An Integrated Decision-Making Model for the Location of a PV Solar Plant

Lee, Amy H. I.; Kang, He‐Yau; Lin, Chun‐Yu; Shen, Kuan-Chin

doi:10.3390/su71013522

Cited by 78 publications

(35 citation statements)

References 39 publications

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“…Implementations of decision-making in risk management in renewable energy are presented in recent studies such as: [23] at work, apply analytical processing network ANP for selecting solar photovoltaic (PV) projects. Another scientific work is quoted: [24] which use experts to evaluate qualitative features, AHP is used to calculate the priorities and a case study is examined to select a photovoltaic solar plant. Sample models in risk identification can also cite [25] that manage the risk of satisfaction/customer dissatisfaction looking for its causes with an Ishikawa diagram.…”

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confidence: 99%

Decision-Making for Risk Management in Sustainable Renewable Energy Facilities: A Case Study in the Dominican Republic

et al. 2016

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Today, Renewable Energy Sources (RES) are a key pillar to achieving sustainable development, which is the main reason why energy projects are being carried out not only in developed countries but also in many emerging countries. Since the technical and financial risk remains a major barrier to financing renewable energy projects, several mechanisms are available to reduce risks on investment into clean energy projects. This paper discusses risk management tools in solar photovoltaic facilities based on the guide to the Project Management (PMBOK Guide). To do this, a combination of different decision-making methodologies will be carried out. These methodologies enable to not only extract the knowledge by experts but also to know the causes and effects that help to make the best decision. In order to do so, techniques to seek information (Delphi and Checklist) as well as diagram techniques such as cause and effect diagrams or Strengths Weaknesses Opportunities and Threats (SWOT) are applied. The categorization and prioritization of risks will be carried out through the Analytic Hierarchy Process (AHP). Finally, a sensitivity analysis will allow for providing consistency to the obtained results. A real case in the Dominican Republic will also be presented as case study.

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confidence: 99%

Decision-Making for Risk Management in Sustainable Renewable Energy Facilities: A Case Study in the Dominican Republic

et al. 2016

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“…Develop a novel performance evaluation method, which integrates both FAHP method and fuzzy DEA for assisting organisations to make the supplier selection decision in auto lighting OEM company by R.J.Kou [22]. A hybrid FAHP-AR-DEA to assess the efficiencies of PV solar plant site candidates by Amy H. I. Lee et al [23]. A integrated TFN-AHP-DEA approaches for renewable energy projects analyzed economic feasibility by Gan et al [24].…”

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confidence: 99%

Solar Panel Supplier Selection for the Photovoltaic System Design by Using Fuzzy Multi-Criteria Decision Making (MCDM) Approaches

Wang

Tsai

2018

Energies

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Abstract:The period of industrialization and modernization has increased energy demands around the world. As with other countries, the Taiwanese government is trying to increase the proportion of renewable energy, especially solar energy resources. Thus, there are many solar power plants built in Taiwan. One of the most important components of a solar power plant is the solar panel. The solar panel supplier selection process is a complex and multi-faceted decision that can reduce the cost of purchasing equipment and supply this equipment on time. In this research, we propose fuzzy MCDM approach that includes fuzzy analytical hierarchy process model (FAHP) and data envelopment analysis (DEA) for evaluation and selection of solar panel supplier for a photovoltaic system design in Taiwan. The main objective of this work is to design a fuzzy MCDM approach for solar panel supplier selection based on qualitative and quantitative factors. In the first step of this research, FAHP is applied to define the priority of suppliers. The AHP combined with fuzzy logic (FAHP) can be used to rank suppliers; however, the disadvantages of the FAHP model is that input data, expressed in linguistic terms, depends on experience of experts and the number of suppliers is practically limited, because of the number of pairwise comparison matrices. Thus, we applied several DEA models for ranking potential suppliers in the final stages. As the result, decision making unit 1 (DMU 1) is the optimal solar panel supplier for photovoltaic system design in Taiwan. The contribution of this research is a new fuzzy MCDM for supplier selection under fuzzy environment conditions. This paper also lies in the evolution of a new approach that is flexible and practical to the decision maker. It provides a useful guideline for solar panel supplier selection in many countries as well as a guideline for supplier selection in other industries.

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“…A. Azadeh et al [13] also presented a hybrid ANN and fuzzy DEA approach to select solar power plant locations. Lee et al [14] introduced a MCDM model to select PV solar plant locations. Ali et al combined GIS and MCDM approaches to determine the best place for wind farm location [15].…”

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Decision Making (MCDM) Approaches for Solar Power Plant Location Selection in Viet Nam

et al. 2018

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Abstract:The ongoing industrialization and modernization period has increased the demand for energy in Viet Nam. This has led to over-exploitation and exhausts fossil fuel sources. Nowadays, Viet Nam's energy mix is primarily based on thermal and hydro power. The Vietnamese government is trying to increase the proportion of renewable energy. The plan will raise the total solar power capacity from nearly 0 to 12,000 MW, equivalent to about 12 nuclear reactors, by 2030. Therefore, the construction of solar power plants is needed in Viet Nam. In this study, the authors present a multi-criteria decision making (MCDM) model by combining three methodologies, including fuzzy analytical hierarchy process (FAHP), data envelopment analysis (DEA), and the technique for order of preference by similarity to ideal solution (TOPSIS) to find the best location for building a solar power plant based on both quantitative and qualitative criteria. Initially, the potential locations from 46 sites in Viet Nam were selected by several DEA models. Then, AHP with fuzzy logic is employed to determine the weight of the factors. The TOPSIS approach is then applied to rank the locations in the final step. The results show that Binh Thuan is the optimal location to build a solar power plant because it has the highest ranking score in the final phase of this study. The contribution of this study is the proposal of a MCDM model for solar plant location selection in Viet Nam under fuzzy environment conditions. This paper also is part of the evolution of a new approach that is flexible and practical for decision makers. Furthermore, this research provides useful guidelines for solar power plant location selection in many countries as well as a guideline for location selection of other industries.

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An Integrated Decision-Making Model for the Location of a PV Solar Plant

Cited by 78 publications

References 39 publications

Decision-Making for Risk Management in Sustainable Renewable Energy Facilities: A Case Study in the Dominican Republic

Decision-Making for Risk Management in Sustainable Renewable Energy Facilities: A Case Study in the Dominican Republic

Solar Panel Supplier Selection for the Photovoltaic System Design by Using Fuzzy Multi-Criteria Decision Making (MCDM) Approaches

Multi-Criteria Decision Making (MCDM) Approaches for Solar Power Plant Location Selection in Viet Nam

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