基于改进逻辑Benders分解的电力交通耦合网络两阶段随机规划方法OA

Against the background of integrated low-carbon development of power-transportation systems,traffic flow uncertainty poses challenges to coordinated optimization and decision-making.This paper proposes a two-stage stochastic programming method for coupled power-transportation networks based on an improved logic-based Benders decomposition.First,a two-stage stochastic model incorporating multi-source carbon emissions is developed to jointly optimize the economics and low-carbon performance of charging station location planning and coupled system operation.Then,to address the nonlinearity,large-scale,and computational complexity of the proposed model,an efficient solution method based on improved logic-based Benders decomposition is developed.Techniques such as piecewise linearization are employed to handle nonlinear constraints associated with travel-time functions and carbon-emission models,reformulating the problem as a mixed-integer linear stochastic programming model,which is subsequently solved using logic-based Benders decomposition.To further enhance the algorithm heuristically,a flow-contribution-based optimal-cut strengthening mechanism and a minimum-infeasible-set-based feasibility-cut generation strategy are proposed to generate high-quality cutting planes and improve computational efficiency.Finally,case studies demonstrate the effectiveness of the proposed method.