Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized FEBRUARY 2015 SOLAR MODELING REPORT Solar Resource Mapping in the Maldives This report was prepared by GeoModel Solar, under contract to The World Bank. It is one of several outputs from the solar Resource Mapping and Geospatial Planning Maldives [Project ID: P146018]. This activity is funded and supported by the Energy Sector Management Assistance Program (ESMAP), a multi-donor trust fund administered by The World Bank, under a global initiative on Renewable Energy Resource Mapping. Further details on the initiative can be obtained from the ESMAP website. This document is an interim output from the above-mentioned project. Users are strongly advised to exercise caution when utilizing the information and data contained, as this has not been subject to full peer review. The final, validated, peer reviewed output from this project will be the Maldives Solar Atlas, which will be published once the project is completed. Copyright © 2015 International Bank for Reconstruction and Development / THE WORLD BANK Washington DC 20433 Telephone: +1-202-473-1000 Internet: www.worldbank.org This work is a product of the consultants listed, and not of World Bank staff. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work and accept no responsibility for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for non-commercial purposes as long as full attribution to this work is given. Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: +1-202-522-2625; e-mail: pubrights@worldbank.org. Furthermore, the ESMAP Program Manager would appreciate receiving a copy of the publication that uses this publication for its source sent in care of the address above, or to esmap@worldbank.org. World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results World Bank Group, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives Project ID: P146018 Solar Modelling Report – Preliminary Results February 2015 GeoModel Solar, Pionierska 15, 831 02 Bratislava, Slovakia http://geomodelsolar.eu Reference No. (GeoModel Solar): 129-01/2015 Page 3 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results TABLE OF CONTENTS Table of contents .....................................................................................................................................................4! Acronyms .................................................................................................................................................................6! Glossary ...................................................................................................................................................................8! 1! Summary ..........................................................................................................................................................10! 2! Introduction .....................................................................................................................................................13! 2.1! Background .........................................................................................................................................13! 2.2! Data needs ..........................................................................................................................................13! 3! Measuring and modelling solar resource .....................................................................................................15! 3.1! Solar basics .........................................................................................................................................15! 3.2! Satellite-based models: SolarGIS approach .......................................................................................18! 3.3! Solar radiation measurements ............................................................................................................22! 3.4! Ground-measured vs. satellite data – adaptation of solar model ........................................................25! 3.5! Typical Meteorological Year ................................................................................................................26! 4! Measuring and modelling meteorological data ............................................................................................29! 4.1! Meteorological data measured at meteorological stations ..................................................................29! 4.2! Data derived from meteorological models ...........................................................................................30! 4.3! Measured vs. modelled data – features and uncertainty.....................................................................31! 5! Solar technologies and solar resource data ................................................................................................33! 5.1! Flat-plate photovoltaic technology .......................................................................................................33! 5.2! Solar concentrating technologies ........................................................................................................37! 6! Geography and air temperature in Maldives ................................................................................................38! 6.1! Representative sites ............................................................................................................................38! 6.2! Geographic data ..................................................................................................................................39! 6.3! Air temperature ....................................................................................................................................43! 7! Solar resource in Maldives ............................................................................................................................47! 7.1! Global Horizontal Irradiation ................................................................................................................48! 7.2! Ratio of diffuse and global irradiation ..................................................................................................52! 7.3! Global Tilted Irradiation .......................................................................................................................55! 7.4! Direct Normal Irradiation .....................................................................................................................60! 8! Photovoltaic power potential .........................................................................................................................65! 8.1! Reference configuration ......................................................................................................................65! 8.2! PV power potential in Maldives ...........................................................................................................67! 8.3! PV integration into the electricity grid in Maldives ...............................................................................71! 9! Solar and meteorological data uncertainty ..................................................................................................75! 10! Application of solar and meteorological data ............................................................................................76! 10.1! Site selection and prefeasibility ...........................................................................................................77! 10.2! Feasibility and project development ....................................................................................................77! 10.3! Due diligence .......................................................................................................................................77! 10.4! Performance assessment and monitoring ...........................................................................................78! Page 4 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 10.5! Operation .............................................................................................................................................78! 11! SolarGIS data delivery for Maldives ............................................................................................................79! 11.1! Spatial data products ...........................................................................................................................79! 11.2! Digital maps .........................................................................................................................................84! 11.3! Site-specific data for four representative sites ....................................................................................88! 12! Meta information ...........................................................................................................................................90! 13! List of figures ................................................................................................................................................91! 14! List of tables ..................................................................................................................................................93! 15! References .....................................................................................................................................................94! 16! Support information .....................................................................................................................................97! 16.1! Background on GeoModel Solar .........................................................................................................97! 16.2! Legal information .................................................................................................................................97! Page 5 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results ACRONYMS AERONET The AERONET (AErosol RObotic NETwork) is a ground-based remote sensing network dedicated to measure atmospheric aerosol properties. It provides a long-term database of aerosol optical, microphysical and radiative parameters. AOD Aerosol Optical Depth at 670 nm. This is one of atmospheric parameters derived from MACC database and used in SolarGIS. It has important impact on accuracy of solar calculations in arid zones. CFSR Climate Forecast System Reanalysis. The meteorological model operated by the US service NOAA CFSv2 The Climate Forecast System Version 2. CFSv2 meteorological models operated by the US service NOAA (Operational extension of Climate Forecast System Reanalysis, CFSR). CPV Concentrated Photovoltaic systems, which uses optics such as lenses or curved mirrors to concentrate a large amount of sunlight onto a small area of photovoltaic cells to generate electricity. DIF Diffuse Horizontal Irradiation, if integrated solar energy is assumed. Diffuse Horizontal Irradiance, if solar power values are discussed. DNI Direct Normal Irradiation, if integrated solar energy is assumed. Direct Normal Irradiance, if solar power values are discussed. ECMWF European Centre for Medium-Range Weather Forecasts is independent intergovernmental organisation supported by 34 states, which provide operational medium- and extended-range forecasts and a computing facility for scientific research. GFS Global Forecast System. The meteorological model operated by the US service NOAA. GHI Global Horizontal Irradiation, if integrated solar energy is assumed. Global Horizontal Irradiance, if solar power values are discussed. GRIB GRIdded Binary data files – special data format used in meteorology to store historical and forecast weather data GTI Global Tilted (in-plane) Irradiation, if integrated solar energy is assumed. Global Tilted Irradiance, if solar power values are discussed. MACC Monitoring Atmospheric Composition and Climate – meteorological model operated by the European service ECMWF (European Centre for Medium-Range Weather Forecasts) Meteosat IODC Meteosat satellite operated by EUMETSAT organization. IODC: Meteosat satellite operating over Indian Ocean and Asia NOCT Nominal Operating Cell Temperature – used in calculation of power performance of PV modules NOAA NCEP National Oceanic and Atmospheric Administration, National Centre for Environmental Prediction NOCT The Nominal Operating Cell Temperature, is defined as the temperature reached by open circuited cells in a module under the defined conditions: Irradiance on cell surface = 800 2 W/m , Air Temperature = 20°C, Wind Velocity = 1 m/s and mounted with open back side. PVOUT Photovoltaic electricity output, often presented as percentage of installed DC power of the photovoltaic modules. This unit is calculated as a ratio between output power of the PV system and the cumulative nominal power at the label of the PV modules (Power at Standard Test Conditions). Page 6 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results RSR Rotating Shadowband Radiometer STC Standard Test Conditions, used for module performance rating to ensure the same measurement conditions: irradiance of 1,000 W/m², solar spectrum of AM 1.5 and module temperature at 25°C. TEMP Air Temperature at 2 m Page 7 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results GLOSSARY AC power output Power output measured at a connection point of a PV system to the distribution of a PV power plant network. Aerosols Solid or liquid particles suspended in air, for example clouds, haze, and air pollution such as smog or smoke. All-sky irradiance The amount of solar radiation reaching the Earth's surface is mainly determined by Earth-Sun geometry (the position of a point on the Earth's surface relative to the Sun which is determined by latitude, the time of year and the time of day) and the atmospheric conditions (the level of cloud cover and the optical transparency of atmosphere). All-sky irradiance is computed with all factors taken into account. Bias Represents systematic deviation (over- or underestimation) and it is determined by systematic or seasonal issues in cloud identification algorithms, coarse resolution and regional imperfections of atmospheric data (aerosols, water vapour), terrain, sun position, satellite viewing angle, microclimate effects, high mountains, etc. Clear-sky irradiance The clear sky irradiance is calculated in a similar way as all-sky irradiance, but without taking into account the impact of cloud cover. Fixed-mounted modules Photovoltaic modules assembled on fixed bearing structure in a defined tilt to the horizontal plane and oriented in fixed azimuth. Frequency of data (30 Period of aggregation of solar data that can be obtained from the SolarGIS minute, hourly, daily, database. monthly, yearly) Installed DC capacity Total sum of nominal power (label values) of all modules installed on photovoltaic power plant. KML Keyhole Markup Language − an XML based file format used to display geographic data in an Earth browser such as Google Earth and Google Maps. Long-term average Average value of selected parameters (GHI, DNI, etc.) based on multiyear historical time series. Long-term averages provide a basic overview of solar resource availability and its seasonal variability. Root Mean Square Represents spread of deviations given by random discrepancies between measured Deviation (RMSD) and modelled data, it is calculated according to this formula: ! ! ! ! !! !! !"#$%&"' − !!"#$%$# !"#$ = ! On the modelling side, the spread can be determined by accuracy of cloud estimate (especially for intermediate clouds), under/over estimation of atmospheric input data, terrain, microclimate and other effects, which are not captured by the model. Part of this discrepancy is natural - as satellite monitors large area (of approx. 3 x 4 km), while sensor can see only area of approx. 1 sq. centimetre. On the measurement side, the discrepancy may be determined by accuracy/quality and errors of the instrument, pollution of the detector, misalignment, data loggers, insufficient quality control, etc. Page 8 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 2 Solar irradiance Solar power (instantaneous energy) falling on a unit area per unit time [W/m ]. Solar resource or solar radiation is used when considering both irradiance and irradiation. 2 Solar irradiation Amount of solar energy falling on a unit area over a stated time interval [Wh/m or 2 kWh/m ]. Spatial grid resolution In digital cartography the term applies to the minimum size of the grid cell of a digital map. Model uncertainty Is a parameter characterizing the possible dispersion of the values attributed to an estimated irradiance/irradiation values. In this report, uncertainty assessment of the solar resource estimate is based on a detailed understanding of the achievable accuracy of the solar radiation model and its data inputs (satellite, atmospheric and other data), which is confronted by an extensive data validation experience. Accuracy of ground measuring instruments, measuring techniques and level of data quality control affect the model uncertainty. In this study, the range of uncertainty assumes 80% probability of occurrence of values. Thus, the lower boundary (negative value) of uncertainty represents 90% probability of exceedance, and it is also used for calculating the P90 value. Water vapour Water in the gaseous state dissolved in the atmosphere. Page 9 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 1 SUMMARY Background This Modelling Report presents preliminary results of the project Renewable Energy Resource Mapping for the Republic of the Maldives. This part of the project focuses on solar resource mapping and measurement services as part of a technical assistance in the renewable energy development implemented by the World Bank in Maldives. It is being undertaken in close coordination with the Ministry of Environment and Energy (MEE) of Maldives, the World Bank’s primary country counterpart for this project. This project is funded by the Energy Sector Management Assistance Program (ESMAP) and Asia Sustainable and Alternative Energy Program (ASTAE), both administered by the World Bank and supported by bilateral donors. Objective and method The objective of the project, in Phase 1, is to increase the knowledge of solar resource potential for solar energy technologies by producing a comprehensive data set based on satellite and meteorological modelling. In Phase 1, SolarGIS approach is used in the preliminary mapping. The satellite-based and meteorological models are used for computing solar resource and meteorological data. These data are validated with ground measurements, available in a wider region representing this climate zone. Geospatial data are delivered in a format suitable for Geographical Information Systems (GIS), and also as digital maps. For four sites, representing variability of climate in Maldives, we delivered site-specific time series and Typical Meteorological Year (TMY) data. Data validation results are presented in the Model Validation Report 129-02/2015. Data delivery The following data parameters are resulting from this solar resource and meteorological modelling: • Global Horizontal Irradiation (GHI) and Global Tilted Irradiation: for assessment of photovoltaic technology • Direct Normal Irradiation (DNI): for Concentrated Solar Power and Concentrated Photovoltaics technologies, also important for accurate simulation of flat plate PV systems • Air temperature: this parameter determines efficiency of solar power plant operation. For site-specific data we delivered also wind speed, wind direction, and relative humidity data. • Photovoltaic electricity potential. The data products that are delivered as part of this preliminary solar modelling: 1. GIS data and digital maps for the whole territory of the Republic of the Maldives, representing long- term monthly and yearly averages. In addition hourly data values are delivered in specific data format: • Raster digital data layers for Geographical Information System (GIS), representing monthly and yearly values • High resolution digital maps of annual values for poster printing • Medium resolution digital maps of annual and monthly values for presentations • Digital image maps of yearly values for Google Earth and GIS • Support maps in a vector data format for GIS Page 10 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 2. Site specific data at hourly resolution are prepared for 4 representative sites: • Time series, for detailed solar resource analysis • Typical Meteorological Year (TMY), for use in solar energy simulation software. 