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Optimal planning of municipal-scale distributed rooftop photovoltaic systems with maximized solar energy generation under constraints in high-density cities

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  • Ren, Haoshan
  • Ma, Zhenjun
  • Chan, Antoni B.
  • Sun, Yongjun

Abstract

Deployment planning of distributed rooftop photovoltaic (PV) systems remains a critical challenge for high-density cities, due to complex shading effects and diversified rooftop availabilities. Furthermore, such planning for large-scale systems could be extremely complex due to high dimensionality caused by the enormous number of buildings. To tackle the challenge, this study proposed an optimal planning strategy for municipal-scale distributed rooftop PV systems in high-density cities. The optimization problem was solved by integer learning programming, based on high-accuracy solar energy potentials characterization. By selecting proper rooftops for PV, the electricity generation was maximized, considering the conflicting budget and peak-export-power constraints. A Hong Kong-based case study (including 582 real building rooftops) was conducted. The effectiveness of the proposed strategy was verified by comparing with 5,000,000 Monte-Carlo-generated alternatives. The strategy more effectively identified the proper rooftops for PV installations, achieving up to 17.7% improvements in performance-cost ratio. Furthermore, the optimal planning strategy was systematically compared with two heuristic planning methods, i.e., total-energy-prioritized and energy-intensity-prioritized methods. The strategy outperformed the heuristic methods by up to 23.3% through well considering trade-off between rooftop total energy and energy intensity. The developed strategy can be used to facilitate rooftop PV deployments, and thus contribute to urban decarbonization.

Suggested Citation

  • Ren, Haoshan & Ma, Zhenjun & Chan, Antoni B. & Sun, Yongjun, 2023. "Optimal planning of municipal-scale distributed rooftop photovoltaic systems with maximized solar energy generation under constraints in high-density cities," Energy, Elsevier, vol. 263(PA).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pa:s0360544222025725
    DOI: 10.1016/j.energy.2022.125686
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