Distributionally robust bi-level optimization for coal-dominant generation expansion planning under deep uncertainty: A decision-dependent framework for Bangladesh’s energy transition

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🔬研究者:Provides a novel bi-level optimization method for generation planning under endogenous uncertainty, with algorithmic improvements and case study results.

🏢実務担当者:Offers a decision-support tool for utilities and energy planners to evaluate expansion strategies under uncertainty, including carbon pricing sensitivity.

🏛政策担当者:Highlights the economic threshold for carbon pricing and a pragmatic transition pathway for coal-dependent economies, informing policy design.

This paper develops a distributionally robust bi-level optimization (DR-BiLevel) framework for generation expansion planning that explicitly models decision-dependent uncertainty through endogenous Wasserstein ambiguity sets. The framework captures how renewable investments increase operational uncertainty while storage investments reduce it—a dynamic ignored by conventional methods. Applied to Bangladesh’s power sector (28.9 GW installed capacity), the DR-BiLevel approach yields expansion plans 8.5% less costly than standard distributionally robust optimization and 20.8% less costly than robust optimization, while maintaining 95% reliability guarantees. The enhanced column-and-constraint generation algorithm achieves finite convergence with practical computation times (8.5 h for the full case study). Results suggest a pragmatic transition pathway: complete committed coal projects (8.2 GW) but halt new coal development; achieve 35% renewable penetration by 2041 through 18 GW solar and 5 GW offshore wind deployment; deploy 4.8 GW nuclear capacity; and expand storage to 4 GW. Carbon pricing sensitivity analysis identifies $25–35/tCO2 as the economic threshold triggering rational fuel switching. The framework demonstrates that explicitly modeling decision-dependent uncertainty fundamentally changes optimal strategies, providing actionable guidance for energy transition planning in coal-dominant developing economies. Novel DR-BiLevel framework models how infrastructure decisions shape future uncertainty structures.Decision-dependent Wasserstein ambiguity sets capture endogenous uncertainty dynamics.8.5% cost reduction versus standard DRO while maintaining 95% reliability guarantees.Identifies $25–35/tCO₂ as economic carbon pricing threshold for Bangladesh.Optimal pathway: 35% renewables, 4.8 GW nuclear, no new coal beyond committed projects. Novel DR-BiLevel framework models how infrastructure decisions shape future uncertainty structures. Decision-dependent Wasserstein ambiguity sets capture endogenous uncertainty dynamics. 8.5% cost reduction versus standard DRO while maintaining 95% reliability guarantees. Identifies $25–35/tCO₂ as economic carbon pricing threshold for Bangladesh. Optimal pathway: 35% renewables, 4.8 GW nuclear, no new coal beyond committed projects. DR-BiLevel framework and key findings for Bangladesh’s energy transition.

• openalex https://figshare.com/articles/dataset/Distributionally_robust_bi-level_optimization_for_coal-dominant_generation_expansion_planning_under_deep_uncertainty_A_decision-dependent_framework_for_Bangladesh_s_energy_transition/32964047first seen 2026-08-01 05:02:56 · last seen 2026-08-01 05:04:28