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Tytuł artykułu

Multi-objective Optimization of the Allocation of DG Units considering technical, economical and environmental attributes

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
PL
Optymalizacja wielozadaniowa rozmieszczenia rozproszonych generatorów energii - parametry techniczne, ekonomiczne i środowiskowe
Języki publikacji
EN
Abstrakty
EN
A multi-objective optimization model which effectively replicates different perspectives is presented to address the optimal allocation of DG units. To offer diverse solutions, NSGA-II is applied to the nonlinear, combinatorial three-objective optimization problem. The encouraging simulation results suggest that the proposed approach not only optimally allocate DG units with benefits of reducing power loss, improving system’s reliability and decreasing pollutant emissions simultaneously but also provide alternative options and facilitate to make more rational evaluations.
PL
W artykule zaproponowano model optymalizacji wielozadaniowej na potrzeby rozmieszczenia rozproszonych generatorów energii. Dla zapewnienia różnorodności aplikacji, zastosowano algorytm NSGA-II do nieliniowej, kombinacyjnej optymalizacji trzyzadaniowej. Przedstawiono wyniki badań symulacyjnych potwierdzających skuteczność działania, ograniczenie strat mocy i redukcję emisji zanieczyszczeń.
Rocznik
Strony
233--237
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
autor
autor
  • State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University. No. 174, Shazhengjie, Shapingba District, Chongqing 400044, China, minyouchen@cqu.edu.cn
Bibliografia
  • [1] Sierra M.R., Coello C.A.C., Multi-objective Particle Swarm optimizers: A survey of the state-of-the-art," Int. J Comput. Intell. Res., 2 (2006), No. 3, 287-308.
  • [2] Farag H.E., El-Saadany E.F., El Shatshat R., Zidan A., A generalized power flow analysis for distribution systems with high penetration of distributed generation, Electr. Power Syst. Res., 81(2011) No. 7, 1499-1506.
  • [3] Pouresmaeil E., Montesinos-Miracle D., Gomis-Bellmunt O., Bergas-Jané J., A multi-objective control strategy for grid connection of DG (distributed generation) resources, Energy, 35(2010) No. 12, 5022-5030.
  • [4] Jahani R., Nejad H.C., Araskalaei A.H., Hajinasiri M., Optimal Distributed Generation Location in Radial Distribution Systems Using A New Heuristic Method, Aust. J. Basic Appl. Sci., 5 (2011) No. 7, 612-621.
  • [5] Singh B., Verma K.S., Singh D., Singh S.N., A Novel Approach for Optimal Placement of Distributed Generation & Facts Controllers in Power Systems: An Overview and Key Issues," Int. J. Rev. Comput., 7(2011), 29-54.
  • [6] Akorede M.F., Hizam H., Pouresmaeil E., Distributed energy resources and benefits to the environment, Renewable Sustainable Energy Rev., 14(2010) No. 2, 724-734.
  • [7] Harrison G.P., Piccolo A., Siano P., Wallace A.R., Hybrid GA and OPF evaluation of network capacity for distributed generation connections, Electr. Power Syst. Res., 78(2008) No. 3, 392-398.
  • [8] Ayres H.M., Freitas W., De Almeida M.C., Da Silva L.C.P., Method for determining the maximum allowable penetration level of distributed generation without steady-state voltage violations, IET Gener. Transm. Distrib., 4(2010) No. 4, 495-508.
  • [9] El-Khattam W., Hegazy Y., Salama M., An integrated distributed generation optimization model for distribution system planning, IEEE Trans. Power Syst., 20(2005) No. 2, 1158-1165.
  • [10] Hedayati H., Nabaviniaki S.A., Akbarimajd A., A Method for Placement of DG Units in Distribution Networks, IEEE Trans. Power Delivery, 23(2008) No. 3, 1620-1628.
  • [11] Akorede M.F., Hizam H., Aris, I., Ab Kadir M.Z.A., Effective method for optimal allocation of distributed generation units in meshed electric power systems, IET Gener. Transm. Distrib., 5(2011) No. 2, 276-287.
  • [12] Abou El-Ela, A.A., Allam S.M., Shatla M.M., Maximal optimal benefits of distributed generation using genetic algorithms," Electr. Power Syst. Res., 80(2010) No. 7, 869-877.
  • [13] Ochoa L.F., Padilha-Feltrin A., Harrison G.P., Evaluating distributed generation impacts with a multiobjective index, IEEE Trans. Power Delivery, 21(2006) No. 31452-1458.
  • [14] Lu Y., Zhou J., Qin, H., Wang Y., Zhang Y., A hybrid multiobjective cultural algorithm for short-term environmental/ economic hydrothermal scheduling, Energy Convers. Manage., 52(2011) No. 5, 2121-2134.
  • [15] Kornelakis A., Multiobjective Particle Swarm Optimization for the optimal design of photovoltaic grid-connected systems, Solar Energy, 84(2010) No. 12, 2022-2033.
  • [16] Hong C., Guo Y. Y., Xia C. J., Research on Optimal Allocation of Distributed Generation by Considering Environmental Benefits, East China Electr. Power, 38(2010) No.12, 1968-1971.
  • [17] Ramakumar R., Butler N.G., Rodriguez A.P., Venkata S.S., Economic aspects of advanced energy technologies, Proc. IEEE, 81(1933) No. 3, 318-332.
  • [18] Liu J., Bi P.X., Dong H. P., Analysis and optimization of complex distribution networks, Beijing: China Electric Power Press, 2002.
  • [19] Deb K., Pratap A., Agarwal S., Meyarivan T., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transa. Evol. Comput., 6(2002) No. 2, 182-197.
  • [20] Baran M.E., Wu F.F., Network Reconfiguration in Distribution Systems for Loss Reduction and Load Balancing, IEEE Power Eng. Rev, 9(1989) No. 4, 101-102.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS1-0050-0074
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