3. NetCDF files with hourly data for the complete period 1999 to 2013 The deliveries for Phase 1 are designed to help effective development of solar energy strategies and projects in their first stages. The innovative features of the delivered data are: • High-resolution, harmonized solar, meteorological and geographical data computed by the best available methods and input data sources; • The data represent a continuous history of last 15 years (1999 to 2013); • The models used are extensively validated by GeoModel Solar and by external organizations. The data are supported by two expert reports: • Solar Modelling Report (129-01/2015, this report), describing the methods and results of Phase 1 activities; • Solar Model Validation Report (129-02/2015), describing the methods and results of the data validation. Phase 1 delivers data computed by SolarGIS model without any support of regional measurements. This phase will be followed by two additional phases: • In Phase 2 we will deploy and operate approx. four solar measuring stations in Maldives to collect high- quality site-specific solar and meteorological time series for adaptation of solar and meteorological models and for detailed analysis of solar climate at representative sites. This Phase is planned for a minimum of 24 months; • Phase 3 aims to combine site measurements with models, and to deliver a new version of the modelled database with reduced uncertainty. Copyright for the delivered data is © 2015 GeoModel Solar. Results This Solar Modelling Report is divided into twelve chapters. Solar radiation basics and collection of solar radiation data from different sources are described in Chapter 2. Characteristics and challenges of using modelled and ground-measured solar parameters are compared in Chapter 3. Chapter 4 describes measurement and modelling approaches for developing reliable meteorological data at any site. Chapter 5 provides a link between solar resource and meteorological parameters and relevant solar technologies. An emphasis is given to photovoltaic (PV) technology, which has high potential for developing larger-scale projects close to larger load centres, as well as deployment of smaller PV hybrid systems and minigrid applications for electrification in islands. Chapters 6 to 8 present developed solar resource and meteorological data in the form of maps. Four representative sites are selected to show potential regional geographical differences in the country through tables and graphs. Chapter 6 introduces some support geographical data that may help in PV development strategies. Chapter 7 summarizes geographical differences and seasonal variability of solar resources in Maldives. Chapter 8 presents PV power generation potential, calculating theoretical specific PV electricity output from the most commonly used PV technology: fixed system with crystalline-silicon (c-Si) PV modules optimally tilted and oriented towards the equator. The expected data uncertainty based on the validation exercise is summarized in Chapter 9. The complete methodology and detailed results can be consulted in the Model Validation Report 129-02/2015. The provided solar resource information, evaluated in the context of other location criteria (demographic, infrastructural, logistic and other constraints and priorities) is a good starting point for building solar energy strategy in Maldives. Chapter 10 outlines the best practices of solar data use in all stages of a project development and operation. Chapters 11 and 12 summarize the technical features of the delivered data products. Page 11 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Conclusions This Solar Modelling Report is supported by the results of preliminary modelling. The maps and site data for four representative sites, serve as an input for knowledge-based decisions targeting development of solar power. The Phase 1 outcomes show very good potential for exploitation of solar resources in Maldives, indicating very good opportunities for photovoltaics, predominantly small to medium size ground-mounted and roof-top systems. Concentrated photovoltaic is not recommended because of its space requirements and lower DNI in the equatorial climate zone. Page 12 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 2 INTRODUCTION 2.1 Background Solar electricity offers a unique opportunity, for each country worldwide, to achieve longterm sustainability goals, such as development of modern economy, healthy and educated society, clean environment, and improved geopolitical stability. Solar power plants exploit local solar resources; they do not require extensive support infrastructure and are scalable. Solar electricity supports diversification of power generation capacities, and improves electricity services also in remote islands, thus opening new potential for development. Solar resources are fuel to solar power plants, but other weather parameters may determine efficiency of their operation. Free fuel makes solar technology very attractive; however effective investment and technical decisions require detailed and validated solar and meteorological data. Such data are also needed for the cost- effective operation of solar power plants. High quality solar resource and meteorological data are available today, and they are based on the use of the modern satellite, atmospheric and meteorological models and operational services. This study describes methods and outcomes of solar resource mapping and photovoltaic power potential analysis of Maldives. 2.2 Data needs Solar resource directly determines how much electricity will be generated from solar power plants. Other meteorological parameters determine operating conditions of solar power plants. Thus, they are also important for accurate energy simulation. A number of data sources are available in the region. These data sources offer diverse information from various models and measurement campaigns, they are static (with no regular update), and with limited information about validation and accuracy. Such situation poses risk to financing the solar electricity projects and is a deterrent to investments. Professional development and operation of solar power plants needs solar resource and meteorological data with the following attributes: • Solar and meteorological data are based on the best available and scientifically-proven models, and the most accurate and detailed input data (satellite, atmospheric and meteorological); • Models are able to deliver harmonized and seamless historical data (for the project development), and systematically updated data (for management of solar power in a local electrical grids); • Historical data should represent a long time period, optimally the most recent 15 years or more; • Models should provide geographically continuous data, covering the whole territory in high resolution: o Temporal resolution of 30 minutes at a site-specific level; o Spatial resolution of derived aggregated maps of 3 km or less; • Standardized site-specific and map products should make the data easy to access and use; • Systematic operation of the models and measuring stations should be able to deliver data for: o Data quality control and model adaptation based on local measurements o Monitoring, performance assessment and forecasting of solar power plants • Data and maps should be supported by technical information and consultancy. Page 13 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results This project aims to deliver the solar resource and meteorological data fulfilling the above listed criteria. Solar and meteorological data are needed in all stages of development and operation of solar power plants: 1. Site prospecting, prefeasibility analysis and site selection; 2. Project assessment, engineering, technical design and financing; 3. Monitoring and performance assessment of solar power plants and forecasting of solar power; 4. Quality control of solar measurements. Table 2.1 shows, which data are needed in different stages of the solar project lifetime, and how they are implemented in solar resource analysis and energy simulation. Solar Resource Mapping in Maldives supports the first two stages of solar development (marked by red box). Parameters delivered as map data and site- specific data products for Maldives within this project are specified in Table 2.2. Table 2.1: Overview of solar and meteorological data needed in different stages of a solar energy project Note: LTA = Longterm averages, P50 = probability of exceedance 50%, P90 = probability of exceedance 90% Table 2.2: Solar and meteorological data parameters delivered for Maldives Parameter Acronym Unit GIS data Site-specific Site-specific Typical and maps time series Meteorological Year 2 Global Horizontal Irradiation GHI kWh/m /day x x x 2 Direct Normal Irradiation DNI kWh/m /day x x x 2 Global Tilted Irradiation GTI kWh/m /day x x - 2 Diffuse Horizontal Irradiation DIF kWh/m /day x x x Air Temperature at 2 metres TEMP °C x x x Wet Bulb Temperature WBT °C - x x Relative Humidity RH % - x x Wind Speed at 10 metres WS m/s - x x Wind Direction at 10 metres WS ° - x x Atmospheric Pressure AP hPa - x x Page 14 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 3 MEASURING AND MODELLING SOLAR RESOURCE 3.1 Solar basics The interactions of extra-terrestrial solar radiation with the Earth’s atmosphere, surface and objects are divided into four groups (Figure 3.1): 1. Solar geometry, trajectory around the sun and Earth's rotation (declination, latitude, solar angle) 2. Atmospheric attenuation (scattering and absorption) by: 2.1 Atmospheric gases (air molecules, ozone, NO2, CO2 and O2) 2.2 Solid and liquid particles (aerosols) and water vapour 2.3 Clouds (condensed water or ice crystals) 3. Topography (elevation, surface inclination and orientation, horizon) 4. Shadows, reflections from surface or local obstacles (trees, buildings, etc.) and re-diffusion by atmosphere. The atmosphere attenuates solar radiation selectively: some wavelengths are associated with high attenuation (e.g. UV) and others with a good transmission. Solar radiation called "short wavelength" (in practice, 300 to 4000 nm) is of main interest to solar power technology. The component that is neither reflected nor scattered, and which directly reaches the surface, is called direct radiation; this is the component that produces shadows, and can be concentrated with solar concentrator. Component scattered by the atmosphere, and which reaches the ground is called diffuse radiation. Small part of the radiation reflected by the surface and reaching an inclined plane is called the reflected radiation. These three components together create global radiation. Figure 3.1: Interaction of solar radiation with the atmosphere and surface. The red numbers refer to the paragraphs below, in which the corresponding effects are discussed. Page 15 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Extra-terrestrial radiation (1) reaching the top of the Earth’s atmosphere above the point on surface depends on the position of the Sun, and varies as a function of the day during the year. This radiation can be accurately calculated using the solar geometry and astronomical equations. In an annual average, extra-terrestrial radiation 2 corresponds approximately to the "solar constant", whose value has been recently estimated at 1362.2 W/m [1]. Solar activity leads to maximum variations of ± 0.5% around this mean value, but in practice these variations are not taken into account. During its passage through the atmosphere, the radiation is attenuated by components such as gases, liquid and solid particles and clouds. The path length of sunrays through the atmosphere is dependent on the thickness of the atmosphere above the considered site. This dimensionless relation is called "air mass" or "relative optical air mass". By definition, it has a value of 1 for a sun at zenith (AM1). The air mass "zero" (AM0) is an abstraction commonly used to refer to extra-terrestrial conditions. The air mass varies according to the position of the Sun; it changes during the day and the year. At sunrise and sunset, it reaches its maximum value of ≈36. Due to its dynamic nature and complex interactions, the atmospheric attenuation cannot be modelled precisely at any time. The so-called uniformly mixed atmospheric gases (2.1) are the components whose concentration is considerably constant throughout the thickness of the atmosphere. The spatial and temporal variability of these gases (N2, O2, N2O, CH4, CO, CO2, etc.) can be considered negligible for all practical purposes. (Even if the CO2 concentration increases steadily, its effect on the solar radiation of short wavelengths is negligible.) Their optical attenuation can be modelled with a good accuracy. Ozone (O3) has a variable concentration but it only has a slight influence on broadband solar radiation. On the other hand, the effect of ozone is pronounced in the UV, for wavelengths below 300 nm. The presence of solid and liquid particles determines the atmospheric turbidity, in other words, the optical density of atmosphere caused by the effect of aerosol scattering. This is considerably higher than scattering caused by the Rayleigh effect on gas molecules, which creates the blue colour of the clear sky. An old definition of turbidity (introduced by Linke in 1922) included the effect of absorption of water vapour, for the sake of simplicity. This amalgam of two very different phenomena does not allow precise calculations, and therefore its use is disconnected in modern calculations (in SolarGIS, water vapour and aerosols are treated separately). The turbidity of the atmosphere is a direct function of its concentration by aerosols (2.2), which have high temporal and spatial variability. Aerosols are normally concentrated in the lower layers of the atmosphere. Large volcanic eruptions can inject large amounts of aerosols into the upper atmosphere, occasional occurrence of which must be also taken into account. High local concentration of aerosols leads to the "haze" and a gradual reduction of horizontal visibility. In such conditions, the sky (usually blue) has colour closer to white, and takes a milky consistency (it is turbid). Diffuse radiation, which is normally low under a blue sky, increases with the presence of high concentration of aerosols. The optical effect of attenuation by aerosol is most often measured by a quantity called "Aerosol Optical Depth" (AOD). Similarly to aerosols, water vapour (2.2) is concentrated in the lower layers of the atmosphere, and is very variable in time and space. From a climate perspective, dry regions normally have little water vapour, while humid regions have high concentrations. The quantity of water vapour can be characterized by "the thickness of condensable water" (Precipitable water, PW). Water vapour is invisible, and its absorption occurs in the infrared, so it cannot be visually detected. This is opposite to the aerosol extinction, which occurs mainly in the visible and UV spectrum. The maximum direct radiation is reached when the sky is cloudless and the atmosphere is "clean and dry", in other words that it contains low concentrations of aerosols and water vapour. Gradually, as their concentration increase, the direct radiation weakens. It also weakens when the air mass increases, so when the sun is closer to the horizon. As mentioned above, the interaction between radiation and atmospheric constituents is considerably complex. Some effects, such as Rayleigh molecular diffusion, and absorption by mixed gases, are well known and do not pose a significant problem in modelling. On the contrary, all effects associated with variable attenuation (clouds, aerosols, and – to a lesser extent – water vapour) remain difficult to model accurately due to the lack of reliable observations with sufficient spatial and temporal resolution throughout the world. The majority of attenuation effects is usually determined by clouds (2.3). In operational numerical models, it is simulated by empirical equations using satellite data. In comparison with all the other effects of atmospheric attenuation, the uncertainty (potential error) of the impact of clouds is the most important. The most difficult cases for modelling are those with scattered and intermittent cloud cover. Radiation that finally reaches the ground is also influenced by local topography (3). The altitude above sea level determines the relative thickness of the atmosphere, and thus the amount of radiation attenuated by Page 16 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results scattering and absorption. The slope and obstructions on the horizon determine access to direct, diffuse and reflected radiation. On a smaller scale, a similar role is played by natural or artificial obstacles such as trees, buildings, etc. (4). This type of attenuation can be measured accurately as long as the geometry of these obstacles and their reflectance are known. According to the generally adopted terminology (project MESoR, IEA SHC Tasks 36 and 46), the two terms are used in the field of radiation of short wavelengths: 2 2 • Irradiance indicates power (instant energy) per second incident on a surface of 1 m (unit: W/ m ). 2 2 • Irradiation, expressed in MJ/ m or Wh/m it indicates the amount of incident solar energy per unit area during a lapse of time (hour, day, month, etc.). Often, the term irradiance is used by the authors of numerous publications in both cases, which can be sometimes confusing. In solar energy applications, the following conventions are commonly used: • Direct Normal Irradiation/Irradiance (DNI): it is the direct solar radiation from the solar disk and the region closest to the sun (circumsolar disk of 5° centred on the sun). DNI is the component that is important to concentrating solar collectors used in Concentrating Solar Power (CSP) and high- performance cells in Concentrating Photovoltaic (CPV) technologies. • Global Horizontal Irradiation/Irradiance (GHI): sum of direct and diffuse radiation received on a horizontal plane. GHI is a reference radiation for the comparison of climatic zones; it is also the essential parameter for calculation of radiation on a flat plate collector, regardless of its orientation. • Global Tilted Irradiation/Irradiance (GTI), or total radiation received on a surface with defined tilt and azimuth, fixed or sun-tracking. This is the sum of the scattered radiation, direct and reflected. In the case of photovoltaic (PV) applications, GTI can be occasionally affected by shadows. Solar resource can be modelled by satellite-based solar models or measured by ground-mounted sensors. Ground-mounted sensors are good in providing high frequency and accurate data (for well-maintained, high accuracy measuring equipment) for a given site. Satellite-based models provide data with lower frequency of measurement, but representing long history over lager areas. Satellite models are not capable of producing instantaneous values at the same accuracy as ground sensors, but can provide robust aggregated values. Chapter 4 summarizes approaches for measuring and computing these parameters, and the main factors and sources of uncertainty. The most effective approach is to correlate multiyear satellite time series with data measured locally over short periods of time (at least one year) to reduce uncertainty and achieve more reliable estimates. Page 17 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 3.2 Satellite-based models: SolarGIS approach Numerical models using satellite and atmospheric data have become a standard for calculating solar resource time series and maps. The same models are also used for real-time data delivery for system monitoring and solar resource forecasting. Reliable solar models exist today. A good description of the current approaches can be consulted in [1]. The state-of-the-art approaches have the following features: • Use of modern models based on sound theoretical grounds, which are consistent and computationally stable; • Use of modern input data: satellite and atmospheric. These input data are systematically quality- controlled and validated; • Models and input data are integrated and regionally adapted to perform reliably at a wide range of geographical conditions. Satellite-based irradiance models range from physically rigorous to purely empirical. At the one end, physical models attempt to explain observed earth’s radiance by solving radiative-transfer equations. Physical models require precise information on the composition of the atmosphere and also depend on accurate calibration from the satellite sensors. At the other end empirical models may consist of a simple regression between the satellite visible channel’s recorded intensity and a measuring station at the earth’s surface. Today, all operational approaches are based on the use of semi-empirical models: they use a simple radiative-transfer approach and some degree of fitting to observations. Old approaches are typically less elaborated, thus cannot reach the accuracy of the modern models. Even if the models are based on similar principles, differences in implementation may result in different outputs. Already today, the information value of the satellite and atmospheric input data used by these models is very high, and most of it still remains unexploited, thus providing room for future improvements. In this study we applied the SolarGIS model, which is operated by GeoModel Solar and applied for calculation and daily update of high- resolution global solar resources and other meteorological parameters. 3.2.1 SolarGIS calculation scheme In comparison to physical models, the algorithms in semi-empirical models are simplified. However, even semi- empirical models consider most of the physical processes of atmospheric attenuation of solar radiation and use some physical parameters in the input. Therefore, this approach is capable of reproducing real atmospheric and weather situations. In satellite-based solar radiation models, the data form meteorological satellites are used for identification of cloud properties, while the atmospheric properties are derived from meteorological models and measurements. The simplification of algorithms is also driven by the availability of the input data. For example, the aerosols may have different optical properties due to diverse chemical composition and particle size, but the data describing these properties are in general not available (except for a limited number of sites). Thus in the semi-empirical models, the aerosols are represented by only one or two parameters, characterizing their properties in an aggregated way with limited accuracy. The SolarGIS model generates updated solar resource data globally, for the land surface between 60º North and 50º South latitudes. For Maldives, the solar resource is computed as a primary time step of 30-minutes. The data and maps for Maldives cover a period 1999 to 2013, i.e. they represent 15 years. Page 18 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Atmospheric parameters Environmental variables Solar geometry Satellite data ! Water vapour ! Altitude ! Zenith angle ! Visible channel ! Aerosol optical depth ! Terrain shading ! Azimuth angle ! Infrared channels ! Aerosol type ! Air temperature ! Extra-terrestrial irrad iance ! Ozone ! … Cloud model Clear-sky model Cloud index Clear-sky irradiance Cloud transmissivity GHIc, DNIc Other models: DNI, DIF, GTI transposition, terrain All-sky irradiance GHI, DNI, DIF, GTI Figure 3.2: Scheme of the semi-empirical solar radiation model (SolarGIS). Figure 3.2 shows the SolarGIS modelling scheme. The solar radiation retrieval is basically split into three steps. First, the clear-sky irradiance (the irradiance reaching ground in the absence of clouds) is calculated using the clear-sky model. Second, the satellite data are used to quantify the attenuation effect of clouds. To retrieve all- sky irradiance the clear-sky irradiance is coupled with a cloud index. The output is direct normal (DNI) and global horizontal irradiance (GHI), which are used for computing diffuse and global tilted irradiance. The data from satellite models are usually further post-processed to get irradiance that fits the needs of specific applications (such as irradiance on tilted or tracking surfaces) and/or irradiance corrected for shading effects from surrounding terrain or objects. 