Yenilenebilir Enerji Kaynaklarının ve Elektrikli Araçların Belirsizlikleri Göz Önüne Alınarak Dağıtım Sisteminin QPSO Yöntemi ile Yeniden Yapılandırılması

Year 2023, Volume: 4 Issue: 2, 187 - 205, 24.12.2023

İbrahim Çağrı Barutçu Faruk Aygün Ali Erduman

https://doi.org/10.58769/joinssr.1401699

Abstract

Bu çalışma kapsamında, IEEE 33 baralı test sistemi üzerinde yenilenebilir enerji kaynaklarının ve elektrikli araçların kullanıldığı dağıtım besleyicisinin yeniden yapılandırması problemini çözmek için kuantum parçacık sürü optimizasyonu (QPSO) uygulanmıştır. Kullanılan yöntem ile QPSO parçacık uzunluğunu değiştirerek daha hızlı bir sürede en yakın çözümün bulunması sağlanmıştır. Optimizasyonun amaç fonksiyonu elektrik dağıtım sistemlerinde aktif güç kaybını en aza indirmektir. Optimizasyonun kısıtları arasında bara gerilimleri, dağıtım hattı taşıma kapasiteleri, üretim kaynaklarının minimum ve maksimum güç değerleri, aktif ve reaktif güç denge denklemleri eşitlik ve eşitsizlik kısıtları olarak alınmıştır. Çalışma kapsamında rüzgâr ve güneş enerji üretim santralleri ve elektrikli araç modellerini dikkate alarak dağıtım sistemi besleyicilerinin yeniden yapılandırılması araştırılmıştır. Dağıtık üretim kaynaklarından olan rüzgar ve güneş enerji santrallerin üretim belirsizliklerinin ve elektrikli araçların yük olarak tüketim belirsizliklerinin ayrı ayrı ve birlikte dikkate alındığı senaryolar oluşturulmuştur.

Keywords

QPSO yöntemi, dağıtım sisteminde yeniden yapılandırma, yenilenebilir enerji belirsizlikleri, elektrikli araçlar

RESTRUCTURING THE DISTRIBUTION SYSTEM WITH QPSO METHOD CONSIDERING THE UNCERTAINTIES OF RENEWABLE ENERGY SOURCES AND ELECTRIC VEHICLES

Abstract

In this study, quantum particle swarm optimisation (QPSO) is applied to solve the distribution feeder reconfiguration problem using renewable energy sources and electric vehicles on the IEEE 33-bus test system. With the method used, the closest solution is found in a faster time by changing the QPSO particle length. The objective function of the optimisation is to minimise the active power loss in electricity distribution systems. Busbar voltages, distribution line carrying capacities, minimum and maximum power values of generation sources, active and reactive power balance equations are taken as equality and inequality constraints. Within the scope of the study, the reconfiguration of distribution system feeders considering wind and solar power generation plants and electric vehicle models is investigated. Scenarios are created in which the production uncertainties of wind and solar power plants, which are distributed generation sources, and the consumption uncertainties of electric vehicles as load are taken into account separately and together.

Keywords

QPSO method, distribution system reconfiguration, renewable energy uncertainties, electric vehicles

