SOLAR RESOURCE AND PHOTOVOLTAIC POTENTIAL OF NEPAL March 2017 This report was prepared by Solargis, under contract to The World Bank. It is one of several outputs from the solar resource mapping component of the activity Energy Resource Mapping and Geospatial Planning Nepal [Project ID: P150328]. 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, and the content is the sole responsibility of the consultant authors. Users are strongly advised to exercise caution when utilizing the information and data contained, as this may include preliminary data and/or findings, and the document has not been subject to full peer review. Final outputs from this project will be marked as such, and any improved or validated solar resource data will be incorporated into the Global Solar Atlas. Copyright © 2017 THE WORLD BANK Washington DC 20433 Telephone: +1-202-473-1000 Internet: www.worldbank.org The World Bank, comprising the International Bank for Reconstruction and Development (IBRD) and the International Development Association (IDA), is the commissioning agent and copyright holder for this publication. However, 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. 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World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 3 of 77 World Bank Group, Global ESMAP Initiative Renewable Energy Resource Mapping and Geospatial Planning – Nepal Project ID: P150328 Solar Resource and Photovoltaic Power Potential of Nepal March 2017 This report has been prepared by Marcel Suri, Tomas Cebecauer, Nada Suriova, Branislav Schnierer, Juraj Betak, Branislav Cief, Veronika Madlenakova, Artur Skoczek and Marek Caltik from Solargis All maps in this report are prepared by Solargis Solargis s.r.o., Pionierska 15, 831 02 Bratislava, Slovakia Reference No. (Solargis): 170-01/2016 http://solargis.com Solargis is ISO 9001:2008 certified company for quality management World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 4 of 77 Table of contents Table of contents ............................................................................................................................................... 4 Acronyms ........................................................................................................................................................... 5 Glossary ............................................................................................................................................................. 7 Executive summary ............................................................................................................................................ 9 1 Introduction ............................................................................................................................................. 11 1.1 Background ................................................................................................................................................... 11 1.2 Objectives ...................................................................................................................................................... 11 1.3 Consultation process ................................................................................................................................... 12 2 Existing solar resource mapping projects................................................................................................. 13 2.1 Review of selected solar resource studies ................................................................................................. 13 2.2 Evaluation of the existing data and studies ................................................................................................ 16 3 Methods and data .................................................................................................................................... 18 3.1 Solar resource data ...................................................................................................................................... 18 3.2 Meteorological data...................................................................................................................................... 28 3.3 Simulation of photovoltaic power potential ................................................................................................ 31 4 Solar resource and PV potential of Nepal ................................................................................................. 37 4.1 Geography ..................................................................................................................................................... 37 4.2 Air temperature ............................................................................................................................................. 43 4.3 Global Horizontal Irradiation ........................................................................................................................ 44 4.4 Direct Normal Irradiation .............................................................................................................................. 48 4.5 Global Tilted Irradiation ................................................................................................................................ 50 4.6 Photovoltaic power potential ....................................................................................................................... 54 4.7 Solar climate ................................................................................................................................................. 58 4.8 Evaluation ...................................................................................................................................................... 61 5 Priority areas for meteorological stations ................................................................................................ 64 5.1 Localisation criteria ...................................................................................................................................... 64 5.2 Areas suitable for solar meteorological stations ....................................................................................... 64 6 Solargis data delivery for Nepal ............................................................................................................... 66 6.1 Spatial data products ................................................................................................................................... 66 6.2 Project in QGIS format .................................................................................................................................. 69 6.3 Digital maps .................................................................................................................................................. 70 6.4 Metainformation related to GIS data layers ................................................................................................ 71 7 List of maps ............................................................................................................................................. 72 8 List of figures .......................................................................................................................................... 73 9 List of tables ............................................................................................................................................ 74 10 References .............................................................................................................................................. 75 World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 5 of 77 Acronyms AC Alternating current 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. CSP Concentrated solar power systems, which use mirrors or lenses to concentrate a large amount of sunlight onto a small area, where it is converted to heat for heat engine connected to an electrical power generator. DC Direct current 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. ESMAP Energy Sector Management Assistance Program, a multi-donor trust fund administered by The World Bank EUMETSAT European Organisation for the Exploitation of Meteorological Satellites, intergovernmental organisation for establishing, maintaining and exploit European systems of operational meteorological satellites 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. GIS Geographical Information System GTI Global Tilted (in-plane) Irradiation, if integrated solar energy is assumed. Global Tilted Irradiance, if solar power values are discussed. KSI Kolmogorov–Smirnov Index, a statistical index for comparing of functions or samples World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 6 of 77 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: Indian Ocean Data Coverage MERRA-2 Modern-Era Retrospective Analysis for Research and Applications, Version 2; a NASA re- analysis for the satellite era using an Earth observing systems NASA National Aeronautics and Space Administration organization NOAA NCEP National Oceanic and Atmospheric Administration, National Centre for Environmental Prediction NOAA ISD NOAA Integrated Surface Database with meteorological data measured by ground-based measurement stations 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. PV Photovoltaic PVOUT Photovoltaic electricity output calculated from solar resource and air temperature time series. RSR Rotating Shadowband Radiometer SOLIS Solar Irradiance Scheme, Solar clear-sky model for convert meteorological satellite images into radiation data SRTM Shuttle Radar Topography Mission, a service collecting topographic data of Earth's land surfaces 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 metres UV Ultraviolet radiation World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 7 of 77 Glossary AC power output Power output measured at the distribution grid at a connection point. of a PV power plant Aerosols Small solid or liquid particles suspended in air, for example desert sand or soil particles, sea salts, burning biomass, pollen, industrial and traffic pollution. 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 similarly to all-sky irradiance but without considering 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 Period of aggregation of solar data that can be obtained from the Solargis database. (30-minute, hourly, daily, monthly, yearly) Installed DC capacity Total sum of nominal power (label values) of all modules installed on photovoltaic power plant. Long-term average Average value of selected parameter (GHI, DNI, etc.) based on multiyear historical time series. Long-term averages provide a basic overview of solar resource availability and its seasonal variability. P50 value Best estimate or median value represents 50% probability of exceedance. For annual and monthly solar irradiation summaries, it is close to average, since multiyear distribution of solar radiation resembles normal distribution. P90 value Conservative estimate, assuming 90% probability of exceedance (with the 90% probability the value should be exceeded). When assuming normal distribution, the P90 value is also a lower boundary of the 80% probability of occurrence. P90 value can be calculated by subtracting uncertainty from the P50 value. In this report, we apply a simplified assumption of normal distribution of yearly values. PV electricity production AC power output of a PV power plant expressed as percentage of installed DC capacity. Root Mean Square Represents spread of deviations given by random discrepancies between measured Deviation (RMSD) and modelled data and is calculated according to this formula: 3 ' 2 '45 ' ()*+,-). − (0.)1). = On the modelling side, this could be low accuracy of cloud estimate (e.g. 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 World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 8 of 77 sensor sees only micro 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. 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 or in the other words minimal size of the pixels in the digital map World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 9 of 77 Executive summary This report presents results of the solar resource mapping and photovoltaic power potential evaluation, as a part of a technical assistance for the renewable energy development in Nepal, implemented by the World Bank. 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. The study has two objectives: • To improve the awareness and knowledge of resources for solar energy technologies by producing a more accurate and detailed data set and maps based on satellite and meteorological modelling. This report evaluates key solar climate features, and geographic and time variability of solar power potential in the country. The outcomes are supported by brief explanation of the methodology and evaluation of the data uncertainty. The context of previous developments focusing on solar resource databases and mapping is shown. • To provide support information for installation of meteorological stations in Nepal by identifying and evaluating the most feasible areas. The data used in this report are based on the satellite and meteorological models. Due to lack of high accuracy solar measurements in Nepal, the data and the outcomes published in this study have higher uncertainty, compared to other regions. The regional uncertainty of models can be reduced by acquiring high quality measurements at several meteorological stations with specialised solar-measuring equipment. Satellite-based and meteorological models are used for computing solar resource and meteorological data that are suitable for production of high-resolution maps and for use in Geographical Information Systems (GIS). The primary data parameters that are relevant for evaluation of energy yield and performance of the solar power plants, especially based on the use of photovoltaic technology: • Global Horizontal Irradiation (GHI), Diffuse horizontal Irradiation (DIF), Global Tilted Irradiation (GTI) and Direct Normal Irradiation (DNI) • Air temperature at 2 metres above ground • Photovoltaic power potential. The deliverables are designed to help effective development of solar energy strategies and projects in their first stages. This phase delivers data computed by Solargis model without support of regional measurements. The model outcomes are delivered in two formats: • Raster GIS data for the whole territory of Nepal, representing long-term monthly and yearly average values. This data layers are accompanied by geographical data layers in raster and vector format. • Digital maps for high resolution poster printing and in medium resolution format In the next phase, it is planned to deploy and operate approximately five solar meteorological stations in Nepal to collect high-quality site-specific solar and meteorological data. Once at least one year of measurements is available, the data can be used for adaptation or solar and meteorological models and for detailed analysis of solar climate at representative sites. This report is divided into the following chapters: Chapter 2 provides an inventory of previous studies and shows the recent initiative in the context of large-scale development of solar power. Solar radiation basics and principles of photovoltaic power potential calculation are described in Chapter 3. Chapters 3.1 and 3.2 describe measuring and modelling approaches for developing reliable solar and meteorological data including information about the solar and meteorological data uncertainty. Chapter 3.3 explains the relevance of solar resource and meteorological information for deployment of solar power technologies. An emphasis is given to photovoltaic (PV) technology, which has high potential for developing World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 10 of 77 utility-scale projects close to larger load centres, as well as deployment of rooftop PV systems, off-grid, hybrid systems and mini-grids for electrification of small communities. Compared to previous initiatives, this work delivers more reliable solar resource databases. Chapter 4 presents analysis and evaluation of meteorological and solar resource data in Nepal. Eight representative sites are selected to show potential regional geographical differences in the country through tables and graphs. Chapter 4.1 introduces support geographical data that influence deployment strategies and performance of solar power plants. Chapter 4.2 to 4.5 summarizes geographical differences and seasonal variability of solar resource in Nepal. Chapter 4.6 presents PV power generation potential of the country. The theoretical specific PV electricity output is calculated from the most commonly used PV technology: fixed system with crystalline-silicon (c-Si) PV modules, optimally tilted and oriented towards South. Chapter 4.7 delineates solar climate zones that are relevant for deployment of solar monitoring stations and PV power systems. Chapter 4.8 summarizes analytical information and brings conclusions. In Chapter 5 the solar resource and meteorological conditions are evaluated in the context of deployment of solar meteorological stations, excluding areas that are not suitable for solar meteorological stations in Nepal. Chapter 6 summarizes the technical features of the delivered data products. This report, supported by the GIS data and maps, serves as an input for knowledge-based decisions targeting development of solar power in Nepal. The outcomes show very good potential for utilization of solar resources in Nepal, indicating good opportunities for photovoltaics, predominantly in small to medium size ground- mounted and mini-grids systems. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 11 of 77 1 Introduction This report is a result of the activity 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. The ESMAP initiative helps developing basic solar and meteorological data infrastructure, and knowledge, based on the best-available meteorological models and measurement practices. Indirect benefit is better understanding of geographical and temporal variability of solar resource and meteorological parameters relevant for solar power industry, in other words better understanding of the weather impact on the PV power generation and performance of PV power systems. The outcomes of this activity are publicly available GIS data sources, maps, and interactive applications; all available at http://globalsolaratlas.info/. 1.1 Background Solar electricity offers a unique opportunity to achieve long-term sustainability goals, such as development of modern economy, healthy and educated society, clean environment, and improvement of geopolitical stability. Solar power technology utilizes local solar resources; it does not require heavy support infrastructure, it is scalable, and improves electricity services. Important feature of solar power is that it is also accessible in remote locations that do not have access to electricity, thus providing development opportunities anywhere. Solar resource is fuel to solar technology. This fuel is free and is available everywhere, which makes solar power attractive. However, detailed understanding of solar resources is needed, especially their geographical and time variability. The local geographical and climate conditions determine the operation efficiency of solar power plants. Effective development and operation of solar power plants require detailed, accurate and validated solar and meteorological data. The requirements for quality data can be met by modern satellite-based meteorological models and by instruments installed at well-selected meteorological stations. Professional knowledge and experience in solar energy assessment supports industry and finance in large-scale development of solar power technology and its effective integration into the existing energy infrastructure. 1.2 Objectives This report provides a country-level evaluation of the solar resources, geographical conditions and photovoltaic power generation potential of Nepal. The study also describes methods, and outcomes of solar resource mapping. The analysis is based on the use of the Solargis model data. The solar model was only validated by measurements only available in a wider region with similar geographical conditions. Because of limited validation, one has to expect higher uncertainty of the model outputs. To reduce this uncertainty and to improve understanding of the solar climate at local scale, it is proposed to install solar meteorological stations with high accuracy instruments. This report evaluates suitable sites for deployment of this type of meteorological stations. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 12 of 77 1.3 Consultation process The outcomes of this study have been discussed with organizations involved in the Nepalese Task Force Members for solar resource mapping. The Task force is managed by Alternative Energy Promotion Center, with Mr. Mukesh Ghimire as the coordinator. The content of this report has been also amended based on the discussion held at the occasion of Solar mapping workshop, organize on 30 November 2016 in Kathmandu. The consultation process helped to better explain the scope of the recent solar mapping initiatives: (i) in the context of historical programs and (ii) of future development of large-scale solar power plants. The modern models and measurement techniques are introduced to deliver high accuracy data for cost-effective engineering, operation and financing of the large-scale solar power plants. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 13 of 77 2 Existing solar resource mapping projects Several solar resource assessment initiatives are documented below, as publications and online data resources. The works show steadily growing interest and different stages of development of solar resource assessment and energy modelling in the region. The review of the available data sources in the country identified several sources of solar radiation and studies in Nepal. This review is not exhaustive. 2.1 Review of selected solar resource studies Coordinated Energy and water cycle Observation Project – High Elevation The CEOP-HE research network operates several automatic stations in the Khumbu Valley, the Sagarmatha National Park. Ground measured GHI data from four stations are available for a period 2002 to 2008 for sites located in the elevation ranging from 2600 to 5100 m a.s.l. The data is measured with lower accuracy sensors (ISO 9060 first class and second class instruments), with no information on maintenance, cleaning, calibration and quality assurance. The quality control of irradiance data identified issues with time shifts and drifts and calibration problems. Moreover, the measurements are influenced by strong shading (and reflections from the surrounding terrain). Due to these issues, the CEOP-HE data do not qualify for the evaluation of Solargis model performance. The comparison of Solargis model with measurements from this network shows bias (systematic deviation) for GHI exceeding -10%. This disagreement may be attributed to the quality of ground measurements as well as to the limits of the Solargis model to represent specific microclimate patterns of narrow valleys, slopes and high ridges the High Himalayas (see Map 3.2). Often the microclimate of the mountains cannot be well captured by spatial resolution of the satellite and atmospheric data. Meteorological measurements of the Department of Hydrology and Meteorology (DHM), Ministry of Population and Environment. Neither the data from this network, nor the information on the applied instrumentation were available at the execution time of this study. Evaluation of the solar measurements, acquired by DHM is possible once this is made available. Data measured by other organizations There are other organizations such as Nepal Academy of Science and Technology (NAST), Nepal Agricultural Research Council (NARC), Department of Hydrology and Meteorology (DHM), Alternative Energy Promotion Center (AEPC), Tribhuvan University Center for Energy Studies (CES), etc. that are in possession of solar data measured within their programs. Due to the narrow scope of this project, the time was limited for detailed exploration of these data sources, and for the analysis of their suitability for the model validation. The project team will be willing to include in-country measurements and data sources in the evaluation process in the revision of this report, once this type of data is made available. Prior to the use of such data, an evaluation of instrument accuracy, calibration, operation and maintenance and data quality control would have to be performed. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 14 of 77 NASA SSE Historically the most popular solar resource data set was developed by NASA Surface meteorology and Solar Energy. The release 6.0 Data Set is the most recent (dated Jan 2008). The data set represent 22-year monthly and annual averages (July 1983 - June 2005); grid resolution is approx. 110 km [1]. Today the features of this data set are to a great extent out-dated, compared to new modern databases. SWERA Project SWERA delivered GIS data layers of monthly and yearly averages for Nepal. Three types of data sources were developed as well as one GIS analysis study. Similarly to NASA SSE, the SWERA data sets do not fulfil the requirements of current solar industry. Data developed from NREL's Climatological Solar Radiation (CSR) Model, it uses information on cloud cover, atmospheric water vapour and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface (resolution of approx. 40 km), [2]. Country-wide GIS data layers were developed by DLR, Germany using three years of Meteosat satellite data (2000, 2001 and 2002) and climate averages of ozone, aerosols and water vapour. The data is available in an ESRI vector shapefile, delineating grid cells 10 x 10 km. The accompanied report [3] presents the maps of the annual average daily total of GHI and DNI (Map 2.1). Hourly time series for the same period were generated for several sites and delivered in a separate ASCII files. Map 2.1: Long-term yearly average of daily totals of GHI developed by DLR (2004). Global Horizontal Solar Irradiance, yearly average, is also developed by Center for Energy Studies (CES), Institute of Engineering, Tribhuvan University in Kathmandu [4]. The method is based on a linear regression between the theoretical and ground measured solar irradiance at three locations: a) Syangboche (Solukhumbu) b) Pulchowk (Lalitpur) and c) Prakashpur (Sunsari). The model was used for converting the theoretical Global Horizontal Solar Irradiance to actual solar irradiance in 15 meteorological stations spread throughout the country. Interpolating the data obtained at these stations, a map has been developed using ArcView GIS software (Map 2.2). Alternative Energy Promotion Centre (AEPC), the national executive agency for promoting and disseminating renewable energy in Nepal has issued a report assessing solar and wind energy potential. The report is based on the GIS analysis of data developed through SWERA program [5]. The GIS part has been conducted with the help of technical support by TERI under the financial support of SWERA/UNEP and GEF. The objective was to enhance the knowledge for the researchers and interested investors, and increase confidence towards investment in this sector. The concentrating solar potential assessment was estimated from direct normal World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 15 of 77 irradiance data developed by DLR. The grid-connected solar power potential was based on global tilted irradiance developed by NREL. The assessment of the solar PV potential in remote areas of Nepal was based on global horizontal irradiance developed by DLR. Compared to the new databases the SWERA data are not so accurate and detailed, but this country-wide GIS analysis is still relevant from the viewpoint of methodology. It is proposed by the authors of this study to repeat the GIS data analysis using the new more accurate and more detailed GIS data that are one of outcomes of this project. Map 2.2: Long-term yearly average of GHI daily totals developed by CES (2015) Map 2.3: Potential for grid-integrated PV analysed using SWERA data calculated by NREL (AEPC 20018) World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 16 of 77 Meteonorm Meteonorm, developed by Meteotest, is a widely used and accepted solar radiation data source in the solar energy industry. It has been around for 30+ years (the first version was released in 1985), and became the standard meteorological database for solar energy simulations. It is also the default meteorological database of some of the popular PV design software, such as PVsyst or PVSOL [6]. The Meteonorm database is based on combination of measured and modelled solar radiation data. Although solar radiation data from approx. 1300 meteorological stations are incorporated, these stations are geographically unevenly distributed and majority of them stopped operating in the past. The data measured in the last decades are often based on the use of lower accuracy sensors, and most of them have not been maintained in the line of today’s standards. In Meteonorm, the estimate at a specified location is based on the interpolation of long-term monthly-averaged values from nearby meteorological stations. The data derived from satellite imagery is incorporated as support information, and used mainly when no meteorological station is available within a distance, starting from approx. 10 km. This approach has several limitations, mainly in regions where solar resource varies considerably at short distances, e.g. on islands, in coastal zones and regions with high and variable terrain. It should also be noted that different sources of data used as inputs represent different periods of time in history, and the database is very heterogeneous from the point of time representation. The data representing the recent years is missing. Meteonorm has very limited capability to be validated by high-resolution measurements. For Asia no validation document has been published, and therefore data accuracy cannot be objectively evaluated. Meteonorm data cannot be used for monitoring and forecasting of PV power. 2.2 Evaluation of the existing data and studies Nepal has considerable potential for deployment of solar power systems. Historically the development focused on small-scale and off-grid photovoltaics. Development of utility-scale projects becomes of interest to many stakeholders. Yet, the older solar and meteorological data sources do not fulfil the requirements for accuracy and reliability. In case of free public sources, the discontinuity in development, missing validation and lack of support are another limiting factors for scaling up solar power. Solargis provides solar and meteorological data and energy-simulation services for development and financing of large-scale solar power plants, worldwide as well as in Nepal. The objective of Solargis is to systematically supply reliable, validated and high-resolution data to solar industry with low uncertainty and systematic quality control. The main features that differ Solargis database from the data sets are mentioned in Chapter 2.2: • The models are based on new and advanced algorithms, validated at different climate zones • Use of modern input data for the models: satellite, atmospheric and meteorological • Data is available globally at very high spatial and temporal resolution • Time series data is available for a long history (18+ years in Nepal) and it is updated in real time • Data can be used for a project development, as well as for monitoring and forecasting • Data is systematically validated and quality controlled • Solargis data can be used in the most popular PV energy simulation software applications • Customers have access to technical support and consultancy. High accuracy solar resource and meteorological data are needed for development of a solar power plant, but also during the years of its operation. In case of large commercial installations, the first calculation of the solar power production is typically based only on the data from solar and meteorological models. When commercial development starts in a selected location, a meteorological station is installed, equipped by high accuracy solar equipment to collect local measurements. Once the local data represents a continuous period of one year it can be used for the accuracy enhancement of the initial assessment. In this procedure, the solar model is adapted by the measurements to better represent the patterns of the local microclimate. This procedure is called site World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 17 of 77 adaptation. Its result in solar resource and meteorological times series representing long-term assessment with higher accuracy compared to the initial calculation. At large-scale commercial power plants, solar measurements are collected continuously over the lifetime of the project. In summary, the solar and meteorological data is used for the following tasks related to solar power generation: 1. Country-level evaluation and strategical assessment 2. Prospection, selection of candidate sites for future power plants, and prefeasibility analysis 3. Project evaluation, solar and energy yield assessment, technical design and financing 4. Monitoring and performance assessment of solar power plants and forecasting of solar power 5. Quality control of solar measurements. This report addresses the first topic, from the list above. Country level evaluation is based on the outputs of solar and meteorological models, which are validated by the measurements available in a wider region or similar climate zones. Therefore, the uncertainty of the outcomes of this study is higher. Once the measured data from a certain number of ground measurements is available in a country or region, these measurements can be used in improving the model accuracy at the regional scale. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 18 of 77 3 Methods and data 3.1 Solar resource data 3.1.1 Introduction Solar resource (physical term solar radiation) is fuel to solar energy systems. The solar radiation available for solar energy systems at the ground level depends on processes in the atmosphere. This leads to a high spatial and temporal variability of at the Earth’s surface. The interactions of extra-terrestrial solar radiation with the Earth’s atmosphere, surface and objects are divided into four groups: 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 and is used as a reference. 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. 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. A proportion of individual components at any time is given by Sun position and by the actual state of atmosphere – mainly occurrence of clouds, air pollution and humidity. According to the generally adopted terminology, in solar radiation two terms are distinguished: 2 2 • Solar irradiance indicates power (instant energy) per second incident on a surface of 1 m (unit: W/m ). 2 2 • Solar 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 three solar resources are relevant: • 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. • 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. A term World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 19 of 77 Plan of Array (POA) irradiation//irradiance is also used. In the case of photovoltaic (PV) applications, GTI can be occasionally affected by shading from surrounding terrain or objects, and GTI is then composed only from diffuse and reflected components. This happens usually for sun at low angles over the horizon. Solar radiation (GHI, DNI and DIF components) can be measured by two complementary approaches: 1. Ground-mounted sensors are good in providing high frequency and accurate data (for well-maintained, high accuracy measuring equipment) for a specific location. 