Multi-Objective Reinforcement Learning based Multi-Microgrid System Optimisation Problem

• URL: http://arxiv.org/abs/2103.06380v1
• Date: Wed, 10 Mar 2021 23:01:22 GMT
• Title: Multi-Objective Reinforcement Learning based Multi-Microgrid System Optimisation Problem
• Authors: Jiangjiao Xu, Ke Li, and Mohammad Abusara
• Abstract summary: Microgrids with energy storage systems and distributed renewable energy sources play a crucial role in reducing the consumption from traditional power sources and the emission of $CO$. Connecting multi microgrid to a distribution power grid can facilitate a more robust and reliable operation to increase the security and privacy of the system. The proposed model consists of three layers, smart grid layer, independent system operator (ISO) layer and power grid layer.
• Score: 4.338938227238059
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Microgrids with energy storage systems and distributed renewable energy sources play a crucial role in reducing the consumption from traditional power sources and the emission of $CO_2$. Connecting multi microgrid to a distribution power grid can facilitate a more robust and reliable operation to increase the security and privacy of the system. The proposed model consists of three layers, smart grid layer, independent system operator (ISO) layer and power grid layer. Each layer aims to maximise its benefit. To achieve these objectives, an intelligent multi-microgrid energy management method is proposed based on the multi-objective reinforcement learning (MORL) techniques, leading to a Pareto optimal set. A non-dominated solution is selected to implement a fair design in order not to favour any particular participant. The simulation results demonstrate the performance of the MORL and verify the viability of the proposed approach.

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