About the growing gap between the peak and trough values of the net load in the new-energy grid-connected virtual power plant, people have already confirmed that the traditional peak-adjustment optimization method does not have effect. This research puts forward a power peak disposition model for the virtual power plant, which depends on the hierarchical particle swarm optimization algorithm. The core part of this model is constituted by three optimization objectives. Firstly, this object has the purpose of reducing to the smallest extent the electricity costs of users. Second, it makes great efforts to realize the maximum value of the net profit of this system. At last, this paper’s goal is to lower the expense of power peak cutting distribution within a fixed time period. When we make the model, practical restrictive conditions are considered by us. These factors include the power balance that is inside the electric power system, the electric energy generation ability of electromechanical devices, and also the climbing capacity. For the solving of the model, the iterative computation capability which is possessed by the layered particle swarm within the hierarchical particle swarm optimization algorithm is utilized by us. By means of a method that is systematic and divided into phases, the most beneficial disposition for the peak load cutting work of the virtual power plant is got decided. This model may obtain an 18.24 percent cut of carbon discharge, and it only lets the cost of power production undergo a 9.56 percent growth. Furthermore, it brings about a notable decrease in the net load peak-valley difference. When make comparison with the strategy of constant power, the strategy which this paper puts forward can completely make use of the constraints of energy storage. This permits the peak scheduling to reach the most superior distribution, hence providing an effective scheme for the safe, cost-cutting, and low-carbon running of the virtual power plant.
