Advanced SearchSearch Tips
Multiobjective Optimal Reactive Power Flow Using Elitist Nondominated Sorting Genetic Algorithm: Comparison and Improvement
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Multiobjective Optimal Reactive Power Flow Using Elitist Nondominated Sorting Genetic Algorithm: Comparison and Improvement
Li, Zhihuan; Li, Yinhong; Duan, Xianzhong;
  PDF(new window)
Elitist nondominated sorting genetic algorithm (NSGA-II) is adopted and improved for multiobjective optimal reactive power flow (ORPF) problem. Multiobjective ORPF, formulated as a multiobjective mixed integer nonlinear optimization problem, minimizes real power loss and improves voltage profile of power grid by determining reactive power control variables. NSGA-II-based ORPF is tested on standard IEEE 30-bus test system and compared with four other state-of-the-art multiobjective evolutionary algorithms (MOEAs). Pareto front and outer solutions achieved by the five MOEAs are analyzed and compared. NSGA-II obtains the best control strategy for ORPF, but it suffers from the lower convergence speed at the early stage of the optimization. Several problem-specific local search strategies (LSSs) are incorporated into NSGA-II to promote algorithm's exploiting capability and then to speed up its convergence. This enhanced version of NSGA-II (ENSGA) is examined on IEEE 30 system. Experimental results show that the use of LSSs clearly improved the performance of NSGA-II. ENSGA shows the best search efficiency and is proved to be one of the efficient potential candidates in solving reactive power optimization in the real-time operation systems.
Optimal reactive power flow;Elitist nondominated sorting genetic algorithm;Pareto optimal;Multiobjective evolutionary algorithms;
 Cited by
Game Model-based Co-evolutionary Algorithm with Non-dominated Memory and Euclidean Distance Selection Mechanisms for Multi-objective Optimization,Park, Seung-Min;Ko, Kwang-Eun;Park, Jun-Heong;Sim, Kwee-Bo;

International Journal of Control, Automation, and Systems, 2011. vol.9. 5, pp.924-932 crossref(new window)
Adaptive multiple evolutionary algorithms search for multi-objective optimal reactive power dispatch, International Transactions on Electrical Energy Systems, 2014, 24, 6, 780  crossref(new windwow)
Game model-based co-evolutionary algorithm with non-dominated memory and Euclidean distance selection mechanisms for multi-objective optimization, International Journal of Control, Automation and Systems, 2011, 9, 5, 924  crossref(new windwow)
Voltage Stability Constrained Optimal Power Flow Using NSGA-II, Computational Water, Energy, and Environmental Engineering, 2013, 02, 01, 1  crossref(new windwow)
Hwa-Chang Song, Byong-Jun Lee, and Young-Hwan Moon, "An Interior Point Method based Reactive Optimal Power Flow Incorporating Margin Enhancement Constraints," KIEE International Transactions on Power Engineering, Vol.5-A, No.2, pp.152-158, 2005.

D. Pudjianto, S. Ahmed, and G. Strbac, "Allocation of VAR supportusing LP and NLP based optimal power flows," IEE Proc. Generation, Transmission, and Distribution, Vol.149, No.4, pp.377-383, 2002. crossref(new window)

R. He, G. A. Taylor, and Y. H. Song, "Multi-objective optimal reactive power flow including voltage security and demand profile classification," International Journal of Electrical Power & Energy Systems, Vol. 30, pp.327-336, 2008. crossref(new window)

Y.-j. Zhang and Z. Ren, "Real-time optimal reactive power dispatch using multi-agent technique," Electric Power Systems Research, Vol.69, pp.259-265, 2004. crossref(new window)

B. Venkatesh and R. Ranjan, "Fuzzy EP algorithm and dynamic data structure for optimal capacitor allocation in radial distribution systems," Generation, Transmission and Distribution, IEE Proceedings-, Vol. 153, pp.80-88, 2006. crossref(new window)