2. 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. Note: Historically also sunshine hours have been recorded/calculated for evaluating type of climate. Sunshine hours (or sunshine duration) is an indicator, measuring duration of sunshine in a given period (a day, month or a year) and it is typically expressed as an averaged value over several years. According to WMO standard, the sunshine duration is defined as the period during which direct solar irradiance exceeds a threshold value of 120 W/m². This parameter is a general indicator of cloudiness of a location, and thus differs from solar radiation, which measures instantaneous power or energy received by a surface over a given period. This parameter is not used any more in solar energy studies as calculation or measurements of GHI/DNI/DIF components is more accurate and the values more suitable as an input to modern energy simulation models. This Chapter summarizes approaches for measuring and computing these parameters, and the main sources of uncertainty. Methods for combination of data acquired by these two complementary approaches with the aim to get maximum from their benefits were developed. 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 long-term estimates. 3.1.2 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. A variety of instruments exists with different properties and achievable accuracy of measurements (Table 3.1). Due to required accuracy in the solar industry and for the solar model adaptation, it is recommended to measure solar 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 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. 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 World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 20 of 77 in case of rapid fluctuations of radiation, when using older instruments, or after prolonged exposure to challenging weather. Table 3.1: Theoretically-achievable uncertainty of pyranometers at 95% confidence level ISO 9060 class Hourly totals Daily totals Secondary standard ±3% ±2% First class ±8% ±5% Second class ±20% ±10% Rotating Shadowband Radiometer (RSR) instruments can be installed as an alternative to the above-mentioned instruments, if measurements take place in a more challenging and remote environment with limited possibilities for 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 accuracy of RSR measurements. The 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 ±4% to 5%. These instruments have narrower spectral sensitivity, thus operating these instruments in very different environmental conditions from those used for calibration may lead to increased uncertainty. Utilization of the state-of-the-art instruments does not alone guarantee good results. 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 3.1). The uncertainty may dramatically increase in extreme operating conditions and in cases of limited or insufficient maintenance. Solar radiation measurements are not only subject to errors in determination of instant values. The radiometric response of the instruments also undergoes seasonal variability and long-term 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 long-term 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 instruments’ behaviour. 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 the data use. 3.1.3 Solargis satellite-based model 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. Data from reliable operational solar models are routinely used by solar industry. In this study, we applied a model developed and operated by the company Solargis. This model operationally calculates a high-resolution solar resource data and other meteorological parameters. Its geographical extent covers most of the land surface between 60º North and 45º South latitudes. A comprehensive overview of the Solargis model was made available in several publications [7,8,9,10]. The related uncertainty and requirements for bankability are discussed in [11,12]. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 21 of 77 In Solargis approach, solar irradiance is calculated in 5 steps: 1. Calculation of clear-sky irradiance, assuming all atmospheric effects except of clouds, 2. Calculation of cloud properties from satellite data, 3. Integration of clear-sky irradiance and clouds and calculation of global horizontal irradiance (GHI) 4. Calculation of direct normal irradiance (DNI) from GHI and clear-sky irradiance. 5. Calculation of global tilted irradiance (GTI) from GHI and DNI. Models used in individual calculation steps are parameterized by a set of inputs characterizing the cloud properties, state of the atmosphere and terrain conditions. The clear-sky irradiance is calculated by the simplified SOLIS model [13]. This model allows fast calculation of clear-sky irradiance from the set of input parameters. Sun position is deterministic parameter, and it is described by the algorithms with satisfactory accuracy. Stochastic variability of clear-sky atmospheric conditions is determined by changing concentrations of atmospheric constituents, namely aerosols, water vapour and ozone. Global atmospheric data, representing these constituents, are routinely calculated by world atmospheric data centres: • In Solargis, the new generation aerosol data set representing Atmospheric Optical Depth (AOD) is used. The core data set is from MACC-II/CAMS project (ECMWF) [14, 15]. An important feature of this data set is that it captures daily variability of aerosols and allows simulating more precisely the events with extreme atmospheric load of aerosol particles. Thus, it reduces uncertainty of instantaneous estimates of GHI and especially DNI, and it allows for improved statistical distribution of irradiance values [16, 17]. For years 1994 to 2002, data from the MERRA-2 model (NASA) [18] homogenized with MACC- II/CAMS model are used. The Solargis calculation accuracy of the clear-sky irradiance is especially sensitive to the information on aerosols. • Water vapour is also highly variable in space and time, but it has lower impact on the values of solar radiation, compared to aerosols. The daily GFS and CFSR values (NOAA NCEP) are used in Solargis, thus representing the daily variability from 1994 to the present [19, 20, 21]. • Ozone absorbs solar radiation at wavelengths shorter than 0.3 µm, thus having negligible influence on the broadband solar radiation. The clouds are the most influencing factor, modulating clear-sky irradiance. Effect of clouds is calculated from the satellite data in the form of cloud index (cloud transmittance). The cloud index is derived by relating irradiance recorded by the satellite in several spectral channels and surface albedo to the cloud optical properties. In this study, a data from the Meteosat MFG IODC satellites is used. Data is available for a period from 1999 till the last day in a time step of 30 minutes. In Solargis, the modified calculation scheme by Cano has been adopted to retrieve cloud optical properties from the satellite data [22]. A number of improvements have been introduced to better cope with specific situations such as snow, ice, or high albedo areas (arid zones and deserts), and with complex terrain. 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 [23]. Diffuse horizontal irradiance is derived from GHI and DNI according to the following equation: DIF = GHI - 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 diffuse 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 [24]. The reflected component is also approximated considering that knowledge of local conditions is limited. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 22 of 77 Model for simulation of terrain effects (elevation and shading) based on high-resolution elevation and horizon data is used in the standard Solargis methodology [25]. Model by Ruiz Arias is used to achieve enhanced spatial representation – from the resolution of satellite (several km) to the resolution of digital terrain model. Solargis model version 2.1 has been used for computing the data. Table 3.2 summarizes technical parameters of the model inputs and of the primary outputs. Table 3.2: Input data in Solargis solar radiation model and related GHI and DNI outputs for Nepal Inputs into the SolarGIS Source Time Original Approx. grid model of input data representation time step resolution Cloud index Meteosat IODC 1999 to date 30 minutes 3.2 x 3.3 km (EUMETSAT) Atmospheric optical depth MACC/CAMS* 2003 to date 3 hours 75 km and 125 km (aerosols)* (ECMWF) MERRA-2 (NASA) 1999 to 2002 1 hour 50 km Water vapour CFSR/GFS 1994 to date 1 hour 35 and 55 km (NOAA) Elevation and horizon SRTM-3 - - 1 km (SRTM) SolarGIS primary data - 1994 to 2015* 15 minutes 1 km outputs (GHI and DNI) * Solargis operational model provides the data updates in real time. The deliverables within this project cover complete calendar years from 1999 till 2015 (see Chapter 6.1) The achievable uncertainty of the solar irradiance derived from the satellite-based models depends on performance and limits of partial models as well as on the quality and the model inputs (mostly aerosols and satellite data). The modelling of solar irradiance in extremely variable geographical conditions of Nepal is very challenging and several features influencing final data uncertainty should be considered: 1. The densely-populated area in the Ganges River Basin is one of the most polluted areas, mainly due to human activities (biomass burning, traffic, industry, fossil energy generation, etc.), thus proper description of aerosols (composition, load) can be provided by aerosol databases only with higher uncertainty. As the aerosols are one of the principal inputs of solar models, the data quality influences also the achievable uncertainty of modelled irradiance. The same applies for polluted cities in hilly and mountainous areas. 2. High mountains are very challenging for solar radiation modelling. The main problem is the identification of clouds using satellite images. Snow cover and presence of terrain shading makes the cloud identification erroneous as the clouds and snow have similar reflectivity in visible wavelengths. The use of satellite data acquired in the infrared spectrum helps in the cloud identification, but the resulting uncertainty is still higher. Moreover, the high elevations in Nepal are at the edge of validity of clear-sky calculation schemes, such as one used also in the Solargis model. 3. Nepal has very fragmented topography. The spatial distribution of valleys and mountain ridges is often below the spatial and temporal resolution of satellite images. Therefore, the specific local microclimatic features cannot be precisely mapped by this approach. The same applies for the aerosol data, which are available in much coarser resolution. Some improvements are done by aerosol elevation correction which is capable to some extent reduce this problem. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 23 of 77 3.1.4 Measured vs. satellite data – adaptation of solar model For qualified solar resource assessment, it is important to understand characteristics of ground measurements and satellite-modelled data (Table 3.3). 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 extrapolation of measured information from nearby station is limited only to short distances. Moreover, the period of measurements is usually too short to describe long-term weather conditions. On the other hand, the satellite data can provide long climatic history (since 1999 to the present time, in Nepal) for any location, but may not accurately represent the micro-climatic conditions of a specific site. Table 3.3: Comparing solar data from solar measuring stations and from satellite models Data from solar measuring stations Data from satellite-based models Availability/ Available only for limited number of sites. Most Data are available for any location within accessibility often, data cover only recent years. latitudes 60N and 45S. Data covers a continuous period from 1999 to the present, in Nepal, . Original spatial Data represent the microclimate of a site. Satellite models represent area with complex resolution spatial resolution: clouds are mapped at approx. 3.4 km, aerosols at 50-125 km and water vapour at 34 km. Terrain can be modelled at spatial resolution of up to 90 metres. Methods for enhancement of spatial resolution are often used. Original time Seconds to minutes 30 minutes in south Asia resolution Quality Data need to go through rigorous quality control, Quality control of the input data is necessary. gap filling and cross-comparison. 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. Long-term 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 and temporal harmonization of inputs is used in state-of-the-art models. 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). The ground measurements and satellite data complement each other and it is beneficial to correlate them and to adapt the satellite model for the specific site. Site-adapted satellite model data provide long history time series with lower uncertainty. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 24 of 77 The model adaptation consists of 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) to cover all seasons; • The solar measuring station is equipped with more than one instrument, 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 long-term quality. • Advanced site-adaptation methods are used, capable to address specific sources of satellite-ground data differences (e.g. correction of aerosols, reduction of cloud identification problems). Besides reduced uncertainty of long-term estimate (lower bias), the model adaptation method should also improve random deviations (lower RMSD) and should provide more representative (sub) hourly values (lower KSI). The site-adaptation of satellite based model data is performed for locations where large-scale solar power plants are planned. During the project development phase, a meteorological station with high-accuracy instruments is installed on the site and operated for at least one year, to get good understanding of local conditions. Local measurements are then used for site-adaptation of satellite model. The site-adapted long-term data are crucial for local solar resource evaluation and for accurate simulation of performance of various solar energy technologies with low uncertainty. The ground measurements can be correlated with satellite data also at a regional level – within so called regional adaptation of the model, focused on broader territory instead of a single site. In the case of regional adaptation, the method aims to identify and reduce regional systematic deviations of a model typically driven by insufficient characterization of aerosols or specific cloud patterns. The regional adaptation requires measurements from several stations, which allows distinguishing the systematic model issues relevant to a large region, from the features related to a microclimate. The result of regional adaptation is improved solar resource database in the regional context with overall reduction of systematic errors. 3.1.5 Validation of Solargis model Accuracy of Solargis data has been compared with high-quality solar measurements at 220+ meteorological stations, distributed worldwide. These stations are equipped by high accuracy equipment, typically secondary standard pyranometers and first class pyrheliometers (Table 3.1). Map 3.1 shows selected validation sites in the broader region, fulfilling the minimum requirements of measured data quality. Even though various projects aimed at measuring solar radiation were taking place in Nepal, they were not used in the validation of the model. The main reason is limited information about the availability and quality of these measurements. