A Mathematical Model for Multi-Region, Multi-Source, Multi-Period Generation Expansion Planning in Renewable Energy for Country-Wide Generation-Transmission Planning

Document Type : Research Paper


1 Associate Prof., Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.

2 PhD Candidate, Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.


Environmental pollution and rapid depletion are among the chief concerns about fossil fuels such as oil, gas, and coal. Renewable energy sources do not suffer from such limitations and are considered the best choice to replace fossil fuels. The present study develops a mathematical model for optimal allocation of regional renewable energy to meet a country-wide demand and its other essential aspects. The ultimate purpose is to minimize the total cost by planning, including power plant construction and maintenance costs and transmission costs. Minimum-cost flow equations are embedded in the model to determine how regions can supply energy to other regions or rely on them to fulfill annual demand. In order to verify the applicability of the model, it is applied to a real-world case study of Iran to determine the optimal renewable energy generation-transmission decisions for the next decade. Results indicate that the hydroelectric and solar power plants should generate the majority of the generated renewable electricity within the country, according to the optimal solution. Moreover, regarding the significant population growth and waste generation in the country’s large cities, biomass power plants can have the opportunity to satisfy a remarkable portion of electricity demand.


Abdelkafi, A., Masmoudi, A., & Krichen, L. (2018). Assisted power management of a stand-alone renewable multi-source system. Energy, 145, 195–205. https://doi.org/10.1016/j.energy.2017.12.133
Afsharzade, N., Papzan, A., Ashjaee, M., Delangizan, S., Van Passel, S., & Azadi, H. (2016). Renewable energy development in rural areas of Iran. Renewable and Sustainable Energy Reviews, 65, 743–755. https://doi.org/10.1016/j.rser.2016.07.042
Aghahosseini, A., Bogdanov, D., Ghorbani, N., & Breyer, C. (2018). Analysis of 100% renewable energy for Iran in 2030: Integrating solar PV, wind energy and storage. International Journal of Environmental Science and Technology, 15(1), 17–36. https://doi.org/10.1007/s13762-017-1373-4
Ajithapriyadarsini, S., Mary, P. M., & Iruthayarajan, M. W. (2019). Automatic generation control of a multi-area power system with renewable energy source under deregulated environment: Adaptive fuzzy logic-based differential evolution (DE) algorithm. Soft Computing, 23(22), 12087–12101. https://doi.org/10.1007/s00500-019-03765-2
Asrari, A., Ghasemi, A., & Javidi, M. H. (2012). Economic evaluation of hybrid renewable energy systems for rural electrification in Iran—A case study. Renewable and Sustainable Energy Reviews, 16(5), 3123–3130. https://doi.org/10.1016/j.rser.2012.02.052
Chassin, D. P., Behboodi, S., & Djilali, N. (2018). Optimal subhourly electricity resource dispatch under multiple price signals with high renewable generation availability. Applied Energy, 213, 262–271. https://doi.org/10.1016/j.apenergy.2018.01.041
Dagoumas, A. S., & Koltsaklis, N. E. (2019). Review of models for integrating renewable energy in the generation expansion planning. Applied Energy, 242, 1573–1587. https://doi.org/10.1016/j.apenergy.2019.03.194
de la Nieta, A. A. S., Gibescu, M., Wang, X., Song, M., Jensen, E., Saleem, A., Bremdal, B., & Ilieva, I. (2018). Local Economic Dispatch with Local Renewable Generation and Flexible Load Management. 2018 International Conference on Smart Energy Systems and Technologies (SEST), 1–6. https://doi.org/10.1109/SEST.2018.8495823
Djebbri, S., Ladaci, S., Metatla, A., & Balaska, H. (2018). Robust MRAC Supervision of a Multi-source Renewable Energy System Using Fractional-Order Integrals. 2018 International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), 1–6. https://doi.org/10.1109/CISTEM.2018.8613425
Droege, P. (Ed.). (2008). Urban energy transition: From fossil fuels to renewable power (1st ed). Elsevier.
Ecer, F., Pamucar, D., Hashemkhani Zolfani, S., & Keshavarz Eshkalag, M. (2019). Sustainability assessment of OPEC countries: Application of a multiple attribute decision making tool. Journal of Cleaner Production, 241, 118324. https://doi.org/10.1016/j.jclepro.2019.118324
Fizaine, F., & Court, V. (2015). Renewable electricity producing technologies and metal depletion: A sensitivity analysis using the EROI. Ecological Economics, 110, 106–118. https://doi.org/10.1016/j.ecolecon.2014.12.001
Ghorbani, N., Aghahosseini, A., & Breyer, C. (2020). Assessment of a cost-optimal power system fully based on renewable energy for Iran by 2050 – Achieving zero greenhouse gas emissions and overcoming the water crisis. Renewable Energy, 146, 125–148. https://doi.org/10.1016/j.renene.2019.06.079
Hosseini, S. E., Andwari, A. M., Wahid, M. A., & Bagheri, G. (2013). A review on green energy potentials in Iran. Renewable and Sustainable Energy Reviews, 27, 533–545. https://doi.org/10.1016/j.rser.2013.07.015
Ilbahar, E., Cebi, S., & Kahraman, C. (2019). A state-of-the-art review on multi-attribute renewable energy decision making. Energy Strategy Reviews, 25, 18–33. https://doi.org/10.1016/j.esr.2019.04.014
