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Deterministically guided differential evolution for constrained power dispatch with prohibited operating zones

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Department of Electrical Engineering, Anna University Regional Centre, Coimbatore, India This paper presents a new approach to solve economic load dispatch (ELD) problem in thermal units with non-convex cost functions using differential evolution technique (DE). In practical ELD problem, the fuel cost function is highly non linear due to inclusion of real time constraints such as valve point loading, prohibited operating zones and network transmission losses. This makes the traditional methods fail in finding the optimum solution. The DE algorithm is an evolutionary algorithm with less stochastic approach to problem solving than classical evolutionary algorithms.DE have the potential of simple in structure, fast convergence property and quality of solution. This paper presents a combination of DE and variable neighborhood search (VNS) to improve the quality of solution and convergence speed. Differential evolution (DE) is first introduced to find the locality of the solution, and then VNS is applied to tune the solution. To validate the DE-VNS method, it is applied to four test systems with non-smooth cost functions. The effectiveness of the DE-VNS over other techniques is shown in general.
Rocznik
Strony
593--603
Opis fizyczny
Bibliogr. 23 poz., rys., wz.
Twórcy
autor
  • Department of Electrical Engineering, Anna University Regional Centre, Coimbatore, India, jasper @gmail.com
  • Department of Electrical Engineering, Anna University Regional Centre, Coimbatore, India, t.aruldoss @gmail.com
Bibliografia
  • [1] Lee F.N., Breipohl A.M., Reserve constrained economic dispatch with prohibited operating zones. IEEE Trans. Power Syst. 8(1): 246-254 (1993).
  • [2] Chen P.H., Chang H.C., Large-scale economic dispatch by genetic algorithm. IEEE Trans. Power Syst. 10(4): 1919-1926 (1995).
  • [3] Aruldoss Albert Victoire T., Ebenezer Jeyakumar A., Reserve constrained Dynamic Dispatch of units with valve point effects. IEEE Trans. Power Syst. 20(3): 1273-1282 (2005).
  • [4] Walters D.C., Sheble G.B., Genetic algorithm solution of economic dispatch with valve point loading. IEEE Trans. Power Syst. 8(3): 1325-1332 (1993).
  • [5] Yang H.T., Yang P.C., Huang C.L., Evolutionary programming based economic dispatch for units with non-smooth incremental fuel cost functions, IEEE Trans. Power Syst. 11(1): 112-118 (1996).
  • [6] Yalcinoz T., Short M.J., Neural networks approach for solving economic dispatch problem with transmission capacity constraints. IEEE Trans. Power Syst. 13: 307-313 (1998).
  • [7] Gaing Z.L., Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans. Power Syst. 18(3): 1187-1195 (2003).
  • [8] Coelho L.S., Mariani V.C., Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect. IEEE Trans. Power Syst. (2006).
  • [9] Storn R., Price K., Differential Evolution – A simple and efficient adaptive scheme for global optimization over continuous spaces. Journal of Global Optimization 11: 341-359 (1997).
  • [10] Hansen P., Mladennovic N., Variable neighborhood search: Principles and applications.European Journal of Operational research 130: 449-467 (2001).
  • [11] Sinha N., Chakrabarti R., Chattopadhyay P.K., Evolutionary programming techniques for economic load dispatch. IEEE Trans. Evol. Compt. 7: 83-94 (2003).
  • [12] Wang S.K., Cjiou J.P., Liu C.W., Non-smooth/non-convex economic dispatch by a novel hybrid differentia evolution algorithm, IET Gener. Transm. Distrib. 1(5): 793-803 (2007).
  • [13] Wong K.P., Wong Y.W., Genetic and genetic/simulated-annealing approaches to economic dispatch. Proc. Inst. Elect. Eng., Gener. Transm. Distrib. 141(5): 507-513 (1994).
  • [14] Price K., Differential Evolution: A Fast and Simple Numerical Optimizer. Biennial Conference of the North American Fuzzy Information Processing Society, NAFIPS: 524-527 (1996).
  • [15] Storn R., On the Usage of Differential Evolution for Function Optimization. Biennial Conference of the North American Fuzzy Information Processing Society, NAFIPS: 519-523 (1996).
  • [16] Bhattacharya A. Chattopadhyay P.K., A modified particle swarm optimization for solving the nonconvex economic dispatch. IEEE international conference, ECTICON: 78-81, (2009).
  • [17] Lingfeng Wang Chanan Singh., Reserve-constrained multiarea environmental/economic dispatch based on particle swarm optimization with local search. Engineering Applications of Artificial Intelligence 22(2): 298-307 (2009).
  • [18] Basu M., Economic environmental dispatch using multi-objective differential evolution. Applied Soft Computing 11(2): 2845-2853 (2011).
  • [19] Xiaohui Yuan, Anjun Su, Yanbin Yuan, Hao Nie, Liang Wang, An improved PSO for dynamic load dispatch of generators with valve-point effects. Energy 34(1): 67-74 (2009).
  • [20] Youlin Lu, Jianzhong Zhou, Hui Qin, Yinghai Li, Yongchuan Zhang., An adaptive hybrid differentia evolution algorithm for dynamic economic dispatch with valve-point effects. Expert Systems with Applications 37(7): 4842-4849 (2010).
  • [21] Dakuo He, Gang Dong, Fuli Wang, Zhizhong Mao., Optimization of dynamic economic dispatch with valve-point effect using chaotic sequence based differential evolution algorithms. Energy Conversion and Management 52(2): 1026-1032 (2011).
  • [22] Duvvuru and Swarup., A Hybrid Interior Point Assisted Differential Evolution Algorithm for Economic Dispatch, IEEE Transactions on Power Systems, 26(2): 541-549, (2011).
  • [23] Aniruddha Bhattacharya, Pranab Kumar Chattopadhyay, Hybrid Differential Evolution With Biogeography-Based Optimization for Solution of Economic Load Dispatch. IEEE Transactions on Power Systems 25(4): 1955-1964 (2010).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2ff5061e-bf3a-434e-951d-15b6c1838d2a
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