3.2.2 Calculation overview Solar radiation is calculated by models, which use inputs characterizing the cloud transmittance, state of the atmosphere and terrain conditions (Chapter 3.2.1 and Figure 3.1). A comprehensive overview of the SolarGIS model is available in [1, 2]. The related uncertainty and requirements for bankability are discussed in [3, 4]. SolarGIS model version 2.0 has been used. The SolarGIS processing chain is summarized below. Table 3.1 shows characteristics of input databases and primary outputs. Clear-sky model SOLIS [5] calculates clear-sky irradiance from a set of input data. Sun position is a deterministic parameter, and it is described by algorithms with good accuracy. Three constituents determine geographical and temporal variability of clear-sky atmospheric conditions: • Aerosols are represented by Aerosol Optical Depth (AOD), which is derived from the global MACC-II database [6, 7]. The SolarGIS model uses daily aerosol data to simulate more precisely the instantaneous estimates of DNI and GHI. Use of daily values reduces uncertainty, especially in regions with variable and high atmospheric load of aerosols [8, 9]. It is to be noted that time coverage of high frequency (daily) aerosol data by the MACC-II database is limited to the period from 2003 onwards; the remaining years (from the beginning of the database to 2002) are represented only by monthly longterm averages. • Water vapour is also highly variable, but compared to aerosols, it has lower impact on magnitude of DNI and GHI change. The daily data are derived from CFSR and GFS databases [10, 11] for the whole historical period up to the present time. Page 19 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results • Ozone has negligible influence on broadband solar radiation and in the model it is considered as a constant value. Cloud model estimates cloud attenuation on global irradiance. Data from meteorological geostationary satellites are used to calculate a cloud index that relates radiance of the Earth’s surface, recorded by the satellite in several spectral channels with the cloud optical transmittance. For Maldives, Meteosat IODC satellite data are used [12]. Conceptually, the modified Heliosat-2 calculation scheme [13] is used, with a number of improvements introduced to better cope with complex identification of albedo in various geographies, e.g. the case of tropical variable cloudiness, complex terrain, presence of snow and ice, etc. Other support data are also used in the model, e.g. altitude and air temperature. To calculate Global Horizontal Irradiance (GHI) for all atmospheric and cloud conditions, the clear-sky global horizontal irradiance is coupled with the cloud index. From GHI, other solar irradiance components (direct, diffuse and reflected) are calculated. Direct Normal Irradiance (DNI) is calculated by the modified Dirindex model [14]. Diffuse horizontal irradiance is derived from GHI and DNI according to the following equation: GHI = DIF +DNI * Cos Z (1) Where Z is the zenith angle between the solar position and the earth’s surface. Calculation of Global Tilted Irradiance (GTI) from GHI deals with direct and disuse components separately. While calculation of the direct component is straightforward, estimation of diffuse irradiance for a tilted surface is more complex, and is affected by limited information about shading effects and albedo of nearby objects. For converting diffuse horizontal irradiance for a tilted surface, the Perez diffuse transposition model is used [15]. The reflected component is also approximated considering that knowledge of local conditions is limited. Model for simulation of terrain effects (elevation and shading) based on high-resolution altitude and horizon data is used in the standard SolarGIS methodology [16]. For Maldives, this model was not used. Table 3.1: Input databases used in the SolarGIS model and related GHI and DNI outputs for Maldives Inputs to SolarGIS Source Time Original Approx. grid model of input data representation time step resolution Aerosol Optical Depth MACC-II reanalysis 1999 to 2002 Monthly longterm 125 km (ECMWF) calculated from reanalysis MACC-II reanalysis 2003 to 2012 Daily (calculated 125 km (ECMWF) from 6-hourly) MACC-II operational 2013 to date Daily (calculated 125 km (ECMWF) from 3-hourly) Water vapor CFSR 1999 to 2010 1 hour 35 km (NOAA NCEP) GFS 2011 to date 3 hours 55 km (NOAA NCEP) Cloud index Meteosat IODC satellite 1999 to date 30 minutes 3 km (EUMETSAT) SolarGIS primary - 1999 to 2013 30 minutes 500 m* outputs GHI and DNI * The spatial resolution of maps for Maldives is achieved by downscaling technique to 500 m. 3.2.3 Sources of uncertainty in the satellite-based model The conceptual limitations of the models and spatial and temporal resolution of the input atmospheric and satellite data are sources of systematic and random deviation of the DNI and GHI estimates at regional and local levels. Accuracy and characteristics of the model inputs vary geographically, but also over longer periods of time and this influences the uncertainty of the resulting DNI and GHI. The main sources of uncertainty come from the cloud and aerosol models. Other factors have smaller contribution [4]. Ground measurements are important for Page 20 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results understanding and reducing the model uncertainty. However, only high-quality ground measurements can be used; low-accuracy instruments and measurements with issues in quality do not contribute to reducing the uncertainty of the models. Clouds Cloud optical transmissivity is mapped from geostationary satellite data, which is measured in several spectral bands. In specific regions quantification of cloud transmissivity is more challenging, e.g. in areas with high reflectivity or with changing ground albedo (salt beds, snow, deserts), or in equatorial tropics (the case of Maldives) with strong variability of clouds. In general, in regions where the satellite-viewing angle is low uncertainty of cloud information is also higher (though this is not an issue in Maldives). Spectral response of different satellite sensors has to be eliminated by data inter-calibration. Similarly, harmonized geometry of satellite data is important for quality of the model outputs. The way, in which these issues are addressed by the satellite-based solar models, determines their computational accuracy and geographical representativeness. Aerosols Atmospheric aerosols have spatial and temporal dynamics, which is quite pronounced in some regions. For a limited number of sites, aerosol data are available from the high-accuracy ground-monitoring network AERONET [17]. However, besides sporadic availability, a serious limitation is short time coverage. Therefore ground-measured data cannot be used in operational models. However AERONET data play an important role in accuracy analysis of map-based aerosol databases. Solar models need global (map-based) aerosol data inputs. Modern aerosol databases are used, and they are capable delivering high frequency and routinely updated data, which improve DNI and GHI modelling because they better capture the daily and seasonal changes of the state of atmosphere. Data developed by two approaches can be used in solar models: • Satellite-based aerosol databases provide data with higher spatial resolution, but they have lower temporal sampling (several days) and the valid data can be only computed for cloudless weather, which is serious limitation for regions with frequent cloudiness. Another limitation is that their accuracy is affected by high surface albedo in desert conditions, which poses computational challenges in deserts. • Databases computed by chemical transport models provide data with higher temporal sampling (3 to 6 hours), but at lower spatial resolution (ca. 85 to 125 km). The models are able to characterize different aerosol types, and they also do not have gaps in data [6, 7]. These databases describe well the temporal variability, but they may have regional bias, which needs to be reduced by regional correction [18]. Data from both satellite and chemical-transport models are available only for the last decade or so (satellite aerosols start around year 2000, the MACC chemical transport model starts in the year 2003), thus averaged aerosol information has to be used for modelling of older era. Recent analysis of aerosol data from chemical transport models shows that due to the complex computing and availability of some input measurements (some measurements are available only with a time delay), differences exist between results from the operational model and from reanalysis model (which is run typically with one- year delay). Solar resource modelling in regions with high aerosol concentrations and large daily and seasonal variability (e.g. West and Africa and Sahel, Gulf region, North India, some parts of China) may be more challenging compared to regions, where concentration of aerosols is lower and relatively stable over time (e.g. Atacama, Northwest of the US, South Africa or Australia). Page 21 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 3.3 Solar radiation measurements Global irradiance for horizontal and tilted plane is most often measured by (i) pyranometers using thermocouple junction or (ii) silicon photodiode cells. Diffuse irradiance is measured with the same sensors as global irradiance, except that the sun is obscured with a sun-tracking disk or rotating shadow band to block the direct component. Direct Normal Irradiance is commonly measured by pyrheliometers, where the instrument always aims directly at the sun using a continuously sun tracking mechanism. Global and diffuse components can also be measured by a Rotating Shadowband Radiometer (RSR) or by an integrated pyranometer such Sunshine Pyranometer (e.g. by SPN1). In such a case, DNI is calculated from global and diffuse irradiance. Due to required accuracy in the solar industry and also for the solar model adaptation, it is recommended to measure soar radiation with the highest-accuracy instruments: • Secondary standard pyranometers for Global Horizontal Irradiation (GHI) and (with shading ball/disc on a tracker) also for Diffuse Horizontal Irradiation (DIF) • First class pyrheliometer for Direct Normal Irradiation (DNI). This instrumentation is more expensive, and it is also more susceptible to failures and soiling, thus they are more demanding in terms of maintenance. However if professional cleaning and operation are rigorously followed, the measuring set-up works reliably, delivering data with the lowest possible uncertainty. Rotating Shadowband Radiometer (RSR) instruments can be installed as an alternative to the above mentioned instruments, if measurements take place in more challenging and remote environment with limited possibilities for frequent cleaning and maintenance. However, if RSR is to be used, it is proposed to add one redundant measurement using a thermopile-type instrument for crosschecking the consistency of GHI, DNI and DIF components. Satellite time series data should be used as an independent source of information for quality control of ground- measured data (see Chapter 3.4). 3.3.1 Theoretical uncertainty of sensors Utilization of the state-of-the-art instruments does not alone guarantee good results. Measurements are subject to uncertainty, and the information is only complete if the measured values are accompanied by information on the associated uncertainty. Sensors and measurement processes have inherent features that must be managed by quality control and correction techniques applied to the raw measured data. Accuracy of Global Horizontal irradiance, measured with a thermopile pyranometer, is affected by two sources of error: the thermal imbalance problem and the cosine error of the sensor, resulting in a minimum uncertainty (for the most accurate sensor) of daily sums at about ±2%. Direct Normal Irradiance, if measured by pyrheliometers, may be measured at daily uncertainty of about ±1% for a freshly calibrated high-accuracy pyrheliometer under ideal conditions. This uncertainty can more than double in case of rapid fluctuations of radiation, when using older instruments, or after prolonged exposure to challenging weather. Photodiodes and RSR devices are also affected by cosine error and temperature. Empirical functions are used to correct the raw data, but theoretical daily uncertainty is around ±3.5% for the best possible cases. Standards for pyrheliometers and pyranometers are defined in [19, 20] and summarized in Table 3.2 and 3.3. Page 22 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Table 3.2: Theoretically-achievable daily uncertainty of Direct Normal Irradiation at 95% confidence level DNI First class RSR pyrheliometers (After data post-processing) Hourly ±1.5% ±3.5% to ±4.5% Daily ±1.0% Approx. ±3.5% Table 3.3: Theoretically-achievable daily uncertainty of Global Horizontal Irradiation at 95% confidence level GHI Pyranometers RSR Secondary standard First class* Second class* (After data post-processing) Hourly ±3% ±8% ±20% ±3.5% to ±4.5% Daily ±2% ±5% ±10% Approx. ±3.5% * Due to limited accuracy, it is not advised to use the first and second-class instruments for monitoring in solar electricity industry. The lowest possible uncertainties of solar measurements are essential for accurate determination of the solar resource. Uncertainty of measurements in outdoor conditions is always higher than the one declared in the technical specifications of the instrument (Table 3.2 and 3.3). The uncertainty may dramatically increase in extreme operating conditions and in cases of limited or insufficient maintenance. The quality of measured data has a significant impact on the validation and regional adaptation of satellite models; therefore use of data with dubious quality must be avoided. 3.3.2 Operation and maintenance of instruments Solar radiation measurements are not only subject to errors in determination of instant values. Radiometric response of the instruments also undergoes seasonal variability and longterm drift. Without careful maintenance, periodical check-up and calibration, the measured values can significantly differ from the “true” ones. Rigorous on-site maintenance is crucial for sustainable quality of the longterm measuring campaign. Not only regular care of instruments is necessary, but also maintaining regular service documentation, changes in instrumentation, calibration, cleaning and variations of the instrument behaviour. The quality of solar measurements from data providers using medium-quality instruments or from those not following the best practices is disputable, and use of such data for validation or adaptation of solar models may be limited or even deceptive. Measuring solar radiation is sensitive to imperfections and errors, which result in visible and hidden anomalies in the output data. The errors may be introduced by measurement equipment, system setup or operation-related problems. Errors in data can severely affect derived data products and subsequent analyses; therefore a thorough quality check is needed prior to using the data. Many problems can be prevented or corrected by proper and continuous maintenance of the measurement station by qualified personnel. Regular cleaning of radiometers is essential to ensure quality measurements. 3.3.3 Quality control of measured data The measurement campaign has to be carefully planned and strict quality control must be applied to the measured data. Once the data are collected, regular procedures have to be employed (ideally every day) to verify the consistency and quality of the dataset and to remove or flag the values not fulfilling pre-defined criteria. Missing data can be substituted or interpolated and marked by another flag. For solar radiation measurements the following issues are known: • Time shift of measurements • Incomplete data • Outliers – data outside physical limits for a given location Page 23 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results • Patterns revealing systematic or occasional shading • Inconsistencies between radiation components (direct, diffuse) Other issues often seen in improperly managed measurements are: • Miss-calibrated or soiled sensors • Data are not quality-controlled • Wrong metadata (site position, time reference) • Wrong or missing description of the file format. Quality control methods relate to all measured components (global, direct, and diffuse) and they are described in several publications, see e.g. SERI QC manual [21], HelioClim quality control [22], BSRN manual [23], ARM [24] and Younes et al [25]. The procedures are typically applied in several steps: • First, the measurements are controlled whether they fall within the physical limits given by the clear sky conditions and heavily overcast conditions calculated for a given location. The tests checking physical limits of solar components are capable of finding only gross errors. The small errors due to miss-calibration or sensor soiling and dirt only produce subtle changes (below approx. 5%) and they are difficult to identify. When all three components (GHI, DNI and DIF) are available, a test of redundancy between the components can help. • Common problem are missing data. They occur due to instrumentation failures and shutdowns or as a result of reasonable or incorrect rejection of values during the quality assessment. It is difficult to use such data when aggregated statistics are to be derived: daily or monthly or yearly sums. If the period of missing data is short, statistical averaging or interpolation techniques to fill such gaps can be employed (see e.g. [26, 27]). The gap-filled data should be labelled by flags that allow distinguishing the measured data from the artificial ones. Satellite data can play an important role in gap filling of ground-measured data. • In addition to measurement errors, the data quality may be reduced by missing or erroneous metadata (descriptive information about the data). Missing information about time reference, time integration (instantaneous vs. averaged data for a given time interval), units, flags, post-processing methods, sensor calibration, etc. may result in incorrect application of the data, especially in the case of error in localisation (latitude and longitude). Examples presented above show that solar radiation measurements are prone to various errors. Therefore quality assessment must be an integral part of the data acquisition and management routines. The complete quality information must be communicated to users along with the data. 3.3.4 Recommendations on solar measuring stations Local ground measurements from high-standard instruments are used for better understanding of site-specific weather conditions, and this knowledge is then translated to an improved accuracy of solar models. The ultimate objective is to reduce uncertainty of solar resource data and achieve more accurate assessment of energy yield and performance of solar power plants, The quality control may identify issues, which may reduce reliability and increase uncertainty of ground measurements or may even result in complete rejection of the data. To avoid such problems, some recommendations are summarized below: • Site selection – a site should be located in geographically representative areas, which are not affected by excessive dust and pollution. Shaded areas, caused by surrounding buildings, structures and vegetation, should be avoided or eliminated as much as possible. If shading takes place, the affected values should be identified and flagged. • Instruments – to achieve quality measurements with high value for solar energy applications, secondary standard pyranometers and first class pyrheliometers (WMO classification) are to be used. Attention should be given also to installation to avoid levelling problems that have a direct effect on the quality of measurements. The sensors should be re-calibrated regularly, according to instructions provided by manufacturers. In remote areas, or sites where maintenance can be difficult, RSR instruments should be preferably deployed. Use of redundant measurements, including satellite-based time series, during quality control, is a good practice. • Rigorous operation practices and regular maintenance are required to achieve high quality of the measured data. The solar sensors are sensitive to dirt and soiling, having a direct effect on data degradation, therefore regular cleaning is very important. Cleaning should take place at least several Page 24 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results times a week, and even daily in more polluted or dusty areas. In addition a regular check of instrument levelling, cabling, logging, etc. is a good practice. Cleaning of RSR instruments can be less frequent. • Regular data quality control – provides fast feedback and is a way to prevent longer data losses or persistent issues. Data delivered to customer should be quality controlled, without gaps and with flags indicating various issues. • Documentation and maintenance information – the documentation about meteorological site, instruments and calibration should be provided along with the data. Good practice is also logging of cleaning and maintenance works. Such information may be later used for explanation of specific data patterns found in the data – e.g. sudden increase of values, change of time stamp etc. • Database management – rather than use of spreadsheet formats, data should be preferably managed within the standard relational databases allowing routine procedures, reporting and back up. 3.4 Ground-measured vs. satellite data – adaptation of solar model It is important to understand characteristics of ground measurements and satellite-modelled data (Table 3.4) for qualified solar resource assessment. In general, top-quality and well-maintained instruments provide data with lower uncertainty than the satellite model. However, such data are rarely available for the required location, and usually the period of measurements is too short to describe longterm weather conditions. On the other hand satellite data can provide long climatic history (since 1999, in case of SolarGIS for the region), but may not accurately represent the micro-climatic conditions of a specific site. Thus, the ground measurements and satellite data complement each other and it is beneficial to correlate both data sources and to adapt the satellite model for the specific site so that long history of time series is computed with lower uncertainty. The model adaptation has two steps: 1. Identification of systematic differences between hourly satellite data and local measurements for the period when both data sets overlap; 2. Development of a correction method that is applied for the whole period represented by the satellite time series. The improvements of such site-adaptation depend on the quality and accuracy of measured and satellite data. In the most favourable cases, the resulting uncertainty is still slightly higher than uncertainty of ground measurements. In general, site adaptation of satellite data by local measurements will result in lower uncertainty under the following conditions: • At least one year of ground-measured data is available (preferably two years or more); • The solar measuring station is equipped by more than one instruments, allowing redundancy checks for GHI, DNI and DIF values; • Ground measurements are of high quality, which should be traceable in the cleaning, maintenance and calibration logs. • High quality satellite data are used - with good representation of irradiance variability, extreme situations and with consistent longterm quality. • Advanced site-adaptation methods are used, capable to address specific sources of satellite-ground data differences (e.g. correction of aerosols, cloud identification). Besides reduced uncertainty of longterm estimate (lower bias), the model adaptation method should also improve random deviations (lower RMSD) and should provide more representative sub(hourly) values (lower KSI). Solar data for Maldives are validated based on the measurements in the wider regions. More information can be consulted in the Model Validation Report 129-02/2015. Page 25 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Table 3.4: Comparing solar data from solar measuring stations and from the satellite-model for Maldives Data from solar measuring stations Data from satellite-based models Availability/ Data from high-standard sensors available for Data are available for any location within accessibility two sites: Gan and Hanimaadhoo (the latter latitudes 60N and 50S. Data cover long time difficult to access), and they represent only short periods; in Maldives, from 1999 onwards. period of time. Original spatial Local measurements represent the microclimate Satellite models represent area with complex resolution of a site. spatial resolution: clouds are mapped at approx. 