Genetic Algorithms-Based Optimum PV Site Selection Minimizing Visual Disturbance

Nikolaos Nagkoulis, Eva Loukogeorgaki () and Michela Ghislanzoni
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Nikolaos Nagkoulis: Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Eva Loukogeorgaki: Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Michela Ghislanzoni: Territoria, Análisis y Gestión del Medio SLU, Calle Inocentes, 41003 Seville, Spain

Sustainability, 2022, vol. 14, issue 19, 1-19

Abstract: In this paper, an integrated methodology is developed to determine optimum areas for Photovoltaic (PV) installations that minimize the relevant visual disturbance and satisfy spatial constraints associated with land use, as well as environmental and techno-economic siting factors. The visual disturbance due to PV installations is quantified by introducing and calculating the “Social Disturbance” (SDIS) indicator, whereas optimum locations are determined for predefined values of two siting preferences (maximum allowable PV locations—grid station distance and minimum allowable total coverage area of PV installations). Thematic maps of appropriate selected exclusion criteria are produced, followed by a cumulative weighted viewshed analysis, where the SDIS indicator is calculated. Optimum solutions are then determined by developing and employing a Genetic Algorithms (GAs) optimization process. The methodology is applied for the municipality of La Palma Del Condado in Spain for 100 different combinations of the two siting preferences. The optimization results are also employed to create a flexible and easy-to-use web-GIS application, facilitating policy-makers to choose the set of solutions that better fulfils their preferences. The GAs algorithm offers the ability to determine distinguishable, but compact, regions of optimum locations in the region, whereas the results indicate the strong dependence of the optimum areas upon the two siting preferences.

Keywords: photovoltaic energy; geographic information system; site selection; visual disturbance; optimization; genetic algorithms; web-GIS application (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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