C. Yuan-Lin, "Weak bus-oriented optimal multi-objective VAr planning," Power Systems, IEEE Transactions on, Vol.11, pp.1885-1890, 1996. crossref(new window)

C. Yuan-Lin and L. Chun-Chang, "Optimal multiobjective VAr planning using an interactive satisfying method," Power Systems, IEEE Transactions on, Vol. 10, pp.664-670, 1995. crossref(new window)

N. Srinivas and K. Deb, "Multiobjective optimization using nondominated sorting in genetic algorithms," Evol. Comp. J., Vol.2, pp.221–248,1995. crossref(new window)

C. M. Fonseca and P. J. Fleming., "Multiobjective optimization and multiple constraint handling with evolutionary algorithms-part I: a unified formulation," System, Man, Cybernetics, IEEE Transaction on, Vol.3, pp.26-37, 1998. crossref(new window)

Horn, J. Nafpliotis, N. Goldberg, D. E. "A niched Pareto genetic algorithm for multiobjective optimization Evolutionary Computation," in Proceedings of the First IEEE World Congress on Computational Intelligence. 1994, crossref(new window)

E. Zitzler and L. Thiele, "Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach," Evolutionary Computation, IEEE Transactions on, Vol.3, pp.257-271, 1999. crossref(new window)

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," Evolutionary Computation, IEEE Transactions on, Vol.6, pp.182-197, 2002. crossref(new window)

Abido M A, Bakhashwain J M "Optimal VAR dispatch using a multiobjective evolutionary algorithm," International Journal of Electrical Power & Energy Systems. Vol.27, pp.13-20, 2005. crossref(new window)

Varadarajan M and Swarup K S. "Solving multiobjective optimal power flow using differential evolution," IET Generation, Transmission & Distribution. Vol.2, No.5, pp.720-730, 2008. crossref(new window)

N. Krasnogor and J. Smith, "A tutorial for competent memetic algorithms: Model, taxonomy and design issues," IEEE Trans. Evol. Comput., Vol.9, No.5, pp. 474-488, Oct., 2005. crossref(new window)

W. Hart, N. Krasnogor, J. Smith, Eds. Recent Advances in Memetic Algorithms. Berlin, Germany: Springer-Verlag, 2004.

Iba K. "Reactive power optimization by genetic algorithm," IEEE Transactions on Power Systems. Vol.9, No.2, pp.685-692, 1994. crossref(new window)

Bhagwan D D and Patvardhan C. "A new hybrid evolutionary strategy for reactive power dispatch," Electric Power Systems Research. Vol.65, No.2, pp.83-90, 2003. crossref(new window)

Bakirtzis A G Biskas P N Zoumas C E, et al. "Optimal power flow by enhanced genetic algorithm," IEEE Transactions on Power Systems. Vol.17, No.2, pp.229-236, 1994. crossref(new window)

Goldberg D E Genetic. Algorithms in Search, Optimization, and Machine Learning. Reading. Reading, Addison-Wesley, 1989

A. Abraham, L. Jain, R. Goldberg. Evolutionary multiobjective optimization: theoretical advances and applications. New York: Springer Science, 2005

E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Swiss Federal Institute of Technology, Lausanne, Switzerlnd, Tech. Rep. TIK-Rep. 103, 2001.

Z. Boming, C. Shousun, High Electric Power Network Analysis, Publisher of Tsinghua University, Beijing, 1996, pp.311-313.

Jaszkiewicz A (2004) On the computational efficiency of multiple objective metaheuristics: the knapsack problem case study. Eur J Oper Res 158:418-433 crossref(new window)

Murata T, Kaige S, and Ishibuchi H. "Generalization of dominance relation-based replacement rules for memetic EMO algorithms," Lect Notes Comput Sci 2723, pp: 1233-1244, 2003

Hisao Ishibuchi and Kaname Narukawa. Some Issues on the Implementation of Local Search in Evolutionary Multiobjective Optimization. GECCO 2004, LNCS 3102, pp.1246-1258, 2004.