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City-scale information modelling for urban energy resilience with optimal battery energy storages in Hong Kong

Author

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  • Ping, Dazhou
  • Li, Chaosu
  • Yu, Xiaojun
  • Liu, Zhengxuan
  • Tu, Ran
  • Zhou, Yuekuan

Abstract

Climate change and extreme weather events are imposing threats to city power systems with regional power shortages. To enhance urban power system's resilience amid climate change, photovoltaic (PV) and battery energy storage systems (BESS) are crucial for maintaining self-sufficient power during outages. However, the optimal installation location and capacity sizing of BESS remain uncertain when considering multi-criteria, including safety, energy flexibility, accessibility and energy resilience. This study proposes a new approach, i.e., Geographic Information System (GIS) integrated with Multi-Criteria Decision-Making (MCDM) and capacitated p-median problem, to identify optimal installation locations and capacity allocation of BESS. This approach comprehensively considers geographical conditions (such as slope, land use, open space), safety, energy flexibility, accessibility and energy resilience, while accounting for the entire distribution network's granularity, intermittent solar supply, and unstable electricity demand. The methodology can guide the optimal BESS siting and sizing for energy resilience under future climate change and associated extreme weather events. Results indicate that suitable installation locations based on the proposed GIS-MCDM method are concentrated in central and southern regions in Yau Tsim Mong. Subsequently, BESS with the optimal and specific installation location and capacity allocation is in districts with high electricity demand and favourable safety geographical conditions. Compared to BESS without GIS-MCDM, the optimal BESS deployment with GIS-MCDM decreases the power shortage from 13,184 MWh to 12,931 MWh. Additionally, it increases the maximum power shortage reduction density from 176.04 kWh/m2 to 364.2 kWh/m2, and the area with a power shortage reduction above 100 kWh/m2 expands from 1.24 × 105 m2 to 2.17 × 105 m2. This study contributes a new approach to determine optimal BESS installation locations and capacity allocation in urban-scale information modelling, planning and deployment, with frontier guidelines for system designers and urban planners to collaboratively develop resilience and survivability of urban power systems under extreme events.

Suggested Citation

  • Ping, Dazhou & Li, Chaosu & Yu, Xiaojun & Liu, Zhengxuan & Tu, Ran & Zhou, Yuekuan, 2025. "City-scale information modelling for urban energy resilience with optimal battery energy storages in Hong Kong," Applied Energy, Elsevier, vol. 378(PA).
    • Handle: RePEc:eee:appene:v:378:y:2025:i:pa:s0306261924021962
    DOI: 10.1016/j.apenergy.2024.124813
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    2. Buonomano, Annamaria & Forzano, Cesare & Giuzio, Giovanni Francesco & Maka, Robert & Palombo, Adolfo & Russo, Giuseppe, 2026. "Optimising renewable energy community aggregation for urban districts decarbonisation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PD).
    3. Li, Yiqun & Pu, Ziyuan & Liu, Pei & Qian, Tao & Hu, Qinran & Zhang, Junyi & Wang, Yinhai, 2025. "Efficient predictive control strategy for mitigating the overlap of EV charging demand and residential load based on distributed renewable energy," Renewable Energy, Elsevier, vol. 240(C).

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