Optimum Design of a Microgrid and Establishment of a Long-Term Electricity Generation Plan with Mixed Integer Nonlinear Programming (MINLP)

Yıl 2023, , 186 - 197, 01.03.2023

Handan Akülker , Hasan Şıldır , Erdal Aydın

https://doi.org/10.35414/akufemubid.1067394

Öz

Mixed integer nonlinear programming (MINLP) is a frequently used optimization method for the optimum design of power grids and the creation of long or short-term power generation plans. Microgrid is a kind of energy grid consisting of storage units, distributed power generators consuming conventional or renewable or hybrid energy sources, and loads. A microgrid can be installed to support the main grid, or it can be installed only to meet the electricity demand of a particular location. The optimum design of micro-grids and the creation of long-term electricity generation plans have become essential in recent years due to Turkey's dependence on imports in terms of energy resources and the energy deficits that occur during the transmission of the electricity produced in the networks. In this study, it is aimed to make the optimum design of a micro-grid with a project life of twenty years and to make a long-term electricity generation plan. A candidate equipment pool including 14 renewable and conventional sourced power generators, 1 electrolyzer and 1 methanation reactor, a synthetic natural gas production system and 1 energy storage unit was created. With MINLP, installation equipment that will minimize the project cost was selected from this pool and electricity generation planning was made with the selected equipment in half-hourly periods. The carbon dioxide emission tax, which has been implemented in some countries that signed the Paris Agreement and and Green Deal is included in the calculations. Two cases where this tax is added and not added are examined, optimum equipment selections and production planning are compared.

Anahtar Kelimeler

Mixed integer nonlinear programming (MINLP), Microgrid optimization, Carbon dioxide emission, Hybrid energy grids, Electricity production scheduling

Karma Tam Sayılı Doğrusal Olmayan Programlama (MINLP) ile Bir Mikro Şebekenin Optimum Tasarımı ve Uzun Vadeli Elektrik Üretim Planının Oluşturulması

Öz

Karma tam sayılı doğrusal olmayan programlama (MINLP), enerji şebekelerinin optimum tasarımı ve uzun ya da kısa vadeli enerji üretim planlarının oluşturulması için kullanılabilecek bir optimizasyon yöntemidir. Literatürdeki pek çok yayın, doğrusal olan karma tam sayılı doğrusal programlama metotlarını kullanırken, önemli detayları bünyesinde bulundurabilen MINLP, çözümünün daha zor olmasından dolayı pek tercih edilmemiştir. Ancak, detaylı ve güvenilir karar verme mekanizmalarının oluşturulabilmesi için, MINLP optimizasyon metotlarının kullanılması kritiktir. Mikro şebeke; geleneksel ya da yenilenebilir ya da hibrit enerji kaynakları kullanan dağıtık güç jeneratörlerinden, depolama birimlerinden ve yüklerden oluşan bir çeşit enerji şebekesidir. Bir mikro şebeke, ana şebekeyi desteklemek amacıyla kurulabileceği gibi yalnıza belli bir lokasyonun elektrik talebini karşılamak amaçlı da kurulabilir. Türkiye’nin hem enerji kaynakları yönünden ithalata bağımlı oluşu hem de şebekelerde üretilen elektriğin iletimi sırasında meydana gelen enerji açıklarından dolayı, mikro şebekelerin optimum tasarımı ve uzun vadeli elektrik üretim planlarının oluşturulması son yıllarda elzem hale gelmiştir. Bu çalışmada, yirmi yıllık proje ömrüne sahip bir mikro şebekenin optimum tasarımının yapılması ve uzun vadeli elektrik üretim planının yapılması hedeflenmiştir. Yenilenebilir ve geleneksel kaynaklı 14 adet güç jeneratörü, 1 adet elektrolizör ve 1 adet metanasyon reaktöründen oluşan sentetik doğalgaz üretim sistemi ve 1 adet enerji depolama birimi içeren aday ekipman havuzu oluşturulmuştur. MINLP ile bu havuzdan proje maliyetini minimize edecek kurulum ekipmanları seçilmiş ve seçilen ekipmanlar ile yarım saatlik periyotlarla elektrik üretim planlaması yapılmıştır. Paris Antlaşması’nı imzalayan bazı ülkelerde uygulanmaya başlanan karbondioksit emisyonu vergisi hesaplamalara dahil edilmiştir. Bu verginin eklendiği ve eklenmediği iki durum incelenmiş, optimum ekipman seçimleri ve üretim planlamaları karşılaştırılmıştır.

