Hybrid MODE/TS for Environmental Dispatch and Economic Dispatch

Article Sidebar

PDF
Published: Sep 9, 2019
DOI: https://doi.org/10.37936/ecti-eec.2019171.215439
Keywords:
Differential Evolution Tabu Search Economic Dispatch Environmental Dispatch Optimization

Main Article Content

Suppakarn Chansareewittaya
School of Information Technology, Mae Fah Luang University

Abstract

The economic dispatch and environmental dispatch are used as problem formulas for this paper. The problem formulations are formulated as multi-objective problems. The hybrid Multi-Objective Differential Evolution/Tabu Search (MODE/TS) is developed to solve this multi-objective problems. The proposed method is applied to 2 test systems. The first test system which contains 6 generations is used as test system without any losses. Another test system which contain 10 generations is used as test system with non-flat losses by using losses co-efficiency. The constraints are used to control the power of each generation in all test systems. Test results from hybrid MODE/TS are compared with test results from original MODE under the same constraints and parameter settings. The test results indicate that the hybrid MODE/TS can determine the better optimal pareto solutions and average solution than those from original MODE. Moreover, the hybrid MODE/TS gives the outstanding solution which is far away from original DE.

Article Details

How to Cite
Chansareewittaya, S. (2019). Hybrid MODE/TS for Environmental Dispatch and Economic Dispatch. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 17(1), 78–86. https://doi.org/10.37936/ecti-eec.2019171.215439
Issue
Vol. 17 No. 1 (2019): Regular Issue (February 2019)
Section
Publish Article
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.

- Creative Commons Copyright License

The journal allows readers to download and share all published articles as long as they properly cite such articles; however, they cannot change them or use them commercially. This is classified as CC BY-NC-ND for the creative commons license. 

- Retention of Copyright and Publishing Rights

The journal allows the authors of the published articles to hold copyrights and publishing rights without restrictions.

References

[1] R. Jomthong, P. Jirapong and S. Chansareewittaya, “Optimal choice and allocation of distributed generations using Evolutionary Programming”, Proceeding of the CIGRE-AORC
2011, October 2011.

[2] S. Chansareewittaya and P. Jirapong, “Power Transfer Capability enhancement with Optimal Maximum Number of FACTS Controllers using Evolutionary Programming,” Proceeding of the
37rd Annual Conference of the IEEE Industrial Electronics Society (IEEE-IECON), November 2011.

[3] J. McCall, “Genetic algorithms for modelling and optimisation,” Journal of Computational and Applied Mathematics, Vol. 184, Issue 1, 1 December 2005, pp. 205-222.

[4] F. Glover, “Tabu Search, Part I,” ORSA Journal on Computing, Vol. 1, no. 3, pp. 190-206, Summer, 1989.

[5] F. Glover, “Tabu Search, Part II,” ORSA Journal on Computing, Vol. 2, No. 1, pp. 4-32, Winter, 1990.

[6] S. Chansareewittaya and P. Jirapong, “Total Transfer Capability Enhancement with Optimal Number of UPFC using Hybrid TSSA,” Proceeding of the IEEE ECTI-CON 2012, Phetchaburi, Thailand, May 2012.

[7] K. Y. Lee and A. E. Mohamed, Modern Heuristics Optimizaion Techniques, New York, John Wiley & Sons, 2008.

[8] L. L. Lai, Intelligent System Applications in Power Engineering: Evolutionary Programming and Neural Networks, New York, John Wiley & Sons, 1998.

[9] M. R. AlRashidi and M. E. El-Hawary, “Applications of computational intelligence techniques for solving the revived optimal power flow problem,” Electric Power Systems Research, vol. 79, issue 4, pp. 694-702, 2009.

[10] M. R. AlRashidi and M. E. El-Hawary, “Applications of computational intelligence techniques for solving the revived optimal power flow problem,” Electric Power Systems Research, vol. 79, issue 4, pp. 694-702, 2009.

[11] S. Chansareewittaya and P. Jirapong, “Optimal Allocation of Multi-type FACTS Controllers by using Hybrid PSO for Total Transfer Capability Enhancement,” ECTI Transactions on Computer and Information Technology (ECTI-CIT), Vol. 9, No. 1 (2015), pp. 55-63, 2015.

