Machine Learning Infused Distributed Optimization for Coordinating
  Virtual Power Plant Assets
Meiyi Li Javad Mohammadi
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.