Evolutionary Algorithms applied to the Intraday Energy Resource Scheduling in the Context of Multiple Aggregators

The growing number of electric vehicles (EVs) on the road and renewable energy production to meet carbon reduction targets has posed numerous electrical grid problems. The increasing use of distributed energy resources (DER) in the grid poses severe operational issues, such as grid congestion and overloading. Active management of distribution networks using the smart grid (SG) technologies and artificial intelligence (AI) techniques by multiple entities. In this case, aggregators can support the grid's operation, providing a better product for the end-user. This study proposes an effective intraday energy resource management starting with a day-ahead time frame, considering the uncertainty associated with high DER penetration. The optimization is achieved considering five different metaheuristics (DE, HyDE-DF, DEEDA, CUMDANCauchy++, and HC2RCEDUMDA). Results show that the proposed model is effective for the multiple aggregators with variations from the day-ahead around the 6 % mark, except for the final aggregator. A Wilcoxon test is also applied to compare the performance of the CUMDANCauchy++ algorithm with the remaining. CUMDANCauchy++ shows competitive results beating all algorithms in all aggregators except for DEEDA, which presents similar results.