As an emerging energy aggregation management technology, virtual power plant can integrate multiple distributed resources such as wind power, photovoltaic and energy storage. Aiming at the multi-objective optimization and uncertainty handling problems in the participation of virtual power plants in the auxiliary services of regional power grids, the study proposes a virtual power plant scheduling method based on multi-objective dynamic planning. The method constructs a virtual power plant model containing micro gas turbine, energy storage unit, wind power generation and demand response, establishes a multi-objective optimization function with the objective of minimizing the operating cost, and solves it with an improved particle swarm algorithm. Through the simulation verification of the IEEE33 node distribution system, the results show that, compared with the traditional peaking method, the proposed method is able to reduce the load variance from 2251kW to 2071kW, the operating cost from 41845.3 yuan to 39,574.63 yuan, and the network loss from 3041kW to 2711kW. In the analysis of the different confidence levels, the system operating benefit reaches 16753 when the confidence level is 0.87 When the confidence level is 0.87, the system operation benefit reaches 16753.68 RMB, while the benefit is 16148.47 RMB when the confidence level is 0.97, which verifies the negative correlation between the confidence level and the operation benefit. The research results show that the multi-objective dynamic planning method can effectively improve the economy and operational stability of virtual power plants, which provides theoretical support for virtual power plants to participate in the electricity market.
