Greenough River Solar Farm case study & validation initialization

Abstract

Large Photovoltaic Solar Power Plants (LPVPPs) and Very Large Photovoltaic Solar Power Plants (VLPVPPs) may have rapid success in the revolutionary change of the national and international power grids to build a 100% renewable power Global Grid. The Australian continent has outstanding solar resource availability, enabling widespread utilization of solar power and power storage technologies (e.g. pumped hydroelectric, thermal, electrochemical). Design and investment modeling of the renewable power grid is the success key for a 100% renewable power grid. The design and investment in LPVPPs and VLPVPPs should preferably be undertaken with the consideration of values such as environmental friendliness, fairness, openness to small private investors, reliability, and accountability. The design of LPVPPs and VLPVPPs should preferably be based on some small-scale PV power plants to help reduce the risks for large to very large investments. Hence, validation and verification efforts of operational PV power plants with different design software are very important for the solar industry. This research paper presents the first specific validation and verification study of the Greenough River Solar Farm (12,68 MWDC, 10,00 MWAC, the first planned expansion to 40,00 MWAC in 2019) near Geraldton in Western Australia with the PVWatts Version 5 model of the National Renewable Energy Laboratory (NREL) System Advisor Model (SAM) Version 2017.9.5. The National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) datasets are used as a weather data source in this study. Location and resource, system design data and information on the Greenough River Solar Farm in this study are presumptions based upon publicly available information without any confirmation of power plant owners and operators. The Greenough River Solar Farm NREL SAM software models' are run on 2 different personal computers (PCs) with internet connection for years 2013 to 2017. The results of 6 simple simulations are compared with actual generation data for 2013 through 2017 with some statistical performance measures of the global unique forecast accuracy metrics pool in the Global Grid Prediction Systems (G2PS). The simulations average total time (milliseconds) are 64.1 and 77.9. The best model/actual accuracy measure is 101.9%. The minimum root mean square error (RMSE) is 254.142 MWh in 2013. The maximum RMSE is 414.931 MWh in 2014.