Optimal site selection for sitting a solar park using multi-criteria decision analysis and geographical information systems

Abstract

Abstract. Among the renewable power sources, solar power is rapidly becoming popular because it is inexhaustible, clean, and dependable. It has also become more efficient since the power conversion efficiency of photovoltaic solar cells has increased. Following these trends, solar power will become more affordable in years to come and considerable investments are to be expected. Despite the size of solar plants, the sitting procedure is a crucial factor for their efficiency and financial viability. Many aspects influence such a decision: legal, environmental, technical, and financial to name a few. This paper describes a general integrated framework to evaluate land suitability for the optimal placement of photovoltaic solar power plants, which is based on a combination of a geographic information system (GIS), remote sensing techniques, and multi-criteria decision-making methods.An application of the proposed framework for the Limassol district in Cyprus is further illustrated. The combination of a GIS and multi-criteria methods produces an excellent analysis tool that creates an extensive database of spatial and non-spatial data, which will be used to simplify problems as well as solve and promote the use of multiple criteria. A set of environmental, economic, social, and technical constrains, based on recent Cypriot legislation, European's Union policies, and expert advice, identifies the potential sites for solar park installation. The pairwise comparison method in the context of the analytic hierarchy process (AHP) is applied to estimate the criteria weights in order to establish their relative importance in site evaluation. In addition, four different methods to combine information layers and check their sensitivity were used. The first considered all the criteria as being equally important and assigned them equal weight, whereas the others grouped the criteria and graded them according to their objective perceived importance. The overall suitability of the study region for sitting solar parks is appraised through the summation rule.Strict application of the framework depicts 3.0 % of the study region scoring a best-suitability index for solar resource exploitation, hence minimizing the risk in a potential investment. However, using different weighting schemes for criteria, suitable areas may reach up to 83 % of the study region. The suggested methodological framework applied can be easily utilized by potential investors and renewable energy developers, through a front end web-based application with proper GUI for personalized weighting schemes.

Multi-Attribute Decision-Making: Applying a Modified Brown–Gibson Model and RETScreen Software to the Optimal Location Process of Utility-Scale Photovoltaic Plants

“…In addition to four constraint factors selected based on the studies carried out in [27][28][29][30][31], the constraints related to the NPV and SPP of the solar PV project were considered. As a rule, a project is accepted if its NPV is higher than 0.…”

“…Seven suitability factors were used instead of the subjective factors of the original model. Those criteria were selected from the evaluation criteria of utility-scale PV plants used in [27][28][29][30][31]. Those already considered in RETScreen simulation like solar radiation were not selected.…”

“…Initially developed by Thomas Saaty in the 1970s, it is one of the most widely used MCDM methods. It has previously been used in [27][28][29][30][31] for calculating the weight of criteria in site selection for utility-scale solar PV projects.…”

“…Often coupled with (GIS) or other MCDM methods, AHP applications in site selection for utility-scale solar PV projects is the most used MCDM approach [23]. It has been successfully applied in site selection for solar PV plants in Ismalia, Egypt [27]; Konya region, Turkey [28]; Ayranci region, Turkey [29]; Eastern Morocco [30]; and Limassol, Cyrus [31]. Table 1 provides a summary of these studies.…”

“…where LMi = location measure of site i, CFIij = critical factor index of the critical factor CFj at location i, Based on previous studies [27][28][29][30][31], the average yearly temperature (AYT), distance from the power grid (DPG) and distance from the road (DR) were considered as criteria of minimum, while the average yearly wind speed (AYWS), carbon emission saving, distance from residential areas (DRA), and land use (LU) were considered as criteria of maximum.…”