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 25 of 77 Map 3.1: Solar radiation sites selected for the model validation Table 3.4: Selected validation sites in the region Site name Country Latitude Longitude Elevation Solar radiometric [º] [º] [m] network Kanpur India 26.5127 80.2319 123 SOLRADNET Pantnagar* India 29.0458 79.5208 241 SOLRADNET Islamabad Pakistan 33.6419 72.9838 558 ESMAP Bahawalpur Pakistan 29.3254 71.8188 123 ESMAP Quetta* Pakistan 30.2708 66.9398 1586 ESMAP Khuzdar* Pakistan 27.8178 66.6294 1254 ESMAP Hyderabad Pakistan 25.4134 68.2595 63 ESMAP Peshawar Pakistan 34.0017 71.4854 367 ESMAP Lahore Pakistan 31.6946 74.2441 207 ESMAP Multan Pakistan 30.1654 71.4978 123 ESMAP Karachi Pakistan 24.9334 67.1116 45 ESMAP * Less than one year of measurements Tables 3.5 and 3.6 show the Solargis model quality indicators for solar primary parameters: DNI and GHI. Comparison of the validation statistics, computed for the solar meteorological sites in the region, shows overall stability of the Solargis model and of the underlying input data. Locally slightly increased bias was identified, that may be effect of the specific local conditions (e.g. anthropogenic pollution), limited accuracy of model and its input data as well as properties of ground measurements (short period of available data, lower accuracy of instruments). World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 26 of 77 Table 3.5: Direct Normal Irradiance – quality indicators in the region Direct Normal Irradiance, DNI Bias Root Mean Square Deviation, RMSD 2 [W/m ] [%] Hourly [%] Daily [%] Monthly [%] Islamabad -5 -1.4 28.0 19.1 2.8 Bahawalpur -33 -8.7 26.6 21.5 14.5 Quetta 31 5.6 26.7 18.6 12.2 Khuzdar 16 2.5 21.6 14.6 6.8 Hyderabad -15 -3.6 24.3 17.3 5.0 Peshawar -17 -4.8 28.8 20.8 9.1 Lahore 1 0.4 32.5 24.2 8.4 Multan 13 4.0 28.7 21.6 10.4 Karachi 10 2.8 25.3 17.1 6.2 Table 3.6: Global Horizontal Irradiance – quality indicators in the region Global Horizontal Irradiance, GHI Bias Root Mean Square Deviation, RMSD 2 [W/m ] [%] Hourly [%] Daily [%] Monthly [%] Kanpur -9 -2.0 15.1 8.2 2.6 Pantnagar -1.5 -0.5 0.5 1.5 2.5 Islamabad 11 2.7 15.0 8.4 3.4 Bahawalpur -8 -1.8 13.6 10.7 7.0 Quetta 8 1.6 13.4 6.9 3.3 Khuzdar 4 0.7 10.7 5.3 1.5 Hyderabad -2 -0.5 8.5 5.0 1.7 Peshawar 22 5.3 15.3 9.8 5.8 Lahore 20 5.2 17.0 12.2 6.4 Multan 16 3.9 13.4 9.5 5.1 Karachi 9 2.0 10.5 6.3 3.8 3.1.6 Uncertainty of solar resource data Based on this analysis, we have only partial local knowledge for estimating the expected uncertainty of yearly solar radiation summaries of the Solargis data. The estimate is based on the data from surrounding regions. This approach is based on the fact that the performance of the Solargis model is relatively stable over larger territories with the same geographical conditions, which was confirmed in other regions. In case of Nepal this approach has some limits, as the available data cannot fully characterize the specific and very variable World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 27 of 77 conditions of the whole country. Therefore, in Nepal, we recognise three main regions with different expected uncertainty (Table 3.7). In the Terai region we can base our estimate on measurements from Kanpur, Pantnagar (India) and Lahore (Pakistan) stations, which show low bias. The uncertainty of Shiwalik Hills and Middle Mountains is based on few stations in Pakistan, but considers also differences in geographical conditions. High Mountains and High Himalaya do not have any analogy in ground measurements from the wider region, and the relatively high uncertainty is estimated based on known issues of satellite based model such as aerosol characterization in very high elevation, difficult cloud identification over snow or high spatial variability of local microclimatic features, which are below the resolution of the satellite images. In the data comparison exercise run for Europe and North Africa, Solargis has been identified as the best performing satellite-based solar database [26]. The Solargis solar resource data layers are more accurate and more detailed than any other data sources available before. Table 3.7: Uncertainty of long-term estimates for GHI, GTI and DNI values in Nepal GHI GTI DNI Terai – Lowlands (plains) <±6% < ±7% < ±12% Siwalik and Middle Mountain (Hill) ±6 to 8% ±7 to 10% ±12 to 18% High Mountain and High Himalaya ±10% and more ±12% and more ±18% and more Map 3.2: Physiographic regions of Nepal Source: Topographic Survey Branch, Department of Survey, Nepal World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 28 of 77 3.2 Meteorological data 3.2.1 Measured vs. modelled data – features and uncertainty 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. The most important meteorological parameter for the operation of photovoltaic power plants is air temperature, which directly impact power production (energy yield is decreasing when temperature is increasing). Meteorological data can be collected by two approaches: (1) by measuring at meteorological sites and (2) computing by meteorological models. The requirements for the meteorological data for solar energy projects are: • Long and continuous record of data, preferably covering the same period as satellite-based solar resource data, • Data should reliably represent the local climate, • Data should be accurate, quality-controlled and without gaps. The best option would be to have continuous measurements from high-accuracy sensors installed on the site in a meteorological station. However, except for sites where long-term meteorological observations are operated as part of national meteorological service or some other observation network, this option is typically not available. Even if some measurements are available, often the time series are incomplete or not reliable. Most often, the only alternative is to derive historical meteorological data from meteorological models. Several 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 (source NOAA, NCEP, USA) covering long period of time with continuous data [20, 21]. The results of these models are implemented in Solargis. The ground measurements play an important role in the assessment of local climate conditions as they determine the efficiency of photovoltaic power production. The role of the measurements in solar energy development has two aspects: • Measurements are used for validation and accuracy enhancement of historical data derived from the solar and meteorological models • The high frequency measurements (typically one-minute data) are used for improved understanding of the microclimate of the site, especially for capturing the extremes. Data inputs and method In this delivery, the air temperature is derived from the meteorological models: CFSR and CFSv2 (Table 3.8). The original spatial resolution of the models is enhanced to 1 km for air temperature by spatial disaggregation and use of the Digital Elevation Model SRTM-3. Table 3.8: Original source of Solargis meteorological data for Nepal: models CFSR and CFSv2. Climate Forecast System Reanalysis Climate Forecast System (CFSR) (CFSv2) Period 1999 to 2010 2011 to the present time Original spatial resolution 30 x 35 km 20 x 22 km Original time resolution 1 hour 1 hour World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 29 of 77 Important note: the numerical weather model has lower spatial and temporal resolution compared to the solar resource data. Local microclimate of the site may deviate from the values derived from the numerical models. Data from the two sources described above have their advantages and disadvantages (Table 3.9). Air temperature retrieved from the meteorological models has 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. Table 3.9: Comparing data from meteorological 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 with all local weather patterns with relatively coarse grid resolution. Therefore, occurrences the local values may be smoothed, especially extreme values. Original time From 1 minute to 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, long-term stability is often Data for operational forecast model may slightly change a challenge. over time, as model development evolves Uncertainty Uncertainty is related to the quality and Uncertainty is given by the resolution and accuracy of the maintenance of sensors and measurement model. Uncertainty of meteorological models is higher practices, usually sufficient for solar energy than high quality local measurements. The data may not applications. exactly represent the local microclimate, but are usually sufficient for solar energy applications. 3.2.2 Validation of Solargis air temperature data The validation was carried out to compare the modelled data with measurements available at selected meteorological stations in Nepal, available through NOAA ISD network (Table 3.10 and Map 3.3). In general, the data from the meteorological models represent larger area, and they may not be capable to represent accurately the local microclimate, especially in mountains of Nepal. Air temperature is derived from both meteorological models to the spatial resolution of 1 km by post-processing and disaggregation (Table 3.11). Considering spatial and time interpolation, for hourly values the deviation of the model to the ground observations can occasionally reach several degrees Celsius. It is to be noted that many stations used in this evaluation have data quality issues (missing measurements), 5 out of 9 stations (Dadeldhura, Surkhet, Nepalgunj, Dang, Bhairahawa) have missing night-time measurements in months from May to July. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 30 of 77 Map 3.3: Meteorological stations considered in validation of air temperature Table 3.10: Meteorological stations and time periods considered in the model validation Meteorological station Data source Validation Latitude* Longitude* Elevation* period [º] [º] [m a.s.l.] Dadeldhura NOAA ISD 01/2010 – 12/2015 29.300 80.583 1848 Surkhet NOAA ISD 01/2008 – 12/2015 28.600 81.617 720 Nepalgunj NOAA ISD 01/2012 – 12/2015 28.100 81.667 165 Dang NOAA ISD 01/2012 – 12/2015 28.050 82.500 634 Bhairahawa NOAA ISD 01/2008 – 12/2015 27.506 83.416 109 Tribhuvan NOAA ISD 01/2008 – 12/2015 27.697 85.359 1338 Okhaldhunga NOAA ISD 01/2010 – 12/2015 27.300 86.500 1720 Taplejung NOAA ISD 01/2010 – 12/2015 27.350 87.667 1732 Biratnagar NOAA ISD 01/2010 – 12/2015 26.481 87.264 72 * Note: the position of meteorological stations from the ISD network can be less accurate The modelled air temperature in Nepal fits quite well the measured data, representing both seasonal and daily patterns with only 2 out of 9 stations having bias higher than ±1.5ºC, and 3 out of 9 having bias higher than ±1.0ºC. Average hourly RMSD for all 9 stations stays below 3.6ºC what indicates that instantaneous temperature values have lower accuracy than long term average temperature. The uncertainty of the estimate for the main meteorological parameters is summarised in Table 3.12. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 31 of 77 Table 3.11: Air temperature at 2 m: accuracy indicators of the model outputs [ºC]. Meteorological Bias RMSD RMSD RMSD station mean hourly daily monthly Dadeldhura 0.0 3.7 2.7 1.5 Surkhet -0.3 4.0 3.2 1.4 Nepalgunj 0.9 4.0 2.8 1.7 Dang -0.4 3.7 2.5 1.4 Bhairahawa 2.1 3.9 3.6 2.7 Tribhuvan -0.2 3.1 1.9 1.1 Okhaldhunga -0.2 3.9 3.2 1.6 Taplejung -1.3 2.7 2.2 1.4 Biratnagar 1.5 3.8 3.3 2.3 Table 3.12: Expected uncertainty of modelled meteorological parameters in Nepal. Unit Annual Monthly Hourly Air temperature at 2 m °C ±1.5 ±2.5 ±4.5 3.3 Simulation of photovoltaic power potential Solar radiation is the most important parameter for PV power simulation, as it is fuel for solar power plants. The intensity of global irradiance received by tilted surface of PV modules (GTI) is calculated from two primary parameters stored in the Solargis database and delivered in this project: • Global Horizontal Irradiance (GHI) • Direct Normal Irradiance (DNI) There are two main types of solar energy technologies: photovoltaic (PV) and concentrating solar power (CSP). There is no reasonable potential for CSP in Nepal, and therefore we will focus only on photovoltaic solar energy technology. Photovoltaics exploit global horizontal or tilted irradiation, which is the sum of direct and diffuse components (see equation (1) in Chapter 3.1). To simulate power production from 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. Similarly, the effect of seasonal variability is to be considered as well. PV technology will presumably dominate in solar energy applications in Nepal. It is gaining popularity, especially in rural areas, where other sources of energy are costly, not feasible or not possible to use [27]. Used units are ranging from small home systems, solar lanterns through community to institutional systems [28]. For PV systems applications, several technical options are available and have been briefly described below. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 32 of 77 Two types of mounting of PV modules can be considered: • Build in an open space, where PV modules are ground-mounted in a fixed position or on sun-trackers • Mounted on roofs or façades of buildings, Three types of a PV system can be considered for Nepal: • Grid-connected PV power plants • Mini-grid PV systems • Off-grid PV systems Most utility scale PV power plants are built on open space and have PV modules mounted at a fixed position with optimum inclination (tilt). Fixed mounting structures offer a simple and efficient 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 the day to a more favourable position in relation to the sun, so the PV modules collect more solar radiation. Roof or façade 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), façades or can be directly integrated as part of a building structure. PV modules in these systems are often installed in a suboptimal position (deviating from the optimum angle), and this results in a lower performance ratio. 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. The main characteristic of grid-connected systems is their geographic dispersion and connection into a 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 can reach higher efficiencies and 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 also used. Mini-grid PV systems provide small isolated distribution grid for local consumers, usually located in remote areas. Typical size of installed PV systems is up to 100 kWp. Mini-grid may be adapted to meet requirements of local needs, sometimes with several types of electricity generators (hybrid systems) and battery storage. This type of electrification gives prospects for development to remote and rural communities, because it is often the sole economically viable option for supply of electricity. Off-grid PV systems are small systems, not connected into distribution grid. They are usually equipped with energy storage (classic lead acid or modern-type batteries) and/or connected to diesel generators. Batteries are maintained through charge controllers for protection against overcharging or deep discharge. Depending on size and functionality of the off-grid PV system, it can work with AC (together with inverter) or DC voltage source. In this study, the PV power potential is studied for a system with fixed-mounted PV modules, considered here as the mainstream technology. Installed capacity of a PV power plant is usually determined by the available space and options to maintain the stability of the power grid. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 33 of 77 3.3.1 Principles of PV energy simulation PV energy simulation results, presented in Chapter 4.6, are based on software developed by Solargis. This Chapter summarizes key elements of the simulation chain. Table 3.13: 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 23° to 34° in Nepal) towards South, derived from GHI and DNI; air temperature at 2 m (TEMP). Spatial grid resolution (approximate) 1 km (30 arc-sec) Time resolution 30-minute Geographical extent (this study) Republic of Nepal Period covered by data (this study) 01/1999 to 12/2015 The PV software has implemented scientifically proven methods [29 to 36] and uses Solargis time series of solar radiation and air temperature data on the input (Table 3.13). Data and model quality are checked using field tests and ground measurements. Figure 3.1: Simplified Solargis PV simulation chain World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 34 of 77 The models in the energy simulation software are scientifically validated and the software outputs have been compared to the real PV power output at many PV power plants. The Solargis model validation is always performed by comparing sub-hourly or hourly values. In PV energy simulation procedure, there are several energy losses occurring in the individual steps of energy conversion (Figure 3.1): 1. Losses due to terrain shading caused by far horizon. Shading of local features such as nearby building, structures or vegetation is not considered in the calculation, 2. Energy conversion in PV modules is reduced by Losses due to angular reflectivity, which depends on relative position of the sun and plane of the module and Temperature Losses, caused by performance of PV modules working outside of STC conditions defined in datasheets, 3. DC output of PV array is further reduced by Losses due to dirt, soiling or snow depending mainly on the environmental factors and module cleaning, Losses by inter-row shading caused by preceding rows of modules and Mismatch and DC cabling losses, which are given by slight differences between nominal power of each module and small losses on cable connections, 4. DC to AC energy conversion is performed by inverter. Efficiency of this conversion step is reduced by Inverter losses, given by inverter efficiency function. Further factors, reducing AC energy output, are Losses in AC cabling and Transformer losses (apply only for large–scale open space systems), 5. 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 approximately 99%. According to experience in many countries, the crystalline silicon PV modules show low performance reduction over time. The rate of the performance degradation 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 [33]. Degradation of PV modules is not considered in this study. Results of calculation of PV power potential for Nepal are shown in Chapter 4.6. 3.3.2 Technical configuration of a reference PV system Photovoltaic power production has been calculated using numerical models developed and implemented in- house by Solargis. As introduced in Chapter 3.1, 30-minute time series of solar radiation and air temperature, representing last 17 years, are used as an input to the simulation. The models are developed based on the advanced algorithms, expert knowledge and recommendations given in [35] and tested using monitoring results from existing PV power plants. Table 3.15 summarizes losses and related uncertainty throughout the PV computing chain. In this study, the reference configuration for the PV potential calculation is a PV system with crystalline-silicon (c-Si) modules mounted in a fixed position on a table facing South and inclined at an angle close to optimum, i.e. at the angle at which the yearly sum of global tilted irradiation received by PV modules is maximized (a range between 23º and 34º depends on a geographical region). The fixed-mounting of PV modules is very common and provides a robust solution with a minimum maintenance effort. Geographic differences in potential PV production are shown at eight selected sites (Chapter 4.1). Map 4.17 shows theoretical potential power production of a PV system installed with a typical technology configuration, for large-scale open space PV power plants. The technical parameters are described in Table 3.14. The results presented in Chapter 4.6 do not consider performance degradation of PV modules due to aging. They also lack a necessary detail; these results cannot be used for financial assumptions of any specific project. Detailed assessment of energy yield of a specific power plant is within a scope of site-specific bankable expert study. