A three-layer game theoretic-based strategy for optimal scheduling of microgrids by leveraging a dynamic demand response program designer to unlock the potential of smart buildings and electric vehicle fleets

TLDR

The growing number of smart buildings and electric vehicle fleets in distribution systems creates an urgent need for new coordination strategies with microgrid scheduling. This paper proposes a three‑layer, risk‑averse, game‑theoretic strategy to coordinate smart buildings and EV fleets with microgrid scheduling. The first layer designs a demand‑response program that sets dynamic incentive tariffs based on subscriber consumption patterns; the second layer schedules the buildings and EV fleets in a decentralized manner within that program; the third layer applies cooperative game theory for day‑ahead microgrid and distribution system scheduling, embedding scenario‑based risk‑averse handling of load, radiation, and wind uncertainties. When applied to a 69‑node distribution system with four microgrids, the strategy improved voltage during peak demand and reduced total daily costs by 13.66 % through the dynamic‑tariff DRP, while peer‑to‑peer power exchange further cut power losses and daily costs by about 8 %.

Abstract

The proliferation of the number of Smart Buildings (SBs) and the fleet of Electric Vehicles (EVs) in Distribution Systems (DSs) makes the need for new strategies to coordinate them with microgrid (MG) scheduling inevitable. Therefore, this article proposes a three-layer risk-averse game theoretic-based strategy to coordinate SBs and EV fleets with MGs scheduling. In the first layer of this strategy, a Demand Response Program (DRP) is designed for SBs where dynamic incentive tariffs are calculated based on the consumption pattern of subscribers. Then, in the second layer, the scheduling of SBs and EV fleets is done in a decentralized space and considering their participation in the designed DRP. Eventually, in the third layer, the operators of MGs have received the power exchange information of SBs in order to carry out their scheduling in accordance with it. In this layer, the Day-Ahead (DA) scheduling of MGs and DS is done through the implementation of a cooperative game theory. Fluctuations of uncertain operational parameters such as load demand, radiation and wind are embedded in the model by scenario-based technique where a Risk-Averse (RA) strategy is adopted to manage them. Running the proposed three-layer strategy on a 69-node DS containing four MGs showed that this strategy can use the potential of SBs and EV fleets to improve the voltage characteristics in the high-demand period and reduce total daily costs by 13.66% with designing a dynamic-tariff DRP. Moreover, the results reveal that MGs using the Peer-to-Peer (P2P) power exchange option have not only reduced the power losses in the system but also reduced the total daily costs by about 8%.