Two-stage self-scheduling of battery swapping station in day-ahead energy and frequency regulation markets

Battery swapping stations (BSS) face two major problems in the frequency regulation market: 1) battery degradation cost with the effect of regulation, and 2) the uncertainties in hourly cumulative regulation signals (HCRS), market prices, and demands. To address the problems, this paper proposes a novel battery model based on the historical data of regulation signals. Furthermore, the ambiguity sets of uncertainties are constructed based on the Wasserstein distance metric. A two-stage self-scheduling model of BSS in the day-ahead energy and regulation markets is presented. The first stage is to determine the batteries swapping plan with the uncertainty in EV demands by robust discrete optimization, while the second stage is formulated by the Wasserstein-distance based distributionally robust chance-constrained program to optimize the bidding strategy with uncertainties in HCRS and market prices. The case studies verify the accuracy and effectiveness of the proposed battery model and the two-stage self-scheduling model of BSS.

Automated summary

This paper proposes a novel battery model based on historical data of regulation signals to address the problems faced by battery swapping stations in the frequency regulation market. Through constructing ambiguity sets of uncertainties and utilizing a two-stage self-scheduling model, the impact of market uncertainties on BSS is effectively addressed.