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COYOTE OPTIMIZATION ALGORITHM TO SOLVE ENERGY HUB ECONOMIC DISPATCH PROBLEM

Yıl 2020, Cilt: 12 Sayı: 1, 20 - 26, 16.06.2020

Öz

Regardless of energy type that we need today, it is important to use it efficiently and economically in the production, transmission and distribution stages. In line with the developing technology and needs, a new energy concept has emerged in which different energy types managed together in the past were managed independently. In this concept, energy infrastructures of more than one energy carrier such as electricity, gas and heat are met as Energy Hub (EH) to supply the demands such as electricity, gas, heating, cooling and compressed air by means of energy conversion, distribution and storage devices. EHs are expected to meet the demands energy with low operating costs. Energy hub economic dispatch problem (EHEDP) is a non-linear, non-convex, uniform and non-differential multidimensional optimization problem. In this study, the energy cost of the system is minimized by using the Coyote Optimization Algorithm (COA) for the solution of the EHEDP. The results obtained with COA have been compared with the results of heuristic algorithms such as Gravitational Search Algorithm (GSA), Enhanced Gravitational Search Algorithm (EGSA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) in the literature. The compared results showed that COA performed better than other algorithms in solving EHED problem.

Kaynakça

  • Abdelaziz, A. Y., El-Zonkoly, A. M., & Eladl, A. M. (2017). Energy hub optimization using modified firefly algorithm. 2017 8th International Renewable Energy Congress, IREC 2017.
  • Andersson, G., & Geidl, M. (2007). Optimal Power Flow of Multiple Energy Carriers. IEEE Transactions on Power Systems, 22(1), 145–155.
  • Beigvand, S. D., Abdi, H., & La Scala, M. (2016). Combined heat and power economic dispatch problem using gravitational search algorithm. Electric Power Systems Research, 133, 160–172.
  • Beigvand, S. D., Abdi, H., & La Scala, M. (2017). A general model for energy hub economic dispatch. Applied Energy, 190, 1090–1111.
  • Das, S., & Suganthan, P. N. (2011). Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 15(1), 4–31.
  • Geidl, Martin, G. K. (2007). IEEE january/february 2007. IEEE Power and Energy Magazine, 5, no(february), 24–30.
  • Guvenc, U., & Kaymaz, E. (2019). Economic Dispatch Integrated Wind Power Using Coyote Optimization Algorithm. 1(1), 179–183.
  • Güvenç, U., Özkaya, B., Bakir, H., Duman, S., & Bingöl, O. (2020). Energy Hub Economic Dispatch by Symbiotic Organisms Search Algorithm. In D. J. Hemanth & U. Kose (Eds.), Artificial Intelligence and Applied Mathematics in Engineering Problems, Cham: Springer International Publishing, 375–385.
  • Ha, T. T., Zhang, Y. J., Hao, J. B., & Pham, T. H. A. (2018). Optimal operation of energy hub with different structures for minimal energy usage cost. 2017 2nd International Conference on Power and Renewable Energy, ICPRE 2017, 31–36.
  • He, S., Wu, Q. H., & Saunders, J. R. (2009). Group search optimizer: An optimization algorithm inspired by animal searching behavior. IEEE Transactions on Evolutionary Computation, 13(5), 973–990.
  • Huband, S., Hingston, P., Barone, L., & While, L. (2006). A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions on Evolutionary Computation, 10(5), 477–506.
  • Huo, D., Gu, C., Yang, G., & Blond, S. Le. (2017). Combined domestic demand response and energy hub optimisation with renewable generation uncertainty. Energy Procedia, 142, 1985–1990.
  • Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 10(3), 281–295.
  • Ma, T., Wu, J., Hao, L., Li, Y., Yan, H., Li, D., & Chen, S. (2018). Energy Flow Modeling and Optimal Operation Analysis of Micro Energy Grid Based on Energy Hub. Dianwang Jishu/Power System Technology, 42(1), 179–186.
  • Moeini-Aghtaie, M., Dehghanian, P., Fotuhi-Firuzabad, M., & Abbaspour, A. (2014). Multiagent genetic algorithm: An online probabilistic view on economic dispatch of energy hubs constrained by wind availability. IEEE Transactions on Sustainable Energy, 5(2), 699–708.
  • Mohammadi, M., Noorollahi, Y., Mohammadi-ivatloo, B., & Yousefi, H. (2017). Energy hub: From a model to a concept – A review. Renewable and Sustainable Energy Reviews, 80(December 2016), 1512–1527.
  • Pazouki, S., & Haghifam, M. R. (2016). Optimal planning and scheduling of energy hub in presence of wind, storage and demand response under uncertainty. International Journal of Electrical Power and Energy Systems, 80, 219–239.
  • Pierezan, J., & Dos Santos Coelho, L. (2018). Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems. 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings, 1–8.
  • Qais, M. H., Hasanien, H. M., Alghuwainem, S., & Nouh, A. S. (2019). Coyote optimization algorithm for parameters extraction of three-diode photovoltaic models of photovoltaic modules. Energy, 187, 116001.
  • Teimourzadeh Baboli, P., Yazdani Damavandi, M., Parsa Moghaddam, M., & Haghifam, M. R. (2015). A mixed integer modeling of micro energy-hub system. IEEE Power and Energy Society General Meeting, 2015-Septe, 1–5.
  • Thiele, L., & Zitzler, E. (1999). Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation, 3(4), 257–271.
  • Timothée, C., Perera, A. T. D., Scartezzini, J., & Mauree, D. (2017). Optimum dispatch of a multi-storage and multi-energy hub with demand response and restricted grid interactions. Energy Procedia, 142, 2864–2869.
  • Zhang, X., Yu, T., Zhang, Z., & Tang, J. (2018). Multi-Agent Bargaining Learning for Distributed Energy Hub Economic Dispatch. IEEE Access, 6, 39564–39573.

