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Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem

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Book cover Computational Intelligence

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 1))

Abstract

The potential of Multiobjective Evolutionary Algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem is comprehensively presented and discussed. In this work, the Non-dominated Sorting Genetic Algorithm (NSGA), Niched Pareto Genetic Algorithm (NPGA), and Strength Pareto Evolutionary Algorithm (SPEA) have been developed and successfully applied to the Environmental/Economic electric power Dispatch (EED) problem. These multiobjective evolutionary algorithms have been individually examined and applied to a standard test system. A hierarchical clustering algorithm is imposed to provide the power system operator with a representative and manageable Pareto set. Moreover, a fuzzy set theory based approach is developed to extract one of the Pareto-optimal solutions as the best compromise solution. Several optimization runs have been carried out on different cases of problem complexity. The results of the MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the performance of MOEA have been assessed and evaluated using different measures of diversity, distribution, and quality of the obtained non-dominated solutions.

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References

  1. Abido, M.A.: A New Multiobjective Evolutionary Algorithm for Environmental/Economic Power Dispatch. In: IEEE Power Engineering Society Summer Meeting, Vancouver, Canada, pp. 1263–1268 (2001)

    Google Scholar 

  2. Abido, M.A.: A Novel Multiobjective Evolutionary Algorithm for Solving Environmental/Economic Power Dispatch Problem. In: 14th Power Systems Computation Conference PSCC 2002, Session 41, Paper 2, Seville, Spain (2002)

    Google Scholar 

  3. Abido, M.A.: A Niched Pareto Genetic Algorithm for Multiobjective Environmental/Economic Dispatch. International Journal of Electrical Power and Energy Systems 25, 79–105 (2003)

    Article  Google Scholar 

  4. Abido, M.A.: A novel multiobjective evolutionary algorithm for environmental/economic power dispatch. Electric Power Systems Research 65, 71–81 (2003)

    Article  Google Scholar 

  5. Abido, M.A.: Environmental/Economic Power Dispatch Using Multiobjective Evolutionary Algorithms. IEEE Trans. on Power Systems 18, 1529–1537 (2003)

    Article  Google Scholar 

  6. Abido, M.A.: Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem. IEEE Trans. on Evolutionary Computation 10, 315–329 (2006)

    Article  Google Scholar 

  7. Abou El-Ela, A.A., Abido, M.A.: Optimal Operation Strategy for Reactive Power Control. Modelling, Simulation & Control, Part A 41, 19–40 (1992)

    Google Scholar 

  8. Brodesky, S.F., Hahn, R.W.: Assessing the Influence of Power Pools on Emission Constrained Economic Dispatch. IEEE Trans. on Power Systems 1, 57–62 (1986)

    Article  Google Scholar 

  9. Xu, J., Chang, C., Wang, X.: Constrained Multiobjective Global Optimization of Longitudinal Interconnected Power System by Genetic Algorithm. IEE Proc.-Gener. Transm. Distrib. 143, 435–446 (1996)

    Article  Google Scholar 

  10. Chang, C., Wong, K., Fan, B.: Security-Constrained Multiobjective Generation Dispatch Using Bicriterion Global Optimization. IEE Proc.-Gener. Transm. Distrib. 142, 406–414 (1995)

    Article  Google Scholar 

  11. Coello, C.A.C., Christiansen, A.D.: MOSES: A Multiobjective Optimization Tool for Engineering Design. Engineering Optimization 31, 337–368 (1999)

    Article  Google Scholar 

  12. Coello, C.A.C., Hernandez, F.S., Farrera, F.A.: Optimal Design of Reinforced Concrete Beams Using genetic Algorithms. Int. J. of Expert systems with Applications 12, 101–108 (1997)

    Article  Google Scholar 

  13. Coello, C.A.C., Pulido, G.T., Lechuga, M.S.: Handling Multiple Objectives with Particle Swarm Optimization. IEEE Trans. on Evolutionary Computation 8, 256–279 (2004)

    Article  Google Scholar 

  14. Das, D.B., Patvardhan, C.: New Multi-objective Stochastic Search Technique for Economic Load Dispatch. IEE Proc.-Gener. Transm. Distrib. 145, 747–752 (1998)

    Article  Google Scholar 

  15. Deb, K., Pratab, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6, 182–197 (2002)

    Article  Google Scholar 

  16. Dhillon, J.S., Parti, S.C., Kothari, D.P.: Stochastic Economic Emission Load Dispatch. Electric Power Systems Research 26, 179–186 (1993)

