暂态安全约束下大规模电力系统最优调度的快速收敛深度强化学习
Fast-Converged Deep Reinforcement Learning for Optimal Dispatch of Large-Scale Power Systems under Transient Security Constraints
AI总结：
本文提出一种用于暂态安全约束最优潮流的快速收敛深度强化学习方法，通过改进MDP建模并引入DDPG-CPEn算法，有效解决了稀疏奖励问题，大幅提升了智能体训练效率和决策准确性。
摘要：
具有暂态安全约束的电力系统最优调度通常被表示为暂态安全约束最优潮流（TSC-OPF）。基于深度强化学习（DRL）的TSC-OPF训练高效的决策智能体，能够适应各种场景并快速提供求解结果。然而，由于状态空间和动作空间的高维性，以及动态约束的非平滑性，现有的基于DRL的TSC-OPF求解方法面临稀疏奖励问题的重大挑战。为了解决这一问题，本文提出了一种用于TSC-OPF的快速收敛DRL方法。通过减小观测空间和平滑奖励设计，改进了TSC-OPF的马尔可夫决策过程（MDP）建模，从而促进智能体训练。引入了一种结合课程学习、并行探索和集成决策的改进型深度确定性策略梯度算法（DDPG-CPEn），以大幅提高智能体训练的效率和决策的准确性。通过在IEEE 39节点系统和实际的710节点区域电网中的实验，证明了所提方法的有效性、效率和准确性。所提方法的源代码已在GitHub上公开。
英文摘要：
Power system optimal dispatch with transient security constraints is commonly represented as Transient Security-Constrained Optimal Power Flow (TSC-OPF). Deep Reinforcement Learning (DRL)-based TSC-OPF trains efficient decision-making agents that are adaptable to various scenarios and provide solution results quickly. However, due to the high dimensionality of the state space and action spaces, as well as the non-smoothness of dynamic constraints, existing DRL-based TSC-OPF solution methods face a significant challenge of the sparse reward problem. To address this issue, a fast-converged DRL method for TSC-OPF is proposed in this paper. The Markov Decision Process (MDP) modeling of TSC-OPF is improved by reducing the observation space and smoothing the reward design, thus facilitating agent training. An improved Deep Deterministic Policy Gradient algorithm with Curriculum learning, Parallel exploration, and Ensemble decision-making (DDPG-CPEn) is introduced to drastically enhance the efficiency of agent training and the accuracy of decision-making. The effectiveness, efficiency, and accuracy of the proposed method are demonstrated through experiments in the IEEE 39-bus system and a practical 710-bus regional power grid. The source code of the proposed method is made public on GitHub.
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