用强化学习优化电网调度，提升稳定性和经济性。
Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes
- 基于马尔可夫决策过程建模电网状态与调控动作
- 对比多种强化学习算法，实现动态负载调度最优策略
- 适合电力系统优化与智能电网研究者参考
电网负载调度是确保发电与用电平衡、降低运营成本并维持系统稳定的关键任务。传统优化方法难以应对可再生能源和波动需求带来的动态随机性。本文提出一种基于强化学习（RL）的马尔可夫决策过程（MDP）框架，定义状态空间为电网运行状态，动作空间包括发电机调节与储能管理，奖励函数兼顾经济效率与系统可靠性。研究了从基础Q-Learning到深度Q网络（DQN）及演员-评论家方法等多种算法，以求解最优调度策略。在模拟电网环境中验证表明，该方法能有效提升调度效率，适应变化的负荷模式。结果表明，基于强化学习的方案具备鲁棒性与可扩展性，为现代电网的实时负载调度提供了高效解决方案。
原文摘要 · Abstract (English)
Power grid load scheduling is a critical task that ensures the balance between electricity generation and consumption while minimizing operational costs and maintaining grid stability. Traditional optimization methods often struggle with the dynamic and stochastic nature of power systems, especially when faced with renewable energy sources and fluctuating demand. This paper proposes a reinforcement learning (RL) approach using a Markov Decision Process (MDP) framework to address the challenges of dynamic load scheduling. The MDP is defined by a state space representing grid conditions, an action space covering control operations like generator adjustments and storage management, and a reward function balancing economic efficiency and system reliability. We investigate the application of various RL algorithms, from basic Q-Learning to more advanced Deep Q-Networks (DQN) and Actor-Critic methods, to determine optimal scheduling policies. The proposed approach is evaluated through a simulated power grid environment, demonstrating its potential to improve scheduling efficiency and adapt to variable demand patterns. Our results show that the RL-based method provides a robust and scalable solution for real-time load scheduling, contributing to the efficient management of modern power grids.
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