Analysis of energy storage demand for peak shaving and frequency regulation of power systems with high penetration of renewable energy

Energy storage (ES) can mitigate the pressure of peak shaving and frequency regulation in power systems with high penetration of renewable energy (RE) caused by uncertainty and inflexibility. However, the demand for ES capacity to enhance the peak shaving and frequency regulation capability of power systems with high penetration of RE has not been clarified at present. In this context, this study provides an approach to analyzing the ES demand capacity for peak shaving and frequency regulation. Firstly, to portray the uncertainty of the net load, a scenario set generation method is proposed based on the quantile regression analysis and Gaussian mixture model clustering. Then, a multi-scenario and multi-time scale optimal operation model is established to handle the uncertainty of net load, and the power correction model for ES operations is established to accommodate the balance of ES charging/discharging and optimization of system operation cost. Finally, based on the solution results of the above models, the method for determining the system's demand for ES capacity is proposed, and the relationship between the penetration of RE, ES power and capacity, and the confidence level of meeting demand is obtained. Numerical studies show that with a confidence level of 90% for satisfying demand, the 49.5% RE penetration system (the maximum load is 9896.42 MW) needs ES power and capacity of 1358 MW and 4122 MWh for peaking and ES power and capacity of 478 MW and 47 MWh for frequency regulation. Further, as the penetration of RE increases, the proportion of ES demand power to the system's power supply capacity and duration demand of ES also increase.

Automated summary

This study proposes an approach to analyze the demand capacity of energy storage (ES) for peak shaving and frequency regulation in power systems with high penetration of renewable energy (RE). It utilizes a scenario set generation method to portray the uncertainty of net load, and establishes a multi-scenario and multi-time scale optimal operation model to handle the uncertainty. The results show the relationship between RE penetration, ES power and capacity, and the confidence level of meeting demand.