Data-Driven Day-Ahead Scheduling Strategy for Virtual Power Plants Considering Energy Data Trading

Virtual Power Plants (VPPs) play a vital role in enhancing the flexibility and coordination efficiency of power systems. However, limited historical data may compromise the accuracy of forecasting and scheduling performance. This paper proposes a scheduling optimization strategy that integrates an energy data trading mechanism. Firstly, an energy data trading model is developed, incorporating meteorological data, electric vehicle charging data, and other external sources. Data acquisition decisions are embedded into the scheduling process, and a functional relationship between the volume of data purchased and the improvement in forecast accuracy is established. Secondly, a day-ahead scheduling model is constructed to coordinate the operation of energy storage systems, flexible loads, and distributed energy resources. Finally, simulation case studies are conducted to compare different data acquisition strategies. The results show that incorporating data trading significantly enhances forecast accuracy, increases renewable energy utilization, and reduces overall system operation costs.