Optimal Bidding Strategy of GENCOs With Volatile Wind Power and Battery Swapping Station

With the traditional vertically integrated electric market transforming into a liberalized one, the generation side consisting of various generation companies (GENCOs) will be separated from the grid. This transformation gives opportunity for GENCOs to enhance their own profits by signing bilateral contracts with users directly, especially newly emerged numerous electric vehicle (EV) users, which could also mitigate the uncertainty. This paper considers a thermal-wind GENCO running a battery swapping station (BSS). The GENCO trades with the grid in the pool-based electricity energy market, and meanwhile signs bilateral contracts with EV users. A two-stage stochastic program is adopted to maximize the profit of the GENCO with the volatile and uncertain wind power and unknown electricity price of the energy market. The Sample Average Algorithm (SAA) method is used to transform the stochastic program into deterministic mixed-integer linear program (MILP). This work provides a feasible operation framework for a single GENCO with uncertain renewable energy to enhance its profit in both pool energy market and bilateral market, and provides optimal bidding strategy and scheduling with uncertain information.