Decision-Making Process for Photovoltaic Solar Energy Sector Development using Fuzzy Cognitive Map Technique

Abstract

Photovoltaic Solar Energy (PSE) sector has sparked great interest from governments over the last decade towards diminution of world's dependency to fossil fuels, greenhouse gas emissions reduction and global warming mitigation. Photovoltaic solar energy also holds a significant role in the transition to sustainable energy systems. These systems and their optimal exploitation require an effective supply chain management system, such as design of the network, collection, storage, or transportation of this energy resource, without disregarding a country's certain socio-economic and political conditions. In Brazil, the adoption of photovoltaic solar energy has been motivated not only by the energy matrix diversification but also from the shortages, problems, and barriers that the Brazilian energy sector has faced, lately. However, PSE development is affected by various factors with high uncertainty, such as political, social, economic, and environmental, that include critical operational sustainability issues. Thus, an elaborate modelling of energy management and a well-structured decision support process are needed to enhance the performance efficiency of Brazilian PSE supply chain management. This study focuses on the investigation of certain factors and their influence on the development of the Brazilian PSE with the help of Fuzzy Cognitive Maps. Fuzzy Cognitive Map is an established methodology for scenario analysis and management in diverse domains, inheriting the advancements of fuzzy logic and neural networks. In this context, a semi-quantitative model was designed with the help of various stakeholders from the specific energy domain and three plausible scenarios were conducted in order to support a decision-making process on PSE sector development and the country's economic potential. The outcome of this analysis reveals that the development of the PSE sector in Brazil is mainly affected by economic and political factors.Energies 2020, 13, 1427 2 of 23 represents the system's behavior using the accumulated knowledge, perceptions, experiences or beliefs from experts, stakeholders or both [3,4], usually expressed in natural language.Over the last years, FCMs have attained great attention and popularity since they are able to implement modelling, analyzing and simulating tasks, as well as test the influence of parameters and predict the behavior of the examined system [2]. Furthermore, they include learning capabilities and characteristics as they can learn from historical data and previous knowledge to overcome the subjectivity of experts' opinions [5][6][7]. In other words, it is a semi-quantitative method that uses qualitative knowledge from experts and stakeholders to form FCM-based models and further increase system understanding and reduce uncertainties.The task of simulation is incorporated by FCMs and can help managers to increase system understanding and reduce uncertainties about how the studied phenomenon may respond to changes in the main system drivers [8].Among other fields, FCMs are also used for ...

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