Optimal Day-Ahead Self-Scheduling and Operation of Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and Load Forecasting

Abstract

Prosumer microgrids (PMGs) are considered as active users in smart grids. These units are able to generate and sell electricity to aggregators or neighbor consumers in the prosumer market. Although the optimal scheduling and operation of PMGs have received a great deal of attention in recent studies, thechallengesofPMG’suncertaintiessuchasstochasticbehaviorofloaddataandweatherconditions(solar irradiance, ambient temperature, and wind speed) and corresponding solutions have not been thoroughly investigated.Inthispaper,anewenergymanagementsystems(EMS)basedonweatherandloadforecasting isproposedforPMG’soptimalschedulingandoperation.Developinganovelhybridmachinelearning-based methodusingadaptiveneuro-fuzzyinferencesystem(ANFIS),multilayerperceptron(MLP)artificialneural network (ANN), and radial basis function (RBF) ANN to precisely predict the load and weather data is one of the most important contributions of this article. The performance of the forecasting process is improved by using a hybrid machine learning-based forecasting method instead of conventional ones. The demand response (DR) program based on the forecasted data and considering the degradation cost of the battery storage system (BSS) are other contributions. The comparison of obtained test results with those of other existing approaches illustrates that more appropriate PMG’s operation cost is achievable by applying the proposed DR-based EMS using a new hybrid machine learning forecasting method.