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 35 of 77 Table 3.14: Reference configuration - photovoltaic power plant with fixed-mounted PV modules Feature Description Nominal capacity Configuration represents a typical PV power plant of 1 MW-peak or higher. All calculations are scaled to 1 kWp, so that they can be easily multiplied for any installed capacity. Modules Crystalline silicon modules with positive power tolerance. NOCT 46ºC and temperature coefficient of the Pmax -0.45 %/K Inverters Central inverter with Euro efficiency 97.5% Mounting of PV modules Fixed mounting structures facing South with optimum tilt (the range from 23º to 34º). Relative row spacing 2.5 (ratio of absolute spacing and table width) Transformer Medium voltage power transformer Table 3.15: Yearly energy losses and related uncertainty in PV power simulation Simulation step Losses Uncertainty Notes [%] [± %] 1 Global Tilted Irradiation N/A 7.0 to 10.0 Annual Global Irradiation falling on the (model estimate with terrain shading) surface of PV modules 2 Module surface angular reflectivity -2.2 to -3.2 1.0 Medium polluted surface of PV modules is (numerical model) considered Temperature losses 0.0 to -13.0 3.5 Depends on the temperature and (numerical model) irradiance. NOCT of 46ºC is considered 3 Polluted surface of modules -4.0 1.5 Losses due to dirt, dust, soiling, snow and (empirical estimate) bird droppings Module inter-row shading -0.5 0.5 Partial shading of strings by modules from (model estimate) the preceding rows Mismatch between modules -0.5 0.5 Well-sorted modules and lower mismatch (empirical estimate) are considered. DC cable losses -2.0 1.5 This value can be calculated from the (empirical estimate) electrical design 4 Conversion in the inverter -2.5 0.5 Given by the Euro efficiency of the inverter, (value from the technical data sheet) which is considered at 97.5% AC cable losses -0.5 0.5 Standard AC connection is assumed (empirical estimate) Transformer losses -1.0 0.5 Standard transformer is assumed (empirical estimate) 5 Availability 0.0 0.0 A theoretical value of 100% technical availability is considered Range of cumulative losses -12.5 to -24.7 8.2 to 10.9 These values are indicative and do not and indicative uncertainty consider many project specific features and performance degradation of a PV system over its lifetime World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 36 of 77 PV electricity potential is calculated based on a set of assumptions shown in Tables 3.14 and 3.15. These assumptions are approximate values, and they will differ in the real projects. As can be seen uncertainty of solar resource is the highest element of energy simulation. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 37 of 77 4 Solar resource and PV potential of Nepal 4.1 Geography This report analyses solar and meteorological data for Nepal that determine photovoltaic power production and influence its performance efficiency. We also analyse other geographical factors that influence development and operation of solar photovoltaic power plants. Nepal is located in South Asia between latitudes 26°20’ and 30°27’ North and longitudes 80°03’ and 88°12’ East. We demonstrate the variability of solar potential in two forms: • At the country level in the form of maps • In the form of graphs and tables for eight sites (Table 4.1). Selection of eight sites is based on the following criteria: • They should represent the main solar climate zones of Nepal (Map 4.20). These zones are delineated using the two principal parameters determining performance of solar power plants, mainly photovoltaics: global horizontal irradiation and air temperature. • Existence of a larger area of relatively monotonous terrain (Map 4.3), this could be local airports in the mountains, as the local climate of narrow valleys or ridges strongly affect the regional weather patterns. • Higher concentration of population. The intention was to have represented some important urban centres of Nepal, which are characterized by a distinct local climate. This criterion is important mainly in remote areas, as solar technology will be deployed mostly in areas where people live (Map 4.9). The position of selected sites coincides to a great deal with position of airports or regionally important cities, and it is summarised in Table 4.1 and Map 4.1. All the data in tables and graphs shown in Chapter 4 is related to these eight sites. Table 4.1: Position of eight selected sites in Nepal ID Site name District Latitude Longitude Elevation [°] [°] [metres a.s.l.] 1 Simikot airport Humla 29.97112 81.81890 2966 2 Budar Kanchanpur 29.08835 80.56712 1362 3 Jomsom airport Mustang 28.78162 83.72337 2737 4 Pokhara airport Kaski 28.20036 83.98195 823 5 Nepalgunj Manikapur airport Banke 28.10182 81.66733 154 6 Kathmandu airport Kathmandu 27.70002 85.35834 1351 7 Khandbari Sankhuwasabha 27.37557 87.20843 1048 8 Biratnagar airport Morang 26.48392 87.26657 75 World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 38 of 77 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 report, we collected the following data that have some relevance to solar energy: • Terrain: physical limitation for development (Maps 4.2 and 4.3); • Land cover: defines primary areas used for human economic activities and settlements (Map 4.4), • Main road network: defining accessibility of sites for location of power plants (Map 4.5) • Protected areas limit size of the power station and intensity of land use for the related infrastructure (Map 4.6) • Days of snow cover, rainfall (precipitation) and air temperature have impact on efficiency (performance ratio) of the PV installations (Maps 4.7 and 4.8) • Population density (Map 4.9). Map 4.1: Position of eight selected sites in Nepal. From the geographical viewpoint, Nepal is a very diverse country, spreading from tropical lowlands with an elevation of 100 m above sea level to the highlands of the central zone ranging from 700 to 3000 m up to more than 8000 m high mountains covered by perpetual snow and ice. Mountains and hills cover more than 75% of Nepal, where terrain represents significant limiting factor for location of solar power facilities. This obstacle can be managed by selecting appropriate combination of technology, location and size. Solar power in Nepal can be spread not only in the populated plains, but also in the remote mountain communities. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 39 of 77 Map 4.2: Terrain elevation above sea level. Source: SRTM-3 Map 4.3: Terrain slope. Source: SRTM-3 and Solargis Map of land cover (Map 4.4) shows that the most appropriate conditions for human activities, including settlements and economic activities (industry, agriculture) that require the bulk of electrical power, are developed in southern lowland and adjacent hills. Smaller settlements are dispersed in higher mountains. The latest available version of the data set represented, was created in the year 2009. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 40 of 77 Map 4.4: Land cover. Source: GlobCover 2009 (ESA 2010 and UCLouvain) Map 4.5: Roads and airports. Source: OpenStreetMap.org contributors; administrative boundaries by Cartography Unit, GSDPM (World Bank Group) World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 41 of 77 Map 4.6: Nature protection areas, glaciers and water bodies. Source: OpenStreetMap.org contributors, adapted by Solargis Concentrated urban centres also constitute the energy demand centres, but at the same time these are the centres of higher air pollution. The Terai Region along the border with India is one of the most polluted areas in South Asia, which must be considered as limitation factor for deployment of solar power. More complex orographic conditions (terrain) are generally less populated and the most often they are not suitable for large- scale solar energy development. Map 4.7: Long-term average sum of days with snow cover. Source: Meteorological model CFSR (NOAA) World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 42 of 77 Map 4.8: Long-term yearly average of rainfall (sum of precipitation). Source: Global Precipitation Climatology Centre (DWD) Map 4.9: Population density. Source: Gridded Population of the World (GPW) v.4. Based on data from Central Bureau of Statistics Nepal, National Population and Housing Census 2011. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 43 of 77 4.2 Air temperature Air temperature determines the operating environment and performance efficiency of the solar power systems. Air temperature is used as one of the inputs in the energy simulation models. In this report, the yearly and monthly average maps are shown. Map 4.10 shows yearly average. Map 4.10: Long-term yearly average of air temperature at 2 metres. Source: Models CFSR and CFSv2, NOAA, post-processed by Solargis The long-term averages of air temperature are derived from the CFSR and CFSv2 meteorological models (see Chapter 3.2) by Solargis post-processing. In the mountains, the hourly values may be partially smoothed and may not represent the local microclimate amplitudes. In case of PV power plants, air temperature has a primary influence on the power conversion efficiency in the PV modules, and it also influences other components (inverters, transformers, etc.). Increased air temperature reduces the power conversion efficiency of a PV power plant. Table 4.2 shows monthly characteristics of air temperature at eight selected sites; they represent statistics calculated over 24-hour diurnal cycle. Minimum and maximum air temperatures are 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 4.1). See Chapter 3.2 for more information about the uncertainty of the air temperature model estimates. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 44 of 77 Table 4.2: Monthly averages and average minima and maxima of air-temperature at 2 m at 8 sites Temperature [°C] Month Simikot Budar Jomsom Pokhara Nepalgunj Kathmandu Khandbari Biratnagar Min Min Min Min Min Min Min Min Average Average Average Average Average Average Average Average Max Max Max Max Max Max Max Max -12,4 4,0 -11,1 6,0 6,3 3,8 5,4 8,3 January -7,3 9,8 -4,8 10,8 13,5 9,7 9,9 16,0 -1,0 18,3 2,8 17,9 23,2 17,9 15,7 24,9 -12,2 6,6 -11,4 8,0 9,5 6,3 7,0 11,2 February -6,3 12,6 -3,8 12,9 17,2 11,9 11,9 19,8 -0,3 20,9 3,2 19,9 26,9 20,1 17,7 28,9 -8,3 11,1 -7,5 11,5 14,5 10,5 9,9 15,8 March -2,5 17,8 -0,5 17,0 23,3 16,4 15,4 25,2 3,2 26,6 6,2 24,7 33,0 24,9 21,8 34,6 -3,6 16,2 -2,8 15,0 20,7 14,5 13,2 21,8 April 1,7 23,4 3,3 21,2 29,7 20,9 19,2 30,0 6,9 32,0 9,2 29,0 38,9 29,4 25,7 38,4 1,1 19,2 2,3 17,4 24,9 16,7 15,6 25,5 May 6,1 26,7 7,1 23,7 33,1 22,8 21,2 31,5 11,4 34,6 12,5 31,2 41,5 31,0 27,2 38,5 5,7 20,4 6,7 18,8 26,6 18,2 17,6 26,4 June 10,4 26,3 10,6 24,6 32,8 23,5 22,8 30,8 15,8 33,1 15,1 31,4 40,0 30,6 28,0 36,7 9,0 19,6 9,7 19,2 25,5 18,1 19,1 25,8 July 12,8 23,4 12,4 23,8 29,6 22,1 22,7 28,7 17,3 28,2 15,6 29,2 35,0 28,0 26,8 32,8 8,9 18,9 9,2 18,5 24,5 17,4 18,8 25,6 August 12,5 22,5 12,3 23,5 28,5 21,8 22,6 28,6 16,6 27,2 15,3 28,9 33,6 27,8 26,6 32,6 5,6 16,9 6,0 16,6 22,7 15,4 17,1 24,2 September 9,9 21,1 10,2 22,1 27,0 20,5 21,4 27,5 14,5 26,9 14,3 28,4 32,8 27,2 25,7 31,7 -0,5 12,5 -0,5 13,1 18,2 11,1 13,4 20,3 October 3,9 18,2 4,8 18,5 23,8 17,4 18,1 25,1 9,2 26,2 10,9 25,9 31,8 25,5 23,4 31,1 -5,5 7,7 -5,2 9,6 12,7 6,8 9,6 15,1 November -0,6 14,3 0,6 14,9 19,1 13,5 14,4 21,4 5,3 23,2 8,0 22,6 28,3 22,5 20,2 29,3 -9,3 4,2 -8,2 6,8 7,7 3,8 6,7 10,7 December -4,2 10,9 -2,1 12,0 14,6 10,6 11,4 17,7 1,8 19,9 5,3 19,5 24,3 19,4 17,2 26,1 YEAR 3,1 18,9 4,2 18,8 24,4 17,6 17,6 25,2 45 35 25 Monthly air temperature [°C] 15 5 -5 -15 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Simikot Budar Jomsom Pokhara Nepalgunj Kathmandu Khandbari Biratnagar Minimum - Maximum Figure 4.1: Monthly averages, minima and maxima of air-temperature at 2 m for selected sites. 4.3 Global Horizontal Irradiation Global Horizontal Irradiation (GHI) is used as a reference value for comparing geographical conditions related to PV electricity systems, as it eliminates the possible variations, given by the choice of components and the PV system design. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 45 of 77 2 The highest GHI is identified in the northwest of the country, where the average daily total up to 5.5 kWh/m or 2 higher is seen (Map 4.11). In southern parts of the country average daily GHI values between 4.4 kWh/m and 2 4.9 kWh/m prevail. The solar resource in these areas is still sufficiently high. Map 4.11: Global Horizontal Irradiation - long-term average of daily and yearly totals. Source: Solargis Table 4.3: Daily averages and average minima and maxima of Global Horizontal Irradiation at 8 sites Global Horizontal Irradiation [kWh/m2] Variability Month Simikot Budar Jomsom Pokhara Nepalgunj Kathmandu Khandbari Biratnagar between Min Min Min Min Min Min Min Min sites [%] Average Average Average Average Average Average Average Average Max Max Max Max Max Max Max Max 65 97 86 103 75 103 83 65 January 95 115 114 119 90 121 105 90 12,1 128 128 136 133 117 134 132 115 71 110 93 97 110 99 80 89 February 97 125 119 124 123 128 112 114 8,4 132 144 152 144 139 144 137 139 115 173 152 151 169 156 138 151 March 156 184 176 171 180 174 152 165 6,6 196 205 203 200 202 204 185 195 134 176 167 152 172 147 123 137 April 165 197 189 176 192 178 155 169 8,1 210 212 218 197 212 210 187 192 142 167 188 156 170 150 141 146 May 175 200 215 185 196 180 161 170 9,6 197 229 235 206 217 204 189 197 119 109 171 145 119 142 123 126 June 154 158 198 166 165 160 150 149 9,6 188 204 226 191 199 188 178 165 115 107 157 105 123 117 110 107 July 135 122 173 146 146 143 149 142 10,0 177 151 199 162 170 160 171 162 121 116 156 133 132 126 131 124 August 133 126 172 148 148 143 150 142 9,3 146 143 194 163 160 157 170 162 109 97 147 125 119 119 117 120 September 138 124 167 144 144 137 139 134 8,7 165 145 187 158 166 154 158 150 141 122 144 122 126 127 123 114 October 158 148 166 153 147 152 146 138 5,6 190 170 189 177 166 167 157 152 120 111 129 85 101 99 79 65 November 131 125 137 120 116 123 110 108 8,1 144 138 146 135 131 139 132 129 98 111 105 101 84 103 76 61 December 114 114 122 111 97 113 97 92 9,9 132 123 131 125 114 127 123 117 1574 1677 1912 1673 1679 1666 1525 1483 YEAR 1651 1739 1949 1763 1745 1753 1627 1614 6,2 1764 1852 2037 1856 1826 1839 1781 1736 Table 4.3 shows long-term averages, and average minima and maxima of daily totals of Global Horizontal Irradiation (GHI) for a period 1999 to 2015 for eight selected sites. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 46 of 77 Figure 4.2 compares monthly averages of daily values of Global horizontal irradiation (GHI). The most stable weather with the highest GHI values is from March to June. July to October is a period with lower, but also stable GHI. Similar pattern of GHI for all representative sites indicates that all sites will experience similar performance of PV power systems, with variability between sites, given by local microclimate. Jomsom site is the least influenced by the rainy/monsoon summer season. 10.0 9.0 8.0 7.0 Daily sums of GHI [kWh/m2] 6.0 5.0 4.0 3.0 2.0 1.0 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Simikot Budar Jomsom Pokhara Nepalgunj Kathmandu Khandbari Biratnagar Min - Max Figure 4.2: Long-term monthly averages, minima and maxima of Global Horizontal Irradiation. The weather changes in cycles and has stochastic nature as well. Therefore, the annual solar radiation in each year can deviate from the long-term average in the range of few percent. Figure 4.3 shows interannual variability, i.e. the magnitude of the year-to-year GHI change. 