Stochastic Dynamic Pricing for EV Charging Stations with Renewables Integration and Energy Storage

摘要

This paper studies the problem of stochastic dynamic pricing and energymanagement policy for electric vehicle (EV) charging service providers. In thepresence of renewable energy integration and energy storage system, EV chargingservice providers must deal with multiple uncertainties --- charging demandvolatility, inherent intermittency of renewable energy generation, andwholesale electricity price fluctuation. The motivation behind our work is tooffer guidelines for charging service providers to determine proper chargingprices and manage electricity to balance the competing objectives of improvingprofitability, enhancing customer satisfaction, and reducing impact on powergrid in spite of these uncertainties. We propose a new metric to assess theimpact on power grid without solving complete power flow equations. To protectservice providers from severe financial losses, a safeguard of profit isincorporated in the model. Two algorithms --- stochastic dynamic programming(SDP) algorithm and greedy algorithm (benchmark algorithm) --- are applied toderive the pricing and electricity procurement policy. A Pareto front of themultiobjective optimization is derived. Simulation results show that using SDPalgorithm can achieve up to 7% profit gain over using greedy algorithm.Additionally, we observe that the charging service provider is able to reshapespatial-temporal charging demands to reduce the impact on power grid viapricing signals.