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The increasing penetration of distributed energy resources in distribution networks introduces significant challenges for congestion management due to their inherent uncertainty. This paper proposes a coordinated congestion management framework that synergistically integrates hydrogen energy storage (HES) systems and incentive-based demand response (IBDR) programs under an information gap decision theory (IGDT)-driven multi-objective optimization approach. The HES operates as both an electrolyzer and a fuel cell, enabling bidirectional energy flow and providing long-duration storage capability, while IBDR delivers short-term, cost-effective demand-side flexibility. The IGDT formulation enhances operational resilience under deep uncertainty in renewable generation without relying on probabilistic data. A virtual pareto front-based multi-objective particle swarm optimization algorithm is developed to balance operational cost minimization, congestion mitigation, and emission reduction objectives. The proposed framework is validated on modified IEEE 33-bus and 69-bus distribution networks with multiple DERs and HES units. Simulation results demonstrate up to 95.43% congestion reduction, 17.4% total operating cost savings, and 27.69% emission reduction compared with initial case. Sensitivity analysis further reveals that optimal performance is achieved with 100–125% HES capacity, 20% DR participation, and a risk-neutral IGDT parameter (α = 0.08). These results confirm that the coordinated HES-IBDR strategy provides complementary flexibility, enabling sustainable and uncertainty-resilient operation of future smart distribution grids.
Abdollahi et al. (Thu,) studied this question.