6.5 2374 Average yearly sum of Global Horizontal Irradiation 6.0 2192 Average daily sum of Global Horizontal Irradiation 5.5 2009 5.0 1826 4.5 1644 4.0 1461 [kWh/m2] [kWh/m2] 3.5 1278 3.0 1096 2.5 913 2.0 730 1.5 548 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Year Simikot 4.5% Budar 3.2% Jomsom 2.5% Pokhara 3.4% Nepalgunj 3.5% Kathmandu 3.5% Khandbari 4.7% Biratnagar 4.9% Figure 4.3: Interannual variability of Global Horizontal Irradiation for selected sites. The interannual variability is calculated from the unbiased standard deviation of GHI over 17 years, considering a simplified assumption of normal distribution of the annual sums. Kathmandu, Nepalgunj, Pokhara and Budar sites show similar varying patterns of GHI over the recorded period. Also, the extremes for those sites (minimum World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 47 of 77 and maximum) or values close to the extremes are reached almost in the same years. Other sites show slightly different characteristics. The highest solar radiation and the most stable GHI (the smallest interannual variability) are observed in Jomsom. The site with highest interannual variability is Biratnagar. Map 4.12 identifies magnitude of terrain shading relative to yearly GHI. Map 4.13 delineates ratio of diffuse to global horizontal irradiation. This ratio is important for the performance of PV systems. Map 4.12: Losses of yearly GHI totals due to terrain shading (high horizon) Source: Solargis Map 4.13: Long-term average for ratio of diffuse and global irradiation (DIF/GHI). Source: Solargis World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 48 of 77 4.4 Direct Normal Irradiation Direct Normal Irradiation (DNI) is one of the primary solar resource parameters, needed for computation of Global Tilted Irradiation (GTI, Chapter 4.5). The highest values are found in the Mustang district. Generally, northwest part of the country has higher DNI potential; lower values in the southeast are influenced by higher presence of aerosols and clouds in the atmosphere. Table 4.4 and Figure 4.4 show long-term average daily totals and average daily minimum and maximum of DNI for eight selected sites, assuming a period 1999 to 2015. The highest DNI is found in the Jomsom and Simikot sites, the lowest in the Biratnagar site. In almost all sites, DNI shows similar pattern with two maximum seasons. The first is during spring from March to May and the second is during autumn and beginning of winter, from October to December. Minimum DNI values are observed during monsoon season in July and August, where DNI is reduced by about 30% to 50%. During this season, the lowest variability (given by minimum and maximum range of values) also occurs. The highest variability is recorded for a season from October to December. Map 4.14: Direct Normal Irradiation - long-term average of daily and yearly totals. Source: Solargis World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 49 of 77 Table 4.4: Daily averages and average minima and maxima of Direct Normal Irradiation at 8 sites Direct Normal Irradiation [kWh/m2] Variability Month Simikot Budar Jomsom Pokhara Nepalgunj Kathmandu Khandbari Biratnagar between Min Min Min Min Min Min Min Min sites [%] Average Average Average Average Average Average Average Average Max Max Max Max Max Max Max Max 57 105 103 103 51 98 51 34 January 134 142 162 140 71 134 96 57 32,2 210 175 213 185 115 180 160 101 45 102 74 70 83 66 39 45 February 104 132 135 119 107 125 87 79 18,4 178 177 196 151 139 166 132 124 85 142 137 95 116 114 73 83 March 152 173 178 140 152 146 99 109 19,3 218 228 237 202 209 215 160 169 82 111 132 74 106 74 49 52 April 128 155 163 112 136 117 83 92 22,8 192 184 214 144 162 160 117 122 66 80 150 74 82 66 54 61 May 115 130 179 111 116 106 78 83 27,3 144 173 219 148 152 146 111 123 53 33 113 67 49 59 41 40 June 87 82 148 93 88 81 68 65 28,9 129 146 193 126 130 118 105 84 41 29 83 32 37 36 37 34 July 62 42 110 75 70 66 73 65 27,0 117 72 156 101 102 90 99 85 48 41 86 60 54 49 53 41 August 65 52 113 78 78 69 76 65 23,9 89 76 150 106 101 85 101 91 78 41 111 64 70 63 60 57 September 110 70 147 96 94 82 83 75 26,0 151 104 174 121 115 113 113 100 170 90 155 100 78 104 87 82 October 201 141 200 152 124 145 125 103 23,7 287 210 261 218 178 173 147 120 179 99 161 73 65 72 52 37 November 213 147 201 127 104 131 102 87 33,2 254 194 228 174 149 187 148 124 156 144 163 96 57 91 40 33 December 202 154 202 133 86 128 91 69 38,1 254 194 230 193 126 176 154 116 1378 1320 1845 1207 1060 1129 915 772 YEAR 1573 1421 1938 1376 1225 1330 1062 949 22,6 1809 1619 2153 1641 1443 1622 1369 1217 10.0 9.0 8.0 7.0 Daily sums of DNI [kWh/m2] 6.0 5.0 4.0 3.0 2.0 1.0 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Simikot Budar Jomsom Pokhara Nepalgunj Kathmandu Khandbari Biratnagar Min - Max Figure 4.4: Daily averages of Direct Normal Irradiation at selected sites. Interannual variability of DNI for selected sites (Figure 4.5) is calculated from the unbiased standard deviation of yearly DNI over 17 years and it is based on a simplified assumption of normal distribution of the yearly sums. Every site has its own variability pattern, only few events were so strong that influenced all sites (for example decline of DNI in years 1999-2000, or high values in year 2003). The most stable and the highest DNI (the smallest interannual variability) is observed in Jomsom, the most unstable and lowest DNI is recorded in Khandbari and Biratnagar sites. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 50 of 77 6.5 2374 Average daily sum of Direct Normal Irradiation Average yearly sum of Direct Normal Irradiation 6.0 2192 5.5 2009 5.0 1826 4.5 1644 [kWh/m2] [kWh/m2] 4.0 1461 3.5 1278 3.0 1096 2.5 913 2.0 730 1.5 548 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Year Simikot 10.1% Budar 7.9% Jomsom 5.6% Pokhara 10.0% Nepalgunj 10.9% Kathmandu 10.7% Khandbari 12.8% Biratnagar 12.7% Figure 4.5: Interannual variability of Direct Normal Irradiation at representative sites Daily totals in a particular year can be displayed for a better visual presentation of DNI in relation to GHI. Figure 4.6 shows daily totals for year 2015 in Jomsom. Blue pattern, representing GHI sums is transparent in order to make visible lower values of DNI pattern (yellow). 12 Direct Normal Global Horizontal 10 Daily sums of irradiation [kWh/m2] 8 6 4 2 0 01.01.2015 01.03.2015 01.05.2015 01.07.2015 01.09.2015 01.11.2015 01.01.2016 Figure 4.6: Daily totals of GHI and DNI in Jomsom in the year 2015 Source: Solargis 4.5 Global Tilted Irradiation Global Tilted Irradiation (GTI) is the key source of energy for flat-plate photovoltaic (PV) technologies (Chapter 4.6). The regional trend of GTI received by PV modules tilted at optimum angle (GTI) is similar to DNI (Map 4.15). PV modules tilted at optimum inclination show increase of average yearly GTI totals to about 2 2 2400 kWh/m (daily totals about 6.5 kWh/m ) and more, especially in Northwest. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 51 of 77 Map 4.15: Global Tilted Irradiation at optimum angle – long-term average of daily and yearly totals. Source: Solargis The main parameter influencing optimum tilt in Nepal is latitude, which spans between 26° and 30° North (Map 4.16). For this region, optimum tilt is southwards between 23° and 34° (increasing from South to North). The optimum tilt is also determined by the ratio between diffuse and global horizontal irradiation, which reduces the effect of latitude in the plain in the South and augments it in the mountains in the North. Map 4.16: Optimum tilt of PV modules to maximize yearly PV power production. Source: Solargis World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 52 of 77 Table 4.5 show long-term averages of average daily total of Global Tilted Irradiation (GTI) for selected sites. It is assumed that solar radiation is received by PV modules surface inclined at the optimum tilt. Table 4.5: Daily averages and average minima and maxima of Global Tilted Irradiation at 8 sites Global Tilted Irradiation [kWh/m2] Variability Month Simikot Budar Jomsom Pokhara Nepalgunj Kathmandu Khandbari Biratnagar between Min Min Min Min Min Min Min Min sites [%] Average Average Average Average Average Average Average Average Max Max Max Max Max Max Max Max 2,74 4,28 3,86 4,43 2,96 4,40 3,25 2,47 January 4,54 5,28 5,35 5,33 3,68 5,30 4,37 3,49 13,8 6,46 5,99 6,56 6,17 4,95 6,09 5,80 4,68 2,93 4,85 4,02 4,10 4,72 4,10 3,20 3,58 February 4,45 5,70 5,43 5,51 5,32 5,67 4,77 4,72 8,6 6,36 6,73 7,15 6,46 6,13 6,46 6,00 5,95 4,09 6,36 5,54 5,33 6,01 5,65 4,77 5,28 March 5,76 6,78 6,53 6,21 6,53 6,31 5,39 5,83 5,5 7,41 7,71 7,66 7,44 7,48 7,57 6,73 7,04 4,44 5,92 5,61 5,08 5,84 4,93 4,12 4,60 April 5,55 6,66 6,44 5,95 6,57 6,00 5,23 5,74 6,9 7,07 7,17 7,43 6,67 7,23 7,09 6,34 6,52 4,14 5,01 5,63 4,73 5,26 4,55 4,31 4,54 May 5,19 5,96 6,46 5,61 6,05 5,44 4,93 5,29 7,9 5,81 6,82 7,04 6,19 6,65 6,17 5,79 6,10 3,51 3,24 5,11 4,38 3,70 4,29 3,82 3,93 June 4,53 4,64 5,89 5,02 5,11 4,83 4,62 4,67 9,7 5,48 6,04 6,71 5,74 6,14 5,70 5,46 5,16 3,31 3,12 4,63 3,13 3,76 3,50 3,32 3,26 July 3,94 3,51 5,10 4,32 4,41 4,24 4,51 4,35 12,4 5,15 4,39 5,85 4,81 5,13 4,73 5,18 4,95 3,66 3,53 4,86 4,12 4,19 3,91 4,13 3,94 August 4,10 3,83 5,41 4,63 4,72 4,48 4,76 4,51 12,0 4,55 4,37 6,18 5,11 5,12 4,92 5,39 5,14 3,90 3,34 5,25 4,35 4,21 4,14 4,08 4,18 September 4,93 4,30 6,06 5,10 5,11 4,81 4,89 4,71 11,4 5,98 5,15 6,82 5,66 5,96 5,49 5,67 5,35 October 5,83 4,63 5,73 4,66 4,75 4,85 4,60 4,18 6,53 5,82 6,74 6,01 5,63 5,92 5,57 5,11 7,1 August 8,09 6,93 7,78 7,14 6,48 6,58 6,05 5,66 5,88 4,87 5,98 3,61 4,10 4,23 3,14 2,50 November 6,59 5,77 6,56 5,33 4,96 5,46 4,67 4,43 11,6 7,44 6,60 7,09 6,21 5,80 6,41 5,81 5,42 5,03 5,26 5,11 4,42 3,37 4,42 2,98 2,33 December 5,96 5,49 6,11 5,07 4,13 5,09 4,13 3,70 13,6 7,15 6,14 6,61 6,07 5,05 5,96 5,52 4,91 4,85 5,19 5,84 5,06 4,97 4,99 4,49 4,29 YEAR 5,18 5,31 6,01 5,34 5,18 5,29 4,82 4,71 5,8 5,60 5,67 6,28 5,74 5,50 5,68 5,39 5,18 Figure 4.7 compares long-term daily averages in selected sites. High GTI, but with higher variability is seen from March to May and September to December. Lower, but stable values of GTI are observed during monsoon season for all sites. Variability of GTI between sites is similar almost through the whole year, except in November and December. This may be related to different winter condition for different climatic zones of selected sites. 10.0 9.0 8.0 7.0 Daily sums of GTI [kWh/m2] 6.0 5.0 4.0 3.0 2.0 1.0 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Simikot Budar Jomsom Pokhara Nepalgunj Kathmandu Khandbari Biratnagar Min - Max Figure 4.7: Global Tilted Irradiation - long-term daily averages, minima and maxima. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 53 of 77 70.0 60.0 50.0 40.0 Relative gain of GTI to GHI [%] 30.0 20.0 10.0 0.0 -10.0 -20.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Simikot Budar Jomsom Pokhara Nepalgunj Kathmandu Khandbari Biratnagar Figure 4.8: Monthly relative gain of GTI relative to GHI at selected sites. Surface inclined at optimum tilt gains more yearly global irradiation compared to the horizontal surface (Figure 4.8). Gains are site-dependent and are pronounced in winter season. The highest gain is recorded in December for all sites: in the range of approximately from 25% in Biratnagar up to 62% in Simikot. On the other side, in monsoon season, with the highest sun position, the horizontal surface receives more global irradiation (about 5% to 12%) compared to the optimally tilted surface. This occurs during period from April to August. As the energy gain in the other months is higher, the overall yearly gains of global irradiation for optimally tilted surface remain higher than for horizontal surface. Detailed comparison of daily GTI and GHI values for Kathmandu is shown in Table 4.6 and Figure 4.9. Table 4.6: Relative gain of daily GTI to GHI in Kathmandu Average daily sum of irradiation [kWh/m2] Kathmandu Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Global Horizontal 3,89 4,54 5,62 5,94 5,82 5,32 4,60 4,63 4,57 4,92 4,12 3,65 4,80 Global Tilted 5,30 5,67 6,31 6,00 5,44 4,83 4,24 4,48 4,81 5,92 5,46 5,09 5,29 Global Tilted vs. Horizontal [%] 36 25 12 1 -6 -9 -8 -3 5 20 33 39 10 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 Global Horizontal Global Tilted Global Tilted vs. Horizontal 0.0 -25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Figure 4.9: GHI and GTI monthly averages and relative gain of GTI to GHI in Kathmandu World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 54 of 77 Daily totals, in one particular year, are shown for better visual presentation of gain for tilted surfaces in comparison to horizontal ones. Figure 4.10 shows daily sums for year 2015 in Kathmandu. Blue pattern, representing GHI totals, is transparent in order to make visible lower values of red, GTI pattern, during monsoon season. The GTI seasonal profile is more stable compared to GHI. 12 Global Tilted Global Horizontal 10 Daily sums of irradiation [kWh/m2] 8 6 4 2 0 01.01.2015 01.03.2015 01.05.2015 01.07.2015 01.09.2015 01.11.2015 01.01.2016 Figure 4.10: Daily values of GHI and GTI for Kathmandu, year 2015 4.6 Photovoltaic power potential Map 4.17 shows the average daily total of specific PV electricity output from a typical open-space PV system with a nominal peak power of 1 kW, i.e. the values are in kWh/kWp. Calculating PV output for 1 kWp of installed power makes it simple to scale the PV power production depending on the size of a power plant. Besides the technology choice, the electricity production depends on a geographical position of the power plant. In Nepal, the average yearly total of specific PV power production from a reference system (Table 3.14) vary between 1200 kWh/kWp (equals to average daily total of about 3.3 kWh/kWp) and 2200 kWh/kWp (about 6.0 kWh/kWp daily) with high values in north-western region of the country. In the mountains, the power production can be reduced by up to 20% (or even more) due to terrain shading. Areas with high PV electricity production potential were previously identified also as areas with high GTI and DNI values. Map 4.18 shows monthly production from a PV power system, and Figure 4.11 breaks down the values for eight sites. Season of relatively high PV yield is long enough for an effective operation of a PV system. As shown in Chapter 4.5, it is recommended to install modules at an optimum tilt rather than on horizontal surface. Besides higher yield, a benefit of tilted modules is improved self-cleaning of the surface pollution by rain. Electricity production in a potential PV power plant depends on the site position and follows a combined pattern of global tilted irradiation and air temperature. High PV power production is identified at Jomsom and Simikot sites; lower potential is in Khandbari and Biratnagar. Difference in PV power production between the sites with higher and lower elevation is quite large: Jomsom (5.07 kWh/kWp) and Biratnagar (3.62 kWh/kWp), which is 28%. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 55 of 77 Map 4.17: PV electricity output from open space fixed-mounted PV system with PV modules mounted at an optimum tilt and a nominal peak power of 1 kWp. Long-term averages of daily and yearly totals. Table 4.7: Annual performance parameters of a PV system with modules fixed at optimum angle Simikot Budar Jomsom Pokhara Nepalgunj Kathmandu Khandbari Biratnagar PVOUT 4.40 4.14 5.07 4.22 3.96 4.19 3.84 3.62 Average daily total [kWh/kWp] PVOUT 1609 1513 1852 1539 1445 1531 1401 1321 Yearly total [kWh/kWp] Optimum angle 34° 31° 30° 29° 26° 28° 26° 23° Annual ratio of 41.9% 47.8% 34.8% 48.4% 52.2% 50.0% 54.5% 58.7% DIF/GHI System PR 85.1% 78.0% 84.4% 78.9% 76.4% 79.2% 79.6% 76.8% PVOUT - PV electricity yield for fixed-mounted modules at optimum angle; DIF/GHI – Ratio of diffuse/global horizontal irradiation; PR - Performance ratio for fixed-mounted PV This also shows potential of selected sites in PV electricity generation. Sites in the mountains can benefit from high elevation and low aerosols, where in extreme cases during good weather, solar radiation may reach values close to the solar constant [37]. Local micro-grid systems will work with higher efficiencies, increased by low temperatures, thus will require less PV modules to be installed. On the opposite side, lower parts of the country still have very good potential for PV electricity generation and for medium-scale grid connected PV projects (see Kathmandu, Pokhara or Budar in Table 4.8). Energy produced by grid connected PV power plants during sunlight hours can decrease amount of imported energy and help to cover increasing electricity demand [38]. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 56 of 77 Monthly power production profiles are very similar for all sites. High production can be reached in two seasons of the year (March to May and October to December). Decreased production in winter season from December to February is lower, but it varies between sites (the highest decrease in sites in the Terai region). In all selected sites, production is reduced by monsoon season with lower irradiation and higher temperatures. Map 4.18: PV power generation potential for an open-space fixed-mounted PV system. Long-term monthly averages of daily totals. Source: Solargis World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 57 of 77 Table 4.8: Average daily sums of PV electricity output from an open-space fixed PV system with a nominal peak power of 1 kW [kWh/kWp] Average daily sum of electricity production [kWh/kWp] Site Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Simikot 4,06 3,95 5,01 4,72 4,32 3,69 3,17 3,33 4,07 5,53 5,72 5,27 4,40 Budar 4,31 4,57 5,27 5,03 4,45 3,48 2,68 2,97 3,36 4,55 4,60 4,47 4,14 Jomsom 4,68 4,72 5,58 5,41 5,36 4,85 4,19 4,46 5,02 5,65 5,61 5,31 5,07 Pokhara 4,39 4,48 4,91 4,60 4,29 3,84 3,34 3,59 3,96 4,73 4,29 4,16 4,22 Nepalgunj 2,96 4,19 4,97 4,85 4,42 3,76 3,32 3,59 3,90 4,32 3,90 3,33 3,96 Kathmandu 4,38 4,61 4,99 4,65 4,18 3,72 3,30 3,50 3,77 4,66 4,40 4,18 4,19 Khandbari 3,63 3,91 4,31 4,11 3,83 3,58 3,51 3,71 3,83 4,43 3,79 3,41 3,84 Biratnagar 2,80 3,71 4,43 4,28 3,94 3,51 3,31 3,45 3,62 3,94 3,48 2,96 3,62 Table 4.9 and Figure 4.12 show monthly and yearly performance ratios (PR) for a reference installation at the selected sites. The range of yearly PR is found in a range between 76.4% (Nepalgunj) and 85.1% (Simikot). Monthly variations in PR fall in the range ±2.5% to ±4.5%; depending on specific climate conditions of a site, especially air temperature. Performance ratio is higher in the season from October to April, when PV output of the modules is less influenced by high air temperature. 6.00 5.50 5.00 Electricity production [kWh/kWp] 4.50 4.00 3.50 3.00 2.50 2.00 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Simikot Budar Jomsom Pokhara Nepalgunj Kathmandu Khandbari Biratnagar Figure 4.11: Monthly averages of daily totals of power production from the fixed tilted PV systems with a nominal peak power of 1 kW at eight sites [kWh/kWp] World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 58 of 77 Table 4.9: Monthly and annual Performance Ratio of a free-standing PV system with fixed modules Monthly Performance Ratio [%] Site Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Simikot 89,4 88,7 86,9 84,9 83,3 81,4 80,5 81,1 82,4 84,7 86,7 88,4 85,1 Budar 81,8 80,2 77,8 75,5 74,7 74,9 76,3 77,4 78,1 78,2 79,7 81,4 78,0 Jomsom 87,5 86,9 85,4 84,0 83,0 82,3 82,3 82,6 82,8 83,8 85,6 86,9 84,4 Pokhara 82,5 81,3 79,0 77,4 76,6 76,6 77,2 77,5 77,7 78,7 80,6 82,1 78,9 Nepalgunj 80,4 78,9 76,0 73,8 73,1 73,6 75,3 76,0 76,3 76,7 78,8 80,6 76,4 Kathmandu 82,6 81,3 79,1 77,5 76,8 77,0 77,9 78,1 78,4 78,8 80,6 82,2 79,2 Khandbari 83,1 82,0 80,0 78,4 77,8 77,6 77,9 78,0 78,4 79,4 81,1 82,6 79,6 Biratnagar 80,2 78,7 76,1 74,6 74,5 75,2 76,2 76,4 76,8 77,1 78,5 80,0 76,8 Impact of air temperature on the performance of PV power plants is seen when comparing monthly temperature profiles in Figure 4.1 with monthly PR profiles in Figure 4.12. The lowest PR values, between April and September, are corresponding to warm season, where PV output is reduced by higher air temperature. 