3 km, aerosols at 125 km and water vapour at 35 km. Methods for enhancement of spatial resolution are used in complex terrain (Terrain can be modelled at spatial resolution of up to 90 metres). Original time Seconds to minutes 30 minutes resolution Quality Data need to go through rigorous quality control, Quality control of the input data is implemented gap filling and cross-comparison. and outputs are regularly validated. Under normal operation, the data have only few gaps, which are filled by intelligent algorithms. Stability Instruments need regular cleaning and control. If data are geometrically and radiometrically pre- Instruments, measuring practices, maintenance processed, a complete history of data can be and calibration may change over time. Thus calculated with one single set of algorithms. Data regular calibration is needed. Longterm stability computed by an operational satellite model may is typically a challenge. change slightly over time, as the model and its input data evolve. Thus, regular reanalysis is needed. Uncertainty Uncertainty is related to the accuracy of the Uncertainty is given by the characteristics of the instruments, maintenance and operation of the model, resolution and accuracy of the input data. equipment, measurement practices, and quality Uncertainty of models is higher than high quality control. local measurements. The data may not exactly represent the local microclimate, but are usually stable and may show systematic deviation, which can be reduced by good quality local measurements (site-adaptation of the model). 3.5 Typical Meteorological Year Along with multiyear time series data, Typical Meteorological Year (TMY) data are delivered for 4 sites in Maldives (Chapter 6.1). TMY contains hourly data derived from the time series covering complete years 1999 to 2013. TMY data is a vital supplement to GIS data and maps, as it can be directly used in energy simulation software, such as SAM, HOMER or PVSYST. Detailed description of the SolarGIS method is given in [28]. Here we summarize only the key principles. In TMY, the history of 15 years is compressed into one year, following two criteria: • Minimum difference between statistical characteristics (annual average, monthly averages) of TMY and longterm time series. This criterion is given about 80% weighting. • Maximum similarity of monthly Cumulative Distribution Functions (CDF) of TMY and full-time series, so that occurrence of typical hourly values is well represented for each month. This criterion is given about 20% weighting. To derive solar resource parameters with an hourly time step, the original satellite data with time resolution of 30-minutes were aggregated in time. The meteorological parameters are derived from the original 1-hourly time steps. The TMY datasets were constructed from original–model solar radiation and meteorological data (Chapters 3.2 and 4.2). Time zone was adjusted to UTC + 05:00. Page 26 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results In assembling TMY for Maldives, the weighting of direct (DNI), global (GHI) and diffuse (DIF) irradiance and also Air Temperature at 2 metres (TEMP) is considered. The weights are showing an importance of parameters that are considered for choosing the representative months and they are set as follows: 0.9 is given to DNI, 0.3 to GHI, 0.05 to DIF, and 0.05 to TEMP (divided by the total of 1.3). For each of four sites two TMY data sets are delivered: • TMY for P50 case, representing a year with the most typical solar radiation and weather conditions; 1 • TMY for P90 case, representing a year with low solar radiation. Statistically, P90 characterizes one year out of ten years with low solar radiation and related weather conditions. A TMY P50 data set is constructed on a monthly basis. For each month the long-term average monthly value and cumulative distribution for each parameters (DNI, GHI, DIF and TEMP) is calculated. Next, the monthly data for each individual year from the set of 15 years are compared to the long-term parameters. The monthly data from the year, which resembles the long-term parameters most closely, is selected. The procedure is repeated for all 12 months, and the TMY is constructed by concatenating the selected months into one artificial (but representative, or typical) year. The method for calculating a TMY P90 data set is based on the TMY P50 method, but modified in the way in which a candidate month is selected. The search for sets of twelve candidate months is repeated in iteration until a condition of minimizing the difference between the annual P90 value and an annual average of new TMY is reached (instead of minimizing the differences in monthly means and CDFs, as applied in the P50 case). Once the selection converges to the minimum difference, the P90 is created by concatenation of selected months. Note: P90 annual values are calculated from the combined uncertainty of the estimate and inter-annual variability, which can occur in any year. Table 3.5: Yearly longterm GHI and DNI averages as represented in time series and TMY data products 2 2 DNI [kWh/m ] GHI [kWh/m ] ID Name Time series TMY P50 TMY P90 Time series TMY P50 TMY P90 1 Gan 1718 1718 1494 2065 2065 1929 2 Hanimaadhoo 1541 1541 1344 2040 2040 1912 3 Hulhulé 1635 1635 1425 2059 2059 1930 4 Kadhdhoo 1691 1691 1474 2062 2062 1930 As a result of generating TMY and mathematical rounding, longterm yearly, and especially monthly, averages calculated from TMY data files may not fit accurately to the statistical information calculated from the multiyear time series. It is important to note that the data reduction in TMY is not possible without loss of information contained in the original multiyear time series. Therefore time series data are considered as the most accurate reference suitable for the statistical analysis of solar resource and meteorological parameters of the site. Only time series data can be used for the statistical analysis of solar climate. 1 It is to be noted that term TMY should be strictly used only when referring to P50 values. Anything, which deviates from a typical year, should be called other way. On the other hand, “TMY P90” term is widely used in industry, and this is why we continue using it also in this report. A consensus on using more appropriate term for P90 or any other Pxx case has to be reached in industry. Page 27 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Figure 3.3: Seasonal profile of GHI, DNI and DIF for P50 Typical Meteorological Year (TMY) 2 Example of Malé (Hulhulé airport): X-axis – day of the year; Y-axis – irradiance W/m Page 28 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 4 MEASURING AND MODELLING METEOROLOGICAL DATA Meteorological parameters are an important part of a solar energy project assessment as they determine the operating conditions and effectiveness of operation of solar power plants. Meteorological data can be collected by two approaches: (1) by measuring at meteorological sites and (2) by meteorological models. The best option is to have locally-measured data, for at least 10 recent years. However, meteorological data are available only for sites where long-term meteorological observations are operated; typically by national meteorological services or some other observation network. Even for such sites, the multiyear time series are not always complete, and there may be periods with missing, incomplete or quality-affected data. Most typically, the meteorological data are not available for a particular site of interest, and the only option is to derive them from meteorological models. Various models are available. A good option is to use Climate Forecast System Reanalysis (CFSR), and its operational extension, the Climate Forecast System Version 2 (CFSv2) models covering long period of time with continuous data (both models are managed by NOAA, NCEP, USA). The disadvantage of using the modelled data is their lower accuracy (for a specific site) compared to measurements from well-maintained meteorological stations with high-quality instruments. In the development of large solar energy projects a good practice is to install a meteorological station at a site of interest, as soon as the site is selected. Even for a period of one year of operation a local meteorological station can provide valuable data for adaptation and validation of meteorological and satellite models. The combined use of modelled data and local measurements makes it possible to achieve low uncertainty data, covering a climatologically representative period of time. In the Maldives project it is planned to install four solar meteorological stations to improve model accuracy across the entire archipelago. 4.1 Meteorological data measured at meteorological stations As a standard practice, a meteorological station is deployed at a site of large solar energy project development. The main objective of measuring data at the project site, during the planning phase, is to record accurate local meteo characteristics, to use them in the adaptation of the models and to reduce uncertainty of the longterm time series and aggregated estimates. Deployment of solar measuring stations in a country has strategic advantage of adapting and validating the model at a country level to provide high-quality data and information for decision-makers and investors. Parameters, relevant for solar energy projects are identical to the list in Chapter 2.2. Uncertainty of the meteorological instruments (according to the WMO standards) is show in Table 4.1. Table 4.1: Uncertainty of meteo sensors by WMO standard (Class A) Parameter Instrument WMO standard 0 Air Temperature at 2 m Thermometer 0.2 K Relative humidity at 2 m Temperature and relative humidity probe 3% Atmospheric pressure Digital barometer 0.3 hPa –1 –1 Wind speed at 10 m Ultrasonic sensor 0.5 m s for ≤ 5 m s –1 10% for > 5 m s Wind direction at 10 m Ultrasonic sensor 5° Rainfall Weighing type rain gauge Amount: larger as 5% or 0.1 mm Intensity: under constant flow conditions in the laboratory, 5% above 2 mm/h, 2% above 10 mm/h; in the field, 5 mm/h and 5% above 100 mm/h Page 29 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 4.2 Data derived from meteorological models Operational models and reanalysis For Maldives, SolarGIS provides a complete 15-years history of solar and also meteorological data. To achieve this objective, numerical meteorological models have to be used and validated by local measurements. SolarGIS reads meteorological data from 3 databases, all operated by NOAA/NCEP: 1. Historical data dataset 1: the Climate Forecast System Reanalysis (CFSR) [10] is a global numerical weather reanalysis model. In SolarGIS, a historical period from 1999 to 2010 has been implemented. The CFSR was designed as global, high-resolution, coupled atmosphere-ocean-land surface-sea-ice system to provide the best estimate of the state of these coupled domains over this period. 2. Historical data dataset 2: the Climate Forecast System Version 2 (CFSv2) [29] is a global numerical weather reanalysis model, developed as extension to CFSR dataset. In SolarGIS, historical period of data from 2011 to date has been implemented. The CFS version 2 was developed at the Environmental Modeling Center at NCEP. It is a fully coupled model representing the interaction between the Earth's atmosphere, oceans, land and sea ice. 3. Operational forecast model: the Global Forecast System (GFS) [11] is a global numerical weather prediction model. This mathematical model runs four times a day and produces forecasts for every third hour up to 16 days in advance, but with decreasing spatial and temporal resolution over time. The data cover period form current date up to the 7 days into the future. GFS is one of the predominant synoptic scale medium-range models in general use. The original temporal resolution of 1 hour (CFSR and CFSv2) and 3 hours (GFS) is interpolated, if necessary, and harmonized to the time step of the final data delivery. The Climate Forecast Model (CFS) is initialized four times per day (00, 06, 12, and 18 UTC). NCEP upgraded their operational CFS to version 2 on March 30, 2011. This is the same model that was used to create the CFS, and the purpose of this dataset is to extend CFSR. This model offers hourly data with a horizontal resolution down to one-half of a degree (approximately 56 km) around the Earth for many variables, further disaggregated to finer spatial resolutions. CFSv2 uses the latest scientific approaches for taking in, or assimilating, observations from data sources including surface observations, upper air balloon observations, aircraft observations, and satellite observations. Original spatial angular resolution of accessible GRIB (GRIdded Binary) files containing the primary parameters is 0.3125° for CFSR and 0.2° for CFSv2 and GFS datasets. This translates into spatial resolution of approx. 34 x 35 km for CFSR and approx. 22 x 23 km for CFSv2 and GFS, for the territory of Maldives. For air temperature, the model resolution is post-processed and recalculated to the spatial resolution of 1 km. The SolarGIS algorithms utilize Digital Elevation Model SRTM-3 for post-processing (downscaling) of air temperature (though, for Maldives, this information is not relevant). Other data (wind speed and direction; wet bulb temperature, relative humidity and air pressure) are used in the original model resolution. As a result occasional blocky features can be seen on the maps. In general, weather data from the meteorological models represent larger area, they are smoothed and therefore they are not capable to represent accurately the local microclimate, especially in rough mountains. The time period covered in site-specific meteorological data is from January 1999 through December 2013 (15 years, models CFSR and CFSv2). For preparation of climate GIS data layers (air temperature only) only the CFSR model was used (15 years, from January 1996 to December 2010) to avoid additional resampling of different data source. GFS data are used only in the delivery of site-specific hourly time series and TMY for four representative sites. The accuracy of meteorological models depends on the input data. Being a mathematical representation of dynamic processes, the models are based on a set of partial differential equations, the solution of which strongly depends on initial and boundary conditions. The initialization parameters come from meteorological measurements at different locations. The accuracy in the lowest layer of the atmosphere (2 m for air temperature, and relative humidity, and 10 m for wind speed and wind direction) depends on the spatial distribution and quality of measurements from the meteorological observation networks. Table 4.2: Availability of CFSR and CFSv2 data from meteorological models for Maldives in SolarGIS Climate Forecast System Reanalysis Climate Forecast System version 2 (CFSR) (CFSv2) Page 30 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Data available 1999 to 2010 2011 to last month of 2014 Original spatial resolution Approx. 34 x 35 km Approx. 22 x 23 km Original time resolution 1 hour 1 hour Numerical meteorological models have lower spatial and temporal resolution, compared to solar resource modelled data. Thus local hourly values from the models may deviate from the local measurements. The data from global meteorological models have to be post-processed in order to provide parameters with local representation. Two approaches are available: 1. Running mesoscale weather prediction models, such as WRF [30] 2. Post processing using simpler methods. The first approach can provide more localized data. The second approach is simpler and may include higher uncertainty. The best practice is to combine modelled data with short-term local measurements to reduce data uncertainty. SolarGIS meteorological parameters, delivered as spatial data products (GIS data layers and maps), include: • Air Temperature at 2 m, TEMP [°C] SolarGIS meteorological parameters, delivered in the site-specific data products (time series and TMY) include: • Air Temperature at 2 m, T [°C] • Wet Bulb Temperature, WBT [°C] • Relative Humidity, RH [%] • Wind Speed at 10 metres, WS [m/s2] • Wind Direction at 10 metres, WD [°] • Air Pressure, AP [hPa]. For time series and TMY data, an hourly temporal resolution is used. In the map products only aggregated Air temperature data is supplied. Validation of the meteorological data is provided in the Model Validation Report 129-02/2015. 4.3 Measured vs. modelled data – features and uncertainty Data from the two sources described above have their advantages and disadvantages (Table 4.3). Meteorological parameters retrieved from the meteorological models have lower spatial and temporal resolution compared to on-site meteorological measurements, and they have lower accuracy. Thus, modelled parameters may characterize only regional climate patterns rather than local microclimate; especially extreme values may be smoothed and not well represented. Page 31 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Table 4.3: Comparing data from meteo stations and weather models Meteorological station data Data from meteorological models Availability/ Available only for selected sites. Data are available for any location accessibility Data may cover various periods of time Data cover long period of time (decades) Original spatial Local measurement representing Regional simulation, representing regional weather resolution microclimate patterns with relatively coarse grid resolution. Therefore the local values may be smoothed, especially extreme values. Original time Sub-hourly, 1 hour 1 hour resolution Quality Data need to go through rigorous quality No need of special quality control. No gaps. control, gap filling and cross-comparison. Relatively stable outputs if data processing systematically controlled. Stability Sensors, measuring practices, In case of reanalysis, long history of data is calculated maintenance and calibration may change with one single stable model. over time. Thus longterm stability is often a Data for operational forecast model may slightly change challenge. over time, as model development evolves Uncertainty Uncertainty is related to the quality and Uncertainty is given by the resolution and accuracy of maintenance of sensors and measurement the model. Uncertainty of meteorological models is practices, usually sufficient for solar higher than high quality local measurements. The data energy applications. may not exactly represent the local microclimate, but are usually sufficient for solar energy applications. Page 32 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 5 SOLAR TECHNOLOGIES AND SOLAR RESOURCE DATA This project delivers two principal solar resource data sets that are exploited by different solar power generation technologies: • Global Horizontal Irradiance (GHI) and Global Tilted Irradiance (GTI) used by photovoltaic (PV) flat plate technologies • Direct Normal Irradiance (DNI) used by Concentrating Photovoltaic (CPV) and by Solar Thermal Power Plants, often denoted as Concentrating Solar Power (CSP) plants. 5.1 Flat-plate photovoltaic technology Photovoltaic technology (PV) exploits global horizontal or tilted irradiation, which is the sum of direct and diffuse components (see equation (1) in Chapter 3.2.2). To simulate power production by a PV system, global irradiance received by a flat surface of PV modules must be correctly calculated. Due to clouds, PV power generation reacts to changes of solar radiation in the matter of seconds or minutes (depending on the size of a module field), thus intermittency (short-term variability) of the PV power production is to be considered. Effect of seasonal variability is also to be considered (though in Maldives seasonal variability is very small). PV will most likely dominate in solar energy applications in Maldives. Therefore, in this project, theoretical photovoltaic electricity production potential has also been calculated for the region. For PV, a number of technical options are available for Maldives, and they are briefly described below. For Maldives two PV system types are relevant: • Grid-connected PV power plants on the larger islands • Off-grid and mini-grid PV systems on the smaller islands. Two types of mounting of PV modules is possible: • Build in open an space, where PV modules are ground-mounted in a fixed position or on sun-trackers • Mounted on roofs or facades of buildings 5.1.1 Open space systems The majority of large-scale PV power plants have PV modules mounted at a fixed position with optimum inclination (tilt). Fixed mounting structures offer a simple and low-cost choice for implementing the PV power plants. A well-designed structure is robust and ensures long-life performance even during harsh weather conditions at low maintenance costs. Sun-tracking systems are the other alternative. Solar trackers adjust the orientation of the PV modules during a day to a more favourable position in relation to the sun, so the PV modules collect more solar radiation during the day: • For equatorial conditions the most feasible tracking system is 1-axis horizontal tracker with North- South orientation of rotating axis. The positive feature - in comparison to fixed mounted systems - is an elongated power generation profile stretching from early morning till late afternoon. The downside of this tracker is its limited power output at the peak of the day during seasons with lower sun angle due to the horizontal position of the PV modules. • Another option is 2-axis tracker, where modules are positioned in both azimuth and zenith axes to direct the modules towards the sun. This option is not economically suitable for equatorial regions. Page 33 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results In this study, the PV power potential is studied for a system with fixed-mounted PV modules, considered here as the mainstream technology in the region. Installed capacity of a PV power plant is usually determined by the available space and options to maintain the stability of the power grid. 5.1.2 Roof (facade) mounted space systems Considering installed power, roof-mounted PV systems are typically small to medium size, i.e. ranging from hundreds of watts to hundreds of kilowatts. Modules can be mounted on roofs (flat or tilted), facades or can be directly integrated as part of a building structure. The main characteristic of these systems is their geographic dispersion and connection into a low voltage distribution grid. Direct connection into grid also means that the inverter must provide all protections required by regulations (voltage, frequency, isolation check, etc.). For comparison, a utility scale power plant has its own protection equipment, separated from the inverter and assembled typically on the high-voltage side. Inverters are required to have anti-islanding protection, which means that they work only if grid voltage is present (due to safety reasons). Other connection options, combined with batteries, are more often used. Since these are low- power systems (compared to open space utility-scale projects), inverters have lower efficiencies, especially those with an internal isolation transformer. In case of roof systems, PV modules are often installed in a suboptimal position (deviating from the optimum angle), and this results in a lower performance ratio. Air circulation between modules in a roof or a facade system is worse, compared to free-standing systems, and thus PV power output is further reduced by the higher temperature of modules. PV modules, which are mounted at low tilt, are affected by higher surface pollution due to less effective natural cleaning. Another reduction of PV power output is often determined by nearby shading structures. Trees, masts, neighbouring buildings, roof structures or self-shading of crystalline silicon modules especially have some influence on reduced PV system performance. Façade systems in the equatorial zone experience too high losses to be economically viable. 