Energy Storage Siting and Capacity Planning Considering Voltage Flexibility Under Extreme Scenarios

The volatility and non-dispatchability of renewable energy sources raise a series of challenges in the context of new energy boom. To cope with these problems, this study proposes a novel energy storage system siting and capacity planning method based on extreme scenarios. The method takes voltage flexibility into full consideration, and employs the K-means algorithm to identify extreme scenarios by analyzing the correlation between temperature and weather and load. Then, load scenarios under extreme meteorological conditions are generated using a generative adversarial network, and a small probability high load scenario generation model is constructed. On the basis of voltage stability and flexibility as indicators, nodes with lower stability and weaker flexibility were prioritized for the layout of energy storage systems. Subsequently, the capacity allocation of the energy storage system is optimized by an improved particle swarm algorithm to reduce the operation cost. Finally, the effectiveness and feasibility of the method in energy storage siting and capacity planning are verified by simulation results of the improved IEEE-33 node distribution system.