Joint Chance-constrained Game for Coordinating Renewable Microgrids with Service Delivery Risk: A Bayesian Optimization Approach
Yifu Ding, Benjamin Hobbs

TL;DR
This paper develops a Bayesian optimization-based game-theoretic framework to coordinate renewable microgrids, managing uncertain reserve services and minimizing risk of contract breaches using distributionally robust optimization.
Contribution
It introduces a novel Bayesian approach to approximate optimal violation rates in a distributionally robust joint chance-constrained game for microgrid coordination.
Findings
Effective regulation of joint violation rate achieved
Microgrid profits secured in reserve market
Framework tested with up to 14 microgrids in simulation
Abstract
Microgrids incorporate distributed energy resources (DERs) and flexible loads, which can provide energy and reserve services for the main grid. However, due to uncertain renewable generations such as solar power, microgrids might under-deliver reserve services and breach day-ahead contracts in real-time. If multiple microgrids breach their reserve contracts simultaneously, this could lead to a severe grid contingency. This paper designs a distributionally robust joint chance-constrained (DRJCC) game-theoretical framework considering uncertain real-time reserve provisions and the value of lost load (VoLL). Leveraging historical error samples, the reserve bidding strategy of each microgrid is formulated into a two-stage Wasserstein-metrics distribution robust optimization (DRO) model. A JCC is employed to regulate the under-delivered reserve capacity of all microgrids in a non-cooperative…
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Taxonomy
TopicsElectric Power System Optimization · Microgrid Control and Optimization · Smart Grid Energy Management
