A two-stage model for urban rail transit energy storage planning optimization with renewable integration

Energy storage planning for urban rail transit (ESPURT) faces new requirements and restrictions due to low carbon and energy efficient transformation. This paper proposes a framework considering renewable energy integration, in which the traffic information and a two-stage optimization model are combined to co-optimize energy storage investment and operation decisions. Firstly, the load characteristics are extracted from the traffic data and for modelling energy interaction variation and fluctuation limitations. Then, the uncertainty set and constraints are formulated to systematically address stochasticity and operational limits. The proposed optimization model utilizes two-stage robust and column and constraint generation algorithm to produce the corresponding energy storage planning and dispatch operation strategy. Case studies on an urban rail transit station demonstrate the effectiveness of the proposed method. The ESPURT improved the comprehensive efficiency of URT compared with traditional mode, while introducing the uncertainty set and character constraints for ESPURT leads to scalability and robustness enhancement.