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High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles

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  • Jiang, Hou
  • Zhang, Xiaotong
  • Yao, Ling
  • Lu, Ning
  • Qin, Jun
  • Liu, Tang
  • Zhou, Chenghu

Abstract

Rooftop photovoltaics (PV) are playing an increasingly important role in building a clean and decarbonized energy system. For such distributed resources, formulating scientific development plans and incentives tailored to local conditions requires a comprehensive potential assessment at high spatial and temporal resolutions. Here, we evaluate the resource volume, power generation potential, economic feasibility, and market returns on electricity sales of rooftop PV in Jiangsu Province, China at hourly and 500-m resolutions by combining remote sensing survey, PV output simulation and load dispatching. The province's annual rooftop PV generation meets approximately 30% of the total social electricity consumption, and the entire region has reached both plant-side and user-side grid parity. Based on the case study, we investigate the suitable development scale of rooftop PV subject to different owners, as well as the impact of grid's system flexibility and energy storage on rooftop PV curtailment. For household use, the installation of a 3-kW rooftop PV is suitable, while for grid power supply, rooftop PV development needs to be sized to accommodate the grid. We find that increasing system flexibility significantly reduces PV curtailment for the same penetration rate, and that the use of energy storage helps increasing PV penetration under the curtailment constraint. This study represents a relatively extreme case of high penetration of variable solar PV generations, high uncertainty of PV outputs, and high variability in user-side loads, providing a reliable reference for rooftop PV development in other China's provinces.

