Day-Ahead Solar Resource Prediction Method Using Weather Forecasts for Peak Shaving

Due to recent concerns about energy sustainability, solar power is becoming more prevalent in distributed power generation. There are still obstacles which need to be addressed before solar power can be provided at the level of reliability that utilities require. Some of these issues can be mitigated with strategic use of energy storage. In the case of load shifting, energy storage can be used to supply solar energy during a time of day when utility customer's demand is highest, thus providing partial peak load burden relief or peak shaving. Because solar resource availability is intermittent due to clouds and other atmospheric factors, charge/discharge planning must take weather into consideration. Many inter-day and intra-day solar resource prediction methods have been developed to aid in rm (high-reliability) resource establishment and peak-shaving through various methods and data sources with different levels of complexity. The purpose of this study was to investigate the use of readily-available, day-ahead National Weather Service (NWS) forecasts to develop a PV resource prediction. Using past day-ahead NWS weather forecasts and historical performance data from the Prosperity Energy Storage Project near Mesa del Sol in Albuquerque, New Mexico, several correlations were created based on regression analysis and optimized for minimal Root Mean Square (RMS) error for daily insolation prediction. Though some other methods such as the National Digital Forecast Database (NDFD) and Global Forecast System (GFS) exhibit greater accuracy, this method could prove to be a relatively simple means of planning the use of energy storage for peak-shaving or arbitrage. Additionally, given appropriate considerations for prediction uncertainty one could establish a rm resource to meet customer demand.United States Department of Energy, Public Service Company of New MexicoMechanical EngineeringMastersUniversity of New Mexico. Dept. of Mechanical EngineeringMammoli, AndreaMammoli, AndreaLavrova, OlgaSorrentino, Francesc