5.1.3 Off-grid and mini-grid systems Off-grid PV systems are equipped with energy storage (classic lead acid or modern-type batteries) and/or connected to diesel generators. Isolated minigrid electrification will be the most used in the majority of islands. 5.1.4 Principles of PV energy simulation PV energy simulation results, presented in Chapter 8, are based on software developed by GeoModel Solar. This Chapter summarizes key elements of the simulation chain. Table 5.1: Specification of SolarGIS database used in the PV calculation in this study Data inputs for PV simulation Global Tilted Irradiation (GTI) for optimum angle (range of -3° to 9°) towards South (in the Southern hemisphere towards North), derived from GHI and DNI; Air Temperature at 2 m (TEMP) is also used Spatial grid resolution (approximate) Primary data (GHI and DNI) are available at 0.5 km (15 arc-sec); meteorological parameters and atmospheric data are resampled to the resolution of supplied data Time resolution 30-minute Geographical extent (this study) Republic of Maldives Period covered by data (this study) 01/1999 to 12/2013 Page 34 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results The PV software has implemented scientifically proven methods [31 to 38] and uses 30-minute time series of solar radiation and air temperature data on the input (Table 5.1). Data and model quality are checked using field tests and ground measurements. The software makes it possible to use historical, near-real time and also forecast data. The interactive version is implemented in online SolarGIS tools (pvPlanner and pvSpot). Figure 5.1: SolarGIS PV simulation chain In PV energy simulation, the energy losses occur in all steps of energy conversion (Figure 5.1): • Losses due to terrain shading. Shading of local features such as from nearby building, structures or vegetation is not considered in the map calculation. For Maldives terrain modelling is not applied. • Losses due to angular reflectivity depend on relative position of the sun and plane of the module. For this calculation the model by Martin and Ruiz is used in SolarGIS [36]. The losses at this stage depend on the module surface type and its cleanness. • Losses due to dirt and soiling. Losses of solar radiation at the level of surface of PV modules depend mainly on the environmental factors and cleaning of the PV modules surface. In Maldives these losses are not high. • Losses due to performance of PV modules outside of STC conditions. Relative change of produced energy at this stage of conversion depends on the module technology and mounting type. Typically, for crystalline silicon modules, these losses are higher when modules are mounted on a tracker rather than at a fixed position [31 to 34]. • Losses by inter-row shading. Row spacing leads to electricity losses due short-distance shading. These losses can be avoided by optimising distances between rows of module tables. For Maldives these losses will be negligible because the sun in very high and tilt of the modules is small. • Power tolerance of modules. Modules are connected in strings, and power tolerance of modules determines mismatch losses for these connections. If modules with higher power tolerance are connected in series, the losses are higher. The higher power tolerance of modules increases uncertainty of the power output estimation. Page 35 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results • Mismatch and DC cabling losses. These are given by slight differences between nominal power of each module and small losses on cable connections. • Inverter losses from conversion of DC to AC. Although the power efficiency of an inverter is high, each type of inverter has its own efficiency function. Losses due to performance of inverters can be estimated using the inverter power curve or using the less accurate pre-calculated value given by the manufacturer. • AC and transformer losses. These losses apply only for large–scale open space systems. The inverter output is connected to the grid through the transformer. The additional AC losses reduce the final system output by a combination of cabling and transformer losses. • Availability. This empirical parameter quantifies electricity losses incurred by shutdown of a PV power plant due to maintenance or failures, including issues in the power grid. Availability of well-operated PV system is approx. 99%. • Longterm degradation. Many years of operation of PV power plants is the ultimate test for all components. Currently produced modules represent a mature technology, and low degradation can be assumed. However, it has been observed that performance degradation rate of PV modules is higher at the beginning of the exposure, and then stabilizes at a lower level, Initial degradation may be close to value of 0.8% for the first year and 0.5% or less for the next years [35]. Results of calculation of PV power potential for Maldives are shown in Chapter 8. Page 36 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 5.2 Solar concentrating technologies Concentrating technologies can only exploit DNI (as diffuse irradiance cannot be concentrated). Instant (short- term) variability of DNI is very high and this is especially relevant for Concentrating PV systems. On the contrary, solar thermal power plants, often denoted as Concentrating Solar Power technology, have the means to control short-term and also daily variability due to the inertia of the whole system (solar field, heat transfer and storage), which in addition can be supported by fossil fuels. This type of technology is mentioned briefly below, for the sake of completeness, although it is not expected that concentrators will be implemented in Maldives. 5.2.1 Concentrating Solar Power A distinctive characteristic of Concentrated Solar Power technology (CSP) is that, when deployed with thermal energy storage, it can produce electricity on demand providing a dispatchable source of renewable energy. Therefore, it can provide electricity whenever needed to meet demand, performing like a traditional base-load power plant. There are several groups of solar thermal power plants: • Parabolic troughs: solar fields using trough systems capture solar energy using large mirrors that track the sun’s movement throughout the day. The curved shape reflects most of that heat onto a receiver pipe that is filled with a heat transfer fluid. The thermal energy from the heated fluid generates steam and electricity in a conventional steam turbine. Heated fluid in the trough systems can also provide heat to thermal storage systems, which can be used to generate electricity at times when the sun is not shining; • Power towers: they use flat mirrors (heliostats) to reflect sunlight onto a solar receiver at the top of a central tower. Water is pumped up the tower to the receiver, where concentrated thermal energy heats it up. The hot steam then powers a conventional steam turbine. Some power towers use molten salt in place of the water and steam. That hot molten salt can be used immediately to generate steam and electricity, or it can be stored and used at a later time. • Fresnel reflectors: they are made of many thin, flat mirror strips to concentrate sunlight onto tubes through which working fluid is pumped. The rest of the energy cycle works similarly as in the above mentioned systems. • Stirling dish: consists of a stand-alone parabolic reflector that concentrates light onto a receiver positioned at the reflector's focal point. The reflector tracks the sun along two axes. The working fluid in the receiver is heated and then used by a Stirling engine to generate power. One of the advantages of concentrated technologies is thermal storage, very often in the form of molten salt. CSP can also be integrated with fossil-based generation sources in a hybrid configuration. 5.2.2 Concentrating photovoltaics Another type of conversion of DNI into electricity is Concentrated Photovoltaic (CPV). This technology is based on the use of lenses or curved mirrors to concentrate sunlight onto a small area of high-efficiency PV cells. High concentration CPV has to use very precise solar trackers. The advantage of CPV over flat plate PV is a potential for cost reduction due to the smaller area of photovoltaic material. The necessity of sun tracking partially balances out the smaller price of semiconductor material used. CPV technology requires also more maintenance during the lifetime of the power plant. Power production from CPV may be more sensitive to changing weather conditions. The advantage of CPV over CSP is full scalability, similar to flat plate PV modules. Page 37 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 6 GEOGRAPHY AND AIR TEMPERATURE IN MALDIVES 6.1 Representative sites To demonstrate the variability of the solar climate and PV power potential, four representative sites in Maldives are selected. The position of these sites coincides with meteorological stations locates at airports of Maldives (see Table 6.1 and Figure 6.1). All the data in tables and graphs in Chapters 7 and 8 relate to these sites. Figure 6.1: Position of four representative sites within the administrative regions. Source: Maldivian Ministry of Planning and National Development. Cartography: GeoModel Solar Page 38 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Table 6.1: Position of four representative sites in Maldives ID Site name Atoll name Airport name Latitude Longitude Altitude (Island) [° ] [° ] [m a.s.l.] 1 Gan Addu (Seenu) Gan International Airport -0.6933 73.1556 2 2 Hanimaadhoo Haa Dhaalu Hanimaadhoo International Airport 6.7442 73.1703 2 3 Hulhulé North Malé (Kaafu) Ibrahim Nasir International Airport 4.1917 73.5292 1 4 Kadhdhoo Laamu Kadhdhoo Airport 1.8590 73.5220 0 6.2 Geographic data Geographic information and maps bring additional value to the solar data. Geographical characteristics of the country from regional to local scale may represent technical and environmental prerequisites, but also constraints for solar energy development. In this Solar Modelling Report we integrated into GIS project the following data: • Population of the islands • Air transport infrastructure/accessibility of sites • Administrative division and towns The population of the islands provides an indication of spatial distribution of energy demand. For the purposes of this project, the location of international or regional airports is important for the siting of solar meteorological stations (from the point of view of equipment maintenance, operation and logistics). Page 39 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Figure 6.2: Populated islands Data provided by Maldivian Ministry of Planning and National Development. Cartography: GeoModel Solar Page 40 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Figure 6.3: Air transport infrastructure Data provided by Maldivian Ministry of Planning and National Development. Cartography: GeoModel Solar Page 41 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Figure 6.4: Administrative division, towns and cities Data provided by: Government of Maldives, Cartography: GeoModel Solar Page 42 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 6.3 Air temperature Figure 6.5: Longterm yearly average of air temperature at 2 m Source: CFSR and CFSv2 Page 43 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Figure 6.6: Longterm monthly average of air temperature. Source: CFSR and CFSv2 Page 44 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Knowledge of air temperature values in the region is important, as it determines the operating environment and performance efficiency of solar power systems. Air temperature is used as one of inputs in energy simulation models. In this report the yearly and monthly average maps are presented (the data are delivered as hourly values). The longterm averages of air temperature are derived from CFSR and CFSv2 meteorological models (see Chapter 4.2) by SolarGIS post-processing. Due to geography (large masses of ocean and tiny spots of land) the hourly values are partially smoothed and they do not represent the local microclimate amplitudes. In case of PV power plants, air temperature is the primary influence on the power conversion efficiency of the PV modules, and it also influences other components (inverters, transformers, etc.). Increasing air temperature reduces power conversion efficiency of a PV power plant. Table 6.2: Monthly averages, minima and maxima of air-temperature at 2 m at selected sites Temperature [°C] Month Gan Hanimaadhoo Hulhulé Kadhdhoo Min Min Min Min Average Average Average Average Max Max Max Max 27.4 26.9 27.1 27.3 January 27.7 27.2 27.5 27.7 28.0 27.6 27.8 27.9 27.7 27.1 27.3 27.5 February 28.0 27.4 27.7 27.9 28.3 27.8 28.0 28.2 28.0 27.8 27.8 27.9 March 28.3 28.2 28.2 28.3 28.7 28.5 28.5 28.6 28.2 28.6 28.3 28.2 April 28.6 28.9 28.7 28.6 29.0 29.2 29.0 28.9 28.2 28.5 28.2 28.2 May 28.6 28.9 28.7 28.6 29.0 29.3 29.0 28.9 27.9 27.9 28.0 28.0 June 28.3 28.3 28.4 28.4 28.6 28.7 28.7 28.7 27.6 27.5 27.7 27.7 July 28.0 28.0 28.2 28.1 28.3 28.4 28.5 28.5 27.5 27.5 27.6 27.6 August 27.9 27.9 28.0 28.0 28.3 28.2 28.4 28.3 27.5 27.4 27.5 27.6 September 27.9 27.8 27.9 28.0 28.3 28.2 28.3 28.4 27.5 27.6 27.6 27.5 October 27.9 27.9 28.0 27.9 28.3 28.2 28.3 28.3 27.4 27.4 27.4 27.4 November 27.8 27.8 27.8 27.8 28.2 28.1 28.1 28.2 27.3 27.1 27.1 27.2 December 27.7 27.5 27.5 27.6 28.1 27.8 27.9 27.9 YEAR 28.1 28.0 28.0 28.1 Table 6.2 shows monthly characteristics of temperatures at four selected sites; they represent statistics calculated over 24-hour diurnal cycle. Minimum and maximum air temperature are both calculated as average of minimum and maximum values of temperature during each day (assuming full diurnal cycle - 24 hours) of the given month. Monthly averages of minimum and maximum daily values show their typical daily amplitude in each month (Figure 6.7). See Chapter 9 discussing the uncertainty of meteorological estimations. Page 45 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 30 29 Monthly air temperature [°C] 28 27 26 25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Hanimaadhoo Hulhulé Kadhdhoo Gan Min - Max Figure 6.7: Monthly averages, minima and maxima of air-temperature at 2 m for selected sites. Page 46 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 7 SOLAR RESOURCE IN MALDIVES • In this chapter the regional differences of basic solar parameters are shown. Global Horizontal Irradiation (GHI) is often considered as a climate reference for a site. Diffuse and direct components of GTI (or GHI) indicate how different PV technologies may perform. • The most important parameter for PV potential evaluation is Global Tilted Irradiation (GTI), i.e. sum of direct and diffuse solar radiation falling at the surface of PV modules. In the Maldives the tilt angle of PV panels should be very small due to their latitude close to the equator. • Direct Normal Irradiation (DNI) is relevant for solar concentrators (CPV and CSP). This analysis is based on the data representing a history of 15 continuous years: from 1999 to 2013. This report may not reflect possible anthropogenic climate change or occurrence of extreme events such as large volcano eruptions in the future [38, 39]. Page 47 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 7.1 Global Horizontal Irradiation Figure 7.1: Global Horizontal Irradiation – annual daily average and yearly totals Page 48 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Figure 7.2: Global Horizontal Irradiation − long-term monthly averages of daily totals Page 49 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 2 The highest GHI is identified in the South of archipelago where average daily sums reach 5.7 kWh/m (yearly 2 sum about 2080 kWh/m ) and more (Figure 7.1). The season of highest irradiation with daily sums above 6.4 2 kWh/km lasts three months (from February to April, Figure 7.2). Second season of higher solar radiation, with 2 daily sums from 5.4 to 5.8 kWh/m , is found in period from August to October. Table 7.1: Daily averages, minima and maxima of Global Horizontal Irradiation at selected sites Global Horizontal Irradiation [kWh/m2] Month Gan Hanimaadhoo Hulhulé Kadhdhoo Min Min Min Min Average Average Average Average Max Max Max Max 4.90 5.20 4.87 5.06 January 5.75 5.64 5.67 5.74 6.53 6.04 6.08 6.37 5.71 5.39 6.03 5.66 February 6.34 6.19 6.43 6.38 6.78 6.65 6.81 6.82 5.97 5.55 5.82 5.98 March 6.52 6.69 6.71 6.64 7.04 7.04 7.16 7.10 5.31 5.81 5.56 5.42 April 5.90 6.24 6.08 5.91 6.34 6.84 6.74 6.27 5.09 4.50 5.07 4.92 May 5.47 5.37 5.45 5.48 5.94 6.23 5.85 5.85 4.18 4.24 4.55 4.73 June 5.05 4.98 5.23 5.30 5.56 5.91 5.89 5.84 4.43 4.47 4.71 4.69 July 4.99 5.12 5.25 5.18 5.65 5.81 5.72 5.66 4.55 4.84 4.61 4.63 August 5.35 5.52 5.55 5.39 5.83 6.20 6.17 5.87 4.73 5.05 4.80 5.17 September 5.73 5.66 5.48 5.67 6.13 6.59 6.65 6.48 4.98 4.58 5.20 4.98 October 5.62 5.54 5.72 5.62 6.67 6.12 6.50 6.20 4.71 3.94 4.12 4.26 November 5.65 5.05 5.14 5.36 6.67 5.75 5.94 6.46 4.57 4.30 3.73 4.00 December 5.51 5.08 5.00 5.12 6.35 5.88 5.86 5.95 5.37 5.38 5.42 5.39 YEAR 5.65 5.58 5.64 5.65 5.76 5.67 5.77 5.79 2 Table 7.1 shows long-term average, minimum and maximum of daily summaries (kWh/m ) of Global Horizontal Irradiation (GHI) for a period 1999 to 2013 for four sites. 8.0 7.0 6.0 Daily sums of GHI [kWh/m2] 5.0 4.0 3.0 2.0 1.0 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Hanimaadhoo Hulhulé Kadhdhoo Gan Min - Max Figure 7.3: Global Horizontal Irradiation − long-term monthly averages, minima and maxima. Page 50 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Figure 7.3 compares daily values of GHI at selected sites. When comparing GHI for these sites, they demonstrate a very similar pattern. The most stable weather with highest GHI values is from February to April. Higher variability of GHI between sites is observed in November and December. These months show also the highest range of minimum and maximum values of GHI. Very small variability of values is determined by similar geographical characteristics, and Fig. 7.3 indicates that all sites will experience similar PV power performance. Weather changes in cycles and has also stochastic nature. Therefore annual solar radiation in each year can deviate from the long-term average in the range of few percent. The estimation of the interannual variability shows the magnitude of this change. 6.0 2188 Average yearly sum of Global Horizontal Irradiation [kWh/m2] Average daily sum of Global Horizontal Irradiation [kWh/m2] 5.5 2006 5.0 1824 4.5 1642 4.0 1460 Hanimaadhoo (1.8%) Hulhulé (1.9%) Kadhdhoo (2.3%) Gan (2.7%) 3.5 1278 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure 7.4: Interannual variability of GHI for selected sites. Numbers in parentheses show unbiased standard deviation of yearly GHI values. The interannual variability of GHI for the representative sites is calculated from the unbiased standard deviation of GHI over 15 years taking into consideration the long-term, normal distribution of the annual sums. All sites show similar patterns of GHI changes over the recorded period (Fig 7.4) and extremes for all sites (minimum and maximum) are reached almost in the same years. The most stable GHI (the smallest interannual variability) is observed in Hanimaadhoo and Hulhulé; these sites have also very similar values of interannual variability. The most variable site is Gan; this site has also the highest GHI values. Page 51 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 7.2 Ratio of diffuse and global irradiation Figure 7.5: Ratio of Diffuse to Global Horizontal Irradiation − long-term annual average Page 52 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Figure 7.6: Ratio of Diffuse to Global Horizontal Irradiation − long-term monthly averages Page 53 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results A higher ratio of diffuse to global horizontal irradiation (DIF/GHI) indicates less stable weather, higher occurrence of clouds, higher atmospheric pollution or water vapour. The lowest DIF/GHI values are identified in South of archipelago, where the yearly average ratio falls to 38%. During the season from June to September all sites show stable, but relatively high DIF/GHI ratio (up to 54%). The best conditions with clear sky and low aerosols typically occur from February to April in all Maldives, when DIF/GHI ratio reach values in range between 30% and 38%. But this period is very short. This indicates that the potential for concentrator technologies (CSP, CPV) in Maldives is limited. Table 7.2: Monthly averages of Ratio of Diffuse to Global Horizontal Irradiation (DIF/GHI) Average Diffuse to Global Horizontal Irradiation Ratio [%] Month Gan Hanimaadhoo Hulhulé Kadhdhoo January 39.0 42.7 41.4 40.5 February 36.4 39.7 36.4 36.1 March 34.1 37.4 34.4 32.7 April 36.8 40.3 37.8 37.7 May 36.5 47.3 42.8 38.4 June 43.1 50.6 44.5 41.1 July 44.9 50.9 45.4 43.6 August 43.1 47.8 44.8 43.4 September 41.1 45.7 45.6 43.0 October 41.6 40.5 39.8 41.7 November 38.1 45.4 42.3 40.1 December 38.6 43.5 44.3 43.0 YEAR 39.3 44.0 41.4 39.9 Table 7.2 and Figure 7.7 show the ratio of long-term averages of DIF to GHI for each of the selected sites in every month of a year (noted also as DIF/GHI). The lowest DIF/GHI ratio is in Gan and Kadhdhoo sites, which are located in the South of the archipelago; the highest DIF/GHI ratio is recorded in Hanimaadhoo. All representative sites show similar pattern of DIF/GHI ratio with high values reached in the same period of year (in 3 sites in July and in Hulhulé in September). 60 Average Diffuse to Global Horizontal 50 40 Irradiation Ratio[%] 30 20 10 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Hanimaadhoo Hulhulé Kadhdhoo Gan Figure 7.7: Monthly averages of DIF/GHI. Page 54 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 7.3 Global Tilted Irradiation Figure 7.8: Global Tilted Irradiation at 7° tilt towards equator – long-term daily average and yearly totals Page 55 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results The regional trend of GTI is similar to GHI. Installing PV modules at an optimum tilt (inclination) and orientated 2 toward the equator can result in annual average daily sum of GTI energy input up to 5.7 kWh/m (yearly sum 2 about 2080 kWh/m ), almost in all territory of Maldives. Figure 7.9: Gain of annual Global Tilted Irradiation relative to Global Horizontal Irradiation, in %. GTI is calculated for equator-oriented PV modules tilted at 7°. Page 56 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Figure 7.9 compares GTI and GHI regionally. GTI represents the global irradiation that is received by a tilted surface. Unlike a horizontal surface, the tilted surface also receives a small amount of ground-reflected radiation. The highest electricity gains from tilted PV modules can be obtained when modules are oriented at an optimum angle (assuming maximising the yearly power production). Higher values are recorded in the North of the archipelago, which are further from the equator (so a horizontal plane receives less irradiance). Table 7.3: Daily averages, minima and maxima of Global Tilted Irradiation at selected sites Global Tilted Irradiation [kWh/m2] Month Gan Hanimaadhoo Hulhulé Kadhdhoo Min Min Min Min Average Average Average Average Max Max Max Max 4.68 5.50 5.10 5.27 January 5.47 5.96 5.97 6.02 6.20 6.41 6.42 6.70 5.54 5.58 6.23 5.81 February 6.15 6.43 6.65 6.58 6.58 6.92 7.06 7.04 5.94 5.61 5.87 6.00 March 6.49 6.78 6.77 6.68 7.01 7.15 7.23 7.14 5.42 5.75 5.47 5.32 April 6.03 6.16 5.98 5.79 6.49 6.76 6.62 6.14 5.30 4.38 4.90 4.72 May 5.71 5.21 5.26 5.25 6.21 6.04 5.62 5.59 4.37 4.09 4.35 4.50 June 5.30 4.80 5.00 5.03 5.86 5.67 5.63 5.54 4.62 4.34 4.54 4.49 July 5.20 4.95 5.05 4.95 5.91 5.61 5.49 5.40 4.66 4.76 4.51 4.51 August 5.50 5.41 5.42 5.24 6.01 6.08 6.02 5.71 4.75 5.06 4.79 5.14 September 5.76 5.68 5.48 5.64 6.16 6.61 6.65 6.45 4.89 4.70 5.32 5.07 October 5.51 5.70 5.86 5.74 6.52 6.30 6.67 6.34 4.53 4.11 4.28 4.42 November 5.41 5.29 5.38 5.58 6.36 6.06 6.23 6.76 4.34 4.53 3.90 4.17 December 5.21 5.39 5.27 5.39 5.99 6.29 6.22 6.29 5.37 5.45 5.44 5.38 YEAR 5.64 5.64 5.67 5.65 5.76 5.73 5.81 5.81 8.0 7.0 6.0 Daily sums of GTI [kWh/m2] 5.0 4.0 3.0 2.0 1.0 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Hanimaadhoo Hulhulé Kadhdhoo Gan Min - Max Figure 7.10: Global Tilted Irradiation − long-term daily averages by month. Vertical bars represent minima and maxima. Table 7.3 shows long-term averages of average daily sums of Global Tilted Irradiation (GTI) for selected sites. It is assumed that the solar radiation is received by plane tilted at optimum tilt and facing towards equator. The Page 57 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results main parameter influencing optimum tilt in Maldives is latitude. For this region, optimum tilt is between 3° and 10° (increasing from the equator towards the poles). Figure 7.10 compares long-term daily averages for selected sites. Stable weather with high GTI values is seen from January to April. Variability of GTI in all selected sites is very small. Lower daily averages in period from August to December are very similar for all sites, which is related to the rainy season. 10.0 7.5 Relative gain of GTI to GHI [%] 5.0 2.5 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec -2.5 -5.0 -7.5 -10.0 Hanimaadhoo Hulhulé Kadhdhoo Gan Figure 7.11: Monthly relative gain of Global Tilted Irradiation to Global Horizontal Irradiation at selected sites. A surface inclined at an optimum angle (tilt) gains more yearly irradiation than a horizontal surface (depending on the latitude of a site). In Maldives, where optimum tilt is close to horizontal position (ranging from 3° to 10°), the yearly gains of GTI are very low in comparison to GHI. This is documented on Figure 7.11, where a positive gain of GTI is about 5% to 6% (in October-March for sites located on Northern hemisphere), but this gain is reduced with almost similar losses during second half of the year (April-September). At Gan, located in the Southern hemisphere, the periods of gains and losses are reversed compared to the other selected sites. The annual gain of a tilted plane is only slightly above the yields of a horizontally mounted plane for all representative sites. Detailed comparisons of daily GTI and GHI values for Gan are shown in Figure 7.12 and Table 7.4. 7.0 55 Average daily sum of irradiation [kWh/m2] 6.0 45 Percentual difference GTI vs. GHI [%] 35 5.0 25 4.0 15 3.0 5 2.0 -5 1.0 -15 0.0 -25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Global Horizontal Global Tilted Global Tilted vs. Horizontal Figure 7.12: Daily GHI (blue), GTI (red) and relative gain of monthly Global Tilted Irradiation relative to Global Horizontal Irradiation (violet) in Gan Page 58 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Table 7.4: Relative gain of daily GTI to GHI in Gan 2 Average daily sum of irradiation [kWh/m ] Site Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Global Horizontal 5.75 6.34 6.52 5.90 5.47 5.05 4.99 5.35 5.73 5.62 5.65 5.51 5.66 Global Tilted 5.47 6.15 6.49 6.03 5.71 5.30 5.20 5.50 5.76 5.51 5.41 5.21 5.65 Global Tilted vs. Horizontal [% ] -5 -3 0 2 4 5 4 3 1 -2 -4 -5 0 Daily sums for any particular year are displayed in Figure 7.13 to better visualize the gain for tilted surfaces in comparison to horizontal ones. Figure 7.13 shows daily sums for the year 2013 in Gan. The blue pattern, representing GHI sums, is transparent in order to make visible the lower values of red (GTI). 10 Global Tilted Global Horizontal Daily sums of irradiation [kWh/m2] 8 6 4 2 0 1.1.2013 1.2.2013 1.3.2013 1.4.2013 1.5.2013 1.6.2013 1.7.2013 1.8.2013 1.9.2013 1.10.2013 1.11.2013 1.12.2013 Figure 7.13: Daily values of GHI and GTI for Gan, year 2013 Page 59 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 7.4 Direct Normal Irradiation Figure 7.14: Direct Normal Irradiation − long-term daily averages and yearly totals Period 1999-2013; source: SolarGIS. Page 60 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Figure 7.15: Direct Normal Irradiation − long-term monthly averages of daily totals. Page 61 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results DNI is the key parameter for concentrated solar technologies. Highest values are in southern part of the archipelago. There are slightly lower values in the North of Maldives due to the higher presence of aerosols and clouds in the atmosphere. 2 The highest DNI in the South region of archipelago represents average daily totals of up to 4.8 kWh/m (equal to 2 2 yearly sum of about 1750 kWh/m , Figure 7.14). The season of high DNI with daily sums above 4.6 kWh/m lasts from January to April (Figure 7.15). When comparing monthly values of DNI with GHI it is apparent,that there is just one season with high DNI yields − from January to April. Table 7.5 shows the long-term average of daily totals and the minimum and maximum of DNI for the selected sites for the period 1999 to 2013. The highest DNI is reached in Gan and Kadhdhoo, the lowest on Hanimaadhoo. Table 7.5: Daily averages, minima and maxima of Direct Normal Irradiation at the selected sites Direct Normal Irradiation [kWh/m2] Month Gan Hanimaadhoo Hulhulé Kadhdhoo Min Min Min Min Average Average Average Average Max Max Max Max 3.62 4.13 3.65 3.54 January 4.88 4.67 4.73 4.84 5.93 5.61 5.44 5.82 4.60 3.76 4.43 3.92 February 5.41 5.13 5.57 5.51 6.40 5.96 6.38 6.41 4.71 4.38 4.71 4.97 March 5.71 5.47 5.80 5.90 6.66 6.16 6.77 6.69 4.21 4.27 4.17 4.32 April 5.12 4.86 5.03 5.01 5.71 5.71 6.13 5.73 4.05 2.54 3.62 3.89 May 4.86 3.73 4.21 4.65 5.57 4.75 5.09 5.63 2.90 2.55 3.18 3.70 June 4.07 3.25 3.91 4.32 4.84 4.44 4.87 5.22 3.29 2.29 3.12 3.33 July 3.80 3.24 3.82 3.99 4.58 4.26 4.40 4.89 3.09 2.85 3.08 3.13 August 4.08 3.68 3.98 4.03 4.79 4.58 4.77 4.74 3.03 3.08 3.03 3.44 September 4.41 3.98 3.87 4.20 5.29 5.32 5.63 5.51 3.54 2.90 3.80 3.70 October 4.44 4.44 4.63 4.42 6.27 5.50 6.19 5.33 3.37 2.76 2.88 3.09 November 4.86 3.94 4.18 4.50 6.63 5.07 5.50 6.21 3.47 2.97 2.21 2.56 December 4.82 4.27 4.05 4.23 6.24 5.84 5.47 5.59 4.39 3.95 4.18 4.32 YEAR 4.70 4.22 4.47 4.63 5.00 4.47 4.72 4.90 Figure 7.16 compares monthly averages of the daily total DNI: Gan, Hulhulé and Kadhdhoo have very similar pattern. Variability of DNI daily totals, given by the minimum and maximum range, are included in the graph (vertical bars). Higher DNI is reached in a period from January to April. Page 62 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 8.0 7.0 6.0 Daily sums of DNI [kWh/m2] 5.0 4.0 3.0 2.0 1.0 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Hanimaadhoo Hulhulé Kadhdhoo Gan Min - Max Figure 7.16: Monthly average daily totals of DNI at selected sites. The interannual variability of DNI for selected representative sites is calculated from the unbiased standard deviation of DNI over 15 years and considering the long-term, normal distribution of the annual totals. Three sites show similar patterns of DNI changes over recorded period (Fig 7.17). The most stable DNI (the lowest interannual variability) is observed in Hanimaadhoo. 6.0 2188 Average yearly sum of Direct Normal Irradiation [kWh/m2] Average daily sum of Direct Normal Irradiation [kWh/m2] 5.5 2006 5.0 1824 4.5 1642 4.0 1460 Hanimaadhoo (4.3%) Hulhulé (4.5%) Kadhdhoo (4.6%) Gan (5.1%) 3.5 1278 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Figure 7.17: Interannual variability of DNI for representative sites Numbers in parentheses show unbiased standard deviation of yearly DNI values. Daily totals for each particular year can be displayed to better visual present portion of DNI in relation to GHI. Figure 7.18 shows daily totals for year 2013 on Gan site. Blue pattern, representing GHI is transparent in order to make visible lower values of orange (DNI). Page 63 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 10 Direct Normal Global Horizontal Daily sums of irradiation [kWh/m2] 8 6 4 2 0 1.1.2013 1.2.2013 1.3.2013 1.4.2013 1.5.2013 1.6.2013 1.7.2013 1.8.2013 1.9.2013 1.10.2013 1.11.2013 1.12.2013 Figure 7.18: Average daily totals of GHI and DNI for Gan, year 2013 Page 64 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 8 PHOTOVOLTAIC POWER POTENTIAL 8.1 Reference configuration The amount of radiation received on a flat plate collector depends on the PV panel mounting configuration, as shown in Figure 7.8.. Map below shows theoretical potential power production of a PV system installed with standard technology configuration, which is described in Table 8.1. In this study, the following configuration of a PV power plant is considered: small ground mounted power plant with PV modules oriented towards the equator (modules on Gan island are oriented towards North, modules on Hanimaadhoo, Hulhulé and Kadhdhoo island oriented towards South). The modules are fixed-mounted (non- tracking) with a tilt angle of 7º and configured so that there is no shading caused by adjacent rows.. The optimum tilt angle for Maldives is in the range between 3º and 10º depending on the geographic location, but due to minimum effect on differences in GTI a one tilt of 7º has been chosen for all sites. Keeping the modules tilted to some extent helps cleaning their surface by rainfall. Further it is considered that DC electricity from the panels first passes through string inverters and then it is fed directly into the distribution grid as AC power. The use of high-power centralised inverters is only suitable for larger PV system, which can only be located on larger islands. No external grid transformer is considered in this reference configuration. Table 8.1: Reference configuration − photovoltaic power plant with fixed-mounted PV modules Feature Description Configuration represents a typical PV power plant of 10 kWp or higher. All calculations are Nominal capacity scaled to 1 kWp, so that they can be easily multiplied for any installed capacity. Crystalline silicon modules with positive power tolerance. Nominal Operating Cell Temperature Modules (NOCT) 45ºC and temperature coefficient of the Pmax -0.44 %/K Inverters String inverter with declared datasheet efficiency (Euro efficiency) 97.0% Ground mounted PV modules, facing towards the equator with 7º tilt, assuming no shading Mounting of PV modules between rows Transformer No transformer: only direct connection into the grid is assumed Photovoltaic power production has been calculated using numerical models developed and implemented in- house by GeoModel Solar. As introduced in Chapter 5.1.4, 30-minute time series of solar radiation and air temperature data, representing the last 15 years, are used as inputs to the simulation of PV power production. Simulations are based on the expert knowledge, monitoring results and recommendations of [24]. Table 8.2 summarizes the losses and related uncertainty throughout the PV computing chain. The reference configuration is very common and provides a robust solution with minimum maintenance effort. Geographic differences in potential PV production are demonstrated at four selected sites. The detailed technical configuration would depend on financial and technical assumptions of a project, and this exceeds the scope of this report. The performance degradation of PV modules due to aging is not considered in this report. Page 65 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Table 8.2: Summary of yearly energy losses and related uncertainty in each step of PV power simulation Simulation step Losses Uncertainty Notes [%] [± %] Global Tilted Irradiation N/A 6.0 Annual Global Irradiation falling on the surface (model estimate) of PV modules Polluted surface of modules -1.5 1.5 Losses due to dirt, dust, soiling, and bird (empirical estimate) droppings Module surface angular reflectivity -3.0 to -3.5 1.0 Slightly polluted surface is assumed in the (numerical model) calculation of the module surface reflectivity Module inter-row shading 0.0 0.0 No shading of strings by modules from (model estimate) adjacent rows Conversion in modules relative to STC -11.0 to -12.0 3.5 Depends on the temperature and irradiance. (numerical model) NOCT of 45ºC is considered Mismatch between modules -0.5 0.5 Well-sorted modules and lower mismatch are (empirical estimate) considered. Power tolerance 0.0 0.0 Value given in the module technical data sheet (value from the data sheet) (modules with positive power tolerance) DC cable losses -1.5 1.5 This value can be calculated from the electrical (empirical estimate) design Conversion losses in the inverter -3.0 0.5 Given by the Euro efficiency of the inverter, (value from the technical data sheet) which is considered at 97.0% AC losses -1.0 0.5 AC connection is assumed without transformer (empirical estimate) Availability 0.0 1.5 100% technical availability is considered; the uncertainty considered here assumes a well- integrated system; the real value strongly depends on the efficiency of PV integration into the existing grid. Range of cumulative losses -20.0 to -21.3 7.5 These values are indicative and do not and approximate uncertainty consider a number of project specific features and performance degradation of a PV system over its lifetime PV electricity potential is calculated based on a set of assumptions shown in Table 8.1 and Table 8.2. These assumptions are approximate, as they will differ in real projects. Page 66 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 8.2 PV power potential in Maldives Figure 8.1: PV electricity output from an open-space fixed PV system with a nominal peak power of 1 kW − long-term daily and annual totals Page 67 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Figure 8.2: PV electricity potential for open-space fixed PV system − long-term monthly average daily totals Page 68 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Figure 8.1 shows the average daily total of specific PV electricity output from a typical PV system with a nominal peak power of 1 kWp, i.e. the values are in kWh/kWp. Calculating PV output for 1 kWp of installed power makes it possible to scale the estimate of PV power production for any size. Figure 8.2 shows monthly production from the fixed tilted PV power systems in the region and Figure 8.3 at four selected locations. In Maldives, the average daily sums of specific PV power production from a reference system vary between 4.4 kWh/kWp (equals to yearly sum of about 1605 kWh/kWp) and 4.5 kWh/kWp (about 1640 kWh/kWp yearly). Average daily totals for the year are very uniform throughout all of Maldives. Local differences in PV production are the most visible in the season from August to December. The best season for PV power production is from January to April, with extreme values in March, when they reach up to 5.6 kWh/kWp. The PV potential from a reference system for four representative sites is shown in Table 8.3. Table 8.3: Annual average of electricity yield for fixed PV system tilted at angle 7º Gan Hanimaadhoo Hulhulé Kadhdhoo Average daily total PV electricity yield for 4.46 4.45 4.47 4.46 fixed-mounted modules at angle 7º kWh/kWp kWh/kWp kWh/kWp kWh/kWp Yearly total PV electricity yield for fixed- 1627 1623 1632 1627 mounted modules at angle 7º kWh/kWp kWh/kWp kWh/kWp kWh/kWp Optimum angle N/A 9º 6º 4º Annual ratio of diffuse/global horizontal 39.3% 44.0% 41.4% 39.9% irradiation (DIF/GHI) System performance ratio (PR) for fixed- 78.9% 78.7% 78.8% 78.8% mounted PV Seasonal differences are relatively small and overall relatively high PV yield gives a good potential for effective operation of PV installations. As presented in Chapter 7.3, it is recommended to install modules at a tilt angle close to optimum. However for sites near equator, it is not recommended to tilt PV modules close to the horizontal position due to limited self-cleaning by rain. Modules installed at very low tilt will collect dirt and dust, which will result in PV output reduction. Despite the geographic distribution of selected sites, electricity production form a PV power system is similar for all sites and follows a combined pattern of global irradiation and air temperature. The difference between production from the “best” site (Hulhulé, 4.47 kWh/kWp) and “the least productive” site (Hanimaadhoo, 4.45 kWh/kWp) is only 0.5%. Also, monthly power production profiles are very similar for all sites. The highest seasonal production occurs from January to April. 6.0 Specific PV power production [kWh/kWp] 5.0 4.0 3.0 2.0 1.0 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Hanimaadhoo Hulhulé Kadhdhoo Gan Min - Max Figure 8.3: Daily total power production from the fixed tilted PV systems at selected sites with a nominal peak power of 1 kWp (long-term monthly averages, minima and maxima). Page 69 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results The monthly and yearly performance ratios (PR) of a reference installation for the selected sites are shown in Table 8.5 and Figure 8.5. The range of yearly PR for the selected sites is negligible: between 78.7% (Hanimaadhoo) and 78.9% (Gan). Monthly variations in PR also fall in a very narrow range, less than ±1%. Table 8.4: Average daily sums of PV electricity output from an open-space fixed PV system with a nominal peak power of 1 kWp Average daily sum of electricity production [kWh/kWp] Site Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Gan 4.32 4.80 5.10 4.75 4.51 4.21 4.13 4.36 4.55 4.36 4.28 4.12 4.46 Hanimaadhoo 4.71 5.03 5.32 4.83 4.09 3.78 3.92 4.28 4.48 4.48 4.18 4.27 4.45 Hulhulé 4.73 5.20 5.32 4.70 4.14 3.95 3.99 4.28 4.32 4.62 4.25 4.18 4.47 Kadhdhoo 4.76 5.13 5.25 4.55 4.15 3.97 3.92 4.14 4.45 4.53 4.41 4.26 4.46 Impact of temperature on the performance of PV power plants is apparent (lower efficiency due to higher air temperature), and due to minimum temperature variability, also the seasonal profile changes minimally − within approx. one percent point. Table 8.5: Monthly and annual Performance ratio of an open-space fixed PV system Monthly Performance Ratio [% ] Site Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Gan 79.0 77.9 78.6 78.8 78.9 79.3 79.3 79.2 79.0 79.0 79.1 79.1 78.9 Hanimaadhoo 79.1 78.2 78.5 78.3 78.4 78.9 79.1 79.0 78.9 78.7 78.9 79.2 78.7 Hulhulé 79.1 78.2 78.6 78.7 78.8 79.0 79.0 79.0 78.8 78.9 79.1 79.2 78.8 Kadhdhoo 78.9 78.0 78.5 78.7 78.9 78.9 79.1 79.0 78.9 79.0 78.9 79.1 78.8 80.0 79.5 79.0 Performance ratio [%] 78.5 78.0 77.5 77.0 76.5 76.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Hanimaadhoo Hulhulé Kadhdhoo Gan Figure 8.4: Monthly performance ratio of a fixed tilted PV system at selected sites assuming the nominal peak power of 1 kWp Page 70 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 8.3 PV integration into the electricity grid in Maldives The electricity system in Maldives is based on diesel generators connected to a local grid with installed capacity reflecting the consumption of the local inhabitants. According to the study prepared by Mercados, financed by the Asian Development Bank [42] the energy needs of Maldives can be categorised into 4 separate groups according to size and scope: very small (~100 kWp), medium/large (several hundred kWp to MWp), privately owned resort islands (300-800 kWp), and the Malé’ region. Due to the limited availability of free space for PV installations it is not possible at the present time to meet all energy consumption with PV systems, but offsetting any portion of diesel would be very beneficial. Maldives archipelago is located in the equatorial zone and experiences climate, where two seasons are observed (as presented in previous chapters): dry season (northeast monsoon) and wet season (southwest monsoon). Months with more sunshine (from January to April) pose challenges to the energy system due to higher electricity needs for air conditioning. Still warm but cloudy months occur for the rest of the year with maximum clouds in November and December. This is shown in Figure 8.5, where monthly cloud occurrence statistics are given for Hulhulé. To remove seasonal variability the daily sums are normalized against the maximum daily sum in the given month. Normalised daily sums of GTI are then classified into three categories: • Low GTI (cloudy day): Daily sums are lower than 30% of maximum in the given month, • Intermediate GTI (variable cloudiness during a day): Daily sums are between 30% and 70% of maximum in the given month, • High GTI (sunny day): Daily sums are higher than 70% of monthly maximum. GTI sum: > 70% GTI sum: 30 to 70% GTI sum: < 30% 100% 90% Percentage of days within given class 80% 70% 65.1 64.7 74.4 72.7 71.6 74.0 70.9 74.6 60% 80.2 84.1 90.6 91.6 50% 40% 30% 20% 26.2 26.9 21.1 22.2 21.8 19.8 21.3 20.2 10% 12.0 16.0 8.0 8.2 7.3 8.7 8.4 3.9 3.8 5.8 6.2 6.2 4.7 5.2 0% 1.4 0.2 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 8.5: Occurrence of days (cloudy/variable/sunny) relative to GTI daily sums for each month in Hulhulé. 