Suggested Citation

  • Jiang, Hou & Zhang, Xiaotong & Yao, Ling & Lu, Ning & Qin, Jun & Liu, Tang & Zhou, Chenghu, 2023. "High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles," Applied Energy, Elsevier, vol. 348(C).
  • Handle: RePEc:eee:appene:v:348:y:2023:i:c:s0306261923009170
    DOI: 10.1016/j.apenergy.2023.121553
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    as
    1. Millot, Ariane & Krook-Riekkola, Anna & Maïzi, Nadia, 2020. "Guiding the future energy transition to net-zero emissions: Lessons from exploring the differences between France and Sweden," Energy Policy, Elsevier, vol. 139(C).
    2. Knuepfer, K. & Rogalski, N. & Knuepfer, A. & Esteban, M. & Shibayama, T., 2022. "A reliable energy system for Japan with merit order dispatch, high variable renewable share and no nuclear power," Applied Energy, Elsevier, vol. 328(C).
    3. Zambrano-Asanza, S. & Quiros-Tortos, J. & Franco, John F., 2021. "Optimal site selection for photovoltaic power plants using a GIS-based multi-criteria decision making and spatial overlay with electric load," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    4. Yasuda, Yoh & Bird, Lori & Carlini, Enrico Maria & Eriksen, Peter Børre & Estanqueiro, Ana & Flynn, Damian & Fraile, Daniel & Gómez Lázaro, Emilio & Martín-Martínez, Sergio & Hayashi, Daisuke & Holtti, 2022. "C-E (curtailment – Energy share) map: An objective and quantitative measure to evaluate wind and solar curtailment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    5. Siddharth Joshi & Shivika Mittal & Paul Holloway & Priyadarshi Ramprasad Shukla & Brian Ó Gallachóir & James Glynn, 2021. "High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    6. Klein, Martin & Ziade, Ahmad & de Vries, Laurens, 2019. "Aligning prosumers with the electricity wholesale market – The impact of time-varying price signals and fixed network charges on solar self-consumption," Energy Policy, Elsevier, vol. 134(C).
    7. Lukač, Niko & Špelič, Denis & Štumberger, Gorazd & Žalik, Borut, 2020. "Optimisation for large-scale photovoltaic arrays’ placement based on Light Detection And Ranging data," Applied Energy, Elsevier, vol. 263(C).
    8. Zhong, Qing & Nelson, Jake R. & Tong, Daoqin & Grubesic, Tony H., 2022. "A spatial optimization approach to increase the accuracy of rooftop solar energy assessments," Applied Energy, Elsevier, vol. 316(C).
    9. Göransson, Lisa & Johnsson, Filip, 2009. "Dispatch modeling of a regional power generation system – Integrating wind power," Renewable Energy, Elsevier, vol. 34(4), pages 1040-1049.
    10. Jiang, Hou & Lu, Ning & Qin, Jun & Tang, Wenjun & Yao, Ling, 2019. "A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    11. Zhong, Teng & Zhang, Zhixin & Chen, Min & Zhang, Kai & Zhou, Zixuan & Zhu, Rui & Wang, Yijie & Lü, Guonian & Yan, Jinyue, 2021. "A city-scale estimation of rooftop solar photovoltaic potential based on deep learning," Applied Energy, Elsevier, vol. 298(C).
    12. Zhang, Xian & Wang, Jia-Xing & Cao, Zhe & Shen, Shuo & Meng, Shuo & Fan, Jing-Li, 2021. "What is driving the remarkable decline of wind and solar power curtailment in China? Evidence from China and four typical provinces," Renewable Energy, Elsevier, vol. 174(C), pages 31-42.
    13. Jun Yin & Annalisa Molini & Amilcare Porporato, 2020. "Impacts of solar intermittency on future photovoltaic reliability," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    14. Lopez-Ruiz, Hector G. & Blazquez, Jorge & Vittorio, Michele, 2020. "Assessing residential solar rooftop potential in Saudi Arabia using nighttime satellite images: A study for the city of Riyadh," Energy Policy, Elsevier, vol. 140(C).
    15. Fan, Jing-Li & Wang, Jia-Xing & Hu, Jia-Wei & Wang, Yu & Zhang, Xian, 2019. "Optimization of China’s provincial renewable energy installation plan for the 13th five-year plan based on renewable portfolio standards," Applied Energy, Elsevier, vol. 254(C).
    16. Bravo, Ruben & Ortiz, Carlos & Chacartegui, Ricardo & Friedrich, Daniel, 2021. "Multi-objective optimisation and guidelines for the design of dispatchable hybrid solar power plants with thermochemical energy storage," Applied Energy, Elsevier, vol. 282(PB).
    17. Debra J. Davidson, 2019. "Exnovating for a renewable energy transition," Nature Energy, Nature, vol. 4(4), pages 254-256, April.
    18. Chyong, Chi Kong & Newbery, David, 2022. "A unit commitment and economic dispatch model of the GB electricity market – Formulation and application to hydro pumped storage," Energy Policy, Elsevier, vol. 170(C).
    19. Sarah Feron & Raúl R. Cordero & Alessandro Damiani & Robert B. Jackson, 2021. "Climate change extremes and photovoltaic power output," Nature Sustainability, Nature, vol. 4(3), pages 270-276, March.
    20. Talavera, D.L. & Pérez-Higueras, P. & Almonacid, F. & Fernández, E.F., 2017. "A worldwide assessment of economic feasibility of HCPV power plants: Profitability and competitiveness," Energy, Elsevier, vol. 119(C), pages 408-424.
    21. Huang, Zhaojian & Mendis, Thushini & Xu, Shen, 2019. "Urban solar utilization potential mapping via deep learning technology: A case study of Wuhan, China," Applied Energy, Elsevier, vol. 250(C), pages 283-291.
    22. Sánchez de la Nieta, Agustín A. & Paterakis, Nikolaos G. & Gibescu, Madeleine, 2020. "Participation of photovoltaic power producers in short-term electricity markets based on rescheduling and risk-hedging mapping," Applied Energy, Elsevier, vol. 266(C).
    23. Denholm, Paul & Hand, Maureen, 2011. "Grid flexibility and storage required to achieve very high penetration of variable renewable electricity," Energy Policy, Elsevier, vol. 39(3), pages 1817-1830, March.
    24. Bódis, Katalin & Kougias, Ioannis & Jäger-Waldau, Arnulf & Taylor, Nigel & Szabó, Sándor, 2019. "A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    25. Sun, Tao & Shan, Ming & Rong, Xing & Yang, Xudong, 2022. "Estimating the spatial distribution of solar photovoltaic power generation potential on different types of rural rooftops using a deep learning network applied to satellite images," Applied Energy, Elsevier, vol. 315(C).
    26. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    27. Shokrzadeh, Shahab & Jafari Jozani, Mohammad & Bibeau, Eric & Molinski, Tom, 2015. "A statistical algorithm for predicting the energy storage capacity for baseload wind power generation in the future electric grids," Energy, Elsevier, vol. 89(C), pages 793-802.
    28. Tian, Jinfang & Yu, Longguang & Xue, Rui & Zhuang, Shan & Shan, Yuli, 2022. "Global low-carbon energy transition in the post-COVID-19 era," Applied Energy, Elsevier, vol. 307(C).
    29. 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).
    30. Henckes, Philipp & Knaut, Andreas & Obermüller, Frank & Frank, Christopher, 2018. "The benefit of long-term high resolution wind data for electricity system analysis," Energy, Elsevier, vol. 143(C), pages 934-942.
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