Energy Management Study of Interconnected Microgrids Considering Pricing Strategy Under the Stochastic Impacts of Correlated Renewables

Abstract

The exponential augmentation of electricity consumption and the restructuring of the conventional power industry resulted in the emergence of microgrids (MGs). The nondispatchability and random attributes of the integrated renewable energy resources (RERs) challenge MGs scheduling operation, for which the multi-MGs system's energy management (EM) study has been gaining paramount importance and studied in this article. This article contemplates both internal and external markets for MGs’ effective participation in energy trading, which involves the energy exchange among MGs and that with the utility grid (UG). The energy pricing considers two conflicting objectives: MGs’ goal to improve their economy by reducing their purchasing prices and reliance on UG; the distribution network operator's aim to maximize its profit from the deployed market. Also, this article implements hybrid scenario and copula-based Monte Carlo techniques to assess the intermittencies associated with load demands, plug-in hybrid vehicle charging demands, and correlated RERs generations. The EM framework is formulated as a max–min optimization problem solved by a metaheuristic fuzzified jellyfish search optimization algorithm. Several simulation outcomes under different charging scenarios are reported, suggesting a 1.5% and 3.2% reduction in the multi-MG system's operational cost compared with the particle swarm optimization algorithm considering the best charging strategy during the summer and winter seasons.