8.3.1 Case study of Malé and Hulhumalé Here we demonstrate the utilization of solar PV–diesel hybrid system. We show simulation examples of PV electricity for two selected days (using the data and the algorithms developed by GeoModel Solar). These results are compared to hourly load profiles, which have been provided by Stelco utility. The size of the PV installation has been adjusted according to recommendation from the literature [40, 41, 42 and 43] and corresponds to PV penetration levels of 20% (low) and 60% (high) of the maximum hourly load (i.e., 20% and 60% of the diesel generator capacity). • Level 20% does not require dedicated control system and the PV installation can be directly coupled with diesel generator (PV penetration from approximately 10 to 30% is safe for the operation stability of the diesel generator, and it is beneficial for generator fuel economy). • Level 60% requires more sophisticated system with dedicated fuel save controlling unit [42, 43]. The fuel save controller ensures the demand-oriented control of the photovoltaic system dependent on the Page 71 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results power plant’s load and the characteristics of the diesel generator. Thus the diesel generator operates in a reliable and stable state even with high levels of photovoltaics. • Higher penetration of PV will require energy storage and this option is not analysed in this study. Figure 8.6 shows load and generation profiles for Hulhumalé (27 Nov 2013, cloudy day) and Malé (20 Feb 2013, sunny day). With a PV penetration of 60%, it is possible to reduce the amount of electricity produced by diesel generators during the peak consumption by up to 60-70%. Figure 8.6: Hourly load and PV electricity generation profiles for Hulhumalé and Malé. Hulhumalé (top) shows a PV contribution during a cloudy day, Malé (bottom) shows a contribution of PV during a sunny day. Page 72 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results The main issue of wide deployment of PV systems in Maldives is limited space. As an example for Malé, with maximum instantaneous consumption of 40 MWp of electricity, even 20% of PV would require installation of a 10 MWp photovoltaic power plant. This translates into approximately 10 ha of free space. Such a space would be available only in Hulhulé and Hulhumalé islands − but targeted planning would be required (unused fragmented space and roofs need to be utilised). Also, transporting the solar electricity to Malé would require deployment of a submarine power cable. 8.3.2 Case study of Addu The same methodology has been applied for Addu Island. Hourly electricity consumption data has been provided by Fenaka utility for almost a complete year 2013 (from 1 January 2013 to 11 December 2013). The impact of PV electricity injected into the system on Addu Island is illustrated in Figure 8.7 on an hourly basis: for the levels of PV power representing 20% and 60% of the maximum consumption. The highest penetration of PV power can be observed during sunny days and obviously for a case of 60% of installed PV capacity. During a cloudy day (18 Feb 2013) the input from the PV installation is also observed, but to a smaller extent: it covers only a portion of the daily peak load. Figure 8.7: Instantaneous (hourly) load and PV electricity generation profiles for Addu Island with PV installation size of 20% and 60% of maximum hourly system load The comparison of energy consumption with PV production presented in Figures 8.8 and 8.9 shows daily electricity generated by a PV installation, energy consumption, and the workload of diesel generators. When considering PV installation size at the levels of 20% and 60% of the maximum hourly load, as has been presented for Malé region, PV electricity would cover approximately 5.4% and 16.3%, respectively, of annual energy consumption. A higher energy share from PV would require battery storage, which can be economically feasible in the close future. Page 73 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Figure 8.8: Daily load and PV electricity energy sums for Addu Island with PV installation size of 20% of maximum hourly system load. Figure 8.9: Daily load and PV electricity energy totals for Addu Island with PV installation size of 60% of maximum hourly system load Page 74 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 9 SOLAR AND METEOROLOGICAL DATA UNCERTAINTY The expected data uncertainty is based on the validation exercise shown in Chapter 6 of the Model Validation Report (129-02/2015) and is summarized in Tables 9.1 and 9.2. Table 9.1: Uncertainty of longterm model estimates for GHI, GTI and DNI values in Maldives Acronym Yearly uncertainty Monthly uncertainty Global Horizontal Irradiation GHI ±6% ±8% Global Tilted Irradiation GTI ±6% ±9% Direct Normal Irradiation DNI ±12% ±15% Table 9.2: Uncertainty of the longterm modelled meteorological parameters in Maldives Acronym Yearly uncertainty Monthly uncertainty Air temperature at 2 m TEMP <1 °C <1.5 °C Relative humidity at 2 m RH <8% <10% Average wind speed at 10 m WS <1.5 m/s <1.5 m/s Page 75 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 10 APPLICATION OF SOLAR AND METEOROLOGICAL DATA Good quality solar resource data are critical for economic and technical assessments of solar electricity infrastructure in a country. Bankability of solar resource data is about achieving the lowest possible uncertainty and understanding and managing the risk. Technically, good bankable solar resource data should: • Be based on proven methods, systematically validated and traceable • Represent at a minimum 10-year continuous time span, • Follow specified quality control standards, • Include information about solar resource uncertainty • Include metadata and be supported by a technical report • Be supported by dedicated professional service providers. An important part of bankable data is the uncertainty assessment, which includes two aspects: • Uncertainty of the estimate • Uncertainty given by longterm weather variability The uncertainty has a probabilistic nature and can be expressed in different levels of confidence. The need for a specific type of data depends on a stage of solar power project development. The data products are described in Chapter 2 and in a different way also in Figure 10.1. This chapter provides general rules, though due to the specific case of Maldives (geographic conditions and dispersion of population) some of them can be simplified. Figure 10.1: Stages of development and operation of a solar power plant (adapted from [47]) Page 76 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 10.1 Site selection and prefeasibility Candidate sites are evaluated to determine which are the most suitable for a project development. Annual longterm averages or aggregated statistics are required at this stage. Monthly longterm averages are also useful, optimally in the form of maps. Additionally, map information on terrain, population, landscape, grid power lines, etc. is used. The comparison of candidate sites and considered technologies requires considering a number of options and discussing them within a group of partners. This task can be effectively performed when using web-based tools with an option for generating PDF reports and downloading data in a format that can be further used in desktop applications or simulation programs. This stage can be documented with reports providing a first estimate of solar resource and local climate. In geographically complex countries thorough GIS analysis capability, involving spatial data and support information can be used to rank the territory and help with preselecting the candidate sites (this does not apply for Maldives). 10.2 Feasibility and project development Once a decision about the prospective site(s) is made for a large project, a ground station should be installed at the site to produce short-term measurements of local solar and meteorological variables. This is particularly important for medium and large size PV projects. For the selected site(s), the next important step is an assessment of possible design and operational variants to optimize energy performance. At this stage, a more comprehensive knowledge of the annual solar resource, as well as an understanding of seasonal and interannual variability and related uncertainties, is required. Hourly (or sub-hourly) times series of GTI or DNI and are needed. Also other meteorological parameters may be relevant, such as air temperature, wind speed and direction and relative humidity. The data are used in the TMY format (typically applied in engineering simulation software) or preferably as multiyear time series. It is generally accepted that a minimum time period of data needed for obtaining a representative picture of solar microclimate is 10 years. In Maldives, 15 or more years of data are available. For larger projects (multi-megawatt solar power plants), a ground measurement campaign is often required for quality enhancement of satellite-derived solar data. Prior to be used, the local measurements have to pass quality control procedures. When a representative data set of local measurements is available (at least one year), the next step is to conduct site adaptation of satellite-based time series. The resulting site-adapted time series should have a minimum bias, minimum RMSD and a more realistic probability distribution function. 10.3 Due diligence Due diligence results in a detailed performance analysis of a solar power plant over its projected economic lifetime and includes elaboration on the following information: • Uncertainty of longterm solar resource estimate and meteorological data; • Seasonal and diurnal variability, including probability distribution and uncertainty of production within a day and for each month/season; • Uncertainty due to variability of the solar resource considering the established confidence limits, most typically P90. Confidence limits are used to describe probability of exceedance values - for any single year (e.g. to assess financial reserve funds for low production years) and also for the lifetime of the energy installation (to asses longterm possible weather fluctuation). In general understanding the impact of weather extremes, including risk of large-scale volcanic eruptions, is to be considered; Page 77 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results These analyses are to assess performance of the solar project from the point of view of technology but also cash flow and the related risk. A typical consultancy report prepared at this stage for a specific development site are Site-specific Solar Resource Assessment Studies. In addition, an Energy Yield Assessment Study is prepared for PV projects, which provide in-depth characteristics of the site, analysis of the performance of considered technology options, optimization of the planned design and calculation of the variability and uncertainty of the power production. 10.4 Performance assessment and monitoring Once a project is commissioned, the monitoring of a solar power plant involves measuring the technological parameters at the level of the components as well as the solar resource and meteorological data. These data are cross-analysed to better characterise a relationship of the power plant performance to the environmental conditions, and to identify potential improvements. For larger PV projects, to obtain high-quality solar resource data, deploying a local meteorological station is a justifiable expense. In case of medium size and small PV projects, solar resource and meteorological data from models are a satisfactory compromise between required accuracy and costs of monitoring and performance assessment. Time series of continuously measured on-site or satellite-based data are used for performance monitoring and reporting. The longterm solar resource monitoring includes systematic collection of measurements, and their quality control to enable: (i) support of the operation and failure assessment during daily routines, (ii) regular technology appraisal and reporting, e.g., on a quarterly or annual basis. High frequency (minute up to sub-hourly) time series of solar irradiance data are used at this stage to systematically check the actual performance characteristics. The requirement is that the data from the most recent period are needed with minimum bias and lowest possible RMSD. The uncertainty of either the installed ground instruments or satellite-derived has to be estimated. Cross-comparison of irradiance data sources (from several radiometers and with satellite-based time series) is used for minimizing errors. Performance of the power plant degrades in the long term, due to technology ageing, and also varies depending on the seasonal cycles and short-term weather changes. In technology performance assessment, real weather and production data are compared with solar radiation and expected (calculated) production to analyse trends and fluctuation of performance in PV projects and detect any possible shortcomings or needs for operational improvements. The objective of the performance assessment report is to (i) confirm the longterm production hypothesis, and to (ii) identify starting conditions for longterm monitoring. Data from the real-time observations for the most recent period are needed with minimum bias, lowest possible RMSD and quantified uncertainty. Even though day-by-day monitoring can be performed by on-site personnel, it is a good practice to involve an independent service provider. Regular reporting keeps track of the production history and makes management routines more efficient. Regular monitoring provides important information about the events affecting production and performance efficiency and their possible deviation from the expected behaviour and trends. Before any analysis, the input measured data have to be validated, cleaned and qualified, otherwise the interpretation of results may be biased or misleading. 10.5 Operation IN general, an important data service of solar power plant operation is forecasting − for optimisation of power generation. For standalone applications and small grids, stability and efficient use of backup solutions depend on solar forecasting. Solar irradiance data products include forecasted time series of GHI, GTI or DNI at hourly time step, and the requirement is zero bias and low RMSD and information availability ahead one day or up to few hours. Page 78 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 11 SOLARGIS DATA DELIVERY FOR MALDIVES The key features of the delivered data and maps are: • Harmonized solar, meteorological and geographical data, based on the best available methods and input data sources. • Historical longterm averages representing 15 years at high spatial and temporal resolution, available for any location. • The SolarGIS database and energy simulation software are extensively validated by GeoModel Solar, and also by independent organizations. They are also verified within monitoring of commercial PV power plants and solar measuring stations worldwide. • Additional data can be accessed online at http://solargis.info. The delivered data and maps offer a good basis for knowledge-based decision-making and project development. These data are updated in real time can be further used in solar monitoring, performance assessment and forecasting. 11.1 Spatial data products High-resolution SolarGIS data have been delivered in the format suitable for common GIS software. The Primary data represent solar radiation, meteorological data and PV potential production. The Supporting data include various vector data, such administrative divisions. 11.1.1 Primary data Tables 11.1 and 11.2 show information about the delivered data layers. Tables 11.3 and 11.5 show technical specifications. File name convention, used for the individual data sets, is described in Tables 11.6 and 11.7. Table 11.1: General information about GIS data layers Geographical extent Region between parallels 7°30’N to 1°00’S and meridians 72°30’E to 74°00’E, covering entire Republic of the Maldives Map projection Geographic (Latitude/Longitude), datum WGS84 (also known as GCS_WGS84; EPSG: 4326) Data format ESRI ASCII raster data format Page 79 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Table 11.2: Description of primary GIS data layers Acronym Full name Unit Type of use Type of data layers 2 GHI Global Horizontal kWh/m Reference information for the 1. Long-term average of Irradiation assessment of flat-plate PV daily sums (photovoltaic) and solar heating 2. Average of daily sums in technologies (e.g. hot water) a particular year and month 3. Hourly data (NetCDF) 2 DNI Direct Normal kWh/m Assessment of Concentrated PV 1. Long-term average of Irradiation (CPV) and Concentrated Solar daily sums Power (CSP) technologies, but also 2. Average of daily sums in two-axis tracking flat plate PV a particular year and month 3. Hourly data (NetCDF) 2 DIF Diffuse Horizontal kWh/m Complementary parameter to GHI 1. Long-term average of Irradiation and DNI daily sums 2. Hourly data (NetCDF) 2 GTI Global Irradiation for kWh/m Assessment of solar resource for Long-term average of daily surface with 7° tilt PV technologies sums towards equator PVOUT Photovoltaic electricity kWh/kWp Assessment of PV power Long-term average of daily output of free-standing production potential for a free sums fixed-mounted c-Si standing PV power plant with modules with 7° tilt modules mounted at optimum tilt to towards equator maximize yearly PV production TEMP Air Temperature at 2 °C Defines operating environment of 1. Long-term (diurnal) m above ground level solar power plants annual and monthly averages 2. Hourly data (NetCDF) GHI_STD Interannual variability % Relative standard deviation of - of Global Horizontal yearly values indicates year-by-year Irradiation variability of GHI DNI_STD Interannual variability % Relative standard deviation of - of Direct Normal yearly values indicates year-by-year Irradiation variability of DNI GTI_STD Interannual variability % Relative standard deviation of - of Global Irradiation at yearly values indicates year-by-year optimum tilt variability of GTI Page 80 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Table 11.3: Technical specification of primary GIS data layers Acronym Full name Data Spatial resolution Time No. of data layers format representati on GHI Global Horizontal Raster 15 arc-sec. 1999 - 2013 12+1+180 Irradiation (approx. 465x465 m) DNI Direct Normal Raster 15 arc-sec. 1999 - 2013 12+1+180 Irradiation (approx. 465x465 m) DIF Diffuse Horizontal Raster 15 arc-sec. 1999 - 2013 12+1 Irradiation (approx. 465x465 m) GTI Global Irradiation for Raster 15 arc-sec. 1999 - 2013 12+1 surface with 7° tilt (approx. 465x465 m) towards equator PVOUT Photovoltaic electricity Raster 15 arc-sec. 1999 - 2013 12+1 output for fixed-mounted (approx. 465x465 m) modules with 7° tilt towards equator TEMP Air Temperature at 2 m Raster 30 arc-sec. 1999 - 2013 12+1 above ground level (approx. 930x930 m) GHISTD Interannual variability of Raster 15 arc-sec. 1999 - 2013 1 Global Horizontal (approx. 465x465 m) Irradiation DNISTD Interannual variability of Raster 15 arc-sec. 1999 - 2013 1 Direct Normal Irradiation (approx. 465x465 m) GTISTD Interannual variability of Raster 15 arc-sec. 1999 - 2013 1 Global Irradiation at (approx. 465x465 m) optimum tilt Table 11.4: Technical specification of NetCDF files (hourly data) Acronym Full name Data Spatial resolution Time Time step format representati on GHI Global Horizontal Raster 15 arc-sec. 1999 - 2013 Hourly Irradiation (approx. 465x465 m) DNI Direct Normal Raster 15 arc-sec. 1999 - 2013 Hourly Irradiation (approx. 465x465 m) DIF Diffuse Horizontal Raster 15 arc-sec. 1999 - 2013 Hourly Irradiation (approx. 465x465 m) TEMP Air Temperature at 2 m Raster 30 arc-sec. 1999 - 2013 Hourly above ground level (approx. 930x930 m) Table 11.5: Characteristics of the raster output data files* Characteristics Range of values West − East 72:30:00E− 74:00:00E North − South 07:30:00N − 01:00:00S Resolution (GHI, DNI, GTI, DIF, PVOUT) 00:00:15 (2040 columns x 360 rows) Resolution (TEMP) 00:00:30 (1020 columns x 180 rows) Data type Float No data value -9999 * see http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#/ESRI_ASCII_raster_format/009t0000000z000000/ Page 81 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Table 11.6: File name convention for GIS data Acronym Full name Filename pattern Number Size of files (approx.) GHI Global Horizontal Irradiation, GHI_MM 13 53 MB long-term monthly (or yearly) sum GHI_TS Global Horizontal Irradiation, GHI_YYYY_MM 180 742 MB monthly sum time-series data DNI Direct Normal Irradiation, DNI_MM 13 53 MB long-term monthly (or yearly) sum DNI_TS Direct Normal Irradiation, DNI_YYYY_MM 180 742 MB monthly sum time-series data DIF Diffuse Horizontal Irradiation, DIF_MM 13 53 MB long-term monthly (or yearly) sum GTI Global Irradiation for surface with 7° tilt towards GTI_MM 13 53 MB equator, long-term monthly (or yearly) sum PVOUT Photovoltaic electricity output for fixed-mounted PVOUT_MM 13 53 MB c-Si modules tilted at 7° towards equator, long-term monthly (or yearly) sum TEMP Air Temperature at 2 m above ground TEMP_MM 13 10 MB GHISTD Interannual variability of Global Horizontal GHI_STD 1 3 MB Irradiation DNISTD Interannual variability of Direct Normal Irradiation DNI_STD 1 3 MB GTISTD Interannual variability of Global Irradiation for GTI_STD 1 3 MB surface tilted at 7° towards equator Explanation: • MM: month of data – from 01 to 12 (13 means yearly average) • YYYY: year of data – from 1999 to 2013 Table 11.7: File name convention for NetCDF files Acronym Full name Filename pattern Number Size of files (approx.) GHI Global Horizontal Irradiance GHI_YYYY 15 - DNI Direct Normal Irradiance DNI_YYYY 15 - DIF Diffuse Horizontal Irradiance DIF_YYYY 15 - TEMP Air Temperature at 2 m above ground TEMP_YYYY 15 - 11.1.2 Support GIS data The support GIS data are provided in a vector format (ESRI shapefile, Table 11.8). Vector data were provided by the Government of Maldives and were adapted by GeoModel Solar for this solar resource study. Page 82 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Table 11.8: Support GIS data Data type Source Data format Island Government of Maldives Polygon shapefile Reef Government of Maldives Polygon shapefile Lagoons Government of Maldives Polygon shapefile Inhabited islands Government of Maldives Point shapefile 11.1.3 Project in QGIS and ESRI ArcGIS format For easy manipulation with GIS data files, selected vector and raster data files are integrated into ready-to-open Quantum GIS (QGIS) project file with colour schemes and annotation (see Figure 11.1). Similarly, the selected data files were integrated also into the ESRI ArcMap 10.2 project file. QGIS is state-of-art GIS software allowing visualization, query and analysis on the provided data. QGIS includes a rich toolbox to manipulate with data. More information about the software and download packages can be found at http://qgis.org. More information about ESRI software can be found at http://esri.com. Figure 11.1: Screenshot of map and data in the ESRI ArcMap environment Page 83 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 11.2 Digital maps Besides GIS data layers, digital maps are also delivered for selected data layers for presentation purposes. Digital maps are prepared in three types; each suitable for different purpose: • High-resolution poster maps • Medium-resolution maps for presentations • Image maps for Google Earth. 