STOKASTİK RÜZGAR VE GÜNEŞ ENERJİSİNİN BİRLEŞTİRİLDİĞİ ENERJİ HUB OPTİMİZASYONU İÇİN GELİŞTİRİLMİŞ LSHADESPACMA ALGORİTMASI

Yıl 2024, Cilt: 10 Sayı: 1, 103 - 112, 30.06.2024

Burçin Özkaya , Uğur Güvenç , Okan Bingöl

https://doi.org/10.22531/muglajsci.1481327

Öz

Günümüzde çeşitli enerji teknolojilerinin kullanılmasıyla birlikte entegre enerji sistemlerine, özellikle enerji hub (EH) problemine olan ilgi artmaktadır. EH optimizasyon problemleri, farklı enerji kaynaklarının birleştirilmesi ve farklı taleplerin üretilmesi nedeniyle karmaşık ve yüksek boyutludur. Bu nedenle EH problemlerinin çözümü için meta-sezgisel arama algoritmalarının kullanılması gerekmektedir. Bu çalışmada, EH optimizasyon problemlerini çözmek için FDB-LSHADESPACMA olarak isimlendirilen uygunluk-mesafe dengesi tabanlı yarı parametre uyarlamalı LSHADE ile hibrit CMA-ES (LSHADE-SPACMA) algoritması önerilmiştir. Çalışmada, beş giriş enerji taşıyıcısı ve dört çıkış enerji kaynağı kullanılarak farklı EH yapıları oluşturulmuş ve üç test sistemi ilk kez literatüre sunulmuştur. Ayrıca, toplam maliyetin ve toplam kaybın minimizasyonu olmak üzere iki amaç fonksiyonu kullanılmıştır. Önerilen algoritmanın performansı, kıyaslama ve EH optimizasyon problemleri üzerinde test edilmiştir. EH optimizasyon problemlerine yönelik deneysel çalışmada altı durum çalışması dikkate alınmıştır. Buna göre, FDB-LSHADESPACMA Durum-1, Durum-2, Durum-3, Durum-4, Durum-5 ve Durum-6 için sırasıyla 3292.2784mu, 1.6753pu, 5052.0203mu, 2.1126pu, 5217.2151mu ve 2.7051pu değerlerini elde etti. Simülasyon sonuçları, FDB-LSHADESPACMA algoritmasının hem EH optimizasyonu hem de kıyaslama problemlerinin çözümünde üstün performans elde ettiğini göstermiştir.

Anahtar Kelimeler

Enerji hub, Optimizasyon, Uygunluk-Mesafe Dengesi, LSHADESPACMA algoritması

ENHANCED LSHADESPACMA ALGORITHM FOR ENERGY HUB OPTIMIZATION INCORPORATING STOCHASTIC WIND AND SOLAR ENERGY

Öz

Nowadays, with the use of various energy technologies, interest in integrated energy systems is increasing, where the energy hub(EH) is the most attractive in them. EH optimization problems are the complex and high-dimensional due to the combining the different energy sources and the generation of different demands at the output. For this reason, the meta-heuristic search algorithms needs to be used to solve the EH problems. In this study, a novel LSHADE with semi-parameter adaptation hybrid with CMA-ES including fitness-distance balance(FDB-LSHADESPACMA) was developed to solve EH optimization problems. Using the five input energy carriers and four output energy sources, different EH structures were created and three test systems were presented to the literature for the first time. Besides, two objective functions were used which are minimization of total cost and total loss. To validate the performance of FDB-LSHADESPACMA, it was applied on benchmark and EH optimization problems. In experimental study about EH optimization problems, six case studies were considered. Accordingly, the FDB-LSHADESPACMA was obtained 3292.2784mu, 1.6753pu, 5052.0203mu, 2.1126pu, 5217.2151mu, and 2.7051pu for Case-1, Case-2, Case-3, Case-4, Case-5, and Case-6, respectively. The simulation results demonstrated that FDB-LSHADESPACMA achieved successful performance for solving both EH optimization and benchmark problems.

