Double layered expansion planning for virtual power plants considering virtual energy storage systems

With the widespread integration of renewable energy sources, power systems increasingly require enhanced flexibility and economic efficiency. To address the constraints imposed by high costs of conventional physical energy storage in virtual power plant planning, a bi-level expansion planning model incorporating virtual energy storage systems is proposed. Initially, a user behavior model for virtual energy storage is developed, where incentive and discount signal mechanisms are integrated to characterize charge-discharge response characteristics. Subsequently, a bi-level optimization model is established, wherein the upper level minimizes energy storage configuration costs through capacity allocation optimization, while the lower level maximizes operational revenue through energy storage scheduling strategy determination. To improve computational efficiency, a hybrid Grey Wolf Optimization algorithm is employed for model solution. The effectiveness of the proposed methodology is evaluated using an industrial park located in the southeast coastal region as a test case. Experimental results indicate that the virtual energy storage system achieved an equivalent storage capacity of 10.4 MWh, reducing total storage investment costs by 18.9% compared to physical-storage-only solutions. The proposed bi-level optimization model improves annual operational revenue by 97.9% and 55.9% compared to the baseline and single-level models, respectively. This approach effectively reduces energy storage investment costs while enhancing operational revenue of virtual power plants and system dispatch flexibility.