References

• [1] J. Torres, J. L. Guardado, F. Rivas-Dávalos, S. Maximov, and E. Melgoza, “A genetic algorithm based on the edge window decoder technique to optimize power distribution systems reconfiguration,” International journal of electrical power & energy systems, vol. 45, no. 1, pp. 28–34, 2013.
• [2] A. Saffar, R. Hooshmand, and A. Khodabakhshian, “A new fuzzy optimal reconfiguration of distribution systems for loss reduction and load balancing using ant colony search-based algorithm,” Appl Soft Comput, vol. 11, no. 5, pp. 4021–4028, 2011.
• [3] S. H. Mirhoseini, S. M. Hosseini, M. Ghanbari, and M. Ahmadi, “A new improved adaptive imperialist competitive algorithm to solve the reconfiguration problem of distribution systems for loss reduction and voltage profile improvement,” International Journal of Electrical Power & Energy Systems, vol. 55, pp. 128–143, 2014.
• [4] T. T. Nguyen and T. T. Nguyen, “An improved cuckoo search algorithm for the problem of electric distribution network reconfiguration,” Appl Soft Comput, vol. 84, p. 105720, 2019.
• [5] H. Karimianfard and H. Haghighat, “An initial-point strategy for optimizing distribution system reconfiguration,” Electric Power Systems Research, vol. 176, p. 105943, 2019.
• [6] M. Cikan and B. Kekezoglu, “Comparison of metaheuristic optimization techniques including Equilibrium optimizer algorithm in power distribution network reconfiguration,” Alexandria Engineering Journal, vol. 61, no. 2, pp. 991–1031, 2022.
• [7] T. T. Nguyen and A. V. Truong, “Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm,” International Journal of Electrical Power and Energy Systems, vol. 68, pp. 233–242, 2015, doi: 10.1016/j.ijepes.2014.12.075.
• [8] T. Tran The, D. Vo Ngoc, and N. Tran Anh, “Distribution network reconfiguration for power loss reduction and voltage profile improvement using chaotic stochastic fractal search algorithm,” Complexity, vol. 2020, pp. 1–15, 2020.
• [9] A. Y. Abdelaziz, F. M. Mohammed, S. F. Mekhamer, and M. A. L. Badr, “Distribution systems reconfiguration using a modified particle swarm optimization algorithm,” Electric Power Systems Research, vol. 79, no. 11, pp. 1521–1530, 2009.
• [10] J. M. Home-Ortiz, R. Vargas, L. H. Macedo, and R. Romero, “Joint reconfiguration of feeders and allocation of capacitor banks in radial distribution systems considering voltage-dependent models,” International Journal of Electrical Power and Energy Systems, vol. 107, pp. 298–310, 2019, doi: 10.1016/j.ijepes.2018.11.035.
• [11] M. Sedighizadeh and R. Bakhtiary, “Optimal multi-objective reconfiguration and capacitor placement of distribution systems with the Hybrid Big Bang–Big Crunch algorithm in the fuzzy framework,” Ain Shams Engineering Journal, vol. 7, no. 1, pp. 113–129, 2016.
• [12] A. O. Salau, Y. W. Gebru, and D. Bitew, “Optimal network reconfiguration for power loss minimization and voltage profile enhancement in distribution systems,” Heliyon, vol. 6, no. 6, 2020.
• [13] M. M. Aman, G. B. Jasmon, A. H. A. Bakar, and H. Mokhlis, “Optimum network reconfiguration based on maximization of system loadability using continuation power flow theorem,” International journal of electrical power & energy systems, vol. 54, pp. 123–133, 2014.
• [14] D. Das, “Reconfiguration of distribution system using fuzzy multi-objective approach,” International Journal of Electrical Power & Energy Systems, vol. 28, no. 5, pp. 331–338, 2006.
• [15] A. Lotfipour and H. Afrakhte, “A discrete Teaching–Learning-Based Optimization algorithm to solve distribution system reconfiguration in presence of distributed generation,” International journal of electrical power & energy systems, vol. 82, pp. 264–273, 2016.
• [16] A. Azizivahed, H. Narimani, E. Naderi, M. Fathi, and M. R. Narimani, “A hybrid evolutionary algorithm for secure multi-objective distribution feeder reconfiguration,” Energy, vol. 138, pp. 355–373, 2017, doi: 10.1016/j.energy.2017.07.102.
• [17] T. T. Nguyen, T. T. Nguyen, N. A. Nguyen, and T. L. Duong, “A novel method based on coyote algorithm for simultaneous network reconfiguration and distribution generation placement,” Ain Shams Engineering Journal, vol. 12, no. 1, pp. 665–676, 2021.
• [18] M. Sedighizadeh, M. Esmaili, and M. Esmaeili, “Application of the hybrid Big Bang-Big Crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems,” Energy, vol. 76, pp. 920–930, 2014.
• [19] B. Arandian, R. A. Hooshmand, and E. Gholipour, “Decreasing activity cost of a distribution system company by reconfiguration and power generation control of DGs based on shuffled frog leaping algorithm,” International Journal of Electrical Power and Energy Systems, vol. 61, pp. 48–55, 2014, doi: 10.1016/j.ijepes.2014.03.001.
