Holistic Hosting Capacity Enhancement Through Sensitivity-Driven Flexibility Deployment and Uncertainty-Aware Optimization in Modern Distribution Networks

Wenjie Pan, Jun Han (), Chao Cai, Haofei Chen, Hong Liu and Zhengyang Xu
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Wenjie Pan: State Grid Jiangsu Electric Power Economic and Technological Research Institute, No. 251 Zhongshan Road, Gulou District, Nanjing 210000, China
Jun Han: State Grid Jiangsu Electric Power Economic and Technological Research Institute, No. 251 Zhongshan Road, Gulou District, Nanjing 210000, China
Chao Cai: State Grid Jiangsu Electric Power Economic and Technological Research Institute, No. 251 Zhongshan Road, Gulou District, Nanjing 210000, China
Haofei Chen: State Grid Jiangsu Electric Power Economic and Technological Research Institute, No. 251 Zhongshan Road, Gulou District, Nanjing 210000, China
Hong Liu: School of Electrical and Information Engineering, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, China
Zhengyang Xu: School of Electrical and Information Engineering, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, China

Energies, 2025, vol. 18, issue 3, 1-30

Abstract: This study presents a novel sensitivity-driven distributionally robust optimization framework designed to enhance hosting capacity in renewable-powered distribution networks through targeted flexibility resource deployment. The proposed approach integrates temporal sensitivity mapping with robust optimization techniques to prioritize resource allocation across high-sensitivity nodes, addressing uncertainties in renewable energy generation and load demand. By leveraging a dynamic interaction between sensitivity scores and temporal system conditions, the framework achieves efficient and resilient operation under extreme variability scenarios. Key methodological innovations include the incorporation of a social force model-based sensitivity mapping technique, a layered optimization approach balancing system-wide and localized decisions, and a robust uncertainty set to safeguard performance against distributional shifts. The framework is validated using a synthesized test system, incorporating realistic renewable generation profiles, load patterns, and energy storage dynamics. Results demonstrate a significant improvement in hosting capacity, with system-wide enhancements of up to 35% and a 50% reduction in renewable curtailment. Moreover, sensitivity-driven resource deployment ensures efficient utilization of flexibility resources, achieving a peak allocation efficiency of 90% during critical periods. This research provides a comprehensive tool for addressing the challenges of renewable integration and grid stability in modern power systems, offering actionable insights for resource allocation strategies under uncertainty. The proposed methodology not only advances the state-of-the-art in sensitivity-based optimization but also paves the way for scalable, resilient energy management solutions in high-renewable penetration scenarios.

Keywords: sensitivity analysis; distributionally robust optimization; hosting capacity enhancement; flexibility resource allocation; renewable energy integration; power distribution networks; uncertainty management (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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