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Optimal deployment of distributed rooftop photovoltaic systems and batteries for achieving net-zero energy of electric bus transportation in high-density cities

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  • Ren, Haoshan
  • Ma, Zhenjun
  • Ming Lun Fong, Alan
  • Sun, Yongjun

Abstract

Using rooftop solar photovoltaics (PV) and batteries together to power electric buses is considered a novel and feasible approach to reducing carbon emissions and tackling street-level air pollution in high-density cities like Hong Kong. However, associated optimal deployment is highly challenging due to the involved high-dimensional system planning, non-linear component sizing, and complex solar potential accurate characterization. To address this challenge, this study proposed a strategy to minimize the payback period of the deployed rooftop PV and batteries for achieving net-zero energy of electric bus transportation by taking associated grid impacts into consideration. In this strategy, a genetic algorithm-integer linear programming (GA-ILP) approach was developed to solve the high-dimensional rooftop PV planning problem and the non-linear PV and battery sizing problem. Our 3D-GIS (geographic information system) and DL (deep learning) integrated approach was used to attain accurate spatiotemporal rooftop solar power generation profiles. To verify the proposed strategy, a case study covering 5 bus terminuses with 28 bus routes and a total of 1,224 building rooftops surrounding the terminuses was conducted in a real region of Hong Kong. The effectiveness of the developed strategy was verified by comparing the optimized deployment with 150 million Monte-Carlo-generated solutions. The optimized deployment achieved the shortest payback period of 3.98 years by effectively tackling design issues such as battery oversizing, PV misallocation, and battery misallocation. To meet the increasing requirement on peak export power reduction (representing the grid impact constraint in the study), the proposed strategy can find a cost-effective balance between shifting PVs from higher-density solar potential terminuses to lower ones and increasing battery capacity. The proposed strategy can be adopted in practice to facilitate the development of cost-effective and grid-friendly community-solar-powered electric bus networks in high-density cities.

Suggested Citation

  • Ren, Haoshan & Ma, Zhenjun & Ming Lun Fong, Alan & Sun, Yongjun, 2022. "Optimal deployment of distributed rooftop photovoltaic systems and batteries for achieving net-zero energy of electric bus transportation in high-density cities," Applied Energy, Elsevier, vol. 319(C).
    • Handle: RePEc:eee:appene:v:319:y:2022:i:c:s0306261922006316
    DOI: 10.1016/j.apenergy.2022.119274
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    4. Liu, Xiaohan & Yeh, Sonia & Plötz, Patrick & Ma, Wenxi & Li, Feng & Ma, Xiaolei, 2024. "Electric bus charging scheduling problem considering charging infrastructure integrated with solar photovoltaic and energy storage systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 187(C).
    5. Shao, Shuai & Tan, Zhijia & Liu, Zhiyuan & Shang, Wenlong, 2022. "Balancing the GHG emissions and operational costs for a mixed fleet of electric buses and diesel buses," Applied Energy, Elsevier, vol. 328(C).
    6. Jiang, Hou & Yao, Ling & Lu, Ning & Qin, Jun & Zhang, Xiaotong & Liu, Tang & Zhang, Xingxing & Zhou, Chenghu, 2024. "Exploring the optimization of rooftop photovoltaic scale and spatial layout under curtailment constraints," Energy, Elsevier, vol. 293(C).
    7. Sebastiano Anselmo & Maria Ferrara, 2023. "Trends and Evolution of the GIS-Based Photovoltaic Potential Calculation," Energies, MDPI, vol. 16(23), pages 1-27, November.
    8. Murugan, Manivel & Marisamynathan, Sankaran, 2024. "Policy analysis for sustainable EV charging facility adoption using SEM-ANN approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).

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