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Licensed Unlicensed Requires Authentication Published by De Gruyter April 21, 2016

Application of Multi-Objective Human Learning Optimization Method to Solve AC/DC Multi-Objective Optimal Power Flow Problem

  • Jia Cao EMAIL logo , Zheng Yan and Guangyu He

Abstract

This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm II (NSGAII) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAII method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

Award Identifier / Grant number: 51377103

Funding statement: This work was supported by the National Natural Science Foundation of China (51377103), and by the technology project of State Grid Corporation of China: Research on Multi-Level Decomposition Coordination of the Pareto Set of Multi-Objective Optimization Problem in Bulk Power System.

Acknowledgment

The authors greatly acknowledge the support from State Energy Smart Grid R&D Center (SHANGHAI). Besides, the authors would like to extend their sincere gratitude to ph.D. Wei Ye, from Ludwig-Maximilians-Universität München, Germany, for his instructive advice and useful materials.

References

1. Rosehart WD, Canizares CA, Quintana VH. Multiobjective optimal power flows to evaluate security costs in power networks. IEEE Trans Power Syst 2003;18:578–87.10.1109/TPWRS.2003.810895Search in Google Scholar

2. Mavrotas G. Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Appl Math Comput 2009;213:455–65.10.1016/j.amc.2009.03.037Search in Google Scholar

3. Roman C, Rosehart W. Evenly distributed pareto points in multi-objective optimal power flow. IEEE Trans Power Syst 2006;21:1011–12.10.1109/TPWRS.2006.873010Search in Google Scholar

4. Vahidinasab V, Jadid S. Normal boundary intersection method for suppliers’s strategic bidding in electricity markets: an environmental/economic approach. Energy Convers Manage 2010;51:1111–19.10.1016/j.enconman.2009.12.019Search in Google Scholar

5. Basu M. Multi-objective optimal power flow with facts devices. Energy Convers Manage 2011;52:903–10.10.1016/j.enconman.2010.08.017Search in Google Scholar

6. Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: nsga-II. IEEE Trans Evol Comput 2002;6:182–97.10.1109/4235.996017Search in Google Scholar

7. Ziizler E, Laumanns M, Thiele L. SPEA2: improving the strength pareto evolutionary algorithm. TIK-Report 2001; 103:1–21.Search in Google Scholar

8. Guo CX, Zhan JP, Wu QH. Dynamic economic emission dispatch based on group search optimizer with multiple producers. Elect Power Syst Res 2012;86:8–16.10.1016/j.epsr.2011.11.015Search in Google Scholar

9. Bhowmik AR, Chakraborty AK. Solution of optimal power flow using nondominated sorting multi objective gravitational search algorithm. Int J Elect Power Energy Syst 2014;62:323–34.10.1016/j.ijepes.2014.04.053Search in Google Scholar

10. Bhowmik AR, Chakraborty AK. Solution of optimal power flow using non dominated sorting multi objective opposition based gravitational search algorithm,. Int J Elect Power Energy Syst 2015;64:1237–50.10.1016/j.ijepes.2014.09.015Search in Google Scholar

11. Wang L, Ni HQ, Yang RX, Pardalos PM, Du X, Fei MR. An adaptive simplified human learning optimization algorithm. Inform Sci 2015;320:126–39.10.1016/j.ins.2015.05.022Search in Google Scholar

12. Wang L, Yang RX, Ni HQ, Ye W, Fei MR, Pardalos PM. A human learning optimization algorithm and its application to multi-dimensional knapsack problems. Appl Soft Comput 2015;34:736–43.10.1016/j.asoc.2015.06.004Search in Google Scholar

13. Wang L, Ni HQ, Yang RX, Fei MR, Ye W. A simple human learning optimization algorithm. Commun Computer Inform Sci 2014;462:56–65.10.1007/978-3-662-45261-5_7Search in Google Scholar

14. Ye W. Multi-objective node placement of large-scale industrial wireless sensor networks based on human learning optimization algorithm. Shanghai, China: Shanghai University, 2013:26–36. Master Degree DissertationSearch in Google Scholar

15. Li X, Du DJ, Pei JX, Menhas M. Probabilistic load flow calculation with latin hypercube sampling applied to grid-connected induction wind power system. Trans Inst Meas Control 2013;35:56–65.10.1177/0142331211410101Search in Google Scholar

16. Cao J, Yan Z, Li JH, Cao L. “Probabilistic power flow calculation for ac/dc hybrid systems including wind farms integration,” Electric Power Automation Equipment, accepted, 2016.Search in Google Scholar

17. Cao J, Ma HY, Liu Y, Yan Z, Liu FB, Yang LB. “Research on demand response strategy based on nodal price,” Power System Technology, accepted, 2016.Search in Google Scholar

18. Li ZY, Li MS, Wu QH. Energy saving dispatch with complex constraints: prohibited zones, valve point effect and carbon tax. Int J Elect Power Energy Syst 2014;63:657–66.10.1016/j.ijepes.2014.06.013Search in Google Scholar

19. Shabanpour-Haghighi A, Seifi AR, Niknam T. A modified teaching-learning based optimization for mullti-objective optimal power flow problem. Energy Convers Manage 2014;77:597–607.10.1016/j.enconman.2013.09.028Search in Google Scholar

20. Niknam T, Narimani MR, Azizipanah-Abarghooee R. A new hybrid algorithm for optimal power flow considering prohibited zones and valve point effect. Energy Convers Manage 2012;58:197–206.10.1016/j.enconman.2012.01.017Search in Google Scholar

21. Yao F, Dong ZY, Meng K, Xu Z, Iu HH, Wong KP. Quantum-inspired particle swarm optimization for power system operations considering wind power uncertainty and carbon tax in australia. IEEE Trans Ind Inform 2012;8:880–8.10.1109/TII.2012.2210431Search in Google Scholar

22. Vahidinasab V, Jadid S. Joint economic and emission dispatch in energy markets: a multiobjective mathematical programming approach. Energy 2010;35:1497–504.10.1016/j.energy.2009.12.007Search in Google Scholar

23. Abido MA. Multiobjective evolutionary algorithms for electric power dispatch problem. IEEE Trans Evol Comput 2006;10:315–29.10.1007/978-3-642-01799-5_3Search in Google Scholar

24. