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 storage systems (ESS) have emerged as a crucial component. However, optimizing the operational control of such an integrated energy system lacks a holistic view of multiple environmental, infrastructural and economic considerations, not to mention the need to factor in the uncertainties from both the supply and demand. This paper presents a holistic datadriven power optimization approach based on deep reinforcement learning (DRL) for microgrid control considering the multiple needs of decarbonization, sustainability and cost-efficiency. First, two data-driven control schemes, namely the prediction-based (PB) and prediction-free (PF) schemes, are devised to formulate the control problem within a Markov decision process (MDP). Second, a multivariate objective (reward) function is designed to account for the market profits, carbon emissions, peak load, and battery degradation of the microgrid system. Third, we develop a Double Dueling Deep Q Network (D3QN) architecture to optimize the power flows for real-time energy management and determine charging/discharging strategies of ESS. Finally, extensive simulations are conducted to demonstrate the effectiveness and superiority of the proposed approach through a comparative analysis. The results and analysis also suggest the respective circumstances for using the two control schemes in practical implementations with uncertainties.

📄 PDF Abstract BibTeX arXiv:2403.01013

Code (1)

flyao123/district-microgrid-dataset 공식 구현

Tasks

Deep Reinforcement Learningenergy management

A Holistic Framework for Parameter Coordination of Interconnected Microgrids against Disasters

2020-06-24

This paper proposes a holistic framework for parameter coordination of a power electronic-interfaced microgrid interconnection against natural disasters. The paper identifies a transient stability issue in a microgrid in…

Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side

2022-11-11 · Jinsong Sang, Hongbin Sun, Lei Kou

As an efficient way to integrate multiple distributed energy resources and the user side, a microgrid is mainly faced with the problems of small-scale volatility, uncertainty, intermittency and demand-side uncertainty of…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)

An Intelligent Control Strategy for buck DC-DC Converter via Deep Reinforcement Learning

2020-08-11 · Chenggang Cui, Nan Yan, Chuanlin Zhang

As a typical switching power supply, the DC-DC converter has been widely applied in DC microgrid. Due to the variation of renewable energy generation, research and design of DC-DC converter control algorithm with outstan…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Reinforcement Learning$\unicode{x2013}$Based Transient Response Shaping for Microgrids

2022-07-11 · Ashwin Venkataramanan, Ali Mehrizi-Sani

This work explores the usage of a supplementary controller for improving the transient performance of inverter$\unicode{x2013}$based resources (IBR) in microgrids. The supplementary controller is trained using a reinforc…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

A New Error Temporal Difference Algorithm for Deep Reinforcement Learning in Microgrid Optimization

2025-11-22 · Fulong Yao, Wanqing Zhao, Matthew Forshaw arxiv

Predictive control approaches based on deep reinforcement learning (DRL) have gained significant attention in microgrid energy optimization. However, existing research often overlooks the issue of uncertainty stemming fr…

Reinforcement Learning