Abstract
In this paper an approach to robustness analysis in evolutionary multi-objective optimization is applied to the problem of locating and sizing capacitors for reactive power compensation (VAR planning) in electric radial distribution networks. The main goal of this evolutionary algorithm is to find a non-dominated front containing the most robust non-dominated solutions also ensuring diversity along the front. A concept of degree of robustness is incorporated into the evolutionary algorithm, which intervenes in the computation of the fitness value assigned to solutions. Two objective functions of technical and economical nature are explicitly considered in the mathematical model: minimization of system losses and minimization of capacitor installation costs. Constraints refer to quality of service, power flow, and technical requirements. It is assumed that some input data are subject to perturbations, both concerning the objective functions and the constraints coefficients.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Branke, J.: Creating Robust Solutions by means of an Evolutionary Algorithm. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 119–128. Springer, Heidelberg (1998)
Branke, J.: Efficient Evolutionary Algorithms for Searching Robust Solutions. In: Adaptive Computing in Design and Manufacture (ACDM), pp. 275–286. Springer, Heidelberg (2000)
Jin, Y., Sendhoff, B.: Trade-Off between Performance and Robustness: An Evolutionary Multiobjective Approach. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 237–251. Springer, Heidelberg (2003)
Tsutsui, S., Ghosh, A.: Genetic Algorithm with a Robust Solution Searching Scheme. IEEE Transactions on Evolutionary Computation 1(3), 201–219 (1997)
Deb, K., Gupta, H.: Introducing Robustness in Multiple-Objective Optimization. Evolutionary Computation 14(4), 463–494 (Winter 2006)
Hughes, E.J.: Evolutionary Multi-Objective Ranking with Uncertainty and Noise. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 329–343. Springer, Heidelberg (2001)
Teich, J.: Pareto-Front Exploration with Uncertain Objectives. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 314–328. Springer, Heidelberg (2001)
Li, M., Azarm, S., Aute, V.: A Multi-Objective Genetic Algorithm for Robust Design Optimization. In: Genetic and Evolutionary Computation Conference (GECCO 2005), Washington, DC, USA, pp. 771–778 (2005)
Barrico, C., Antunes, C.H.: Robustness Analysis in Multi-Objective Optimization Using a Degree of Robustness Concept. In: IEEE Congress on Evolutionary Computation (CEC 2006), Vancouver, Canada, pp. 1887–1892. IEEE Press, Los Alamitos (2006)
Barrico, C., Antunes, C.H.: A New Approach to Robustness Analysis in Multi-Objective Optimization. In: 7th International Conference on Multi-Objective Programming and Goal Programming (MOPGP 2006), Loire Valley, City of Tours, France (2006)
Barrico, C., Antunes, C.H.: An Evolutionary Approach for Assessing the Degree of Robustness of Solutions to Multi-Objective Models. In: Ong, Y.S., Yaochu, J., Shengxiang, Y. (eds.) Evolutionary Computation in Dynamic and Uncertain Environments. Studies in Computational Intelligence, vol. 51, pp. 565–582. Springer, Heidelberg (2007)
Jin, Y., Branke, J.: Evolutionary Optimization in Uncertain Environments – A Survey. IEEE Transactions on Evolutionary Computation 9(3), 1–15 (2005)
Antunes, C.H., Barrico, C., Gomes, A., Pires, D., Martins, A.: On the Use of Evolutionary Algorithms for Reactive Power Compensation in Electrical Distribution Networks - Experiments on a Case Study. In: 6th Metaheuristics International Conference (MIC 2005), Viena, Austria, pp. 514–519 (2005)
Antunes, C.H., Pires, D., Barrico, C., Gomes, A., Martins, A.: A Multi-objective Evolutionary Algorithm for Reactive Power Compensation in Distribution Networks. In: Applied Energy. Elsevier, Amsterdam (2009) (accepted)
Pires, D.F., Martins, A.G., Antunes, C.H.: A multiobjective model for VAR planning in radial distribution networks based on Tabu Search. IEEE Transactions on Power Systems 20(2), 1089–1094 (2005)
Fonseca, C.M., Fleming, P.J.: An Overview of Evolutionary Algorithms in Multiobjective Optimization. Evolutionary Computation 3(1), 1–16 (1995)
Coello, C., Veldhuizen, D., Lamont, G.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, Dordrecht (2002)
Gomes, A., Antunes, C.H., Martins, A.: A multiple objective evolutionary approach for the design and selection of load control strategies. IEEE Transactions on Power Systems 19(2), 1173–1180 (2004)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley and Sons, New York (2001)
Iba, K.: Reactive power optimization by genetic algorithm. IEEE Transactions on Power Systems 9(2), 685–692 (1994)
Lee, K.-Y., Bai, X., Park, Y.-M.: Optimization method for reactive power planning by using a modified simple genetic algorithm. IEEE Transactions on Power Systems 10(4), 1843–1850 (1995)
Kim, K.-H., You, S.-K.: Voltage profile improvement by capacitor placement and control in unbalanced distribution systems using GA. In: IEEE Power Engineering Society Summer Meeting, vol. 2, pp. 800–805. IEEE Press, Los Alamitos (1999)
Levitin, G., Kalyuhny, A., Shenkman, A., Chertkov, M.: Optimal capacitor allocation in distribution systems using a genetic algorithm and a fast energy loss computation technique. IEEE Transactions on Power Delivery 15(2), 623–628 (2000)
Delfanti, M., Granelli, G., Marannino, P., Montagna, M.: Optimal capacitor placement using deterministic and genetic algorithms. IEEE Transactions on Power Systems 15(3), 1041–1046 (2000)
Baran, B., Vallejos, J., Ramos, R., Fernandez, U.: Reactive power compensation using a multi-objective evolutionary algorithm. In: IEEE Porto Power Tech. Conference. IEEE Press, Los Alamitos (2001)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions Evolutionary Computation 6(2), 182–197 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Barrico, C., Antunes, C.H., Pires, D.F. (2009). Robustness Analysis in Evolutionary Multi-Objective Optimization Applied to VAR Planning in Electrical Distribution Networks. In: Cotta, C., Cowling, P. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2009. Lecture Notes in Computer Science, vol 5482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01009-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-01009-5_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01008-8
Online ISBN: 978-3-642-01009-5
eBook Packages: Computer ScienceComputer Science (R0)