Abstract

Today's electrical power grid is going through an important transition, where the integration of clean and renewable energy sources is an essential fact. The increasing penetration of renewable energy sources such as photovoltaic (PV) and wind power impact the function of bulk power system. The renewable energy generation is intermittent and uncertain, which introduces challenges in power system operation and control. Solutions to overcome the frequency and power flow fluctuations due to the uncertain energy generation needs further research and development. Predicting variable generation of renewable energy sources in short term intervals has become an interesting area of study, which can be applied to control the power and frequency fluctuations. In this study, a cellular computational network (CCN) based prediction method is presented to predict the solar irradiance for utility-scale PV plants. Modern utility-scale PV plants are designed as spatially distributed number of modular PV plants. Use of spatial information can improve the prediction accuracy in a single PV plant location. Neighboring plant units' weather information is used to predict the solar irradiance in individual locations. Predictions are done using archived data from Oahu solar measurement grid, Oahu, Hawaii, National Renewable Energy Laboratory (NREL). Typical prediction results are compared with different CCN configurations.

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