95.0 90.0 Performance ratio [%] 85.0 80.0 75.0 70.0 65.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Simikot Budar Jomsom Pokhara Nepalgunj Kathmandu Khandbari Biratnagar Figure 4.12: Monthly performance ratio of a PV system at selected sites. Fixed mounted modules at optimum tilt are considered 4.7 Solar climate The power production and performance efficiency of photovoltaic systems is primarily driven by global horizontal irradiance, GHI (Map 4.11). GHI determines absolute values of energy production of a PV system, and variability patterns of PV power production (interannual, seasonal, daily and very short-term variability). In general, the higher GHI, the higher PV energy is expected. Patterns of power generation are also determined by the ratio of diffuse to global radiation (Figure 4.13). This simplified assumption is modulated by air temperature, TEMP (Map 4.10), as it affects operation efficiency of a PV system. In general, high-temperature reduces performance efficiency of the PV system and low temperature makes power conversion in PV modules more efficient. Additionally, low and high temperature areas in Nepal represent regions where PV operates under World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 59 of 77 higher stress. Low temperature to a certain degree correlates with presence of snow and high temperature with higher air humidity. For development of solar power systems, it is also important to take into account other geographical and meteorological factors, e.g. terrain elevation, terrain shading, wind speed, rainfall, snow cover and land cover. They are also important for development and operation of solar power systems: • Terrain: maps of elevation, slope inclination and terrain shading show limitations of installation and operating the meteorological stations, but also PV power systems. The major effect in the mountains is terrain shading (Map 4.12) and this effect is significant in high mountains of Nepal. Terrain shading blocks direct sunlight and it reduces power generation. Therefore, if possible, a solar meteorological station and also a PV system should be located in a place with no or limited shading. Higher elevation above sea level (Map 4.2) reduces atmospheric load by aerosols and thickness of atmosphere and this way it increases solar radiation under clear sky conditions. On the other hand, high elevation may pose increased risk of degradation of components due to low temperature and higher UV radiation. High slope inclination (Map 4.3) is challenging for logistics and operation, and also poses various geo- hazards, such as landslides, avalanches and floods. • Wind speed: Low and medium speed winds, close to the ground, have cooling effect on PV modules, which in turn increases their conversion efficiency and increases power production. However, the occurrence of stronger winds poses a risk of damaging the modules and construction components. Wind speed map is not supplied in this report. • Snow (Map 4.7): higher frequency of snowfall and higher snow cover reduce PV power production by blocking solar radiation from reaching the surface of PV modules. Higher tilt of PV modules and higher construction help reducing time of the snow presence on the surface of PV modules. • Rainfall (Map 4.8): amount and periodicity of rainfall determine cleaning efficiency of surface of the PV modules. Manual cleaning of PV modules requires special attention in dry and dusty climate. • Water bodies (Map 4.6): areas close to lakes may be affected by microclimate features such as local fog, humidity or increased creation of due. • Industrial and highly urbanised areas: these areas typically pose a higher risk of air pollution that triggers higher intensity of soiling of PV modules. PV modules that are covered by dust or atmospheric pollution may show substantial reduction of power production and require more frequent cleaning of surface of the PV modules. The close proximity of a meteorological station or a PV power plant to heavy industry or traffic should be avoided. Note: maps of rainfall and snow cover provide only indicative information at a regional level. They do not respect detailed and complex orography, and thus they do not represent local climate. Below, the geographical diversity of Nepal is described by several climate zones relevant to PV power production. These climate zones are identified by combining maps of yearly GHI and TEMP (Tables 4.10 and 4.11). Table 4.10: Categories of long-term yearly average of global horizontal irradiation Category Yearly average of daily global horizontal irradiation (GHI) 2 A Low < 4.0 kWh/m 2 B Middle range From 4.0 to 4.8 kWh/m 2 C High > 4.8 kWh/m World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 60 of 77 Table 4.11: Categories of long-term yearly average of air temperature Category Yearly average of air temperature (TEMP) 1 Low < 4°C 2 Middle range From 4°C to < 20°C 3 High > 20°C Map 4.19: Solar climate zones of Nepal – indicative classification based on a combination of GHI and TEMP. Source: Solargis We recognize several climate regions that have some relevancy from the perspective of solar power generation and performance efficiency of PV systems (Map 4.19, Tables 4.10 and 4.11, see also Maps 3.2 and 4.4): 2 • Solar climate zone A shows low solar radiation areas (GHI long-term yearly average below 4.0 kWh/m per day): This zone is mostly represented by ridges and slopes of high mountains with high occurrence of clouds and air temperature in the middle and low range (A1 and A2). In many areas, the solar resource is reduced by terrain shading. This climate zone is less populated with dominating forests and permanent snow and ice. The main challenge here is accessibility and harsh conditions and lower resource for PV power systems. Low range of solar resource is also seen in the lower hills of Southwest with prevailing air temperature in the middle to higher range and higher rainfall (A2 and A3). World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 61 of 77 • Solar climate zone B is the most predominant in Nepal, with GHI yearly average between 4.0 and 2 4.8 kWh/m per day: Low temperature zone (B1) includes the high mountains with shrubland and grassland, as well as bare areas and permanent snow and ice. This zone has a relatively low population and the main challenge is accessibility and harsh conditions. In the middle range of air temperature (B2) in middle-elevation mountains, the land cover consists mainly of croplands and forests. The zones with high and middle range temperature are also the most populated. This region offers opportunities for installing solar meteorological stations and also PV power systems. The limitation factors are terrain (slope and shading) and accessibility (proximity to roads or airports). The dominating land cover in high temperature zone (B3) is cropland, in the Terai (plains) mostly irrigated, and combined with mosaics of shrubland and forest in Sivalik. This region is also very favourable for solar meteorological stations and for PV power plants with not many limiting conditions. 2 • Solar climate zone C indicates areas with high solar radiation (average above 4.8 kWh/m per day): The low temperature zone (C1) is represented by high mountains with less rainfall and higher occurrence of snow and ice. Land cover is represented by bare land, occasionally shrubland with little or no population. In the most cases, these areas are difficult to access and may pose challenge to operation of solar meteorological stations and PV systems. From the perspective of deployment of PV power plants and solar meteorological stations, the most interesting are zones C2 and C3 with middle and higher range air temperature. These zones are represented by hills and middle-elevation mountains with mosaic of forests, cropland, shrubland and grassland and relatively high population. The deployment of solar meteorological stations and PV systems is favourable and limited only by terrain (slope and shading) and accessibility. 4.8 Evaluation The Chapters above describe various aspects of PV power generation potential in Nepal, and its relevance for development and operation of photovoltaic systems. A large extent of the country has specific PV electricity output in the range between 1400 kWh/kWp and 1600 kWh/kWp (equals to average daily totals between 3.8 and 4.4 kWh/kWp). This positions Nepal into the category of regions with very feasible potential for PV power generation (Map 4.20). Additionally, the seasonal variability in the country is very low, compared to other regions further away from the equator. The ratio between months with maximum and minimum GHI is about 1.63, while the same ratio in Upington, South Africa, is 2.29 and in Sevilla, Spain, it is 3.54 (Figure 4.13). Figure 4.14 demonstrates how PV power production depends on solar resource and air temperature in non- linear way. Even that the PV power production is feasible everywhere, for the case of large-scale PV power plants there might be some geographical preferences. Due to high elevation and low air temperature, high PV energy yield can be seen in high mountains with lower cloudiness. However, these regions have low density of population and are difficult to access. On the other hand, comparable GHI with high air temperature of the plains reduces efficiency of PV systems. Therefore, the hills and lower-elevation mountains with good GHI and lower temperature seem to have the best conditions for development of large-scale PV power plants. Of course, the microclimate factors have to be considered for a best choice of a site (see Chapter 4.7). Based on the outcomes of this study, Table 4.12 provides indicative SWOT analysis relative to the exploitation of solar resource in Nepal. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 62 of 77 Map 4.20: PV power potential of Nepal in the global context. Fixed mounted modules at optimum tilt are considered. Figure 4.13: Comparing seasonal variability in three locations World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 63 of 77 Figure 4.14: Comparing yearly GHI and TEMP with potential PV power output at selected sites Table 4.12: SWOT analysis relative to the solar resource and photovoltaic potential in Nepal Strengths Weaknesses • Good solar resource and PV power potential • Large areas of dispersed small settlements • Flat terrain (or low slopes) is available and existing • High costs of grid connection, in remote areas is not electricity infrastructure give prospects for feasible development of medium to large scale PV power • Terrain constrains: high elevation and slope, shading, plants accessibility • Existing off-grid and minigrids technology for remote • Air pollution in urbanized areas communities • Relative short history of solar PV and only for rural electrification [28] Opportunities Threats • Dependency on fuel imports • Geographical risks (e.g. extreme terrain, landslides, • Growing demand for electricity avalanches, floods, snow, extreme weather events) • International support programs • Short term variability of resource has to be analyzed • Positive attitude to renewable energy for more effective PV integration • Reduced cost of PV • Combination with other renewable energy sources (mainly hydro) helps dealing with variability of solar resource • Approx. 220 MW of solar electricity can be produced in Kathmandu that would substantially cover current demand and reduce environmental pollution [39] World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 64 of 77 5 Priority areas for meteorological stations 5.1 Localisation criteria Based on the analysis of maps presented in this report, and set of criteria we propose areas suitable for deployment of solar meteorological stations. We have in mind the need for the validation of models and reduction of the data uncertainty for development of photovoltaic power systems. The methodology includes two steps: • Identification of climate regions in Nepal relevant to photovoltaic power production, • Identification of areas preferred for deployment of solar meteorological stations. From the regional perspective, suitable areas for deployment of solar meteorological stations should be geographically representative, i.e. they should represent in a wider territory, certain type of climate where solar resource, terrain, air temperature and land use are similar, and where they do not change abruptly. We also identify exclusion areas, where installation of stations is not recommended, for reasons such as fast changing terrain and landscape, in industrial zones, but also in remote and difficult-to-access areas. From the local perspective, there are several additional localisation criteria for deployment of solar meteorological stations, such as accessibility, availability of personnel for maintenance and cleaning, security, sustainability of running the measurement campaign in a long term, acceptance/interest of the land owner, and other economical and logistical criteria. This report focuses on the regional selection criteria. The local criteria are not considered in this report. 5.2 Areas suitable for solar meteorological stations Based on the analysis in Chapter 4.7, we propose location of solar measuring stations by excluding areas that are not suitable. One of requirements of the solar measurement campaign is to receive high quality, continuous data sets that can be used for satellite data validation and regional adaptation of the solar model. Three viewpoints are considered in identification of areas that are suitable for deployment of solar measuring stations. Map 5.1 is based on the overlay of the following factors: Exclusion areas: • Areas with slope inclination higher than 20 degrees; these are areas with rapidly changing terrain and landscape. • In the low populated zones: areas at a distance more than 5 km from the nearest main road or airport • Areas affected by excessive terrain shading (Map 4.12) • Proximity to water bodies • Industrial areas and areas with high air pollution • Remote areas Suitable (preferred) areas for solar meteorological stations: • Close to the transport means and populated areas, due to requirements for regular maintenance and also likelihood that the meteo station will serve the data needs of local power systems, World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 65 of 77 • Airports, as they offer open space, with good level of security. Typically, there is also personnel who is experience in meteorological measurements, • Important inhabited and tourist centres that are far from the electrical grid and are good candidate for development of off-grid systems or minigrids, • Close to the electricity grid and power infrastructure. Close position has relevance especially for utility scale PV projects. It is to be noted that a detailed analysis of suitable sites for PV power plants is outside of the scope of this report. Map 5.1: Preferred areas for deployment of solar meteorological stations (green colour) Note: areas with high air pollution are not shown in this map Map 5.1 presents areas excluded (white colour on the map) and prioritized (green colour on the map) from the point of view of deploying the solar measuring stations. Consulting with Map 4.19 we propose that the meteorological stations are located in the solar climate B and C regions, with medium and higher levels of GHI. The sites should be located in the green-indicated areas (see Map 5.1), a good option are airports: • Terai region (lowlands and low hills): with candidate sites located e.g. around Biratnagar, Ghorahi Nepalgunj or any other location; • Lower mountains: good candidate sites could possibly be located around Pokhara or Khandbari. Finding a site in or around Kathmandu is of strong importance, even if the valley is affected by air pollution; • Higher mountains: candidate site(s) could be found around Jomsom or other airports located in higher mountains. Quality and reliability of measuring campaign is also of the highest importance. Implementation of the best measurement practices is necessary precondition for achieving reliable data sets required for calibration and validation of solar models. The meteorological instruments must be regularly maintained, cleaned and calibrated. Measurement sites should be located in the areas, which are not affected by excessive dust and pollution. Locally shaded areas, caused by surrounding buildings, structures and vegetation, should also be avoided. If shading takes place, the affected solar radiation values should be identified and flagged. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 66 of 77 6 Solargis data delivery for Nepal The key features of the delivered data and maps for Nepal are: • Harmonized solar, meteorological and geographical data based on the best available methods and input data sources. • Historical long-term averages representing 17 years at high spatial and temporal resolution, available for any location. • The Solargis database and energy simulation software is extensively validated by company Solargis, as well as 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.com. 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. 6.1 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 power potential. The Supporting data includes various vector data, such as administrative divisions, etc. Tables 6.1 and 6.2 show information about the data layers, and the technical specification is summarized in Tables 6.3 and 6.4. File name convention, used for the individual data sets, is described in Table 6.5. Table 6.1: General information about GIS data layers Geographical extent Federal Democratic Republic of Nepal with buffer 10 km along the borders 2 (approx. 