[12] S. Chansareewittaya and P. Jirapong, “Optimal Allocation of Multi-type FACTS Controllers for Total Transfer Capability Enhancement using Hybrid Particle Swarm Optimization,” Proceedings of the IEEE ECTI-CON 2014, Nakhon Ratchasima, Thailand, May 2014.

[13] S. Chansareewittaya and P. Jirapong, “Power Transfer Capability Enhancement with Multitype FACTS Controllers using Hybrid Particle Swarm Optimization,” Electrical Engineering, Vol. 97, Issue 2 (2015), pp. 119-127,2015.

[14] S. Chansareewittaya, “Hybrid BA/TS for Economic Dispatch Considering the Generator Constraint,” Proceeding of 2017 International Conference on Digital Arts, Media and Technology
(ICDAMT), Thailand, March 2017.

[15] P. Bhasaputra and W. Ongsakul, “Optimal power flow with multitype FACTS devices by hybrid TS/SA approach,” Proceeding of the IEEE ICIT’02, Bangkok, Thailand, 2002.

[16] J. David Schaffer, “Multiple Objective Optimization with Vector Evaluated Genetic Algorithms,” Proceedings of the 1st International Conference on Genetic Algorithms, pp. 93-100.

[17] F. Mendoza, J. L. Bernal-Agustin, and J. A. Dominguez-Navarro, “NSGA and SPEA Applied to Multiobjective Design of Power Distribution Systems,” IEEE Transactions on Power Systems, Vol. 21, Issue: 4, 2006.

[18] N. Srinivas and K. Deb, “Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms,” Evolutionary Computation, MIT Press Journals, 1994, Vol. 2, Issue 3, pp. 221 - 248.

[19] H. Verdejo, D. Gonzalez, J. Delpiano, and C. Becker, “Tuning of Power System Stabilizers using Multiobjective Optimization NSGA II,” IEEE Latin America Transactions, Vol. 13, Issue 8, pp. 2653–2660, 2015.

[20] M. R. Aghaebrahimi, R. K.Golkhandan, and S. Ahmadnia, “Application of non-dominated sorting genetic algorithm (NSGA-II) in siting and sizing of wind farms and FACTS devices for optimal power flow in a system,” Proceeding of the 2017 IEEE AFRICON, pp.44–50, 2017.

[21] W. Wu and L. Li, “Optimization Method of Control for Transformer DC Bias due to Multi Factors Based on NSGA-III,” Proceeding of the 9th International Conference on Intelligent HumanMachine Systems and Cybernetics (IHMSC) 2017, Vol. 2, pp. 308–311, 2017.

[22] B. V. Babu and A. M. Gujarathil, “Multiobjective differential evolution (MODE) for optimization of supply chain planning and management,” Proceeding of the IEEE Congress on Evolutionary Computation 2007, pp. 2732–2739

[23] D. C. Walters and G. B. Sheble, “Genetic Algorithm Solution of Economic Dispatch with Valve Point Loading,” IEEE Transaction in Power System, vol. 8, pp.1325–1332, 1993.

[24] M. Basu, “Economic environmental dispatch using multi-objective differential evolution,” Applied Soft Computing, Vol. 11, pp. 2845–2853, 2011.

[25] R. Storn and K. Price, “Differential Evolution –A Simple and Efficient Heuristic for global Optimization over Continuous Spaces,” Journal of Global Optimization, Vol. 11, Issue 4, pp. 341–359, 1997.

[26] F. W. Glover and M. Laguna, Tabu Search, Springerlink publishing, 1997.

[27] S. Chansareewittaya and P. Jirapong, “Power transfer capability enhancement with Optimal Number of FACTS Controllers using hybrid TSSA,” Proceedings of the IEEE SouthEastCon 2012, Florida, USA., March 2012.

[28] T. Phongkidakarn and D. Rerkpreeapong, “Economic dispatch using cuckoo search algorithm,” Kasetsart Engineering Journal, Vol. 27(90), pp. 57–66, 2014.