    Article  Google Scholar 

  17. El-Keib, A.A., Ma, H., Hart, J.L.: Economic Dispatch in View of the Clean Air Act of 1990. IEEE Trans. On Power Systems 9, 972–978 (1994)

    Article  Google Scholar 

  18. Farag, A., Al-Baiyat, S., Cheng, T.C.: Economic Load Dispatch Multiobjective optimization Procedures Using Linear Programming Techniques. IEEE Trans. on Power Systems 10, 731–738 (1995)

    Article  Google Scholar 

  19. Farina, M., Amato, P.: A Fuzzy Definition of Optimality for Many Criteria Optimization Problems. IEEE Trans. On Systems, Man, and Cybernetics-Part A: Systems and Humans 34, 315–326 (2004)

    Article  Google Scholar 

  20. Farina, M., Deb, K., Amato, P.: Dynamic Multiobjective Optimization Problems: Test Cases, Approximations, and Applications. IEEE Trans. on Evolutionary Computation 8, 425–442 (2004)

    Article  Google Scholar 

  21. Fuller, R., Carlsson, C.: Fuzzy Multiple Criteria Decision Making: Recent Developments. Fuzzy Set and Systems 78, 139–153 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  22. Fonseca, C.M., Fleming, P.J.: An Overview of Evolutionary Algorithms in Multiobjective Optimization. Evolutionary Computation 3, 1–16 (1995)

    Article  Google Scholar 

  23. Granelli, G.P., Montagna, M., Pasini, G.L., Marannino, P.: Emission Constrained Dynamic Dispatch. Electric Power Systems Research 24, 56–64 (1992)

    Article  Google Scholar 

  24. Helsin, J.S., Hobbs, B.F.: A Multiobjective Production Costing Model for Analyzing Emission Dispatching and Fuel Switching. IEEE Trans. on Power Systems 4, 836–842 (1989)

    Article  Google Scholar 

  25. Herrera, F., Lozano, M., Verdegay, J.L.: Tackling Real-Coded genetic Algorithms: operators and Tools for Behavioral Analysis. Artificial Intelligence Review 12, 265–319 (1998)

    Article  MATH  Google Scholar 

  26. Horn, J., Nafpliotis, N., Goldberg, D.E.: A Niched Pareto Genetic Algorithm for Multiobjective Optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, vol. 1, pp. 67–72. IEEE Service Center, Piscataway (1994)

    Google Scholar 

  27. Huang, C.M., Yang, H.T., Huang, C.L.: Bi-Objective Power Dispatch Using Fuzzy Satisfaction-Maximizing Decision Approach. IEEE Trans. on Power Systems 12, 1715–1721 (1997)

    Article  Google Scholar 

  28. Kwang, Y.L., El-Sharkawi, M.A.: Modern Heuristic Optimization Techniques Theory and Applications to Power Systems. IEEE Press/ Wiley-Interscience, USA (2008)

    Google Scholar 

  29. Jensen, M.T.: Reducing the Run-Time Complexity of Multiobjective EAs: The NSGA-II and Other Algorithms. IEEE Trans. on Evolutionary Computation 7, 503–515 (2003)

    Article  Google Scholar 

  30. Park, J.-B., Lee, K.-S., Shin, J.-R., Kwang, Y.L.: A Particle Swarm Optimization for Economic Dispatch With Nonsmooth Cost Functions. IEEE Trans. on Power Systems 20, 34–42 (2005)

    Article  Google Scholar 

  31. Laumanns, M., Thiele, L., Zitzler, E.: Running Time Analysis of Multiobjective Evolutionary Algorithms on Pseudo-Boolean Functions. IEEE Trans. on Evolutionary Computation 8, 170–182 (2004)

    Article  Google Scholar 

  32. Lingfeng, W., Chanan, S.: Stochastic Combined Heat and Power Dispatch Based on Multi-Objective Particle Swarm Optimization. In: Proceedings of 2006 IEEE Power Engineering Society General Meeting (2006)

    Google Scholar 

  33. Lis, J., Eiben, A.E.: A Multi-Sexual Genetic Algorithm for Multiobjective Optimization. In: Proceedings of the 1996 International Conference on Evolutionary Computation IEEE ICEC 1996, Nagoya, Japan, pp. 59–64 (1996)