165 000 km ) Map projection Geographic (Latitude/Longitude), datum WGS84 (also known as GCS_WGS84; EPSG: 4326) Data formats ESRI ASCII raster data format (asc) GeoTIFF raster data format (tif) Notes: • Data layers of both formats (asc and tif) contain the same information and the operator is free to choose the preferential data format. Data layers can be also converted to other standard raster formats. • More information about ESRI ASCII grid format can be found at http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#/ESRI_ASCII_raster_format/009t0000000z000000/ • More information about GeoTIFF format can be found at https://trac.osgeo.org/geotiff/ World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 67 of 77 Table 6.2: 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 Long-term yearly and monthly Irradiation assessment of flat-plate PV average of daily totals (photovoltaic) and solar heating technologies (e.g. hot water) 2 DNI Direct Normal kWh/m Assessment of Concentrated PV (CPV) Long-term yearly and monthly Irradiation and Concentrated Solar Power (CSP) average of daily totals technologies, but also calculation of GTI for fixed mounting and sun- tracking flat plate PV 2 DIF Diffuse Horizontal kWh/m Complementary parameter to GHI and Long-term yearly and monthly Irradiation DNI average of daily totals 2 GTI Global Irradiation kWh/m Assessment of solar resource for PV Long-term yearly and monthly at optimum tilt technologies average of daily totals OPTA Optimum angle ° Optimum tilt to maximize yearly PV - production PVOUT Photovoltaic power kWh/kWp Assessment of power production Long-term yearly and monthly potential potential for a PV power plant with average of daily totals free-standing fixed-mounted c-Si modules, mounted at optimum tilt to maximize yearly PV production TEMP Air Temperature at °C Defines operating environment of solar Long-term (diurnal) annual and 2 m above ground power plants monthly averages level Table 6.3: Technical specification of primary GIS data layers Acronym Full name Data Spatial resolution Time No. of data layers format representation GHI Global Horizontal Raster 30 arc-sec. 1999 - 2015 12+1 Irradiation (approx. 825x925 m) DNI Direct Normal Raster 30 arc-sec. 1999 - 2015 12+1 Irradiation (approx. 825x925 m) DIF Diffuse Horizontal Raster 30 arc-sec. 1999 - 2015 12+1 Irradiation (approx. 825x925 m) GTI Global Irradiation Raster 30 arc-sec. 1999 - 2015 12+1 at optimum tilt (approx. 825x925 m) OPTA Optimum angle Raster 2 arc-min - 1 (approx. 3300x3700 m) PVOUT Photovoltaic power Raster 30 arc-sec. 1999 - 2015 12+1 potential (approx. 825x925 m) TEMP Air Temperature at 2 m Raster 30 arc-sec. 1999 - 2015 12+1 above ground level (approx. 825x925 m) World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 68 of 77 Table 6.4: Characteristics of the raster output data files Characteristics Range of values West − East 79:00:00E − 89:00:00E North − South 31:00:00S − 29:00:00S Resolution (GHI, DNI, GTI, DIF, PVOUT, TEMP) 00:00:30 (1200 columns x 600 rows) Resolution (OPTA) 00:02 (300 columns x 150 rows) Data type Float No data value -9999, NaN Explanation: • MM: month of data – from 01 to 12 • ext: file extension (asc or tif) Data layers are provided as separate files in a tree structure, organized according to • File format (ASCII or GEOTIF) • Time summarization (yearly and monthly) Complementary files: • Project files (*.prj) complement ESRI ASCII grid files (*.asc) • World files (*.tfw) complement GeoTIFF files (*.tif) The support GIS data are provided in a vector format (ESRI shapefile, Table 6.6). Table 6.5: File name convention for GIS data Acronym Full name Filename pattern Number Size of files (approx.) GHI Global Horizontal Irradiation, long-term yearly GHI.ext 1+1 5 MB average of daily totals GHI Global Horizontal Irradiation, long-term 12+12 60 MB monthly averages of daily totals GHI_MM.ext DNI Direct Normal Irradiation, long-term yearly DNI.ext 1+1 5 MB average of daily totals DNI Direct Normal Irradiation, long-term monthly DNI_MM.ext 12+12 60 MB averages of daily totals DIF Diffuse Horizontal Irradiation, long-term DIF.ext 1+1 5 MB yearly average of daily totals DIF Diffuse Horizontal Irradiation, long-term DIF_MM.ext 12+12 60 MB monthly averages of daily totals GTI Global Irradiation at optimum tilt, long-term GTI.ext 1+1 5 MB yearly average of daily totals World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 69 of 77 GTI Global Irradiation at optimum tilt, long-term GTI_MM.ext 12+12 60 MB monthly averages of daily totals OPTA Optimum angle OPTA.ext 1+1 0.2 MB PVOUT Photovoltaic power potential, long-term PVOUT.ext 1+1 5 MB yearly average of daily totals PVOUT Photovoltaic power potential, long-term PVOUT_MM.ext 12+12 60 MB monthly averages of daily totals TEMP Air Temperature at 2 m above ground, long- TEMP.ext 1+1 5 MB term yearly average TEMP Air Temperature at 2 m above ground, long- TEMP_MM.ext 12+12 60 MB term monthly averages Table 6.6: Support GIS data Data type Source Data format City location OpenStreetMap.org contributors, GeoNames.org, Point shapefile adapted by Solargis Airports Wikipedia.org, adapted by Solargis Point shapefile Administrative boundaries Cartography Unit, GSDPM, World Bank Group Polyline shapefile Roads OpenStreetMap.org contributors Polyline shapefile Water bodies OpenStreetMap.org contributors Polygon shapefile 6.2 Project in QGIS 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 6.1). QGIS is state-of-art open- source 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. World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 70 of 77 Figure 6.1: Screenshot of the map and data in the QGIS environment 6.3 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 Digital images for high-resolution poster printing (size 120 x 80 cm). The colour-coded maps are prepared in a 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 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 long-term averages of Global Horizontal Irradiation • Annual and monthly long-term averages of ratio Diffuse/Global Horizontal Irradiation • Annual and monthly long-term averages of Global Tilted Irradiation (for optimum tilt) • Annual and monthly long-term averages of Direct Normal Irradiation • Annual and monthly long-term averages of Air Temperature • Annual and monthly long-term averages of Photovoltaic (PV) Electricity Potential • High resolution Terrain Elevation World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 71 of 77 • Nepal in the world context of Global Horizontal Irradiation map The maps also include visualization of the following layers: • Main cities, location and names • Administrative borders • Water bodies 6.4 Metainformation related to GIS data layers • Global Horizontal Irradiation; long-term yearly and monthly average of daily totals • Direct Normal Irradiation; long-term yearly and monthly average of daily totals • Diffuse Horizontal Irradiation; long-term yearly and monthly average of daily totals • Global Tilted Irradiation; long-term yearly and monthly average of daily totals • Photovoltaic electricity output for c-Si fixed-mounted modules, optimally tilted Northwards; long-term yearly and monthly average of daily totals • Air Temperature, long-term (diurnal) annual and monthly averages World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 72 of 77 7 List of maps Map 2.1: Long-term yearly average of daily totals of GHI developed by DLR (2004). .................................................. 14 Map 2.2: Long-term yearly average of GHI daily totals developed by CES (2015) ........................................................ 15 Map 2.3: Potential for grid-integrated PV analysed using SWERA data calculated by NREL (AEPC 20018) .............. 15 Map 3.1: Solar radiation sites selected for the model validation ................................................................................... 25 Map 3.2: Physiographic regions of Nepal ........................................................................................................................ 27 Map 3.3: Meteorological stations considered in validation of air temperature ............................................................ 30 Map 4.1: Position of eight selected sites in Nepal. ......................................................................................................... 38 Map 4.2: Terrain elevation above sea level. ..................................................................................................................... 39 Map 4.3: Terrain slope....................................................................................................................................................... 39 Map 4.4: Land cover. ......................................................................................................................................................... 40 Map 4.5: Roads and airports. ............................................................................................................................................ 40 Map 4.6: Nature protection areas, glaciers and water bodies. ....................................................................................... 41 Map 4.7: Long-term average sum of days with snow cover. .......................................................................................... 41 Map 4.8: Long-term yearly average of rainfall (sum of precipitation). ........................................................................... 42 Map 4.9: Population density. ............................................................................................................................................ 42 Map 4.10: Long-term yearly average of air temperature at 2 metres. ........................................................................... 43 Map 4.11: Global Horizontal Irradiation - long-term average of daily and yearly totals. ............................................... 45 Map 4.12: Losses of yearly GHI totals due to terrain shading (high horizon) ............................................................... 47 Map 4.13: Long-term average for ratio of diffuse and global irradiation (DIF/GHI)...................................................... 47 Map 4.14: Direct Normal Irradiation - long-term average of daily and yearly totals...................................................... 48 Map 4.15: Global Tilted Irradiation at optimum angle – long-term average of daily and yearly totals........................ 51 Map 4.16: Optimum tilt of PV modules to maximize yearly PV power production. ...................................................... 51 Map 4.17: PV electricity output from open space fixed-mounted PV system............................................................... 55 Map 4.18: PV power generation potential for an open-space fixed-mounted PV system. ........................................... 56 Map 4.19: Solar climate zones of Nepal – indicative classification .............................................................................. 60 Map 4.20: PV power potential of Nepal in the global context. ....................................................................................... 62 Map 5.1: Preferred areas for deployment of solar meteorological stations (green colour) ......................................... 65 World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 73 of 77 8 List of figures Figure 3.1: Simplified Solargis PV simulation chain ....................................................................................................... 33 Figure 4.1: Monthly averages, minima and maxima of air-temperature at 2 m for selected sites. ............................. 44 Figure 4.2: Long-term monthly averages, minima and maxima of Global Horizontal Irradiation. ............................... 46 Figure 4.3: Interannual variability of Global Horizontal Irradiation for selected sites. ................................................. 46 Figure 4.4: Daily averages of Direct Normal Irradiation at selected sites. .................................................................... 49 Figure 4.5: Interannual variability of Direct Normal Irradiation at representative sites ................................................ 50 Figure 4.6: Daily totals of GHI and DNI in Jomsom in the year 2015 ............................................................................. 50 Figure 4.7: Global Tilted Irradiation - long-term daily averages, minima and maxima. ................................................ 52 Figure 4.8: Monthly relative gain of GTI relative to GHI at selected sites. ..................................................................... 53 Figure 4.9: GHI and GTI monthly averages and relative gain of GTI to GHI in Kathmandu .......................................... 53 Figure 4.10: Daily values of GHI and GTI for Kathmandu, year 2015 ............................................................................. 54 Figure 4.11: Monthly averages of daily totals of power production from the fixed tilted PV systems ....................... 57 Figure 4.12: Monthly performance ratio of a PV system at selected sites. .................................................................. 58 Figure 4.13: Comparing seasonal variability in three locations ..................................................................................... 62 Figure 4.14: Comparing yearly GHI and TEMP with potential PV power output at selected sites ............................... 63 Figure 6.1: Screenshot of the map and data in the QGIS environment.......................................................................... 70 World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 74 of 77 9 List of tables Table 3.1: Theoretically-achievable uncertainty of pyranometers at 95% confidence level ................................ 20 Table 3.2: Input data in Solargis solar radiation model and related GHI and DNI outputs for Nepal .................. 22 Table 3.3: Comparing solar data from solar measuring stations and from satellite models .............................. 23 Table 3.4: Selected validation sites in the region ................................................................................................... 25 Table 3.5: Direct Normal Irradiance – quality indicators in the region .................................................................. 26 Table 3.6: Global Horizontal Irradiance – quality indicators in the region ............................................................ 26 Table 3.7: Uncertainty of long-term estimates for GHI, GTI and DNI values in Nepal .......................................... 27 Table 3.8: Original source of Solargis meteorological data for Nepal: models CFSR and CFSv2. ...................... 28 Table 3.9: Comparing data from meteorological stations and weather models .................................................. 29 Table 3.10: Meteorological stations and time periods considered in the model validation .................................. 30 Table 3.11: Air temperature at 2 m: accuracy indicators of the model outputs [ºC]. ............................................. 31 Table 3.12: Expected uncertainty of modelled meteorological parameters in Nepal. ........................................... 31 Table 3.13: Specification of Solargis database used in the PV calculation in this study ....................................... 33 Table 3.14: Reference configuration - photovoltaic power plant with fixed-mounted PV modules ...................... 35 Table 3.15: Yearly energy losses and related uncertainty in PV power simulation ................................................ 35 Table 4.1: Position of eight selected sites in Nepal................................................................................................ 37 Table 4.2: Monthly averages and average minima and maxima of air-temperature at 2 m at 8 sites ................ 44 Table 4.3: Daily averages and average minima and maxima of Global Horizontal Irradiation at 8 sites ............ 45 Table 4.4: Daily averages and average minima and maxima of Direct Normal Irradiation at 8 sites ................. 49 Table 4.5: Daily averages and average minima and maxima of Global Tilted Irradiation at 8 sites.................... 52 Table 4.6: Relative gain of daily GTI to GHI in Kathmandu..................................................................................... 53 Table 4.7: Annual performance parameters of a PV system with modules fixed at optimum angle.................. 55 Table 4.8: Average daily sums of PV electricity output from an open-space fixed PV system ........................... 57 Table 4.9: Monthly and annual Performance Ratio of a free-standing PV system with fixed modules .............. 58 Table 4.10: Categories of long-term yearly average of global horizontal irradiation.............................................. 59 Table 4.11: Categories of long-term yearly average of air temperature .................................................................. 60 Table 4.12: SWOT analysis relative to the solar resource and photovoltaic potential in Nepal ............................ 63 Table 6.1: General information about GIS data layers ............................................................................................ 66 Table 6.2: Description of primary GIS data layers................................................................................................... 67 Table 6.3: Technical specification of primary GIS data layers ............................................................................... 67 Table 6.4: Characteristics of the raster output data files ....................................................................................... 68 Table 6.5: File name convention for GIS data ......................................................................................................... 68 Table 6.6: Support GIS data ...................................................................................................................................... 69 World Bank Group (ESMAP): Solar Resource and Photovoltaic Power Potential of Nepal 75 of 77 10 References [1] NASA SSE. 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