Operational solar forecasting for the real-time market

Despite the significant progress made in solar forecasting over the last decade, most of the proposed models cannot be readily used by independent system operators (ISOs). This article proposes an operational solar forecasting algorithm that is closely aligned with the real-time market (RTM) forecasting requirements of the California ISO (CAISO). The algorithm first uses the North American Mesoscale (NAM) forecast system to generate hourly forecasts for a 5-h period that are issued 12 h before the actual operating hour, satisfying the lead-time requirement. Subsequently, the world's fastest similarity search algorithm is adopted to downscale the hourly forecasts generated by NAM to a 15-min resolution, satisfying the forecast-resolution requirement. The 5-h-ahead forecasts are repeated every hour, following the actual rolling update rate of CAISO. Both deterministic and probabilistic forecasts generated using the proposed algorithm are empirically evaluated over a period of 2 years at 7 locations in 5 climate zones

Development of Regional Wind Resource and Wind Plant Output Datasets: Final Subcontract Report

This is the Final Report for the project “Development of Regional Wind Resource and Wind Plant Output Datasets“(NREL subcontract number: AAM-8-77556-01 under prime contract number: DE-AC36-99GO10337). The report covers the period of the contract from October 15, 2007 through March 15, 2009. The final delivered outcomes of this project include: • This report detailing the work produced. • 30 validation reports for the purpose of tuning the mesoscale models. • The numerical weather prediction (NWP) simulations for 2004-2006 in NetCDF format with a spatial resolution of one arc-minute and a temporal resolution of ten minutes. • 30,544 original sites and 1499 additional that were extracted into time series data files for 2004-2006. These sites had the following information provided: o Wind speed at 100 meters (m) o Rated power output at 100 m o SCORE-lite o Mesoscale forecasts at 100 m power output at 100 m o “Perfect” forecasts o “Two-hourly” persistence forecasts o By-hour monthly climatology forecasts • Solar forecasts from mesoscale models for a regular grid of 8736 points • 28 validation reports for the final data set on publicly available data • 2 validation reports on confidentially sourced data • SCORE-lite validation report • Web-based graphical file server • Extension to the contract where each of the 32,043 sites had wind speed and wind direction pulled from the model runs at 10 m, 20 m, 50 m, 100 m, and 200 m. A paper was written based on this collaboration between the National Renewable Energy Laboratory (NREL) and 3TIER. The paper was presented at the 7th International Workshop on Large Scale Integration of Wind Power and on Transmission Networks for Offshore Wind Farms, Madrid, May 2008. It was subsequently invited for publication in the Wind Engineering Journal, Volume 32, Number 4, 2008

Estimation of the instantaneous downward surface shortwave radiation using MODIS data in Lhasa for all-sky conditions

Measuring the solar irradiance with high accuracy is the basis of PV power forecasting. Although the downward surface shortwave radiation (DSSR) data derived from satellite images are widely used in the PV industry, the instantaneity and accuracy of these data are not suitable for PV power forecasting in a short-time period. In this study, an algorithm to calculate instantaneous DSSR for all-sky conditions was developed by combining clear-sky radiative transfer model and 3D radiative transfer model using MODIS products (MOD03-07, 09). The algorithm was evaluated by ground measurements from a station in Lhasa and a reference dataset from FLASHFlux. The results indicate that the errors of DSSR using combining model are less than FLASHFlux. The time consuming of running 3D radiative transfer model can be reduced by narrowing down the extent of input data to 8km

