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

Abstract

Due to environmental and economic drawbacks of fossil fuels, global renewable energy (RE) capacity has increased significantly over the last decade. Solar photovoltaic (PV) is one of the fastest-growing RE technologies. Selecting an appropriate site is one of the most critical steps in utility-scale solar PV planning. This paper aims at proposing a rational multi-criteria decision-making (MCDM) approach based on the Brown–Gibson model for optimal site selection for utility-scale solar PV projects. The proposed model considers the project’s net present value (NPV) along with seven suitability factors and six critical (constraint) factors. The RETScreen software was applied in calculating the NPV, the simple payback period and the carbon emission savings of the project at each alternative site. The weights of the suitability factors were determined using the analytical hierarchy process. Applied to the case study of finding the best location for a 5 MW solar PV project in northern Cameroon, the optimization results showed that Mokolo was the optimal location. The sensitivity analysis results revealed that the rankings of alternative sites based on the project’s NPV and the proposed model are not consistent. Compared to the traditional MCDM approaches, the proposed model provides decision-makers with a more practical thinking method in the optimal location process of utility-scale solar projects.

Methods of Multi-Criteria Analysis in Technology Selection and Technology Assessment: A Systematic Literature Review

“…Analysing search results in terms of categories (in WoS) or the subject area (Scopus), over 8 % in Scopus and around 12% in Web of Science were evidently or partly related to the energy and renewable energy technologies. The most explored subjects are technology selection of solar and photovoltaic systems (Fang et al, 2020;Dat et al, 2014;Ghasempour et al, 2019;Yimen & Dagbasi, 2019;Sellak et al, 2017;Ma et al, 2013), renewable energy storage (Liu & Du, 2020;Zhang et al, 2019;Qie et al, 2021) wind energy technologies (Onar et al, 2015;Narayanamoorthy, 2012) or hybrid renewable technology solutions (Ali et al, 2020;Peterseim et al, 2013). Within the second subarea, sustainable technologies, there are studies on choosing the best alternative industrial technology selection problems considering the sustainability perspective in general (Ibanez-Forez et al, 2014;Gilde-Castro et al, 2009;Jin & Gambatese, 2020;Ren & Lützen, 2015) or some particular issues like greenhouse gas emission (Streimikiene et al, 2013;Streimikiene & Balezentiene, 2012), transportation technologies (Štreimikiene, 2013;Oztaysi et al, 2017) or sustainable supply chain technologies (Khatri & Srivastava, 2016;Buyukozkan & Gocer, 2019).…”

The Residence Plot Selection Model for Family House in Vilnius by Neutrosophic Waspas Method

“…Land selection is treated as a multifaceted problem that requires multi-criteria evaluation. Researches have proven this approach to be an effective tool for selecting the most appropriate places (land-plots) for objects of various uses (Schitea et al, 2019;Yimen & Dagbasi, 2019;Aktas & Kabak, 2019). Also, we took into account that the peer-reviewed reviews supported the validity of MCDM approaches as new development principles (Mardani et al, 2017;Yap et al, 2019).…”

Building Sustainable Development through Technology Transfer Offices: An Approach Based on Levels of Maturity

“…It does not present the qualification of decision-makers. Yimen and Dagbasi [41] point out that the FAHP method is based on paired comparisons, leading to the construction of decision matrices in each level of the hierarchical structure of the criteria. This point is not clear in the maturity model.…”