Multi-Stage Adaptive Robust Scheduling Framework for Nonlinear Solar-Integrated Transportation Networks

Puyu He, Jie Jiao, Yuhong Zhang, Yangming Xiao, Zhuhan Long, Hanjing Liu, Zhongfu Tan () and Linze Yang
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Puyu He: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Jie Jiao: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Yuhong Zhang: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Yangming Xiao: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Zhuhan Long: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Hanjing Liu: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Zhongfu Tan: School of Economics and Management, North China Electric Power University, Changping District, Beijing 102206, China
Linze Yang: School of Economics and Management, North China Electric Power University, Changping District, Beijing 102206, China

Energies, 2025, vol. 18, issue 21, 1-20

Abstract: The operation of modern power networks is increasingly exposed to overlapping climate extremes and volatile system conditions, making it essential to adopt scheduling approaches that are resilient as well as economical. In this study, a two-stage stochastic formulation is advanced, where indicators of system adaptability are embedded directly into the optimization process. The objective integrates standard operating expenses—generation, reserve allocation, imports, responsive demand, and fuel resources—with a Conditional Value-at-Risk component that reflects exposure to rare but damaging contingencies, such as extreme heat, severe cold, drought-related hydro scarcity, solar output suppression from wildfire smoke, and supply chain interruptions. Key adaptability dimensions, including storage cycling depth, activation speed of demand response, and resource ramping behavior, are modeled through nonlinear operational constraints. A stylized test system of 30 interconnected areas with a 46 GW demand peak is employed, with more than 2000 climate-informed scenarios compressed to 240 using distribution-preserving reduction techniques. The results indicate that incorporating risk-sensitive policies reduces expected unserved demand by more than 80% during compound disruptions, while the increase in cost remains within 12–15% of baseline planning. Pronounced spatiotemporal differences emerge: evening reserve margins fall below 6% without adaptability provisions, yet risk-adjusted scheduling sustains 10–12% margins. Transmission utilization curves further show that CVaR-based dispatch prevents extreme flows, though modest renewable curtailment arises in outer zones. Moreover, adaptability provisions promote shallower storage cycles, maintain an emergency reserve of 2–3 GWh, and accelerate the mobilization of demand-side response by over 25 min in high-stress cases. These findings confirm that combining stochastic uncertainty modeling with explicit adaptability metrics yields measurable gains in reliability, providing a structured direction for resilient system design under escalating multi-hazard risks.

Keywords: stochastic optimization; conditional value-at-risk; adaptability resources; power system adequacy; compound climate stressors; renewable integration (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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