Study on Photovoltaic Uncertainty and Optimal Allocation of Energy Storage in Traction Power Supply System Considering Geographical Differences

With the large-scale configuration and planning of traction power supply systems (TPSS) combining photovoltaic (PV) and energy storage, current research on PV uncertainty and PV storage capacity allocation needs to be based on large sample PV data. Influenced by geographic differences and climate extremes, it is difficult to converge the PV fluctuation range calculations with a small amount of sample data; in most regions, it is even more difficult to find geographically compatible sample data for capacity allocation planning for supercapacitors (SC). This challenges existing capacity allocation schemes and PV uncertainty resolution methods. In order to solve the PV uncertainty and capacity allocation problem when there is no PV sample data or only short-term sample data, a two-tier optimization model configuration based on PV prediction model is developed. The PSO algorithm embedded in the CPLEX solver is used to solve this optimization model. Finally, the economy and effectiveness of the model are verified by comparing the results with the existing schemes.