Peak Shaving Strategy of Electric Vehicle in Industrial Park Considering Differentiated Subjective Willingness

As a typical flexibility resource, electric vehicles (EVs) have considerable load storage capacity if scheduled properly. This paper established a two-level optimization model of EV for optimal load scheduling of industrial large users, in which the upper model searched the optimal price incentive strategy on which basis the lower model give the optimal scheduling strategy of EV. To portray the external characteristics of the EV aggregation, a virtual battery (VB) based EV power capacity model was established to conduct peak shaving for the industrial user. The subjective willingness of EV owners was a modification to the VB model. The subjective willingness was quantified through fuzzy evaluation and the value determined the EV response rate which affected the available power capacity of the VB. The optimal price incentive coefficient was sought by particle swarm optimization (PSO) to reach the balance between the subjective satisfaction of the EV owner and the revenue of the industrial user, then the charging/discharging strategy of the EV was optimized through the modified VB model and the total revenue of industrial user was calculated considering the profits of peak load shaving the costs of price incentive. We validated the model with real-world load data, proving that the EV optimal scheduling model is practical and profitable, and that an optimal price incentive strategy can increase the total revenue of industry users.