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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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    as
    1. Huang, Pei & Lovati, Marco & Zhang, Xingxing & Bales, Chris & Hallbeck, Sven & Becker, Anders & Bergqvist, Henrik & Hedberg, Jan & Maturi, Laura, 2019. "Transforming a residential building cluster into electricity prosumers in Sweden: Optimal design of a coupled PV-heat pump-thermal storage-electric vehicle system," Applied Energy, Elsevier, vol. 255(C).
    2. Zhang, Yelin & Zhang, Xingxing & Huang, Pei & Sun, Yongjun, 2020. "Global sensitivity analysis for key parameters identification of net-zero energy buildings for grid interaction optimization," Applied Energy, Elsevier, vol. 279(C).
    3. Kavlak, Goksin & McNerney, James & Trancik, Jessika E., 2018. "Evaluating the causes of cost reduction in photovoltaic modules," Energy Policy, Elsevier, vol. 123(C), pages 700-710.
    4. Brito, M.C. & Freitas, S. & Guimarães, S. & Catita, C. & Redweik, P., 2017. "The importance of facades for the solar PV potential of a Mediterranean city using LiDAR data," Renewable Energy, Elsevier, vol. 111(C), pages 85-94.
    5. Florian Knobloch & Steef V. Hanssen & Aileen Lam & Hector Pollitt & Pablo Salas & Unnada Chewpreecha & Mark A. J. Huijbregts & Jean-Francois Mercure, 2020. "Net emission reductions from electric cars and heat pumps in 59 world regions over time," Nature Sustainability, Nature, vol. 3(6), pages 437-447, June.
    6. Shabani, Masoume & Dahlquist, Erik & Wallin, Fredrik & Yan, Jinyue, 2020. "Techno-economic comparison of optimal design of renewable-battery storage and renewable micro pumped hydro storage power supply systems: A case study in Sweden," Applied Energy, Elsevier, vol. 279(C).
    7. Ren, Haoshan & Xu, Chengliang & Ma, Zhenjun & Sun, Yongjun, 2022. "A novel 3D-geographic information system and deep learning integrated approach for high-accuracy building rooftop solar energy potential characterization of high-density cities," Applied Energy, Elsevier, vol. 306(PA).
    8. Bellocchi, Sara & Klöckner, Kai & Manno, Michele & Noussan, Michel & Vellini, Michela, 2019. "On the role of electric vehicles towards low-carbon energy systems: Italy and Germany in comparison," Applied Energy, Elsevier, vol. 255(C).
    9. Huang, Pei & Ma, Zhenjun & Xiao, Longzhu & Sun, Yongjun, 2019. "Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities," Applied Energy, Elsevier, vol. 255(C).
    10. Zhou, Boya & Wu, Ye & Zhou, Bin & Wang, Renjie & Ke, Wenwei & Zhang, Shaojun & Hao, Jiming, 2016. "Real-world performance of battery electric buses and their life-cycle benefits with respect to energy consumption and carbon dioxide emissions," Energy, Elsevier, vol. 96(C), pages 603-613.
    11. Khalilpour, Kaveh Rajab & Vassallo, Anthony, 2016. "Technoeconomic parametric analysis of PV-battery systems," Renewable Energy, Elsevier, vol. 97(C), pages 757-768.
    12. Yang, Ying & Campana, Pietro Elia & Stridh, Bengt & Yan, Jinyue, 2020. "Potential analysis of roof-mounted solar photovoltaics in Sweden," Applied Energy, Elsevier, vol. 279(C).
    13. Ayetor, G.K. & Mbonigaba, Innocent & Sunnu, Albert K. & Nyantekyi-Kwakye, Baafour, 2021. "Impact of replacing ICE bus fleet with electric bus fleet in Africa: A lifetime assessment," Energy, Elsevier, vol. 221(C).
    14. Majumder, Suman & De, Krishnarti & Kumar, Praveen & Sengupta, Bodhisattva & Biswas, Pabitra Kumar, 2021. "Techno-commercial analysis of sustainable E-bus-based public transit systems: An Indian case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    15. de Oliveira e Silva, Guilherme & Hendrick, Patrick, 2016. "Lead–acid batteries coupled with photovoltaics for increased electricity self-sufficiency in households," Applied Energy, Elsevier, vol. 178(C), pages 856-867.
    16. Arteconi, Alessia & Ciarrocchi, Eleonora & Pan, Quanwen & Carducci, Francesco & Comodi, Gabriele & Polonara, Fabio & Wang, Ruzhu, 2017. "Thermal energy storage coupled with PV panels for demand side management of industrial building cooling loads," Applied Energy, Elsevier, vol. 185(P2), pages 1984-1993.
    17. Han, Dongho & Lee, Jay H., 2021. "Two-stage stochastic programming formulation for optimal design and operation of multi-microgrid system using data-based modeling of renewable energy sources," Applied Energy, Elsevier, vol. 291(C).
    18. Luthander, Rasmus & Widén, Joakim & Nilsson, Daniel & Palm, Jenny, 2015. "Photovoltaic self-consumption in buildings: A review," Applied Energy, Elsevier, vol. 142(C), pages 80-94.
    19. González, L.G. & Cordero-Moreno, Daniel & Espinoza, J.L., 2021. "Public transportation with electric traction: Experiences and challenges in an Andean city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    20. O'Shaughnessy, Eric & Cutler, Dylan & Ardani, Kristen & Margolis, Robert, 2018. "Solar plus: A review of the end-user economics of solar PV integration with storage and load control in residential buildings," Applied Energy, Elsevier, vol. 228(C), pages 2165-2175.
    21. Li, Hangxin & Wang, Shengwei, 2019. "Coordinated optimal design of zero/low energy buildings and their energy systems based on multi-stage design optimization," Energy, Elsevier, vol. 189(C).
    22. Nadjemi, O. & Nacer, T. & Hamidat, A. & Salhi, H., 2017. "Optimal hybrid PV/wind energy system sizing: Application of cuckoo search algorithm for Algerian dairy farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1352-1365.
    23. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).
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    2. Sebastiano Anselmo & Maria Ferrara, 2023. "Trends and Evolution of the GIS-Based Photovoltaic Potential Calculation," Energies, MDPI, vol. 16(23), pages 1-27, November.

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