A comprehensive model of the regional portfolio of renewable energies in Iran, focusing on arid land

Document Type : Research Paper

Authors

1 Ph.D. student in production and operations management, Faculty of Economics, Management and Accounting, Yazd University, Yazd, Iran

2 Associate Professor of Production and Operations Management, Faculty of Economics, Management and Accounting, Yazd university, Yazd, Iran

10.29252/aridbiom.2023.19507.1914

Abstract

In addition to rich resources of fossil fuels, Iran has a lot of renewable energy potential. On the other hand, considering the climate diversity in the country and the natural conditions and potentials in different regions, instead of national planning, we should move towards regional energy planning and develop a regional renewable energy portfolio. In the present research, firstly, the potential measurement criteria of different types of renewable energy, including solar, wind, geothermal, hydroelectric and biomass, based on geographic information system maps and data received from the Meteorological Organization and SATBA, for 1361 latitudes and longitudes, has been scored. Then, using the Rapidminer software, the geographic points were divided into 5 clusters, each cluster includes areas of equal potential with the greatest similarity. Two of these 5 clusters are considered to be among the dry lands of the country. Then, based on the review of library resources and usage from the opinions of SATBA experts (Renewable Energy Research Group), a fuzzy inference model based on 5 sustainable development criteria including: access to technology, investment costs, capital productivity, employment rate, and environmental consequences along with design potential measurement criteria and based on the fuzzy rules defined on these criteria. The percentage share of each type of energy in the energy portfolio of each cluster was calculated. In the final step, based on demographic criteria including unemployment rate, population growth rate, acceptance culture (literacy rate), investment security, to prioritize clusters for strategic planning of the government and other influential institutions such as governorates, municipalities and chambers of commerce. For example, in cluster 4, includes some cities in the provinces of Isfahan, Khorasan, Yazd, Kermanshah, Fars, and Kohkiloyeh, which are classified as arid and semi-arid regions of the country according to the criteria of potential measurement and development criteria that has an energy portfolio with 25% share of wind energy, 39% share of solar energy, 10% share of hydroelectric energy and 26% share of biomass energy. Population growth and investment security are the first priority.

Keywords

References

‎[1]. Behbood, V., Lu, J., & Zhang, G. (2010). Adaptive Inference-based learning and rule generation algorithms in fuzzy neural network for failure prediction. In 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, pp. 33-38.
[2]. Cucchiella, F., D’Adamo, I., & Gastaldi, M. (2013). Italian energy portfolio analysis: an interactive renewable investments tool. Advanced Materials Research, 739, 768-776.
[3]. Hocine, A., Kouaissah, N., Bettahar, S., & Benbouziane, M. (2018). Optimizing renewable energy portfolios.
[4]. Houshmandynia, S., Hamdi, K., Mohebi, S., & Zamanimoghadam, A. (2022). Providing a Model of Effective Components for the Renewable Energy Business Model (By Predicting the Status of Renewable Energy in Iran and the World by 2030). Jounal of Marketing Management, 17(57), 27-49. [in Farsi]
[5]. Idrus, A., Nuruddin, M. F., & Rohman, M. A. (2011). Development of project cost contingency estimation model using risk analysis and fuzzy expert system. Expert Systems with Applications, 38(3), 1501-1508.
[6]. IRENA, I. (2019). Renewable energy and jobs: Annual review 2019. International Renewable Energy Agency (IRENA), United Arab Emirates.
[7]. Manzano-Agugliaro, F., Alcayde, A., Montoya, F. G., Zapata-Sierra, A., & Gil, C. (2013). Scientific production of renewable energies worldwide: An overview. Renewable and Sustainable Energy Reviews, 18, 134-143.
[8]. Mroue, A. M., Mohtar, R. H., Pistikopoulos, E. N., & Holtzapple, M. T. (2019). Energy Portfolio Assessment Tool (EPAT): Sustainable energy planning using the WEF nexus approach–Texas case. Science of The Total Environment, 648, 1649-1664.
[9]. Nouri, E., & Nouri, M. (2014). Bazaar as a context for clean energy policies, opportunities and threats, the first national conference on civil engineering, architecture and sustainable development, Yazd, Yazd Payam Noor University. [in Farsi]
[10]. Osamu, I., Takashi, O., Izumi, K., & Hiroshi, M. (2005). Current status and future prospect of PV development in Japan: beyond 1GW of PV installed capacity. Proceedings of the 20th European photovoltaic solar energy conference and exhibition, Barcelona.
[11]. Rabbani, M., Mamaghani, M. G., Farshbaf-Geranmayeh, A., & Mirzayi, M. (2016). A Novel Mixed Integer Programming Formulation for Selecting the Best Renewable Energies to Invest: A Fuzzy Goal Programming Approach. International Journal of Operations Research and Information Systems (IJORIS), 7(3), 1-22.
[12]. Scala, A., Facchini, A., Perna, U., & Basosi, R. (2019). Portfolio analysis and geographical allocation of renewable sources: A stochastic approach. Energy Policy, 125, 154-159.
[13]. Waqif Kodhi, A. (2015). Strategic policy making in renewable energy for sustainable development The 5th Conference on New Energy and Distributed Production of Iran.
[14]. Yel, E., & Yalpir, S. (2011). Prediction of primary treatment effluent parameters by Fuzzy Inference System (FIS) approach. Procedia computer science, 3, 659-665.
[15]. Yuregir, O. H., & Sagiroglu, C. (2016). Solar energy validation for strategic investment planning via comparative data mining methods: an expanded example within the cities of Turkey. International Journal of Photoenergy, 2016, 1-16.
[16]. Zargar, B., Emami Meibodi, A., Jahangirnia, H., & Safa, M. (2020). A Financing Model of Photovoltaic Industry in Iran: Combination of Grounded Theory and Neural NetworksModel. Iranian Energy Economics, 10(37), 73-97. doi: 10.22054/jiee.2022.63746.1867. [in Farsi]
[17]. Zhang, M., Tang, Y., Liu, L., & Zhou, D. (2022). Optimal investment portfolio strategies for power enterprises under multi-policy scenarios of renewable energy. Renewable and Sustainable Energy Reviews, 154, 111879.
[18]. Zhuang, Z. Y., Hocine, A., Kouaissah, N., & Kiker, G. A. (2023). Optimising sustainable renewable energy portfolios using a multi-tolerance fuzzy goal programming approach. International Journal of Green Energy, 20(6), 640-655.
Journal of Arid Biome
Volume 12, Issue 1
April 2022
Pages 171-187

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