Optimal sizing and localization of multiple distributed generations in distribution systems using an improved grey wolf optimization algorithm

• Autorzy: Benahcour Souheyla , Bendjeghaba Omar

Identyfikatory

DOI

10.15199/48.2024.06.59

Warianty tytułu

PL

Optymalny rozmiar i lokalizacja wielu rozproszonych generacji w systemach dystrybucyjnych przy użyciu ulepszonego algorytmu optymalizacji Szarego Wilka

• Języki publikacji: EN

Abstrakty

EN

This study investigates the impact of the localization and sizing of distributed generations in distribution systems using a combined approach of improved grey wolf optimizer (IGWO) and Newton-Raphson load flow algorithms. The suggested method optimizes the size and position of distributed generation generating both real and reactive power while ensuring power system constraints are not violated. The suggested algorithm optimizes the location and sizing of dis-tributed generations. Nevertheless, investigations show that the proposed method outperforms the PSO optimizer and takes less calculation time. Moreover, in contrast with other meta-heuristic algorithms such as JAYA, PSO, SFO, BO, SMA, GA, and GJO, the proposed approach produces a better voltage profile of the distribution system with smaller distributed generator sizes. To demonstrate the advantages of the suggested approach, the IEEE-13, IEEE-37, and IEEE-123 bus distribution systems are used as test cases, and the outcomes are contrasted with those of other meta-heuristic methods. According to simulation data, IGWO outperforms other meta-heuristic algorithms when it comes to the quality of the solution while satisfying all system constraints.

PL

W tym badaniu zbadano wpływ lokalizacji i rozmiaru generacji rozproszonych w systemach dystrybucyjnych przy użyciu połączonego podejścia ulepszonego optymalizatora szarego wilka (IGWO) i algorytmów przepływu obciążenia Newtona-Raphsona. Zaproponowana metoda optymalizuje wielkość i położenie generacji rozproszonej generującej zarówno moc czynną, jak i bierną, przy jednoczesnym zapewnieniu nienaruszania ograniczeń systemu elektroenergetycznego. Zaproponowany algorytm optymalizuje lokalizację i wielkość generacji rozproszonych. Niemniej jednak badania pokazują, że proponowana metoda przewyższa optymalizator PSO i zajmuje mniej czasu obliczeniowego. Co więcej, w przeciwieństwie do innych algorytmów metaheurystycznych, takich jak JAYA, PSO, SFO, BO, SMA, GA i GJO, proponowane podejście zapewnia lepszy profil napięcia systemu dystrybucyjnego przy mniejszych rozmiarach generatorów rozproszonych. Aby zademonstrować zalety sugerowanego podejścia, jako przypadki testowe wykorzystano systemy dystrybucji magistrali IEEE-13, IEEE-37 i IEEE-123, a wyniki porównano z wynikami innych metod metaheurystycznych. Jak wynika z danych symulacyjnych, IGWO przewyższa inne algorytmy metaheurystyczne pod względem jakości rozwiązania przy jednoczesnym spełnieniu wszystkich ograniczeń systemowych.

Słowa kluczowe

EN

distributed generator; DG; improved grey wolf algorithm; power losses; voltage improvement

PL

generator rozproszony; DG; ulepszony algorytm szarego wilka; strata mocy; poprawa napięcia

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2024
• Tom: R. 100, nr 6
• Strony: 283--288
• Opis fizyczny: Bibliogr. 26 poz., rys., tab.

