Optimal energy trading in cooperative microgrids considering hybrid renewable energy systems

This study presents a novel method for optimal energy trading within microgrids considering renewable energy (RE) integration. The proposed approach uses the hybridization of particle swarm optimization and gravitational search algorithms (PSO-GSA) with Nash Bargaining to optimize power flow and energy trading between inter-connected MGs and the main utility grid. Unlike existing solutions, the proposed model promotes cooperative energy trading among MGs and the main grid, considering network constraints and RE's inherent uncertainty; consequently, it addresses key challenges related to model design, pricing fairness, power flow optimization, and network constraints. The proposed method is implemented in MATLAB (R) considering the interconnection of four different MGs, called cooperative MGs, which optimally enable energy trading within the cooperative MGs and main utility. Simulation results, case studies and comparisons demonstrate the relevance of the proposed hybrid PSO-GSA in terms of maximizing the RE utilization, reducing load burden on the main grid and substantial cost reductions, with monthly energy costs decreased by $60,720 compared to the base case of $94,551. Also, the robustness and efficiency of the proposed PSOGSA algorithm are evaluated by comparing it with other well -established metaheuristic optimization methods under the same system data, control variables, and con-straints. This research significantly contributes to MG energy trading by proactively optimizing economic op-erations and increasing RE utilization while adapting cooperation among interconnected MGs.

Automated summary

This study presents a novel method for optimizing energy trading within microgrids by using a hybrid of particle swarm optimization and gravitational search algorithms. The proposed approach promotes cooperative energy trading among microgrids and the main grid, considering network constraints and the uncertainty of renewable energy. Simulation results show that this method maximizes renewable energy utilization, reduces load burden on the main grid, and significantly decreases energy costs.