Research Specifications

This study puts forward a bilevel optimization framework to evaluate the tactical price-setter strategy of a multienergy system (MES) in the wholesale electricity market (WEM). The MES is integrated with smart electric vehicle (EV) parking lots and the compressed air energy storage system (CAES). As the master-level player, the MES operator deploys coordinated EV charging strategies and CAES flexibilities to minimize the operational costs and submit the best offer/bid in WEM. At this stage, the WEM operator (follower-level player) collects offers/bids from MES and other market participants to clear the WEM with the optimal public interest and announce the market-clearing price. The vehicles were congregated in electric vehicle fleets via Kmeans clustering according to their uncertain specifications, such as daily travelled miles and arrival/departure times. Afterward, the stochastic programming scenarios were generated for each fleet according to their user-based probability distribution functions. However, more volatile parameters, e.g., wind speed, were handled by the data-driven distributionally robust optimization method, which is embodied by an ambiguity set within the Wasserstein ball. The proposed approach is simulated in two 24-bus and 39-bus IEEE standard test systems.