Application Status of Deep Reinforcement Learning in Optimal Power Flow (OPF) Problem in Renewable Energy Power System

DOI

10.2991/978-94-6239-648-7_77How to use a DOI?

Keywords

Deep reinforcement learning; Optimal power flow; Power system

Abstract

This paper makes an in-depth analysis of the challenges faced by the optimal power flow (OPF) of the power system due to the high proportion of renewable energy access, which makes the uncertainty increase and the real-time requirements improve. Since the traditional iterative optimization method only relies on mathematical modeling (such as the interior point method) and it is difficult to meet the real-time decision-making needs of large-scale power systems, deep reinforcement learning (DRL) provides a key idea for solving the optimal power flow (OPF) problem with its dynamic adaptation and online rapid decision-making advantages. This paper first gives an overview of the optimal power flow related algorithms of the power system (such as single-agent, multi-agent deep reinforcement learning algorithms, and safe deep reinforcement learning algorithms), and then aims to solve the four defects of the traditional DRL algorithm design. From the dimensions of algorithm design, multi-agent collaboration, physical constraint fusion, and data and knowledge enhancement, this paper reviews the application of deep reinforcement learning in solving the optimal power flow problem, analyzes the technical bottlenecks, and provides future research directions, so as to provide reference for future related research.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shuai Yuan
PY  - 2026
DA  - 2026/04/24
TI  - Application Status of Deep Reinforcement Learning in Optimal Power Flow (OPF) Problem in Renewable Energy Power System
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 713
EP  - 721
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_77
DO  - 10.2991/978-94-6239-648-7_77
ID  - Yuan2026
ER  -