Dynamic energy system risk management under the pressures of GHG- and pollutant-emission mitigation for Hebei Province, China

Chong Zhang, Guohe Huang and Chenglong Zhang

Energy, 2025, vol. 335, issue C

Abstract: This study develops dynamic energy system risk management model (DERM) to mitigate emission in Hebei Province energy system and support decision making under uncertainty. The DERM integrates modified fuzzy chance-constrained programming, interval linear programming, and mixed-integer programming into an energy system planning model. Nine scenarios of attitude for decision-makers (γ = 0.9, …, 0.1) and three credibility levels (λ = 0.9, 0.8, 0.7) for the environmental loading capacity scenarios are provided in the case study. Weather conditions (mildly, moderately, and severely smoggy weather) and environmental loading capacity of different emission (NOx, SO2, dust, and CO2) are concurrently considered in this model. The proposed DERM effectively captures both the uncertainties and dynamics characteristics of energy systems. Its application in Hebei Province demonstrates its practical value, and the results align with the long-term planning objective of Hebei. The obtained result indicates that a pessimistic attitude of policymakers toward energy availability can significantly improve the energy system and the air quality compared to a positive attitude. Such pessimism will promote the development of new energy, especially wind power generation, resulting in a [106, 236]% increase in renewable capacity during planning period. Meanwhile, the diminutive degrees of credibility would result in a slight reduction in overall system cost, while leading to a sharp increase in the risk of system failure to the maximum acceptable environmental pollutant capacity. These findings could help to investigate uncertainty features for a domestic energy system and identify desirable attitude alternatives from decision markers under the trade-off between economic and environment.

Keywords: Energy system; GHG emissions; Uncertainty; Integer; Fuzzy chance-constrained programming (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:335:y:2025:i:c:s0360544225035558

DOI: 10.1016/j.energy.2025.137913

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