Amid the increasing interest in the deployment of Distributed Energy Resources (DERs), the Virtual Power Plant (VPP) has emerged as a pivotal tool for aggregating diverse DERs and facilitating their participation in wholesale energy markets. These VPP deployments have been fueled by the Federal Energy Regulatory Commission's Order 2222, which makes DERs and VPPs competitive across market segments. However, the diversity and decentralized nature of DERs present significant challenges to the scalable coordination of VPP assets. To address efficiency and speed bottlenecks, this paper presents a novel machine learning-assisted distributed optimization to coordinate VPP assets. Our method, named LOOP-MAC(Learning to Optimize the Optimization Process for Multi-agent Coordination), adopts a multi-agent coordination perspective where each VPP agent manages multiple DERs and utilizes neural network approximators to expedite the solution search. The LOOP-MAC method employs a gauge map to guarantee strict compliance with local constraints, effectively reducing the need for additional post-processing steps. Our results highlight the advantages of LOOP-MAC, showcasing accelerated solution times per iteration and significantly reduced convergence times. The LOOP-MAC method outperforms conventional centralized and distributed optimization methods in optimization tasks that require repetitive and sequential execution.

翻译：随着分布式能源资源部署日益受到关注，虚拟电厂已成为聚合多种分布式能源并促进其参与电力批发市场的关键工具。这些虚拟电厂的部署得益于美国联邦能源监管委员会第2222号令，该法令使分布式能源和虚拟电厂在各市场细分领域具有竞争力。然而，分布式能源的多样性和去中心化特性给虚拟电厂资产的可扩展协调带来了重大挑战。为解决效率和速度瓶颈问题，本文提出了一种新颖的机器学习辅助分布式优化方法来协调虚拟电厂资产。我们提出的方法名为LOOP-MAC（学习优化多智能体协调的优化过程），采用多智能体协调视角，每个虚拟电厂智能体管理多个分布式能源，并利用神经网络近似器加速求解过程。LOOP-MAC方法采用规范映射确保严格满足局部约束，有效减少了额外后处理步骤的需求。实验结果凸显了LOOP-MAC的优势，展示了每次迭代的加速求解时间以及显著缩短的收敛时间。在需要重复顺序执行的优化任务中，LOOP-MAC方法优于传统的集中式和分布式优化方法。