Distributionally Robust Chance-Constrained Scheduling Strategy for Community Microgrid Under A Peer-to-Peer Trading Framework

With burgeoning numbers of renewable generators being connected to distribution networks worldwide, novel business models like peer-to-peer (P2P) energy trading are being proposed to fully unlock the economic benefits of renewable generation for the community. Using a small residential community comprising several prosumers as a test system, this paper presents the day-ahead scheduling strategy of a community microgrid manager (CMM) wherein prices for the intra-community energy trade between prosumers are determined under a P2P framework. The impact of prosumers' uncertain renewable generation and demands on the CMM's scheduling strategy is modelled using distributionally-robust chance constraints. Detailed sensitivity studies are conducted and the impacts of prosumers' risk preferences on the community's total energy import costs and on the total trade conducted in the intra-community P2P market are investigated. Finally, recommended settings for the prosumers' risk preferences are determined to arrive at an efficient trade-off between the CMM's objective of minimizing the community's total day-ahead energy import costs on one hand, and the prosumers' aim to manage their real-time risk exposures on the other.