A two-stage stochastic programming method for coupled power-transportation networks based on improved logic-based Benders decomposition

ZENG Xianming; LI Haixiao; DIAO Xing; XIE Bao; WANG Haohuan; BAO Shiyuan

doi:10.19783/j.cnki.pspc.260147

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A two-stage stochastic programming method for coupled power-transportation networks based on improved logic-based Benders decomposition

更新时间：2026-09-23

- A two-stage stochastic programming method for coupled power-transportation networks based on improved logic-based Benders decomposition
- Power System Protection and Control Vol. 54, Issue 18, (2026)
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- DOI：10.19783/j.cnki.pspc.260147
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- Published：2026
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ZENG Xianming, LI Haixiao, DIAO Xing, et al. A two-stage stochastic programming method for coupled power-transportation networks based on improved logic-based Benders decomposition[J]. Power System Protection and Control, 2026, 54(18).
DOI：

ZENG Xianming, LI Haixiao, DIAO Xing, et al. A two-stage stochastic programming method for coupled power-transportation networks based on improved logic-based Benders decomposition[J]. Power System Protection and Control, 2026, 54(18). DOI： 10.19783/j.cnki.pspc.260147.

摘要

在电力-交通一体化低碳发展的背景下，针对交通流不确定性导致的电力-交通协同优化决策难问题，提出了一种基于改进逻辑Benders分解的电力-交通耦合网络两阶段随机规划方法。首先，构建计及多源碳排放的两阶段随机规划模型，实现经济性与低碳性协同优化的充电站选址规划与耦合系统运行决策。然后，针对所建模型非线性、规模大、复杂难解的特征，提出基于改进逻辑Benders分解的两阶段随机规划高效求解方法。采用分段线性化等技术处理时间函数、碳排放模型等非线性约束，将模型重构为混合整数线性随机规划，并采用逻辑Benders分解算法进行求解。对算法启发式增强改进，提出基于流量贡献度的最优割强化机制与基于最小不可行集的可行割生成策略，以生成高质量割平面并有效提升计算效率。最后，基于算例仿真，验证所提方法有效性。

Abstract

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.

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references

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