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<p>The increasing installation of renewable sources, particularly wind energy, has introduced significant uncertainty into power system operations, requiring enhanced flexibility and advanced scheduling strategies. This work suggests a novel hybrid uncertainty controlling framework for the optimal day-ahead scheduling of integrated electricity-gas networks connected to industrial energy hub, with consideration of hydrogen vehicles and multiple flexibility resources. The proposed framework enables simultaneous meet of electricity, gas, heat, and hydrogen demands, while integrating advanced technologies such as transmission line switching, demand response programs, hydrogen storage systems, fuel cells, and gas pipeline line-pack modeling. To manage uncertainties in wind power generation and electrical load, a scenario-free hybrid method combining robust optimization and the information-gap decision theory (IGDT) is developed. This hybrid approach eliminates the dependency on probability distributions and scenario generation, thereby reducing computational burden. The resulting tri-level optimization model is transformed into a single-level mixed-integer linear programming model using duality theory and solved via CPLEX. Comprehensive case studies demonstrate that the coordinated integration of flexible technologies significantly reduces operational costs, improves renewable energy utilization, and eliminates wind curtailment and load shedding. The proposed robust-IGDT method increases the system’s capability to withstand uncertainty while incurring only a moderate cost increase (18.34%) compared to the deterministic model.</p>

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A new hybrid uncertainty controlling framework for optimal scheduling of power-gas networks connected to industrial energy hubs considering hydrogen vehicles and flexibility resources

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Behrouz Moarref,
  • Seyyed Mostafa Abedi,
  • Seyed Mahmoud Modaresi

摘要

The increasing installation of renewable sources, particularly wind energy, has introduced significant uncertainty into power system operations, requiring enhanced flexibility and advanced scheduling strategies. This work suggests a novel hybrid uncertainty controlling framework for the optimal day-ahead scheduling of integrated electricity-gas networks connected to industrial energy hub, with consideration of hydrogen vehicles and multiple flexibility resources. The proposed framework enables simultaneous meet of electricity, gas, heat, and hydrogen demands, while integrating advanced technologies such as transmission line switching, demand response programs, hydrogen storage systems, fuel cells, and gas pipeline line-pack modeling. To manage uncertainties in wind power generation and electrical load, a scenario-free hybrid method combining robust optimization and the information-gap decision theory (IGDT) is developed. This hybrid approach eliminates the dependency on probability distributions and scenario generation, thereby reducing computational burden. The resulting tri-level optimization model is transformed into a single-level mixed-integer linear programming model using duality theory and solved via CPLEX. Comprehensive case studies demonstrate that the coordinated integration of flexible technologies significantly reduces operational costs, improves renewable energy utilization, and eliminates wind curtailment and load shedding. The proposed robust-IGDT method increases the system’s capability to withstand uncertainty while incurring only a moderate cost increase (18.34%) compared to the deterministic model.