A model predictive control method for multi-energy flow of smart community combined with stochastic response surface method is proposed. In this method

the stochastic response surface method is adopted to obtain the distribution characteristics of forecasting errors of the distributed wind power outputs and photovoltaic power outputs

the load demand as well as the spot price

and the distribution curve of probability density is obtained and discretized. Then the roulette algorithm is used to get the initial scene set and the nearest neighbor clustering method is adopted to reduce the number of scenes. A multi-objective optimal scheduling model for multi-energy flow considering the economic and environmental effect is built by taking into account the technologic and economic characteristics of combined cooling heating and power

electric vehicles and energy storage equipment. The stochastic model predictive control method is adopted to implement the online receding horizon optimization of the multi-energy flow scheduling model. The effective unification of the day-ahead optimization and the real-time receding horizon optimization is realized. The simulation results verify the effectiveness and superiority of the proposed method.