Robust self-scheduling of parking lot microgrids leveraging responsive electric vehicles

The penetration of plug-in electric vehicles is increasing in power systems all over the world. Due to imperfect data about the availability of electric vehicles, the uncertainty of the demand-side increases considerably. To overcome the problem, the power storage capacity of electric vehicles is addressed to provide demand flexibility for the supply-side. This paper proposes a novel structure for parking lot microgrids to provide day-ahead peak-shaving and valley-filling for power systems with distinct peak hours in the daily operation. The microgrid is comprised of electrical demands, e.g. heat ventilation and air conditioning, lighting, escalators, as well as self-generation facilities, including roof-top photovoltaic sites and gas-fired engines. Besides, the parking lot has smart charging stations to charge/discharge the electric vehicles based on the flexibility requirements of power systems and/or owners’ preferences. The microgrid is supplied by the wholesale electricity market and bilateral contracts. To leverage the flexibility potentials of electric vehicles, a data-driven approach is suggested that classifies the electric vehicles based on distinct characteristics, e.g. dwell time and preferred state of charge. The robust optimization approach is adopted to determine the optimum procurement strategies for the microgrid in the worst-case realization of the wholesale market price uncertainty. Finally, the robust-mixed integer linear programming is examined in Iran Power Grid not only to provide flexibility for the power network but also to make a profit for the microgrid.