Abstract: Aiming at the charging load optimization problem of electric vehicles, this paper proposes a charging load optimization strategy for electric vehicles based on stage-sharing tariff response. The strategy establishes an electric vehicle charging load model and simulates the load variation of uncontrolled charging of electric vehicles by Monte Carlo algorithm, taking into account the electric vehicle load factor. On the basis of the elasticity matrix of peak-valley leveling tariffs, the stage time-sharing tariff model is established by subdividing different equal time periods, and the optimization model of time-sharing tariffs for electric vehicles is constructed with the optimization objectives of minimizing the mean square deviation of the total charging costs of the users and the total load of the system, and the improved chaotic multi-objective genetic algorithm is used to solve and optimize the model. Finally, the effectiveness and economy of the strategy are verified by the experimental comparison results, where the total user charging cost and the total system load mean squared deviation are reduced by 4091 yuan and 37.151 MW respectively, compared with the traditional time-sharing tariff scheme under the given data conditions.

Key words: electric vehicle, time-sharing tariff, demand response, price elasticity matrix