11.2.1 High-resolution poster maps Digital images for high-resolution poster printing (size 65 x 170 cm). The colour-coded maps are prepared in TIFF format at 300 dpi density and lossless compression. Following four map files are delivered for high-resolution poster printing: • Global Horizontal Irradiation – Yearly average of the daily totals • Direct Normal Irradiation − Yearly average of the daily totals • Air temperature at 2 metres − Long term yearly average • Photovoltaic electricity production from a free-standing power plant with optimally tilted c-Si modules − Yearly average of the daily totals Besides the main parameter, the poster maps include visualization of the following data layers: • Longitude and latitude lines • Islands location and names • Reef and lagoon borders • Graphs showing monthly data for four selected sites Page 84 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Figure 11.2: Example of high-resolution DNI poster map prepared in a resolution suitable for large format printing (original size 65 x 170 cm) Page 85 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 11.2.2 Medium-resolution maps for presentations Digital images prepared in a resolution suitable for A4 printing or on-screen presentation. The colour-coded maps are prepared in PNG format at 300 dpi density and lossless compression. Following map files are delivered: • Annual and monthly averages of Global Horizontal Irradiation • Annual and monthly averages of ratio Diffuse/Global Horizontal Irradiation • Annual and monthly averages of Global Tilted Irradiation • Annual and monthly averages of Direct Normal Irradiation • Annual and monthly averages of Air Temperature • Annual and monthly averages of Photovoltaic (PV) Electricity Potential • Maldives in the world context of Global Horizontal Irradiation map • Population map • Airports The maps also include visualization of the following layers: • Main cities location and names • Borders of administrative regions Figure 11.3: Example of medium resolution DNI map prepared in a resolution suitable for A4 printing or on-screen presentation Page 86 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 11.2.3 Image maps for Google Earth Spatially referenced digital image maps with corresponding KML file can be displayed in Google Earth application or any other GIS software (KML stands for “Keyhole Markup Language”). Map layers representing the following datasets are delivered: • Annual average of Global Horizontal Irradiation • Annual average of Direct Normal Irradiation Figure 11.4: Screenshot of DNI data displayed in Google Earth application Page 87 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 11.3 Site-specific data for four representative sites For demonstration of climate diversity four representative sites were selected in Maldives. Position of these sites was selected to coincide with meteorological station positions to obtain comparative data sets for further analysis. Representative sites are summarised in Table 11.9 and their position is marked in Figure 11.5. Table 11.9: Selected representative sites ID Site name Atoll name Airport name Latitude Longitude Altitude (Island) [° ] [° ] [m a.s.l.] 1 Gan Addu (Seenu) Gan International Airport -0.6933 73.1556 2 2 Hanimaadhoo Haa Dhaalu Hanimaadhoo International Airport 6.7442 73.1703 2 3 Hulhulé North Malé (Kaafu) Ibrahim Nasir International Airport 4.1917 73.5292 -8 4 Kadhdhoo Laamu Kadhdhoo Airport 1.8590 73.5220 0 Figure 11.5: Position of selected representative sites in Maldives Page 88 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 11.3.1 Multiyear Time Series Time representation: 1999 – 2013 Time step: hourly data, daily and monthly summaries Time series represent 15 years and include the following parameters: 2 • Direct Normal Irradiation, DNI [Wh/m ] 2 • Global Horizontal Irradiation, GHI [Wh/m ] 2 • Diffuse Horizontal Irradiation, DIF [Wh/m ] • Global Tilted Irradiation, GTI [Wh/m2] for 10-degree tilt of PV panels • Azimuth and solar angle, SA and SE [°] • Air temperature at 2 metres, T [°C] • Relative air humidity, RH [%] • Wet bulb temperature, WBT [°C] • Wind speed at 10 metres, WS [m/s] • Wind direction at 10 metres, WD [°] 11.3.2 Typical Meteorological Year (TMY) data Delivery of the site-specific TMY (Typical Meteorological Year) data is described in detail in Chapter 3.5. Time representation: 1999 − 2013 Time step: hourly summaries TMY for P50 and P90 represent a one representative year and they include the following parameters: 2 • Global horizontal irradiance, GHI [W/m ] 2 • Direct normal irradiance, DNI [W/m ] 2 • Diffuse horizontal irradiance, DIF [W/m ] • Azimuth and solar angle, SA and SE [°] • Air temperature at 2 metres, TEMP [°C] • Wet bulb temperature, WBT [°C] • Relative humidity, RH [%] • Wind speed at 10 metres, WS [m/s] • Wind direction at 10 metres, WD [°] • Atmospheric pressure, AP [°] Page 89 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 12 META INFORMATION Meta Information for GIS data • Global Horizontal Irradiation; long-term average of daily totals • Global Horizontal Irradiation; average of daily totals in a particular year and month • Direct Normal Irradiation; long-term average of daily totals • Direct Normal Irradiation; average of daily totals in a particular year and month • Diffuse Horizontal Irradiation; long-term average of daily totals • Global Tilted Irradiation; long-term average of daily totals • Photovoltaic electricity output for c-Si fixed-mounted modules, tilted towards equator at 7°; long-term average of daily totals • Air Temperature, long-term (diurnal) annual and monthly averages • Interannual variability of Global Horizontal Irradiation • Interannual variability of Direct Normal Irradiation • Interannual variability of Global Irradiation at optimum tilt Meta information for NetCDF data • Global Horizontal Irradiance; hourly averages • Direct Normal Irradiance; hourly averages • Diffuse Horizontal Irradiance; hourly averages • Air Temperature at 2 m; hourly averages Meta information for GeoTIFF/KML image data • Map of Global Horizontal Irradiation • Map of Direct Normal Irradiation Page 90 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 13 LIST OF FIGURES Figure 3.1: Interaction of solar radiation with the atmosphere and surface. ......................................................... 15! Figure 3.2: Scheme of the semi-empirical solar radiation model (SolarGIS). ....................................................... 19! Figure 3.3: Seasonal profile of GHI, DNI and DIF for P50 Typical Meteorological Year (TMY) ........................... 28! Figure 5.1: SolarGIS PV simulation chain ............................................................................................................ 35! Figure 6.1: Position of four representative sites within the administrative regions. .............................................. 38! Figure 6.2: Populated islands ............................................................................................................................... 40! Figure 6.3: Air transport infrastructure .................................................................................................................. 41! Figure 6.4: Administrative division, towns and cities ............................................................................................ 42! Figure 6.5: Longterm yearly average of air temperature at 2 m ........................................................................... 43! Figure 6.6: Longterm monthly average of air temperature. .................................................................................. 44! Figure 6.7: Monthly averages, minima and maxima of air-temperature at 2 m for selected sites. ....................... 46! Figure 7.1: Global Horizontal Irradiation – annual daily average and yearly totals............................................... 48! Figure 7.2: Global Horizontal Irradiation − long-term monthly averages of daily totals ........................................ 49! Figure 7.3: Global Horizontal Irradiation − long-term monthly averages, minima and maxima. ........................... 50! Figure 7.4: Interannual variability of GHI for selected sites. ................................................................................. 51! Figure 7.5: Ratio of Diffuse to Global Horizontal Irradiation − long-term annual average .................................... 52! Figure 7.6: Ratio of Diffuse to Global Horizontal Irradiation − long-term monthly averages ................................. 53! Figure 7.7: Monthly averages of DIF/GHI. ............................................................................................................ 54! Figure 7.8: Global Tilted Irradiation at 7° tilt towards equator – long-term daily average and yearly totals .......... 55! Figure 7.9: Gain of annual Global Tilted Irradiation relative to Global Horizontal Irradiation, in %. ...................... 56! Figure 7.10: Global Tilted Irradiation − long-term daily averages by month. ........................................................ 57! Figure 7.11: Monthly relative gain of Global Tilted Irradiation to Global Horizontal Irradiation at selected sites. . 58! Figure 7.12: Daily GHI (blue), GTI (red) and relative gain of monthly Global Tilted Irradiation ............................ 58! Figure 7.13: Daily values of GHI and GTI for Gan, year 2013 .............................................................................. 59! Figure 7.14: Direct Normal Irradiation − long-term daily averages and yearly totals ............................................ 60! Figure 7.15: Direct Normal Irradiation − long-term monthly averages of daily totals. ........................................... 61! Figure 7.16: Monthly average daily totals of DNI at selected sites. ...................................................................... 63! Figure 7.17: Interannual variability of DNI for representative sites ....................................................................... 63! Figure 7.18: Average daily totals of GHI and DNI for Gan, year 2013 .................................................................. 64! Figure 8.1: PV electricity output from an open-space fixed PV system ................................................................ 67! Figure 8.2: PV electricity potential for open-space fixed PV system − long-term monthly average daily totals ... 68! Figure 8.3: Daily total power production from the fixed tilted PV systems at selected sites ................................. 69! Figure 8.4: Monthly performance ratio of a fixed tilted PV system at selected sites ............................................. 70! Figure 8.5: Occurrence of days (cloudy/variable/sunny) relative to GTI daily sums for each month in Hulhulé. .. 71! Page 91 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results Figure 8.6: Hourly load and PV electricity generation profiles for Hulhumalé and Malé. ...................................... 72! Figure 8.7: Instantaneous (hourly) load and PV electricity generation profiles for Addu Island ........................... 73! Figure 8.8: Daily load and PV electricity energy sums for Addu Island ................................................................ 74! Figure 8.9: Daily load and PV electricity energy totals for Addu Island ................................................................ 74! Figure 10.1: Stages of development and operation of a solar power plant (adapted from [47]) ........................... 76! Figure 11.1: Screenshot of map and data in the ESRI ArcMap environment ....................................................... 83! Figure 11.2: Example of high-resolution DNI poster map ..................................................................................... 85! Figure 11.3: Example of medium resolution DNI map .......................................................................................... 86! Figure 11.4: Screenshot of DNI data displayed in Google Earth application ........................................................ 87! Figure 11.5: Position of selected representative sites in Maldives ....................................................................... 88! Page 92 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 14 LIST OF TABLES Table 2.1:! Overview of solar and meteorological data needed in different stages of a solar energy project .... 14! Table 2.2:! Solar and meteorological data parameters delivered for Maldives .................................................. 14! Table 3.1:! Input databases used in the SolarGIS model and related GHI and DNI outputs for Maldives......... 20! Table 3.2:! Theoretically-achievable daily uncertainty of Direct Normal Irradiation at 95% confidence level .... 23! Table 3.3:! Theoretically-achievable daily uncertainty of Global Horizontal Irradiation...................................... 23! Table 3.4:! Comparing solar data from solar measuring stations and from the satellite-model for Maldives..... 26! Table 3.5:! Yearly longterm GHI and DNI averages as represented in time series and TMY data products ..... 27! Table 4.1:! Uncertainty of meteo sensors by WMO standard (Class A) ............................................................ 29! Table 4.2:! Availability of CFSR and CFSv2 data from meteorological models for Maldives in SolarGIS ......... 30! Table 4.3:! Comparing data from meteo stations and weather models ............................................................. 32! Table 5.1:! Specification of SolarGIS database used in the PV calculation in this study ................................... 34! Table 6.1:! Position of four representative sites in Maldives .............................................................................. 39! Table 6.2:! Monthly averages, minima and maxima of air-temperature at 2 m at selected sites ....................... 45! Table 7.1:! Daily averages, minima and maxima of Global Horizontal Irradiation at selected sites................... 50! Table 7.2:! Monthly averages of Ratio of Diffuse to Global Horizontal Irradiation (DIF/GHI) ............................. 54! Table 7.3:! Daily averages, minima and maxima of Global Tilted Irradiation at selected sites .......................... 57! Table 7.4:! Relative gain of daily GTI to GHI in Gan .......................................................................................... 59! Table 7.5:! Daily averages, minima and maxima of Direct Normal Irradiation at the selected sites .................. 62! Table 8.1:! Reference configuration − photovoltaic power plant with fixed-mounted PV modules .................... 65! Table 8.2:! Summary of yearly energy losses and related uncertainty in each step of PV power simulation .... 66! Table 8.3:! Annual average of electricity yield for fixed PV system tilted at angle 7º ......................................... 69! Table 8.4:! Average daily sums of PV electricity output from an open-space fixed PV system ......................... 70! Table 8.5:! Monthly and annual Performance ratio of an open-space fixed PV system .................................... 70! Table 9.1:! Uncertainty of longterm model estimates for GHI, GTI and DNI values in Maldives ....................... 75! Table 9.2:! Uncertainty of the longterm modelled meteorological parameters in Maldives................................ 75! Table 11.1:! General information about GIS data layers .................................................................................... 79! Table 11.2:! Description of primary GIS data layers .......................................................................................... 80! Table 11.3:! Technical specification of primary GIS data layers ........................................................................ 81! Table 11.4:! Technical specification of NetCDF files (hourly data) .................................................................... 81! Table 11.5:! Characteristics of the raster output data files* ............................................................................... 81! Table 11.6:! File name convention for GIS data ................................................................................................ 82! Table 11.7:! File name convention for NetCDF files .......................................................................................... 82! Table 11.8:! Support GIS data ........................................................................................................................... 83! Table 11.9:! Selected representative sites ......................................................................................................... 88! Page 93 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 15 REFERENCES [1] Perez R., Cebecauer T., Suri M., 2014. Semi-Empirical Satellite Models. In Kleissl J. (ed.) Solar Energy Forecasting and Resource Assessment. Academic press. [2] Cebecauer T., Šúri M., Perez R., High performance MSG satellite model for operational solar energy applications. ASES National Solar Conference, Phoenix, USA, 2010. 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Long-term variability of solar direct and global radiation derived from ISCCP data and comparison with reanalysis data, Solar Energy, 80, 11, 1390-1401. [40] Gueymard C., Solar resource e assessment for CSP and CPV. Leonardo Energy webinar, 2010. http://www.leonardo-energy.org/webfm_send/4601 [41] Azoumah Y., Yamegueu D. and Py X. 2012. Sustainable electricity generation by solar pv/diesel hybrid system without storage for off grids areas. IOP Conf. Ser.: Mater. Sci. Eng. 29 012012 doi:10.1088/1757- 899X/29/1/012012 [42] Apoorv V., Pandi T. 2012, Optimum Sizing Of Photovoltaic Diesel-Generator Hybrid Power System, IJREAS, Volume 2, Issue 6, ISSN: 2249 -3905, International Journal of Research in Engineering & Applied Sciences [43] SMA Hybrid Systems Leaflet, http://www.sma.de/fileadmin/intersolar/Hybrid_Systems- Leaflet_Fuel_Save_Solution_EN.pdf [44] SMA Fuel Save Solution Product brochure, http://www.sma.de/fileadmin/intersolar/Hybrid_Systems- Brochure_Fuel_Save_Solution_EN.pdf Page 95 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results [45] Regional Technical Assistance (Ta 7950-Reg) For Smart Grid Capacity Development Solar PV Integration in Maldives, Prepared For: Maldives Energy Authority Asian Development Bank (Under Ta 7950-Reg), AF- Mercados EMI, January 2013 [46] Renné D., George R., Marion B., Heimiller D., Gueymard Ch., 2003. Solar Resource Assessment for Sri Lanka and Maldives. NREL Technical Report, NREL/TP-710-34645. http://www.nrel.gov/docs/fy03osti/34645.pdf [47] Stoffel T., Renné D., Myers D., Wilcox S., Sengupta M., George R., Turchi C., 2010. Concentrating Solar Power. Best Practices Handbook for the Collection and Use of Solar Resource Data, NREL Technical Report, NREL/TP-550-47465. http://www.nrel.gov/docs/fy10osti/47465.pdf. Page 96 of 98 World Bank, Global ESMAP Initiative, Renewable Energy Resource Mapping: Solar − Maldives (Project ID: P146018) Solar Modelling Report – Preliminary Results 16 SUPPORT INFORMATION 16.1 Background on GeoModel Solar Primary business of GeoModel Solar is in providing support to the site qualification, planning, financing and operation of solar energy systems. We are committed to increase efficiency and reliability of solar technology by expert consultancy and access to our databases and customer-oriented services. The Company builds on 25 years of expertise in geoinformatics and environmental modeling, and more than 15 years in solar energy and photovoltaics. We strive for development and operation of new generation high- resolution quality-assessed global databases with focus on solar resource and energy-related weather parameters. We are developing simulation, management and control tools, map products, and services for fast access to high quality information needed for system planning, performance assessment, forecasting and management of distributed power generation. Members of the team have long-term experience in R&D and are active in the activities of International Energy Agency, Solar Heating and Cooling Program, Task 46 Solar Resource Assessment and Forecasting. ® GeoModel Solar operates a set of online services, integrated within SolarGIS information system, which ® includes data, maps, software, and geoinformation services for solar energy. SolarGIS is the registered trademark of GeoModel Solar. Other brand names and trademarks that may appear in this study are the ownership of their respective owners. http://geomodelsolar.eu http://solargis.info 16.2 Legal information Considering the nature of climate fluctuations, interannual and long-term changes, as well as the uncertainty of measurements and calculations, GeoModel Solar cannot take guarantee of the accuracy of estimates. GeoModel Solar has done maximum possible for the assessment of climate conditions based on the best ® available data, software and knowledge. SolarGIS is the registered trademark of GeoModel Solar. Other brand names and trademarks that may appear in this study are the ownership of their respective owners. © 2015 GeoModel Solar, all rights reserved GeoModel Solar is ISO 9001:2008 certified company for quality management since 2011 Page 97 of 98