Analysis of the potential of renewable energy development in Saudi Arabia

Saudi Arabia is one nation that has been exploring the potential of renewable energy for many years. Saudi authorities, scientists, and researchers view renewable energy as a preferable long-term energy strategy. Despite this, because Saudi Arabia is one of the leading oil producing nations and relies heavily on it as a form of energy, solar energy has not been given much serious consideration. Solar and wind energy are the best sources of renewable energy in Saudi Arabia; however, because of the large amount of oil in the country, most do not want to explore the option of renewable energy. Hence, it is essential to explore the alternative sources to insure reliable supply for potential future need. This will be investigated through this research, in which three different forecasting methods were generated for 32 cities: the decomposition method, multiple linear regressions (linear trend model), and multiple linear models (seasonal model). These three methods have developed a preferred model that can forecast renewable energy in the future. The main objectives of this research are i) to establish the potential of solar and wind energy generation as a suitable, cost-effective alternative to petroleum products and ii) to establish the potential for maximizing renewable power generation to support the grid supply to Saudi cities. The software developed for this thesis (Visual Basic) is aimed at enabling a user to use advanced data analysis techniques to handle a given research issue. Moreover, the results of this research demonstrated that the total of the output of solar and wind power in 32 locations in Saudi Arabia are 162.032 GW, and 1.298 GW, respectively. Thus, in order to reduce the cost of energy, installing renewable farms is recommended

Weather impact on solar farm Performance : A comparative analysis of machine learning techniques

Forecasting the performance and energy yield of photovoltaic (PV) farms is crucial for establishing the economic sustainability of a newly installed system. The present study aims to develop a prediction model to forecast an installed PV system’s annual power generation yield and performance ratio (PR) using three environmental input parameters: solar irradiance, wind speed, and ambient air temperature. Three data-based artificial intelligence (AI) techniques, namely, adaptive neuro-fuzzy inference system (ANFIS), response surface methodology (RSM), and artificial neural network (ANN), were employed. The models were developed using three years of data from an operational 2MWp Solar PV Project at Kuzhalmannam, Kerala state, India. Statistical indices such as Pearson’s R, coefficient of determination (R2), root-mean-squared error (RMSE), Nash-Sutcliffe efficiency (NSCE), mean absolute-percentage error (MAPE), Kling-Gupta efficiency (KGE), Taylor’s diagram, and correlation matrix were used to determine the most accurate prediction model. The results demonstrate that ANFIS was the most precise performance ratio prediction model, with an R2 value of 0.9830 and an RMSE of 0.6. It is envisaged that the forecast model would be a valuable tool for policymakers, solar energy researchers, and solar farm developers

Development of a Short-Term Forecast System for Solar Surface Irradiance Based on Satellite Imagery and NWP Data

The increasing use of renewable energies as a source of electricity has lead to a fundamental transition of the power supply system. The integration of fluctuating weather-dependent energy sources into the grid already has a major impact on its load flows and associated with this economic effects. As a result, the interest in forecasting wind and solar radiation with a sufficient accuracy over short time periods (0-4 h) has grown. In this study, a novel approach for forecasting solar surface irradiance is developed which is based on the optical flow of the effective cloud albedo and SPECMAGIC NOW. This short-term forecast is combined seamlessly with the numerical weather prediction (NWP) to expand the forecast horizon up to 12 h. The optical flow method utilized here is TV-L1 from the open source library OpenCV. This method uses a multi-scale approach to capture cloud motions on various spatial scales. After the clouds are displaced by extrapolating the optical flow into the future, the solar surface radiation will be calculated with SPECMAGIC NOW, which computes the global irradiation spectrally resolved from satellite imagery. Due to the high temporal and spatial resolution of satellite measurements, the effective cloud albedo and thus solar radiation can be forecasted from 15 min up to 4 h with a resolution of 0.05°. The combination of the displacement of clouds by TV-L1 and the calculation of solar surface irradiance by SPECMAGIC NOW is innovative and promising. Finally, a procedure for a seamless blending between a NWP model and the presented nowcasting is developed. For this purpose the software tool ANAKLIM++ is utilized which was originally designed for the efficient assimilation of two-dimensional data sets using variational approach. ANAKLIM++ blends the nowcasting, ICON and IFS between 1-5 h in such a way that the combined forecast delivers a smaller forecast error than the individual forecasts for each lead time

Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids

This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader