The stochastic, intermittent and fluctuating characteristics of wind and photovoltaic power generation make the stable operation of a high percentage of renewable energy power systems potentially risky. The market mechanism and regulation strategy of the power system are important means to ensure the stable and reliable operation of the system. In this paper, wind turbines, gas turbines, energy storage and load and a high proportion of renewable energy are aggregated into a power plant, and the power plant is considered to participate in the multi-species trading consisting of the energy market, demand response market and frequency regulation market, and the optimal regulation model and regulation strategy of the power plant is established by considering the impact of the appraisal mechanism and the multiple uncertainty factors. Then a multi-objective particle swarm algorithm based on the dynamic collaboration of multiple swarms is proposed, and the optimal regulation strategy is obtained by solving the model using the DCMOPSO algorithm. The experiment is based on the node system to verify the model proposed in this paper, and it is found that the wind energy storage plant participates in the market with lower offer price, while in the DAM scenario, it participates in the market before the day of the market with higher market offer price, and at the same time, with the enhancement of its own offer price, the market price of electricity will be increased accordingly. It is verified that the method proposed in this paper can optimize the offer strategy of power storage stations and enrich the energy storage regulation means.
