Characteristics and scenarios of solar energy development in Iran: Fuzzy cognitive map-based approach

Abstract

Widespread use of promising solar energy is an integral attribute of governments toward global efforts in mitigating future emission and reducing the fossil fuel consumption trajectory. However, the outlook of solar energy development at the strategic level is driven by inherent uncertainties in a long-term perspective. As these unpredictable volatilities come into play, they heavily influence the future trends formed by a range of exogenous and endogenous factors including Political, Economic, Social, Technological, Environmental, and Legal (PESTEL). This study aimed at identifying, characterizing, and analyzing influential PESTEL factors shaping the dynamics of Solar Photovoltaics (SPV) in an uncertain environment. For this purpose, the Fuzzy Cognitive Mapping (FCM) method was employed for the case of Iran to investigate how involved factors dynamically interact and collectively behave in the system. A participatory stepwise framework was developed consisting of three steps by conducting a survey and two workshops with the participation of multidisciplinary stakeholders. Aa a result, a semi-quantitative model was formed in an integrated FCM comprising 31interwoven concepts. To further outline possible future trends, five key concepts were determined that explored four plausible projections of Iran's SPV sector. The scenario outcome suggests that the industry is profoundly affected by external economic and political factors. Two scenarios "Cornered in a Dead-End" and "At Wits' End" project that, by 2030, the sector experiences a declining growth trend. "The Period of Tolerance" scenario does not envisage a dramatic change from the current trajectory, while in only "Blue Sky" the country's SPV sector anticipate a moderate growth.

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

“…The initial stimulus state vector, the inference rule's type, the transfer function, its learning parameter, and the number of iterations or the convergence step need to be properly defined. Users are also offered the open/closed lock option to set and keep the value of a concept unchained throughout iterations, as being "clamped" [25].…”

“…For a deeper understanding of the examined problem, such as the concept's behavior, their relations, and the extent to which one concept has an impact on others, an FCM simulation process is needed. This study employs the FCMWizard tool to simulate and analyze the problem [25]. This process entails the multiplication of the input vector with the adjacency matrix, and in the output vector a squashing function (Equation (2)) is applied each time as a threshold function, until the system reaches a stable state.…”

“…We also need to report on [23], in which an FCM methodology was applied for assessing energy efficiency strategies and electricity demand prediction, whereas Nikas et al, dealt with policy-making in the Renewable Energy sector, employing a stakeholder-driven approach based on FCMs [24]. Alipour et al, in a more recent study, explored the FCM capabilities on identifying and analyzing unpredictable factors that have a direct impact on solar photovoltaics in an uncertain socio-economic, political and technological environment [25]. It is worth mentioning that this is the first research study that implements FCM-based scenario analysis in the renewable energy field.In order to tackle the vast amount of data, various parameters, modelling, learning, and simulation tasks of complex systems, researchers need a simple but powerful software tool that could help Energies 2020, 13, 1427 3 of 23 them in this direction.…”

“…This tool provides an opportunity to incorporate different types of knowledge into environmental decision-making, define hypotheses to be tested, and run scenarios to determine perceived outcomes of proposed policies.However, a gap is noticed in the relevant literature, regarding the existence of a more user-friendly tool that could provide extra capabilities for aggregating FCM models and learning them using supervised and unsupervised algorithms. Considering this gap, the team of Papageorgiou E. has developed a new web-based tool, called FCMwizard [25], that incorporates these functionalities for helping research community to freely experiment with them and exploit all the capabilities offered. Some preliminary work with the use of the FCMWizard tool has been recently made in the environmental domain, dealing with decision making problems such as (i) the economic sustainability of poor women in rural areas [32] and (ii) climate change adaptations [33].In this work, the FCM technique is applied to perform a decision-making process for PSE sector development, providing at the same time the researchers and the FCM community, wth an efficient simulation tool called FCMWizard, to conduct policy making simulations in their own case studies.…”

“…However, a gap is noticed in the relevant literature, regarding the existence of a more user-friendly tool that could provide extra capabilities for aggregating FCM models and learning them using supervised and unsupervised algorithms. Considering this gap, the team of Papageorgiou E. has developed a new web-based tool, called FCMwizard [25], that incorporates these functionalities for helping research community to freely experiment with them and exploit all the capabilities offered. Some preliminary work with the use of the FCMWizard tool has been recently made in the environmental domain, dealing with decision making problems such as (i) the economic sustainability of poor women in rural areas [32] and (ii) climate change adaptations [33].…”