Charging and discharging optimization strategy for electric vehicles considering elasticity demand response

Abstract

The electrification of urban transportation systems is a critical step toward achieving low-carbon transportation and meeting climate commitments. With the support of the Chinese government for the electric vehicle industry, the penetration rate of electric vehicles has continued to increase. In the context of large-scale electric vehicles connected to the grid, a coordinated charging-discharging system is particularly vital studied to avoid grid overload caused by customers' random charging. In this paper, a two-stage optimization strategy for electric vehicle charging and discharging that considers elasticity demand response based on particle swarm optimization was proposed, allowing the user to respond autonomously according to the reference value of the charge and discharge demand response and select the optimization weight independently to meet their travel and charging needs. To facilitate the user to balance the charging cost and the charging energy, we have introduced the virtual SOC to calculate the optimization result in advance. The results show that the optimized scheme can reduce the charging cost by 40%∼110%, and the load variance of the distribution network can be reduced by 19%∼100%, realizing the "win-win" benefit of the grid side and the user side. In addition, our research found that under the proposed strategy, the cost of battery loss caused by cyclic charging and discharging is negligible compared to the discharge benefit.