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<p>This study presents a time-coupled, multi-objective distributionally robust chance-constrained (MODRCC) framework for resilient grid restoration using Mobile Emergency Generators (MEGs). The model unifies (i) time-expanded logistics for MEG routing, crew scheduling, and refuelling, (ii) islanding-feasible DC-OPF under line outages, and (iii) Wasserstein-ball ambiguity to hedge uncertainty in attack severity and travel-time delays. Disjunctive linearization and second-order-cone (SOC) embeddings yield a tractable MISOCP that is evaluated inside an NSGA-II evolutionary search to generate the Pareto frontier between total cost and resilience. Experiments on IEEE-24 and IEEE-118 (12-hour horizon, 24 periods) show that, at comparable budgets, the proposed method reduces expected unserved energy (EUE) by 14–20% relative to static DRCC and classical robust baselines. On the IEEE-118 case, representative operating points illustrate a ~ 54% decrease in EUE (92→42 MWh) for a ~ 10% increase in cost along the frontier, evidencing smooth, convex trade-offs induced by Wasserstein regularization. The solver stack (Gurobi 12.0 + NSGA-II) scales efficiently; with parallel fitness evaluation it converges in ~ 2.8&#xa0;h for IEEE-118 (16 MEGs). Results confirm that explicitly coupling mobility realism with distributionally robust modelling yields operationally credible, cost-aware restoration schedules suitable for disaster-prone regions.</p>

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A time-coupled multi-objective distributionally robust chance-constrained framework for grid resilience enhancement using mobile emergency generators

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月25日

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  • D. Ashokaraju,
  • M. L. Ramamoorthy,
  • Deepa Simon,
  • N. Ashok,
  • Abhijit Bhowmik

摘要

This study presents a time-coupled, multi-objective distributionally robust chance-constrained (MODRCC) framework for resilient grid restoration using Mobile Emergency Generators (MEGs). The model unifies (i) time-expanded logistics for MEG routing, crew scheduling, and refuelling, (ii) islanding-feasible DC-OPF under line outages, and (iii) Wasserstein-ball ambiguity to hedge uncertainty in attack severity and travel-time delays. Disjunctive linearization and second-order-cone (SOC) embeddings yield a tractable MISOCP that is evaluated inside an NSGA-II evolutionary search to generate the Pareto frontier between total cost and resilience. Experiments on IEEE-24 and IEEE-118 (12-hour horizon, 24 periods) show that, at comparable budgets, the proposed method reduces expected unserved energy (EUE) by 14–20% relative to static DRCC and classical robust baselines. On the IEEE-118 case, representative operating points illustrate a ~ 54% decrease in EUE (92→42 MWh) for a ~ 10% increase in cost along the frontier, evidencing smooth, convex trade-offs induced by Wasserstein regularization. The solver stack (Gurobi 12.0 + NSGA-II) scales efficiently; with parallel fitness evaluation it converges in ~ 2.8 h for IEEE-118 (16 MEGs). Results confirm that explicitly coupling mobility realism with distributionally robust modelling yields operationally credible, cost-aware restoration schedules suitable for disaster-prone regions.