ENERJİ MERKEZİ EKONOMİK YÜK DAĞITIM PROBLEMİ ÇÖZÜMÜ İÇİN KIR KURDU OPTİMİZASYON ALGORİTMASI

Yıl 2020, Cilt: 12 Sayı: 1, 20 - 26, 16.06.2020

Öz

Günümüzde ihtiyacımız olan enerjinin türü ne olursa olsun üretim, iletim, dağıtım aşamasında verimli ve ekonomik olarak kullanımı önemli hale gelmiştir. Gelişen teknoloji ve ihtiyaçlar doğrultusunda, geçmişte birbirinden bağımsız olarak yönetilen farklı enerji türlerinin bir arada yönetildiği yeni bir enerji konsepti ortaya çıkmıştır. Bu konseptte elektrik, gaz ve ısı gibi birden fazla enerji taşıyıcısının, enerji dönüşüm, dağıtım ve depolama cihazları vasıtasıyla, talep edilen elektrik, gaz, ısıtma, soğutma ve basınçlı hava gibi ihtiyaçların karşılanabilmesini sağlayan enerji alt yapıları Enerji Merkezi (EM) olarak kabul edilir. EM’ lerinin, talep edilen enerjiyi düşük işletme maliyeti ile karşılaması beklenir. Enerji merkezi ekonomik dağıtım problemi (EMEDP) doğrusal, konveks, düzgün ve diferansiyel olmayan çok boyutlu bir optimizasyon problemidir. Bu çalışmada EMEDP çözümü için Kır Kurdu Optimizasyon Algoritması (KKOA) kullanılarak sistem enerji maliyeti minimize edilmiştir. KKOA ile elde edilen sonuçlar, literatürde yer alan Yerçekimsel Arama Algoritması (YAA), Gelişmiş Yerçekimsel Arama Algoritması (GYAA), Parçacık Sürü Optimizasyonu (PSO), ve Genetik Algoritma (GA) gibi sezgisel algoritmaların sonuçları ile karşılaştırılmıştır. Karşılaştırılan sonuçlar, KKOA’ nın EMED problemi çözümünde diğer algoritmalara göre daha iyi performans gösterdiğini ortaya koymuştur.

