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

2021-03-10 · Jiangjiao Xu, Ke Li, Mohammad Abusara

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.

📄 PDF Abstract BibTeX arXiv:2103.06380

Code (0)

등록된 구현이 없습니다.

Tasks

energy managementManagementMulti-Objective Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization

2025-07-22 · Yunyi Zhao, Wei Zhang, Cheng Xiang, Hongyang Du 외 arxiv

This paper introduces DiffCarl, a diffusion-modeled carbon- and risk-aware reinforcement learning algorithm for intelligent operation of multi-microgrid systems. With the growing integration of renewables and increasing …

Reinforcement Learning

A Multiobjective Reinforcement Learning Framework for Microgrid Energy Management

2023-07-17 · M. Vivienne Liu, Patrick M. Reed, David Gold, Garret Quist 외

The emergence of microgrids (MGs) has provided a promising solution for decarbonizing and decentralizing the power grid, mitigating the challenges posed by climate change. However, MG operations often involve considering…

energy management Management Multi-Objective Reinforcement Learning reinforcement-learning+1

A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning

2024-03-01 · Fulong Yao, Wanqing Zhao, Matthew Forshaw, Yang song

The global energy landscape is undergoing a transformation towards decarbonization, sustainability, and cost-efficiency. In this transition, microgrid systems integrated with renewable energy sources (RES) and energy sto…

Deep Reinforcement Learning energy management

Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations

2023-11-21 · Sayak Mukherjee, Ramij R. Hossain, Sheik M. Mohiuddin, YuAn Liu 외

Improving system-level resiliency of networked microgrids is an important aspect with increased population of inverter-based resources (IBRs). This paper (1) presents resilient control design in presence of adversarial c…

OpenAI Gym Reinforcement Learning (RL)

Cooperative Dispatch of Microgrids Community Using Risk-Sensitive Reinforcement Learning with Monotonously Improved Performance

2023-10-17 · Ziqing Zhu, Xiang Gao, Siqi Bu, Ka Wing Chan 외

The integration of individual microgrids (MGs) into Microgrid Clusters (MGCs) significantly improves the reliability and flexibility of energy supply, through resource sharing and ensuring backup during outages. The disp…