Anahtar Kelimeler

Karma tam sayılı doğrusal olmayan programlama (MINLP), Mikro şebeke optimizasyonu, Karbondioksit emisyonu, Hibrit enerji şebekeleri, Elektrik Üretim Planlaması

Kaynakça

• Abo-Elyousr, F. K., and Elnozahy, A. , 2018. Bi-objective economic feasibility of hybrid micro-grid systems with multiple fuel options for islanded areas in Egypt. Renewable Energy, 128, 37–56.
• Aghaei, J., and Alizadeh, M. I., 2013. Demand response in smart electricity grids equipped with renewable energy sources: A review. Renewable and Sustainable Energy Reviews, 18, 64-72.
• Alipour, M., Zare, K., and Abapour, M., 2018. MINLP Probabilistic Scheduling Model for Demand Response Programs Integrated Energy Hubs. IEEE Transactions on Industrial Informatics, 14(1), 79–88.
• Alvarado-Barrios, L., Rodríguez del Nozal, Á., Boza Valerino, J., García Vera, I. and Martínez-Ramos, J. L., 2020. Stochastic unit commitment in microgrids: Influence of the load forecasting error and the availability of energy storage. Renewable Energy, 146, 2060–2069.
• Babacan, H., and Unvan, Y. A. (Eds.), 2020. Academic Studies in Economic and Administrative Sciences. Difiglio C, Güray BŞ, and Merdan E. , 2020. Turkey Energy Outlook.
• EIA, 2020. Capital Cost and Performance Characteristic Estimates for Utility Scale Electric Power Generating Technologies.
• Farrokhifar, M., Aghdam, F. H., Alahyari, A., Monavari, A., and Safari, A. , 2020. Optimal energy management and sizing of renewable energy and battery systems in residential sectors via a stochastic MILP model. Electric Power Systems Research, 187(June), 106483.
• Feng, Z. Kai, Niu, W. Jing, Wang, W. Chuan, Zhou, J. Zhong, and Cheng, C. Tian. , 2019. A mixed integer linear programming model for unit commitment of thermal plants with peak shaving operation aspect in regional power grid lack of flexible hydropower energy. Energy, 175, 618–629.
• Fu, Y., Huang, G., Liu, L., and Zhai, M., 2021. A factorial CGE model for analyzing the impacts of stepped carbon tax on Chinese economy and carbon emission. Science of The Total Environment, 759, 143512.
• Giddey, S., Badwal, S. P. S., Kulkarni, A., and Munnings, C. , 2012. A comprehensive review of direct carbon fuel cell technology. Progress in Energy and Combustion Science, 38(3), 360–399.
• Gorre, J., Ortloff, F., and van Leeuwen, C. , 2019. Production costs for synthetic methane in 2030 and 2050 of an optimized Power-to-Gas plant with intermediate hydrogen storage. Applied Energy, 253(June), 113594.
• Guo, X., Bao, Z. and Yan, W., 2019. Stochastic model predictive control based scheduling optimization of multi-energy system considering hybrid CHPs and EVs. Applied Sciences (Switzerland), 9(2).
• Honarmand, H. A., Shamim, A. G., and Meyar-Naimi, H., 2021. A robust optimization framework for energy hub operation considering different time resolutions: A real case study. Sustainable Energy, Grids and Networks, 100526.
• Ko, W., and Kim, J., 2019. Generation expansion planning model for integrated energy system considering feasible operation region and generation efficiency of combined heat and power. Energies, 12(2).
• Kocis, G. R., and Grossmann, I. E., 1989. Computational experience with DICOPT solving MINLP problems in process systems engineering. Computers & Chemical Engineering, 13(3), 307-315.
