Dynamic decision model of critical peak pricing considering electric vehicles´ charging load

The continuous increase of peak load in China has made significant impact on the safety and stability of power grid. Meanwhile, electric vehicles are developing rapidly in China and whether their charging load can be controlled effectively by reasonable guidance without worsening the peak load problem becomes a challenge for the power system. Traditional solutions to balance electricity demand and supply, such as adding generators and using power orderly, are less economically efficient than DR programs. Critical peak pricing (CPP), which is a type of price-based DR program, can encourage customers to reduce or shift load during critical peak hours and alleviate the pressure of power supply. In this paper, a CPP dynamic decision-making model is proposed in which a trigger condition is set to determine critical days and hours and the particle swarm optimization algorithm is adopted to optimize the peak rate and the rebate. Additionally, the influence of electric vehicles´ charging load is considered in the proposed model. Finally, the numerical results show that the proposed CPP is apparently effective at reducing peak load and with the increase of the number of electric vehicles, the optimized peak electricity price gradually reduces to reach a stable level.