基于强化学习的综合能源系统管理综述

为了满足日益增长的能源需求并减少对环境的破坏, 节能成为全球经济和社会发展的一项长远战略方针, 加强能源管理能够提高能源利用效率、促进节能减排. 然而, 可再生能源和柔性负载的接入使得综合能源系统(Integrated energy system, IES)发展成为具有高度不确定性的复杂动态系统, 给现代化能源管理带来巨大的挑战. 强化学习(Reinforcement learning, RL)作为一种典型的交互试错型学习方法, 适用于求解具有不确定性的复杂动态系统优化问题, 因此在综合能源系统管理问题中得到广泛关注. 本文从模型和算法的层面系统地回顾了利用强化学习求解综合能源系统管理问题的现有研究成果, 并从多时间尺度特性、可解释性、迁移性和信息安全性4个方面提出展望.

Reinforcement Learning Based Integrated Energy System Management: A Survey

In order to meet the growing energy demand and reduce the damage to the environment, energy conservation has become a long-term strategic policy for global economic and social development. The enhancement of energy management can improve energy efficiency, as well as promote energy conservation and emission reduction. However, the integration of renewable energy and flexible load makes the integrated energy system (IES) become a complex dynamic system with high uncertainty, which brings great challenges to modern energy management. Reinforcement learning (RL), as a typical interactive trial-and-error learning method, is suitable for solving optimization problems of complex dynamic systems with uncertainty, and therefore it has been widely considered in integrated energy system management. This paper systematically reviews the existing works of using reinforcement learning to solve integrated energy system management problems from the perspective of models and algorithms, and puts forward prospects from four aspects: Multi-time scale, interpretability, transferability, and information security.