Flexibility Demand Analysis and Regulation Capacity Sharing Decisions Between Interconnected Power Systems Considering Differences in Regulation Performance

To cope with the demand for large amount of flexibility regulation caused by high penetration of intermittent renewable energy, it is necessary to classify and measure the demand capacity for different regulation performance, and to reasonably allocate flexibility resources for different regions and different regulation capacities. This study proposes a flexibility demand analysis and regulation capacity sharing decisions between interconnected power systems considering differences in regulation performance. Firstly, the empirical mode decomposition (EMD) method is used to decompose the historical operating load curves of each sub-region, and the demand capacities of different regulation performances are calculated based on the obtained decomposition results of trend components and fluctuation components. Then, the probability density and the regulation demand capacity interval at different confidence levels are calculated based on the regulation capacity statistics of the sample of historical operation days. Finally, the regulation capacity sharing decisions between the interconnected regions are made based on the cost of various regulation resources in different sub-regions and the confidence level requirements of internal resources in sub-regions to meet regulation demand. A scenario based on the interconnection operation of two regional grids and the self-sufficiency rate of regulation capacity in each sub-region is no less than 0.95 confidence level is used to verify the effectiveness and feasibility of the proposed method. The simulation results demonstrate that the regulation capacity demand considering the difference in regulation quality can provide a detailed basis for the cross-region deployment of different quality flexibility resources, and the total cost of regulation capacity of the regional grid after adopting the cross-region sharing decision model is reduced by about 4.51% compared with the system independent optimization model.