Journal Article

Optimization and Scheduling Method for Wind-Solar-Thermal-Storage Power System of Multiple Energy Stations Using Correlation-IGDT

Lu, Qiuyu, Xu, Zhanpeng, Liu, Yang, Huang, Zejie, Xie, Pingping, Chen, Yue, Yang, Yinguo

2026

Overview

With the large-scale integration of wind and solar energy into the power grid, the power system is facing uncertainty challenges in multiple links, such as source, grid, and load. How to efficiently dispatch flexible resources, such as energy storage, has become an urgent problem to be solved. To this end, this paper considers the correlation between new energy stations due to natural conditions, uses Vine-Copula theory to describe the correlation characteristics of the output of multiple new energy stations, and proposes a wind solar new energy output scenario generation method based on Vine-Copula theory; Then, to develop the optimal scheduling and operation plan, considering the goal of minimizing operating costs within a scheduling cycle, combined with the scenario of output of wind and solar energy, an optimization and scheduling model for wind-solar-thermal-storage power system operation of multiple energy stations was constructed; On this basis, considering the difficulty in obtaining the probability distribution of load uncertainty, a risk-averse model and a risk-seeking model based on information Gap Decision Theory (IGDT) were constructed, and a multi energy station power system operation optimization scheduling method based on correlation-IGDT was proposed. By setting risk strategies and risk deviation factors, the power system operation scheduling scheme under this strategy can be obtained. Simulation experiments were conducted based on an improved IEEE39 node system for verification, and the results showed that compared to traditional methods that do not consider correlation, this method can reduce thermal power costs by 0.63% and energy storage costs by 10.56%. Meanwhile, Monte Carlo sampling analysis shows that the model has good accuracy and stability within the range of load disturbances. Further analysis shows that under the risk avoidance strategy, the maximum power variation of thermal power is controlled at 284 MW, with an average of 172 MW; while under the risk acceptance strategy, the maximum variation is 198 MW, with an average of 127 MW, significantly improving the system’s adaptability and operational efficiency to uncertain environments. The main contribution of this article is to integrate the modeling of new energy correlation with information gap decision-making and construct a power system scheduling optimization framework for multiple uncertain factors, which has good promotion value and practical application potential.

• Publisher: Tech Science Press
• Subject: Correlation / Decision theory / Electrical loads / Energy costs / Energy storage / Maximum power / Operating costs / Operation scheduling / Optimization / Scheduling / Solar energy / Strategy / Thermoelectricity / Uncertainty