Latterly, central governments and local authorities have been establishing various constraints on the construction of new large ground-mounted photovoltaic (PV) plants, because of the soil consumption, landscape impact, and also competitiveness with the crop production. This is particularly important in contexts where the agricultural sector is closely linked to the territory. With the aim of providing a decision support tool based on quantitative indicators for the site selection of large ground-mounted PV plants, in this article the criteria for the identification of areas suitable for the installation of ground-mounted photovoltaic systems, recently emerged by regional government or in the technical and scientific literature, are applied to the entire territory of the Piedmont region (25,000 km(2)). Both qualitative criteria for inclusion/exclusion (e.g., exclusion from areas of great value) and criteria for quantification (e.g., solar resource availability) were considered. The aggregation of the quantitative criteria into the final indicator is done by means of an Artificial Neural Network (ANN) trained with values corresponding to sites of existing PV plants in the Region. It emerges that the available areas are very limited, concentrated, and strongly influenced by the criteria of exclusion/inclusion. Some considerations on the significance of the results for the region of analysis are finally made.
机构:
Indian Inst Trop Meteorol, Pune 411008, India
Univ Tenaga Nas, Inst Energy Infrastruct, Kajang 43000, Malaysia
Al Ayen Univ, Sci Res Ctr, New Era & Dev Civil Engn Res Grp, Nasiriyah 64001, Thi Qar, IraqVivekanand Educ Soc Coll Architecture VESCOA, Architecture, Mumbai 400074, India
机构:
North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R ChinaNorth China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
Gao, Jianwei
Wang, Yaping
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机构:
North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R ChinaNorth China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
Wang, Yaping
Guo, Fengjia
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Capital Univ Econ & Business, Sch Management & Engn, Beijing 100070, Peoples R ChinaNorth China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
Guo, Fengjia
Chen, Jiayi
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North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R ChinaNorth China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China