新误差时序差分算法提升微电网强化学习控制稳定性
A New Error Temporal Difference Algorithm for Deep Reinforcement Learning in Microgrid Optimization
- 用新误差时序差分算法量化预测不确定性
- 在真实美国数据集上使DRL优化性能显著提升
- 适合关注能源系统鲁棒控制的研究者
基于深度强化学习（DRL）的预测控制方法在微电网能源优化中备受关注。然而，现有研究常忽视由预测模型不完善带来的不确定性，导致控制策略次优。本文提出一种新的误差时序差分（ETD）算法，用于缓解预测不确定性，提升微电网运行性能。首先，构建了集成可再生能源（RES）与储能系统（ESS）的微电网系统及其马尔可夫决策过程（MDP）模型；其次，设计了一种基于深度Q网络（DQN）的预测控制方法，结合加权平均算法与新型ETD算法，分别实现对不确定性的量化与处理；最后，在真实美国数据集上的仿真结果表明，所提出的ETD算法有效提升了DRL在微电网优化中的表现。
原文摘要 · Abstract (English)
Predictive control approaches based on deep reinforcement learning (DRL) have gained significant attention in microgrid energy optimization. However, existing research often overlooks the issue of uncertainty stemming from imperfect prediction models, which can lead to suboptimal control strategies. This paper presents a new error temporal difference (ETD) algorithm for DRL to address the uncertainty in predictions,aiming to improve the performance of microgrid operations. First,a microgrid system integrated with renewable energy sources (RES) and energy storage systems (ESS), along with its Markov decision process (MDP), is modelled. Second, a predictive control approach based on a deep Q network (DQN) is presented, in which a weighted average algorithm and a new ETD algorithm are designed to quantify and address the prediction uncertainty, respectively. Finally, simulations on a realworld US dataset suggest that the developed ETD effectively improves the performance of DRL in optimizing microgrid operations.
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