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.
Highlights
The overall demand for energy consumption has drastically increased over recent years, and it is expected to reach up to 1000 Exajoule by the end of 2050 [1]
We propose a comprehensive Bayesian reinforcement learning framework for the problem of coalition formation in microgrid communities, which helps agents make a system of beliefs about the types of other agents and learn from their past experiences simultaneously
Our goal is to find the optimal coalition formation in the Bayesian coalition formation game that can be modeled as a partially observable MDP (POMDP)
Summary
The overall demand for energy consumption has drastically increased over recent years, and it is expected to reach up to 1000 Exajoule by the end of 2050 [1]. We employ a Bayesian reinforcement-based coalition formation scheme for energy trading among microgrids to deal with this uncertainty. This algorithm was first introduced in [9], and an application of this model was developed for device-to-device communications in wireless networks [10]. We develop the Bayesian reinforcement learning model, which enhances the conventional Bayesian coalition formation by learning from past observations and experiences We employ this approach in the energy trading problem among microgrids under uncertainty.
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