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Feasibility analysis of community-based PV systems for residential districts: A comparison of on-site centralized and distributed PV installations

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  • Aghamolaei, Reihaneh
  • Shamsi, Mohammad Haris
  • O’Donnell, James

Abstract

Photovoltaic systems are one of the most promising renewable energy technologies for on-site generation. Most of the techno-economic studies consider distributed standalone photovoltaic generation with little consideration of community-based standalone photovoltaic systems. Location-based case studies are required to provide economic and reliable photovoltaic systems to meet the peak loads of residential neighbourhoods in an optimized manner. This paper devises an integrated evaluation methodology; a combination of white-box energy modelling and black box photovoltaic design optimization. This research uses optimization methods to develop a quantitative optimized model for analysing the opportunities of centralized systems to adequately meet the demands of a residential neighbourhood and support the grid. This analysis includes three metrics including the level of the energy production, reliability of system for peak power and finally the capital cost of implementation in residential districts. Results indicate that the size of a centralized photovoltaic installation is less when compared to distributed installations to support a similar single peak load. The required converter size is reduced for the centralized system owing to the reduced system size. Centralized installations require fewer batteries to store surplus energy produced due to increased interaction of energy flows. Centralized installations are economically more viable than distributed ones.

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  • Aghamolaei, Reihaneh & Shamsi, Mohammad Haris & O’Donnell, James, 2020. "Feasibility analysis of community-based PV systems for residential districts: A comparison of on-site centralized and distributed PV installations," Renewable Energy, Elsevier, vol. 157(C), pages 793-808.
    • Handle: RePEc:eee:renene:v:157:y:2020:i:c:p:793-808
    DOI: 10.1016/j.renene.2020.05.024
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    1. Di Lorenzo, Gianfranco & Rotondo, Sara & Araneo, Rodolfo & Petrone, Giovanni & Martirano, Luigi, 2021. "Innovative power-sharing model for buildings and energy communities," Renewable Energy, Elsevier, vol. 172(C), pages 1087-1102.
    2. Guo, Jiacheng & Zhang, Peiwen & Wu, Di & Liu, Zhijian & Liu, Xuan & Zhang, Shicong & Yang, Xinyan & Ge, Hua, 2022. "Multi-objective optimization design and multi-attribute decision-making method of a distributed energy system based on nearly zero-energy community load forecasting," Energy, Elsevier, vol. 239(PC).

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