Machine Learning Infused Distributed Optimization for Coordinating Virtual Power Plant Assets

TL;DR

This paper introduces LOOP-MAC, a machine learning-assisted distributed optimization method that significantly improves the speed and efficiency of coordinating diverse Virtual Power Plant assets, facilitating their market participation.

Contribution

It proposes a novel multi-agent coordination approach using neural network approximators and gauge maps to enhance VPP asset management, outperforming traditional methods.

Findings

Accelerated solution times per iteration

Reduced convergence times

Outperforms conventional optimization methods

Abstract

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.