• [20] H.-J. Wang, J.-S. Pan, T.-T. Nguyen, and S. Weng, “Distribution network reconfiguration with distributed generation based on parallel slime mould algorithm,” Energy, vol. 244, p. 123011, 2022.
• [21] R. Rajaram, K. Sathish Kumar, and N. Rajasekar, “Power system reconfiguration in a radial distribution network for reducing losses and to improve voltage profile using modified plant growth simulation algorithm with Distributed Generation (DG),” Energy Reports, vol. 1, pp. 116–122, 2015, doi: 10.1016/j.egyr.2015.03.002.
• [22] J. Siahbalaee, N. Rezanejad, and G. B. Gharehpetian, “Reconfiguration and DG Sizing and Placement Using Improved Shuffled Frog Leaping Algorithm,” Electric Power Components and Systems, vol. 47, no. 16–17, pp. 1475–1488, 2019, doi: 10.1080/15325008.2019.1689449.
• [23] A. Shaheen, A. Elsayed, A. Ginidi, R. El-Sehiemy, and E. Elattar, “Reconfiguration of electrical distribution network-based DG and capacitors allocations using artificial ecosystem optimizer: Practical case study,” Alexandria Engineering Journal, vol. 61, no. 8, pp. 6105–6118, 2022, doi: 10.1016/j.aej.2021.11.035.
• [24] A. Jafar-Nowdeh et al., “Meta-heuristic matrix moth–flame algorithm for optimal reconfiguration of distribution networks and placement of solar and wind renewable sources considering reliability,” Environ Technol Innov, vol. 20, 2020, doi: 10.1016/j.eti.2020.101118.
• [25] S. Nikkhah and A. Rabiee, “Multi-objective stochastic model for joint optimal allocation of DG units and network reconfiguration from DG owner’s and DisCo’s perspectives,” Renew Energy, vol. 132, pp. 471–485, 2019.
• [26] R. Fathi, B. Tousi, and S. Galvani, “A new approach for optimal allocation of photovoltaic and wind clean energy resources in distribution networks with reconfiguration considering uncertainty based on info-gap decision theory with risk aversion strategy,” J Clean Prod, vol. 295, p. 125984, 2021.
• [27] A. Zidan and E. F. El-Saadany, “Distribution system reconfiguration for energy loss reduction considering the variability of load and local renewable generation,” Energy, vol. 59, pp. 698–707, 2013, doi: 10.1016/j.energy.2013.06.061.
• [28] R. V. A. Monteiro, J. P. Bonaldo, R. F. da Silva, and A. S. Bretas, “Electric distribution network reconfiguration optimized for PV distributed generation and energy storage,” Electric Power Systems Research, vol. 184, 2020, doi: 10.1016/j.epsr.2020.106319.
• [29] E. Hooshmand and A. Rabiee, “Energy management in distribution systems, considering the impact of reconfiguration, RESs, ESSs and DR: A trade-off between cost and reliability,” Renew Energy, vol. 139, pp. 346–358, 2019, doi: 10.1016/j.renene.2019.02.101.
• [30] Z. Li, S. Wang, Y. Zhou, W. Liu, and X. Zheng, “Optimal distribution systems operation in the presence of wind power by coordinating network reconfiguration and demand response,” International Journal of Electrical Power and Energy Systems, vol. 119, 2020, doi: 10.1016/j.ijepes.2020.105911.
• [31] L. W. De Oliveira, F. D. S. Seta, and E. J. De Oliveira, “Optimal reconfiguration of distribution systems with representation of uncertainties through interval analysis,” International Journal of Electrical Power and Energy Systems, vol. 83, pp. 382–391, 2016, doi: 10.1016/j.ijepes.2016.04.020.
• [32] W. Guan, Y. Tan, H. Zhang, and J. Song, “Distribution system feeder reconfiguration considering different model of DG sources,” International Journal of Electrical Power and Energy Systems, vol. 68, pp. 210–221, 2015, doi: 10.1016/j.ijepes.2014.12.023.
• [33] M. J. Sanjari and H. Karami, “Optimal control strategy of battery-integrated energy system considering load demand uncertainty,” Energy, vol. 210, p. 118525, 2020.
• [34] S. Huang and O. Abedinia, “Investigation in economic analysis of microgrids based on renewable energy uncertainty and demand response in the electricity market,” Energy, vol. 225, p. 120247, 2021.
• [35] T. M. Alabi, L. Lu, and Z. Yang, “Stochastic optimal planning scheme of a zero-carbon multi-energy system (ZC-MES) considering the uncertainties of individual energy demand and renewable resources: An integrated chance-constrained and decomposition algorithm (CC-DA) approach,” Energy, vol. 232, p. 121000, 2021.
• [36] J. Sun, B. Feng, and W. Xu, “Particle swarm optimization with particles having quantum behavior,” in Proceedings of the 2004 congress on evolutionary computation (IEEE Cat. No. 04TH8753), IEEE, 2004, pp. 325–331.