    Google Scholar 

  34. Mahfoud, S.: Niching Methods for Genetic Algorithms, Ph. D. Thesis, Univ. of Illinois at Urbana-Champaign (1995)

    Google Scholar 

  35. Morse, J.N.: Reducing the Size of Nondominated Set: Pruning by Clustering. Computers and Operations Research 7, 55–66 (1980)

    Article  Google Scholar 

  36. Sakawa, M., Yano, H., Yumine, T.: An Interactive Fuzzy Satisficing Method for Multiobjective Linear Programming Problems and Its Application. IEEE Trans. On Systems, Man, and Cybernetics 17, 654–661 (1987)

    Article  MathSciNet  Google Scholar 

  37. Schaffer, J.D.: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In: Proceedings of the International Conference on Genetic Algorithms and Their Applications, Pittsburgh, July 24-26, 1985, pp. 93–100 (1985)

    Google Scholar 

  38. Selvakumar, A.I., Thanushkodi, K.: A New particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems. IEEE Trans. on Power Systems 22, 42–51 (2007)

    Article  Google Scholar 

  39. Shin, S.Y., Lee, I.H., Kim, D., Zhang, B.T.: Multiobjective Evolutionary Optimization of DNA Sequences for Reliable DNA Computing. IEEE Trans. on Evolutionary Computation 9, 143–158 (2005)

    Article  Google Scholar 

  40. Srinivas, N., Deb, K.: Multiobjective Function Optimization Using Nondominated Sorting Genetic Algorithms. Evolutionary Computation 2, 221–248 (1994)

    Article  Google Scholar 

  41. Srinivasan, D., Chang, C.S., Liew, A.C.: Multiobjective Generation Schedule using Fuzzy Optimal Search Technique. IEE Proc.-Gener. Transm. Distrib. 141, 231–241 (1994)

    Google Scholar 

  42. Srinivasan, D., Tettamanzi, A.: An Evolutionary Algorithm for Evaluation of Emission Compliance Options in View of the Clean Air Act Amendments. IEEE Trans. on Power Systems 12, 152–158 (1997)

    Article  Google Scholar 

  43. Talaq, J.H., El-Hawary, F., El-Hawary, M.E.: A Summary of Environmental/Economic Dispatch Algorithms. IEEE Trans. on Power Systems 9, 1508–1516 (1994)

    Article  Google Scholar 

  44. Veldhuizen, D.A.V., Lamont, G.B.: Multiobjective Evolutionary algorithms: Analyzing the State-of-the-Art. Evolutionary Computation 8, 125–147 (2000)

    Article  Google Scholar 

  45. Yokoyama, R., Bae, S.H., Morita, T., Sasaki, H.: Multiobjective Generation dispatch Based on Probability Security Criteria. IEEE Trans. on Power Systems 3, 317–324 (1988)

    Article  Google Scholar 

  46. Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8, 173–195 (2000)

    Article  Google Scholar 

  47. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. TIK-Report, No. 103 (2001)

    Google Scholar 

  48. Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms – A Comparative Case Study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998, vol. 1498, pp. 292–301. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  49. Zitzler, E., Thiele, L.: An Evolutionary Algorithm for Multiobjective optimization: The Strength Pareto Approach. TIK-Report, No. 43 (1998)

    Google Scholar 

  50. Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Trans. on Evolutionary Computation 3, 257–271 (1999)

    Article  Google Scholar 

  51. Gaing, Z.-L.: Particle Swarm Optimization to Solving the Economic Dispatch Considering the Generator Constraints. IEEE Trans. on Power Systems 18, 1187–1195 (2003)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia

    Mohammad A. Abido

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  1. Mohammad A. Abido
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Editors and Affiliations

  1. School of Computer Science, Cardiff University , 5 The Parade,Roath, CF24 3AA, Cardiff, UK

    Christine L. Mumford

  2. University of South Australia Adelaide , Mawson Lakes Campus, South Australia, Australia

    Lakhmi C. Jain

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© 2009 Springer-Verlag Berlin Heidelberg

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Abido, M.A. (2009). Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem. In: Mumford, C.L., Jain, L.C. (eds) Computational Intelligence. Intelligent Systems Reference Library, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01799-5_3

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  • DOI: https://doi.org/10.1007/978-3-642-01799-5_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01798-8

  • Online ISBN: 978-3-642-01799-5

  • eBook Packages: EngineeringEngineering (R0)

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