How Much Energy Storage is Required in High-Penetration Renewable Power Systems? A Simulation-based Optimization Approach

Energy storage (ES) has been recognized as one of the most promising technologies to cope with the increasing peakshaving challenge in high-penetration renewable power systems. With the restriction on the curtailment ratio of renewable power, how to find the minimal requirement of ES in the power systems is of great importance for secure and economic system operations. State-of-the-art stochastic and robust optimization methods are not well-behaved when coming to massive historical/prediction data. In this regard, we propose a simulation-based optimization algorithm with an iterative paradigm to analyze the minimal requirement of ES. First, the initial requirement to the energy capacity and rated power is obtained by solving a stochastic programming problem. Second, hourly chronological operational simulations on each historical/prediction scenario is conducted, thereby the expectation of the curtailment ratio is estimated. Third, the required size of ES is corrected according to the expected curtailment ratio. Repeat the chronological operational simulations and ES size corrections until the targeted curtailment ratio is satisfied. Numerical experiments using real data of a provincial power grid in China validate the effectiveness and efficiency of the proposed algorithm.