Coordinated Optimization of Multi-station Integration Systems Considering Uncertainties

Under the general development trend of the power Internet of Things, multi-station integration with high computing efficiency and local load consumption will become the main form of the future energy system. However, with the large number of renewable energy stations and charging stations connected into multi-station integration systems, the uncertainties and volatilities of new energy output and charging time of charging stations adversely affect the economic, efficient, and stable operation of the system. To achieve the economic, efficient, and stable operation of multi-station integration energy system, this paper establishes an integration station model with five stations in one: substation, charging station, energy storage station, data center station and renewable energy station. This paper adopts Monte Carlo simulation method to obtain renewable energy station output scenarios and K-means algorithm to perform scenario reduction. Probability density function is used to describe the uncertainty of random charging at charging station. Considering time-of-use price, a multi-objective optimization model is established with respect to costs and risks. NSGA-II and multi-attribute decision method are combined to find the optimal solution. The simulation results show that the proposed method significantly reduces the total operating cost and risk of the system, indicating that the proposed model and method are feasible.