Cost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning

Abstract

Microgrids are empowered by the advances in renewable energy generation, which enable the microgrids to generate the required energy for supplying their loads and trade the surplus energy to other microgrids or the macrogrid. Microgrids need to optimize the scheduling of their demands and energy levels while trading their surplus with others to minimize the overall cost. This can be affected by various factors such as variations in demand, energy generation, and competition among microgrids due to their dynamic nature. Thus, reaching optimal scheduling is challenging due to the uncertainty caused by the generation/consumption of renewable energy and the complexity of interconnected microgrids and their interplay. Previous works mainly rely on modeling-based approaches and the availability of precise information on microgrid dynamics. This paper addresses the energy trading problem among microgrids by minimizing the cost while uncertainty exists in microgrid generation and demand. To this end, a Bayesian coalitional reinforcement learning-based model is introduced to minimize the energy trading cost among microgrids by forming stable coalitions. The results show that the proposed model can minimize the cost up to 23% with respect to the coalitional game theory model.

Buyers Collusion in Incentivized Forwarding Networks: A Multi-Agent Reinforcement Learning Study

“…In [55], Bayesian reinforcement learning coalition formation is used to solve the problem of distributed resource sharing in device-to-device enabled heterogeneous networks. In [56], micropower grids co-schedule their demands and generate energy levels to minimize the overall costs using Bayesian coalition reinforcement learning. While in [57], micropower grids form a coalition to save power losses of energy exporting from the distant grid.…”