Dynamic Optimal Allocation Method of Multi-Energy Unit Integrated System

In this paper, a two-layer stochastic optimization model based on Stackelberg game is proposed for integrated energy system scheduling problem including electric vehicle (EV), renewable energy and energy storage system. The upper layer model takes the grid operator as the main body, optimizes the power purchase cost and load peak-valley difference penalty through dynamic time-price strategy, and considers the uncertainty of wind power and photovoltaic output and energy storage economy. The lower model takes the user-side EV cluster and adjustable load as the main body, and minimizes the energy consumption cost through charge and discharge scheduling and load transfer. Latin Hypercube sampling (LHS) and Euclidean distance reduction method are used to generate and simplify the renewable energy prediction error scenario. Taking a park in Shanghai as an example, the simulation results show that the optimal scheduling strategy significantly reduces the system operating cost and load peak-valley difference, and reduces the frequency deviation through the rapid power regulation of energy storage and EV. The study verifies the effectiveness of the proposed model in improving the consumption capacity of renewable energy, reducing the abandonment of wind and light, and enhancing the stability of the power grid, which provides theoretical support for the demand side response and multi-energy cooperative dispatch.