Multi-Area Wind Power Planning with Storage Systems for Capacity Credit Maximization Using Fuzzy-Based Optimization Strategy

Generation expansion planning is critical for the sustainable development of power systems, particularly with the increasing integration of renewable energy sources like wind power. This paper presents an innovative generation expansion model identifying the optimal strategy for constructing new wind power plants. The model determines the ideal size of wind power generation and strategically allocates wind resources across multi-area power systems to maximize their capacity credit. A novel fuzzy set approach addresses wind power’s inherent uncertainty and variability, which models wind data uncertainty through membership functions for each stochastic parameter. This method enhances the accuracy of capacity credit calculations by effectively capturing the unpredictable nature of wind power. The model uses the Effective Load Carrying Capability (ELCC) as the objective function to measure the additional load that can be reliably supported by wind generation. Additionally, integrating a compressed-air energy storage system (CAESS) is introduced as a novel solution to mitigate the intermittency of wind power, further boosting the wind power plants’ capacity credit. By incorporating an energy storage system (ESS), the model ensures greater resource availability and flexibility. The study evaluates a multi-area power network, where each area has distinct conventional generation capacity, reliability metrics, load profiles, and wind data. A three-interconnected power system case study demonstrates the model’s effectiveness in increasing the load carrying capability of intermittent renewable resources, improving system reliability, and enhancing resilience. This study provides new insights into optimizing renewable energy integration by leveraging advanced uncertainty modeling and energy storage, contributing to the long-term sustainability of power systems.