Iqbal, M., Azam, M., Naeem, M., Khwaja, A. S., & Anpalagan, A. (2014). Optimization classification, algorithms and tools for renewable energy: A review. Renewable and Sustainable Energy Reviews, 39, 640–654. https://doi.org/10.1016/j.rser.2014.07.120
Iran Ministry of Energy. (2020, August 16). List of Iran’s regional power companies. List of Regional Power Companies. http://moe.gov.ir/Sites-of-Water-Electricity/Regional-power-companies
Keles, C., Alagoz, B. B., & Kaygusuz, A. (2017). Multi-source energy mixing for renewable energy microgrids by particle swarm optimization. 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 1–5. https://doi.org/10.1109/IDAP.2017.8090163
Khojasteh, D., Khojasteh, D., Kamali, R., Beyene, A., & Iglesias, G. (2018). Assessment of renewable energy resources in Iran; with a focus on wave and tidal energy. Renewable and Sustainable Energy Reviews, 81, 2992–3005. https://doi.org/10.1016/j.rser.2017.06.110
Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., & Bansal, R. C. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69, 596–609. https://doi.org/10.1016/j.rser.2016.11.191
Kumar, K. P., & Saravanan, B. (2017). Recent techniques to model uncertainties in power generation from renewable energy sources and loads in microgrids – A review. Renewable and Sustainable Energy Reviews, 71, 348–358. https://doi.org/10.1016/j.rser.2016.12.063
Li, Z., Qiu, F., & Wang, J. (2016). Data-driven real-time power dispatch for maximizing variable renewable generation. Applied Energy, 170, 304–313. https://doi.org/10.1016/j.apenergy.2016.02.125
Mahmud, N., & Zahedi, A. (2016). Review of control strategies for voltage regulation of the smart distribution network with high penetration of renewable distributed generation. Renewable and Sustainable Energy Reviews, 64, 582–595. https://doi.org/10.1016/j.rser.2016.06.030
Melamed, M., Ben-Tal, A., & Golany, B. (2018). A multi-period unit commitment problem under a new hybrid uncertainty set for a renewable energy source. Renewable Energy, 118, 909–917. https://doi.org/10.1016/j.renene.2016.05.095
Mollahosseini, A., Hosseini, S. A., Jabbari, M., Figoli, A., & Rahimpour, A. (2017). Renewable energy management and market in Iran: A holistic review on current state and future demands. Renewable and Sustainable Energy Reviews, 80, 774–788. https://doi.org/10.1016/j.rser.2017.05.236
Naval, N., Sánchez, R., & Yusta, J. M. (2020). A virtual power plant optimal dispatch model with large and small-scale distributed renewable generation. Renewable Energy, 151, 57–69. https://doi.org/10.1016/j.renene.2019.10.144
Nazir, N., Racherla, P., & Almassalkhi, M. (2020). Optimal Multi-Period Dispatch of Distributed Energy Resources in Unbalanced Distribution Feeders. IEEE Transactions on Power Systems, 35(4), 2683–2692. https://doi.org/10.1109/TPWRS.2019.2963249
Office of Energy and Electricity Planning at Ministry of Energy. (2016). Iran Energy Balance Sheet 2016.
Oree, V., Sayed Hassen, S. Z., & Fleming, P. J. (2017). Generation expansion planning optimisation with renewable energy integration: A review. Renewable and Sustainable Energy Reviews, 69, 790–803. https://doi.org/10.1016/j.rser.2016.11.120
Quaschning, V. (2016). Understanding renewable energy systems (Revised edition). Routledge, Taylor & Francis Group, Earthscan from Routledge.
Radovanović, M., Filipović, S., & Pavlović, D. (2017). Energy security measurement – A sustainable approach. Renewable and Sustainable Energy Reviews, 68, 1020–1032. https://doi.org/10.1016/j.rser.2016.02.010
San Cristóbal, J. R. (2012). A goal programming model for the optimal mix and location of renewable energy plants in the north of Spain. Renewable and Sustainable Energy Reviews, 16(7), 4461–4464. https://doi.org/10.1016/j.rser.2012.04.039
Schwerhoff, G., & Sy, M. (2017). Financing renewable energy in Africa – Key challenge of the sustainable development goals. Renewable and Sustainable Energy Reviews, 75, 393–401. https://doi.org/10.1016/j.rser.2016.11.004
Szargut, J., Ziębik, A., & Stanek, W. (2002). Depletion of the non-renewable natural exergy resources as a measure of the ecological cost. Energy Conversion and Management, 43(9–12), 1149–1163. https://doi.org/10.1016/S0196-8904(02)00005-5
Theo, W. L., Lim, J. S., Ho, W. S., Hashim, H., & Lee, C. T. (2017). Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods. Renewable and Sustainable Energy Reviews, 67, 531–573. https://doi.org/10.1016/j.rser.2016.09.063
Twidell, J., & Weir, T. (2015). Renewable energy resources (Third edition). Routledge, Taylor & Francis Group.
Wang, H., Lei, Z., Zhang, X., Zhou, B., & Peng, J. (2019). A review of deep learning for renewable energy forecasting. Energy Conversion and Management, 198, 111799. https://doi.org/10.1016/j.enconman.2019.111799
Wei, W., Liu, F., Mei, S., & Hou, Y. (2015). Robust Energy and Reserve Dispatch Under Variable Renewable Generation. IEEE Transactions on Smart Grid, 6(1), 369–380. https://doi.org/10.1109/TSG.2014.2317744
Zaibi, M., Cherif, H., Champenois, G., Sareni, B., Roboam, X., & Belhadj, J. (2018). Sizing methodology based on design of experiments for freshwater and electricity production from multi-source renewable energy systems. Desalination, 446, 94–103. https://doi.org/10.1016/j.desal.2018.08.008