Sizing Hybrid Energy Storage Systems for Distributed Power Systems under Multi-Time Scales

With the rapid development of industry, more fossil energy is consumed to generate electricity, which increases carbon emissions and aggravates the burden of environmental protection. To reduce carbon emissions, traditional centralized power generation networks are transforming into distributed renewable generation systems. However, the deployment of distributed generation systems can affect power system economy and stability. In this paper, under different time scales, system economy, stability, carbon emissions, and renewable energy fluctuation are comprehensively considered to optimize battery and super-capacitor installation capacity for an off-grid power system. After that, based on the genetic algorithm, this paper shows the optimal system operation strategy under the condition of the theoretical best energy storage capacity. Finally, the theoretical best capacity is tested under different renewable energy volatility rates. The simulation results show that by properly sizing the storage system’s capacity, although the average daily costs of the system can increase by 10%, the system’s carbon emissions also reduce by 42%. Additionally, the system peak valley gap reduces by 23.3%, and the renewable energy output’s fluctuation range and system loss of load probability are successfully limited in an allowable range. Lastly, it has less influence on the theoretical best energy storage capacity if the renewable energy volatility rate can be limited to within 10%