IPSO-based Charging Price Optimization for Charging Stations with Cooperative and Competitive Relationship

With the rapid growth of electric vehicles (EVs), the charging demand has become an inevitable load type in the power grid. Without reasonable guidance, disorderly charging behavior will further aggravate the pressure on the grid. Rational charging service price is the key to guide the orderly charging of electric vehicles. This paper focuses on the charging pricing problem for multiply charging stations (CS) with competitive and cooperative relationship. The following contributions are made. Firstly, a pricing game model based on the Stackelberg game framework is proposed to generate the optimal charging price. In order to allocate benefits fairly, the Shapley value is applied to ensure equitable distribution among cooperative charging stations. Secondly, prospect theory is introduced to depict users' risk preferences and psychological expectations in uncertain environments. This can be used to represent the uncertain response of the EV users to the charging price. Finally, an improved particle swarm optimization (IPSO) is proposed to solve this large scale pricing optimization problem, which incorporates nonlinear inertia weights, dynamic learning factor, and Levy flight strategy. Numerical results show that the method can find the optimal pricing solution within a small number of iterations compared with traditional intelligent algorithms.