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arXiv 2203.04166eess.SYcs.SY
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基于课程学习的配电网关键负荷恢复强化学习方法

Curriculum-based Reinforcement Learning for Distribution System Critical Load Restoration

Xiangyu Zhang, Abinet Tesfaye Eseye, Bernard Knueven, Weijia Liu, Matthew Reynolds, Wesley Jones

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IEEE Transactions on Power Systems
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AI总结：

针对配电网大停电后关键负荷恢复问题，提出结合课程学习的强化学习方法，通过渐进式训练提升策略性能，实验验证其在可再生能源预测误差下比模型预测控制器更可靠。

摘要：

本文聚焦重大停电后配电网的关键负荷恢复问题。为提供快速在线响应与最优序贯决策支持，提出一种基于强化学习（RL）的恢复优化方法。由于复杂电网控制问题存在策略搜索空间大、可再生能源不确定性、非线性等带来的复杂性，直接应用RL算法训练出满意策略需要大量调参才能成功。为应对这一挑战，本文利用课程学习（CL）技术设计了包含更简单垫脚石问题的训练课程，引导RL智能体以渐进且更高效的方式学习解决原始难题。本文证明，与直接学习相比，CL能助力控制器训练实现更优性能。为研究决策所用可再生能源预测通常存在误差的现实场景，实验将训练好的RL控制器与两种模型预测控制器（MPC）在不同误差水平的可再生能源预测下进行对比，观察这些控制器如何对冲不确定性。结果表明，与基线MPC相比，RL控制器受预测误差的影响更小，能够提供更可靠的恢复过程。

英文摘要：

This paper focuses on the critical load restoration problem in distribution systems following major outages. To provide fast online response and optimal sequential decision-making support, a reinforcement learning (RL) based approach is proposed to optimize the restoration. Due to the complexities stemming from the large policy search space, renewable uncertainty, and nonlinearity in a complex grid control problem, directly applying RL algorithms to train a satisfactory policy requires extensive tuning to be successful. To address this challenge, this paper leverages the curriculum learning (CL) technique to design a training curriculum involving a simpler steppingstone problem that guides the RL agent to learn to solve the original hard problem in a progressive and more effective manner. We demonstrate that compared with direct learning, CL facilitates controller training to achieve better performance. To study realistic scenarios where renewable forecasts used for decision-making are in general imperfect, the experiments compare the trained RL controllers against two model predictive controllers (MPCs) using renewable forecasts with different error levels and observe how these controllers can hedge against the uncertainty. Results show that RL controllers are less susceptible to forecast errors than the baseline MPCs and can provide a more reliable restoration process.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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