用物理约束强化学习，协同调控风电储能系统，降波动增收益。
Coordinated Power Smoothing Control for Wind Storage Integrated System with Physics-informed Deep Reinforcement Learning
- 分层协同控制框架，融合尾流与电池衰减模型。
- 利润提升11%，功率波动降低19%。
- 适合关注风电并网稳定与经济性的研究者。
风储联合系统的功率平滑控制（PSC）是提升风电高效可靠并网的重要方案。现有策略忽视电池与风机间复杂的交互关系及不同控制频率，且未考虑尾流效应和电池退化成本。本文提出一种分层协同控制框架，融合尾流模型与电池退化模型；将问题重构为马尔可夫决策过程，引入多智能体强化学习方法以应对双层特性；进一步提出基于物理信息神经网络的多智能体深度确定性策略梯度（PAMA-DDPG）算法，融入功率波动微分方程，加速学习过程。通过在WindFarmSimulator（WFSim）中四个场景的仿真验证，结果表明所提方法相比传统方法使总利润提升约11%，功率波动降低19%，有效兼顾经济性与电网接入可靠性。
原文摘要 · Abstract (English)
The Wind Storage Integrated System with Power Smoothing Control (PSC) has emerged as a promising solution to ensure both efficient and reliable wind energy generation. However, existing PSC strategies overlook the intricate interplay and distinct control frequencies between batteries and wind turbines, and lack consideration of wake effect and battery degradation cost. In this paper, a novel coordinated control framework with hierarchical levels is devised to address these challenges effectively, which integrates the wake model and battery degradation model. In addition, after reformulating the problem as a Markov decision process, the multi-agent reinforcement learning method is introduced to overcome the bi-level characteristic of the problem. Moreover, a Physics-informed Neural Network-assisted Multi-agent Deep Deterministic Policy Gradient (PAMA-DDPG) algorithm is proposed to incorporate the power fluctuation differential equation and expedite the learning process. The effectiveness of the proposed methodology is evaluated through simulations conducted in four distinct scenarios using WindFarmSimulator (WFSim). The results demonstrate that the proposed algorithm facilitates approximately an 11% increase in total profit and a 19% decrease in power fluctuation compared to the traditional methods, thereby addressing the dual objectives of economic efficiency and grid-connected energy reliability.
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