Anahtar Kelimeler

Energy hub, Optimization, Fitness-Distance Balance, LSHADESPACMA algorithm

Kaynakça

• Geidl, M., Koeppel, G., Favre-Perrod, P., Klockl, B., Andersson, G., and Frohlich, K., “Energy hubs for the future”, IEEE Power and Energy Magazine, 5(1), 24-30, 2006.
• Mohammadi, M., Noorollahi, Y., Mohammadi-Ivatloo, B., and Yousefi, H., “Energy hub: From a model to a concept–A review”, Renewable And Sustainable Energy Reviews, 80, 1512-1527, 2017.
• Salehi, J., Namvar, A., and Gazijahani, F. S., “Scenario-based Co-Optimization of neighboring multi carrier smart buildings under demand response exchange”, Journal of Cleaner Production, 235, 1483-1498, 2019.
• Najafi, A., Falaghi, H., Contreras, J., and Ramezani, M., “Medium-term energy hub management subject to electricity price and wind uncertainty”, Applied Energy, 168, 418-433, 2016.
• Dolatabadi, A., Mohammadi-Ivatloo, B., Abapour, M., and Tohidi, S., “Optimal stochastic design of wind integrated energy hub”, IEEE Transactions on Industrial Informatics, 13(5), 2379-2388, 2017.
• Geidl, M. and Andersson, G., “A modeling and optimization approach for multiple energy carrier power flow”, In 2005 IEEE Russia Power Tech, 1-7, 2005.
• Geidl, M. and Andersson, G., “Optimal power flow of multiple energy carriers”, IEEE Transactions on Power Systems, 22(1), 145-155, 2007.
• Geidl, M. and Andersson, G., “Optimal power dispatch and conversion in systems with multiple energy carriers”, In Proc. 15th Power Systems Computation Conference, 2005.
• Moeini-Aghtaie, M., Abbaspour, A., Fotuhi-Firuzabad, M., and Hajipour, E., “A decomposed solution to multiple-energy carriers optimal power flow”, IEEE Transactions on Power Systems, 29(2), 707-716, 2013.
• Shams, M. H., Shahabi, M., Kia, M., Heidari, A., Lotfi, M., Shafie-Khah, M., and Catalão, J. P., “Optimal operation of electrical and thermal resources in microgrids with energy hubs considering uncertainties”, Energy, 187, 115949, 2019.
• Ebrahimi, J., Niknam, T., and Firouzi, B. B., “Electrical and thermal power management in an energy hub system considering hybrid renewables”, Electrical Engineering, 103(4), 1965-1976, 2021.
• Chamandoust, H., Derakhshan, G., Hakimi, S. M., and Bahramara, S., “Tri-objective optimal scheduling of smart energy hub system with schedulable loads”, Journal of Cleaner Production, 236, 117584, 2019.
• Beigvand, S. D., Abdi, H., and La Scala, M., “Economic dispatch of multiple energy carriers”, Energy, 138, 861-872, 2017.
• Beigvand, S. D., Abdi, H., and La Scala, M., “A general model for energy hub economic dispatch”, Applied Energy, 190, 1090-1111, 2017.
• Ozkaya, B., Guvenc, U., and Bingol, O., “Fitness Distance Balance based LSHADE algorithm for energy hub economic dispatch problem”, IEEE Access, 10, 66770-66796, 2022.
• Biswas, P. P., Suganthan, P. N., and Amaratunga, G. A., “Optimal power flow solutions incorporating stochastic wind and solar power”, Energy Conversion and Management, 148, 1194-1207, 2017.
• Biswas, P. P., Suganthan, P. N., Qu, B. Y., and Amaratunga, G. A., “Multiobjective economic-environmental power dispatch with stochastic wind-solar-small hydro power”, Energy, 150, 1039-1057, 2018.
• Kahraman, H. T., Aras, S., and Gedikli, E., “Fitness-distance balance (FDB): a new selection method for meta-heuristic search algorithms”, Knowledge-Based Systems, 190, 105169, 2020.
• Mohamed, A. W., Hadi, A. A., Fattouh, A. M., and Jambi, K. M., “LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems”, In 2017 IEEE Congress on evolutionary computation (CEC), 145-152, 2017.
• Mohamed, A. W., Hadi, A. A., and Jambi, K. M., “Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization”, Swarm and Evolutionary Computation, 50, 100455, 2019.
• Storn, R., and Price, K., “Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces”, Journal of Global Optimization, 11, 341-359, 1997.
• Faramarzi, A., Heidarinejad, M., Mirjalili, S., and Gandomi, A. H., “Marine Predators Algorithm: A nature-inspired metaheuristic”, Expert Systems with Applications, 152, 113377, 2020.
• Faramarzi, A., Heidarinejad, M., Stephens, B., and Mirjalili, S., “Equilibrium optimizer: A novel optimization algorithm”, Knowledge-Based Systems, 191, 105190, 2020.
• Sulaiman, M. H., Mustaffa, Z., Saari, M. M., and Daniyal, H., “Barnacles mating optimizer: A new bio-inspired algorithm for solving engineering optimization problems”, Engineering Applications of Artificial Intelligence, 87, 103330, 2020.
• Yadav, A., “AEFA: Artificial electric field algorithm for global optimization”, Swarm and Evolutionary Computation, 48, 93-108., 2019.
• Zhao, W., Wang, L., and Zhang, Z., “Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm”, Neural Computing and Applications, 32(13), 9383-9425, 2020.
• Zhao, W., Wang, L., and Zhang, Z., “Supply-demand-based optimization: A novel economics-inspired algorithm for global optimization”, IEEE Access, 7, 73182-73206, 2019.
• Mohamed, A. W. and Mohamed, A. K., “Adaptive guided differential evolution algorithm with novel mutation for numerical optimization”, International Journal of Machine Learning and Cybernetics, 10, 253-277, 2019.
• Pierezan, J. and Coelho, L. D. S., “Coyote optimization algorithm: a new metaheuristic for global optimization problems”, In 2018 IEEE congress on evolutionary computation (CEC), 2018.
• Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., and Mirjalili, S. M., “Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems”, Advances in Engineering Software, 114, 163-191, 2017.
• Mirjalili, S., “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm”, Knowledge-Based Systems, 89, 228-249, 2015.
• Tanabe, R. and Fukunaga, A. S., “Improving the search performance of SHADE using linear population size reduction”, In 2014 IEEE Congress on Evolutionary Computation, 1658-1665., 2014.
• Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S., “GSA: a gravitational search algorithm”, Information Sciences, 179(13), 2232-2248, 2009.
• Chu, P. C. and Beasley, J. E., “A genetic algorithm for the multidimensional knapsack problem”, Journal of Heuristics, 4, 63-86, 1998.
• Awad, N. H., Ali, M. Z., Liang, J. J., Qu, B. Y., and Suganthan, P. N., “Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization”, Technical Report, 2016.
• Yue, C. T., Price, K. V., Suganthan, P. N., Liang, J. J., Ali, M. Z., Qu, B. Y., ... & Biswas, P. P., “Problem Definitions and Evaluation Criteria for the CEC 2020 Special Session and Competition on Single Objective Bound Constrained Numerical Optimization”, Technical Report, 2019.
• Özkaya, B., Güvenç, U, and Bingöl, O., “Sezgisel Optimizasyon Algoritmaları ile Enerji Hub Optimizasyonu”, PhD Thesis, Düzce University, 2022.