Kaynakça

  • Abdelaziz, A. Y., El-Zonkoly, A. M., & Eladl, A. M. (2017). Energy hub optimization using modified firefly algorithm. 2017 8th International Renewable Energy Congress, IREC 2017.
  • Andersson, G., & Geidl, M. (2007). Optimal Power Flow of Multiple Energy Carriers. IEEE Transactions on Power Systems, 22(1), 145–155.
  • Beigvand, S. D., Abdi, H., & La Scala, M. (2016). Combined heat and power economic dispatch problem using gravitational search algorithm. Electric Power Systems Research, 133, 160–172.
  • Beigvand, S. D., Abdi, H., & La Scala, M. (2017). A general model for energy hub economic dispatch. Applied Energy, 190, 1090–1111.
  • Das, S., & Suganthan, P. N. (2011). Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 15(1), 4–31.
  • Geidl, Martin, G. K. (2007). IEEE january/february 2007. IEEE Power and Energy Magazine, 5, no(february), 24–30.
  • Guvenc, U., & Kaymaz, E. (2019). Economic Dispatch Integrated Wind Power Using Coyote Optimization Algorithm. 1(1), 179–183.
  • Güvenç, U., Özkaya, B., Bakir, H., Duman, S., & Bingöl, O. (2020). Energy Hub Economic Dispatch by Symbiotic Organisms Search Algorithm. In D. J. Hemanth & U. Kose (Eds.), Artificial Intelligence and Applied Mathematics in Engineering Problems, Cham: Springer International Publishing, 375–385.
  • Ha, T. T., Zhang, Y. J., Hao, J. B., & Pham, T. H. A. (2018). Optimal operation of energy hub with different structures for minimal energy usage cost. 2017 2nd International Conference on Power and Renewable Energy, ICPRE 2017, 31–36.
  • He, S., Wu, Q. H., & Saunders, J. R. (2009). Group search optimizer: An optimization algorithm inspired by animal searching behavior. IEEE Transactions on Evolutionary Computation, 13(5), 973–990.
  • Huband, S., Hingston, P., Barone, L., & While, L. (2006). A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions on Evolutionary Computation, 10(5), 477–506.
  • Huo, D., Gu, C., Yang, G., & Blond, S. Le. (2017). Combined domestic demand response and energy hub optimisation with renewable generation uncertainty. Energy Procedia, 142, 1985–1990.
  • Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 10(3), 281–295.
  • Ma, T., Wu, J., Hao, L., Li, Y., Yan, H., Li, D., & Chen, S. (2018). Energy Flow Modeling and Optimal Operation Analysis of Micro Energy Grid Based on Energy Hub. Dianwang Jishu/Power System Technology, 42(1), 179–186.
  • Moeini-Aghtaie, M., Dehghanian, P., Fotuhi-Firuzabad, M., & Abbaspour, A. (2014). Multiagent genetic algorithm: An online probabilistic view on economic dispatch of energy hubs constrained by wind availability. IEEE Transactions on Sustainable Energy, 5(2), 699–708.
  • Mohammadi, M., Noorollahi, Y., Mohammadi-ivatloo, B., & Yousefi, H. (2017). Energy hub: From a model to a concept – A review. Renewable and Sustainable Energy Reviews, 80(December 2016), 1512–1527.
  • Pazouki, S., & Haghifam, M. R. (2016). Optimal planning and scheduling of energy hub in presence of wind, storage and demand response under uncertainty. International Journal of Electrical Power and Energy Systems, 80, 219–239.
  • Pierezan, J., & Dos Santos Coelho, L. (2018). Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems. 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings, 1–8.
  • Qais, M. H., Hasanien, H. M., Alghuwainem, S., & Nouh, A. S. (2019). Coyote optimization algorithm for parameters extraction of three-diode photovoltaic models of photovoltaic modules. Energy, 187, 116001.
  • Teimourzadeh Baboli, P., Yazdani Damavandi, M., Parsa Moghaddam, M., & Haghifam, M. R. (2015). A mixed integer modeling of micro energy-hub system. IEEE Power and Energy Society General Meeting, 2015-Septe, 1–5.
  • Thiele, L., & Zitzler, E. (1999). Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation, 3(4), 257–271.
  • Timothée, C., Perera, A. T. D., Scartezzini, J., & Mauree, D. (2017). Optimum dispatch of a multi-storage and multi-energy hub with demand response and restricted grid interactions. Energy Procedia, 142, 2864–2869.
  • Zhang, X., Yu, T., Zhang, Z., & Tang, J. (2018). Multi-Agent Bargaining Learning for Distributed Energy Hub Economic Dispatch. IEEE Access, 6, 39564–39573.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Uğur Güvenç 0000-0002-5193-7990

Onur Battal 0000-0003-4581-2857

Yayımlanma Tarihi 16 Haziran 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 12 Sayı: 1

Kaynak Göster

IEEE U. Güvenç ve O. Battal, “COYOTE OPTIMIZATION ALGORITHM TO SOLVE ENERGY HUB ECONOMIC DISPATCH PROBLEM”, UTBD, c. 12, sy. 1, ss. 20–26, 2020.

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