Twórcy

autor

Benahcour Souheyla

• M’hamed Bougara University, LREEI, Boumerdes, Algeria

autor

Bendjeghaba Omar

• M’hamed Bougara University, LREEI, Boumerdes, Algeria

Bibliografia

• [1] Ang, S., Leeton, U., Chayakulkheeree, K., Kulworawanichpong, T. “Sine cosine for optimal placement and sizing of distributed generation in radial distribution network”. GMSARN International Journal, 12(4):202-202. 2018.
• [2] M. P. Lalitha, V. C. Reddy, V. Usha, “Optimal DG placement for minimum real power loss in radial distribution systems using PSO”, Journal of Theoretical and Applied Information Technology, pp. 107–116, 2010.
• [3] Prakash, P. Khatod, D.K. “An analytical approach for optimal sizing and placement of distributed generation in radial distribution systms”. Proceeding of 1st IEEE international Conference on Power Electronic, Intelligent Control and Energy System; Rookee, India, p. 1-5,2016.
• [4] Singh D, Verma KS. “Multiobjective optimization for DG planning with load models. Power Syst IEEE Trans; 24:427e36, 2009.
• [5] Moradi, M.H. and Abedinie. “A combination of genetic algorithm and partical swarm optimization for optimal DG location and sizing in distribution systems”. Electr. Power Energy Syst., 34:66-74, 2012.
• [6] Karami H, Anaraki MV, Farzin S, Mirjalili S. “Flow direction algorithm (FDA): a novel optimization approach for solving optimization problems. Comput Ind Eng; 156:107224, 2021.
• [7] Ullah Z, Elkadeem MR, Wang S, Radosavljevi c J. “A novel PSOS-CGSA method for state estimation in unbalanced DG integrated distribution systems”. IEEE Access; 8:113219–29, 2020.
• [8] Sharma, S., Bhattacharjee, S., Bhattacharya, A. “Grey wolf optimisation for optimal sizing of battery energy storage device to minimise operation cost of microgrid”. IET Gener. Transm. Distrib. 1-13,2015.
• [9] Sovann Ang, Vannak Heak, Udoum Chhor, Sokun Ieng, Uthen Leeton, Keerati Chayakulkheeree, “Grey wolf optimizer for optimal allocation and sizing of distributed generation for loss reduction and voltage improvement in distribution system." Suranaree Journal of Science & Technology; 29.3 2022.
• [10] Mistry K, Bhavsar V, Roy R. “GSA based optimal capacity and location determination of distributed generation in radial distribution system for loss minimization”. In Proc. of 11th international conference on Environment and electrical engineering (EEEIC); p. 513e8,2012.
• [11] Nasri A, Hamedani Golshan ME. Mortaza Saghaian Nejad S. “Optimal planning of dispatchable and non-dispatchable distributed generation units for minimizing distribution system's energy loss using particle swarm optimization”. Int Trans Electr Energy Syst; 24:504e19,2014.
• [12] Ibrahim B.M. Taha Ehab E. Elattar. “Optimal reactive power resources sizing for power system operations enhancement based on improved grey wolf optimiser”. IET Gener. Transm. Distrib. 12(14):3,421-3,434,2018.
• [13] Chang GW, Chinh NC. “Coyote optimization algorithm-based approach for strategic planning of photovoltaic distributed generation”. IEEE Access; 8:36180–90,2020.
• [14] Martins ASC, Araujo LRd, Penido DRR. “Sensibility analysis with genetic algorithm to allocate distributed generation and capacitor banks in unbalanced distribution systems”. Electr Power Syst Res; 209:107962,2022.
• [15] Dahal, Samir, Hossein Salehfar. “Impact of distributed generators in the power loss and voltage profile of three phase unbalanced distribution network”. International Journal of Electrical Power & Energy Systems 77, 256-262,2016.
• [16] Electric Power Research Institute, Inc.2021, Reference Guide: The Open Distribution System Simulator (OpenDSS), Roger C. Dugan, Davis Montenegro.
• [17] Mirjalili, S., Mirjalili, S.M., Lewis, A. “Grey wolf optimizer”, Adv. Eng.Softw69, pp. 46–61, 2014.
• [18] Malik, M., Mohideen, E., Ali, L. “Weighted distance grey wolf optimizer for global optimization problems”. IEEE Int. Conf. Computational Intelligence and Computing Research (ICCIC), Madurai, India, pp. 1–6,2015.
• [19] Willis, H. L. Power Distribution Planning Reference Book. New Tork, NY: CRC Press;2 edition, 1244 p, 2004.
• [20] DSA Subcommittee.test feeder cases. Available from:https://cmte.ieee.org/pestestfeeders/resources/ [accessed Nov. 17, 2022]
• [21] Castillo, T. D., & Saad, M. Optimal location and size for various renewable distributed generators in distribution networks”. In IEEE PES Innovative Smart Grid Technologies Conference Latin America (ISGT Latin America), pp. 1-6.2017.
• [22] Dahal, S. “Optimal allocation of distributed renewable energy sources in power distribution networks”. PhD thesis, University of North Dakota. 2014.
• [23] M. Kumawat, N. Gupta, N. Jain, and R. C. Bansal, “Optimally allocation of distributed generators in three-phase unbalanced distribution network,” Energy Procedia, vol. 142, pp. 749–754, 2017.
• [24] A. Anwar and H. R. Pota, “Optimum allocation and sizing of DG unit for efciency enhancement of distribution system,” in Proceedings of the IEEE International Power Engineering and Optimization Conference, Melaka, Malaysia, 165–170, 2012.
• [25] S. Dahal H. Salehfar, “Impact of distributed generators in the power loss and voltage profile of three phase unbalanced distribution network,” International Journal of Electrical Power & Energy Systems, vol. 77, pp. 256–262, 2016.
• [26] Pham, T. D., Nguyen, T. T., & Kien, L. C. “Optimal Placement of Photovoltaic Distributed Generation Units in Radial Unbalanced Distribution Systems Using MATLAB and OpenDSS-Based Co-simulation and a Proposed Metaheuristic Algorithm”. International Transactions on Electrical Energy Systems, 2022.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).