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

Abstract

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.