• Mansouri, S. A., Ahmarinejad, A., Javadi, M. S., and Catalão, J. P. S., 2020. Two-stage stochastic framework for energy hubs planning considering demand response programs. Energy, 206.
• Mohammadi-Ivatloo, B., Moradi-Dalvand, M., and Rabiee, A., 2013. Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients. Electric Power Systems Research, 95, 9–18.
• Morshed, M. J., Hmida, J. Ben, and Fekih, A., 2018. A probabilistic multi-objective approach for power flow optimization in hybrid wind-PV-PEV systems. Applied Energy, 211(August 2017), 1136–1149.
• Nazari-Heris, M., Mirzaei, M. A., Mohammadi-Ivatloo, B., Marzband, M., and Asadi, S. ,2020. Economic-environmental effect of power to gas technology in coupled electricity and gas systems with price-responsive shiftable loads. Journal of Cleaner Production, 244, 118769.
• Onat, N., 2010. Transmission and distribution losses of Turkey’s power system. 4th WSEAS International Conference on Energy Planning, Energy Saving, Environmental Education, EPESE’10, 170–175.
• Ren, H. and Gao, W. , 2010. A MILP model for integrated plan and evaluation of distributed energy systems. Applied Energy, 87(3), 1001–1014. https://doi.org/10.1016/j.apenergy.2009.09.023
• Shi, X., Dini, A., Shao, Z., Jabarullah, N. H. ve Liu, Z., 2019. Impacts of photovoltaic/wind turbine/microgrid turbine and energy storage system for bidding model in power system. Journal of Cleaner Production, 226, 845–857. Talebi, S., Ariza, A. F., and Nguyen, T. V., 2016. High-level multi-objective model for microgrid design. 10th Annual International Systems Conference, SysCon 2016 - Proceedings.
• Tenfen, D. ve Finardi, E. C. , 2015. A mixed integer linear programming model for the energy management problem of microgrids. Electric Power Systems Research, 122, 19– 28. UEDAŞ, Uludağ Elektrik Dağıtım Şirketi, 2020.
• Vergara, P. P., López, J. C., Rider, M. J., Shaker, H. R., da Silva, L. C. P., and Jørgensen, B. N., 2020. A stochastic programming model for the optimal operation of unbalanced three-phase islanded microgrids. International Journal of Electrical Power & Energy Systems, 115, 105446.
• Wang, T. , 2017. An overview of IGCC systems. Integrated Gasification Combined Cycle (IGCC) Technologies (pp. 1–80). Elsevier.
• Xia, C., Ye, B., Jiang, J., and Shu, Y. , 2020. Prospect of near-zero-emission IGCC power plants to decarbonize coal-fired power generation in China: Implications from the GreenGen project. Journal of Cleaner Production, 271, 122615.
• Zhang, Y., Yao, F., Iu, H. H. C., Fernando, T. , and Wong, K. P. , 2013. Sequential quadratic programming particle swarm optimization for wind power system operations considering emissions. Journal of Modern Power Systems and Clean Energy, 1(3), 231–240.
• Zheng, X., Wu, G., Qiu, Y., Zhan, X., Shah, N., Li, N., and Zhao, Y., 2018. A MINLP multi-objective optimization model for operational planning of a case study CCHP system in urban China. Applied Energy, 210, 1126–1140.
• https://www.aa.com.tr/en/energy/regulation-renewable/turkey-expects-up-to-21-drop-in-emissions-until-2030/32513, (01.01.2022).
• https://darksky.net/dev, (08.06.2021).