TY - JOUR
T1 - Resilient co-optimization of distribution network hardening and mobile resource scheduling with decision-dependent uncertainty
AU - Ma, Donglai
AU - Cao, Xiaoyu
AU - Zeng, Bo
AU - Chen, Chen
AU - Zhai, Qiaozhu
AU - Jia, Qing Shan
AU - Guan, Xiaohong
N1 - Publisher Copyright:
© 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
PY - 2026/10/1
Y1 - 2026/10/1
N2 - This paper studies the robust co-planning of proactive network hardening and mobile hydrogen energy resource (MHER) scheduling, which is to enhance the resilience of power distribution network (PDN) against the disastrous events. A decision-dependent robust optimization model is formulated with a (Formula presented) resilience constraint and discrete recourse structure, which helps achieve the load survivability target considering endogenous uncertainties. Different from the traditional model with a fixed uncertainty set, we adopt a dynamic representation that explicitly captures the endogenous uncertainties of network contingency as well as the available hydrogen storage levels of MHERs, which induces a decision-dependent uncertainty (DDU) set. Also, the multi-period adaptive routing and energy scheduling of MHERs are modeled as a mixed-integer recourse problem for further decreasing the resilience cost. Then, a nested parametric column-and-constraint generation (N-PC&CG) algorithm is customized and developed to solve this challenging formulation. By leveraging the structural property of the DDU set as well as the combination of discrete recourse decisions and the corresponding extreme points, we derive a strengthened solution scheme with nontrivial enhancement strategies to realize efficient and exact computation. Numerical results on a 14-bus test system and a 56-bus real-world distribution network demonstrate the resilience benefits and economic feasibility of the proposed method. For the 56-bus instance, the proposed co-planning strategy can reduce the system reinforcement cost by up to 72.44% compared with hardening-only planning while meeting pre-specified survivability targets. Moreover, the enhanced N-PC&CG can significantly reduce the solution time by 57.8%–85.4% on the 14-bus system and exhibits strong scalability for large-scale instances.
AB - This paper studies the robust co-planning of proactive network hardening and mobile hydrogen energy resource (MHER) scheduling, which is to enhance the resilience of power distribution network (PDN) against the disastrous events. A decision-dependent robust optimization model is formulated with a (Formula presented) resilience constraint and discrete recourse structure, which helps achieve the load survivability target considering endogenous uncertainties. Different from the traditional model with a fixed uncertainty set, we adopt a dynamic representation that explicitly captures the endogenous uncertainties of network contingency as well as the available hydrogen storage levels of MHERs, which induces a decision-dependent uncertainty (DDU) set. Also, the multi-period adaptive routing and energy scheduling of MHERs are modeled as a mixed-integer recourse problem for further decreasing the resilience cost. Then, a nested parametric column-and-constraint generation (N-PC&CG) algorithm is customized and developed to solve this challenging formulation. By leveraging the structural property of the DDU set as well as the combination of discrete recourse decisions and the corresponding extreme points, we derive a strengthened solution scheme with nontrivial enhancement strategies to realize efficient and exact computation. Numerical results on a 14-bus test system and a 56-bus real-world distribution network demonstrate the resilience benefits and economic feasibility of the proposed method. For the 56-bus instance, the proposed co-planning strategy can reduce the system reinforcement cost by up to 72.44% compared with hardening-only planning while meeting pre-specified survivability targets. Moreover, the enhanced N-PC&CG can significantly reduce the solution time by 57.8%–85.4% on the 14-bus system and exhibits strong scalability for large-scale instances.
KW - Decision-dependent robust optimization
KW - Distribution network hardening
KW - Mobile hydrogen energy resources
KW - Nested parametric C&CG
KW - Resilience constraint
UR - https://www.scopus.com/pages/publications/105045577996
U2 - 10.1016/j.energy.2026.142045
DO - 10.1016/j.energy.2026.142045
M3 - 文章
AN - SCOPUS:105045577996
SN - 0360-5442
VL - 361
JO - Energy
JF - Energy
M1 - 142045
ER -