系统仿真学报 ›› 2026, Vol. 38 ›› Issue (10): 2964-2979.doi: 10.16182/j.issn1004731x.joss.26-0275

• 复杂系统调度与资源配置 • 上一篇    下一篇

面向多点应急的移动电力资源分配与调度优化

马丁, 张涛, 江波, 刘亚杰, 李水佳   

  1. 国防科技大学 系统工程学院,湖南 长沙 410073
  • 收稿日期:2026-04-01 修回日期:2026-08-18 出版日期:2026-10-31 发布日期:2026-10-20
  • 通讯作者: 李水佳
  • 第一作者简介:马丁(2001-),男,硕士生,研究方向为资源调度与系统优化。
  • 基金资助:
    国家自然科学基金(62403477)

Optimization of Mobile Power Resource Allocation and Scheduling for Multi-point Emergency Response

Ma Ding, Zhang Tao, Jiang Bo, Liu Yajie, Li Shuijia   

  1. College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
  • Received:2026-04-01 Revised:2026-08-18 Online:2026-10-31 Published:2026-10-20
  • Contact: Li Shuijia

摘要:

极端事件场景下城市移动电力资源存在空间分布不均衡和应急调度时的容量错配问题,为实现全局合理分配、提高资源配置和供电保障水平,提出一种面向多点应急电力需求的移动电力资源分配与调度两阶段决策方法。对移动电力资源特征和行驶电量消耗进行分析和建模;第一阶段建立考虑需求满足、容量匹配和调度时间的分配模型,解决资源与需求点的宏观匹配问题;第二阶段基于分配结果构建接口时序模型生成调度方案的具体顺序。仿真结果表明:在资源受限场景下该方法能够满足最低保障要求并实现高优先级重点保障,与距离优先贪心分配算法相比,加权短缺量降低16.3%、平均调度距离和总调度距离分别降低5.04%和5.1%。模型能根据目标偏好和供需紧张程度调整资源调用规模、空间调度范围和需求保障水平。

关键词: 极端事件, 应急供电, 移动电力资源, 资源分配, 调度

Abstract:

In extreme event scenarios, urban mobile power resources (MPRs) suffer from uneven spatial distribution and capacity mismatch during emergency scheduling. To achieve globally rationalallocation and improve resource configuration and power supply guarantee, a two-stage decision-making method for MPR allocation and scheduling is proposed for multi-point emergency power demands. First, the characteristics of MPRs and travel energy consumption are modeled. In the first stage, an allocation model considering demand satisfaction, capacity matching, and dispatch time is established to solve the macro-level matching problem between MPRs and demand points. In the second stage, an interface scheduling model is constructed based on the allocation results to generate the specific sequence of the scheduling scheme. Simulation results show that, under resource-constrained scenarios, the proposed method can satisfy minimum guarantee requirements and achieve prioritized supply for high-priority demand points. Compared with the distance-based greedy assignment (DGA), it reduces the weighted shortage by 16.3%, average scheduling distance by 5.04%, and total scheduling distance by 5.1%. The model can adjust the scale of resource utilization, spatial scheduling range, and demand guarantee level according to objective preferences and the degree of supply-demand conditions.

Key words: extreme event, emergency power supply, mobile power resource, resource allocation, scheduling

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