Adaptive Risk-Driven Control Strategy for Enhancing Highway Renewable Energy System Resilience Against Extreme Weather

Peiqiang Cui, Hongde Li, Wenwu Zhao, Xiaowu Tian, Jin Liu, Weijie Qin, Liya Hai and Fan Wu ()
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Peiqiang Cui: Gezhouba Group Transportation Investment Co., Ltd., Wuhan 430000, China
Hongde Li: School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Wenwu Zhao: China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan 430074, China
Xiaowu Tian: Gezhouba Group Transportation Investment Co., Ltd., Wuhan 430000, China
Jin Liu: Gezhouba Group Transportation Investment Co., Ltd., Wuhan 430000, China
Weijie Qin: Gezhouba Group Transportation Investment Co., Ltd., Wuhan 430000, China
Liya Hai: School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Fan Wu: School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2025, vol. 18, issue 20, 1-20

Abstract: Traditional centralized highway energy systems exhibit significant resilience shortcomings in the face of climate change mitigation requirements and increasingly frequent extreme weather events. Meanwhile, prevailing microgrid control strategies remain predominantly focused on economic optimization under normal conditions, lacking the flexibility to address dynamic risks or the interdependencies between transportation and power systems. This study proposes an adaptive, risk-driven control framework that holistically coordinates power generation infrastructures, microgrids, demand-side loads, energy storage systems, and transport dynamics through continuous risk assessment. This enables the system to dynamically shift operational priorities—from cost-efficiency in stable periods to robustness during emergencies. A multi-objective optimization model is established, integrating infrastructure resilience, operational costs, and traffic impacts. It is solved using an enhanced evolutionary algorithm that combines the non-dominated sorting genetic algorithm II with differential evolution (NSGA-II-DE). Extensive simulations under extreme weather scenarios validate the framework’s ability to autonomously reconfigure operations, achieving 92.5% renewable energy utilization under low-risk conditions while elevating critical load assurance to 98.8% under high-risk scenarios. This strategy provides both theoretical and technical guarantees for securing highway renewable energy system operations.

Keywords: grid-connected highway; resilience enhancement; risk-driven adaptive control; multi-objective optimization; NSGA-II-DE algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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