A New Decision Framework for Hybrid Solar and Wind Power Plant Site Selection Using Linear Regression Modeling Based on GIS-AHP

Abstract

Currently, worldwide attention to clean energy and sustainable energy has been expedited because of its many environmental benefits. In fact, wind and solar energies play a prime role in decarbonizing the energy market. However, finding the most suitable locations for wind/solar power plants is difficult because of the non-homogeneous distribution of these sources. This paper presents a novel method for selecting the optimal locations for wind and solar farms by mapping the space of the decision criteria to the site score. In addition, the multiple linear regression model was used, with the help of the combination of GIS and AHP methods, to model the siting of wind and solar power plants. The site scoring method used in this study is reliable and globally evaluated; therefore, the scores are accurate and effective. To reveal the ability of the proposed method, two study areas were investigated and researched. The results achieved based on the introduced method showed that, in case study 1, areas with an area of about 9, 4 and 7 km2 are suitable for the construction of wind, solar and wind/solar power plants, respectively. This paper also used fourteen existing wind/solar, wind and solar farms from five continents around the world. The results showed that the suggested model acts the same as the real data. In addition to the interest these results hold for the development of renewable energy in the study area, this novel approach may be applied elsewhere to select optimum sites for wind, solar, and combined wind and solar farms.

Assessment of the Geographic and Technical Potential for Large-Scale Wind Energy Production Using a GIS-Based MCDM-AHP Multi-criteria Analysis Method and Sensitivity Analysis: Case of Cameroon

“…Although wind speed (m/s) is an essential parameter to consider when choosing a large-scale wind farm connected to the electricity grid, technical-economic and socio-environmental factors are also important when choosing a wind farm [22,24]. In addition to optimal electricity production over their life cycle, wind farms should be placed in locations that would reduce installation and operating costs, as well as socio-environmental impacts such as electromagnetic radiation interference, noise, flickering shadows and vibrations [30].…”

“…[23] applied an AHP-GIS model to five ( 5) criteria (Wind energy potential, Land use, Slope, Proximity to power lines, Proximity to main roads). Ref [24] applied an innovative approach combining AHP-GIS-based linear regression modeling with five (5) criteria (Wind power density, Distance from roads and network, Distance from power lines, Land slope, Distance from urban and protected areas) to identify suitable sites for wind farms in Varzeghan city in East Azarbaijan province. Also in the case of Sudan, Ref [25] applied a high-resolution GIS model coupled with Fuzzy-AHP to the same criteria as Ref [7] for wind farm suitability analysis.…”

Unlocking renewable energy potential: A case study of solar and wind site selection in the Kasserine region, central‐western Tunisia

“…It is insufficient to develop a solar or wind facility based merely on the abundance of solar and wind sources, as there are many other factors that have a direct impact on the output power, such as the topography of the location, infrastructure, and costs. [25][26][27] In addition, solar and wind potential vary significantly from one region to another according to weather patterns and seasonal variations. 20 Therefore, collecting and analyzing data on a potential site is crucial to determine its energy generation capacity.…”

“…20 Therefore, collecting and analyzing data on a potential site is crucial to determine its energy generation capacity. 20,26,28 However, this is not a straightforward task in light of the various and often overlapping criteria illustrated in Table 1. In this context, the integration of GIS along with MCDM approaches has emerged as a valuable tool in handling decision-making complexities, including solar and wind site selection.…”

“…Global Horizontal Irradiance (GHI) [25,26,[29][30][31] Average Wind Speed [25,26,29,30,32] Wind power density [26,[33][34][35] Percentage of windy days [33] Ambient temperature [31,[36][37][38] Slope [26,31,32,34,36] Aspect [20,37,39] Distance to grid lines [20,21,26,31,32] Distance to major roads [20,21,26,31,32] Distance to residential areas [20,21,26,31,32] Elevation [21,25,29,30,32] Land use [21,25,[29]…”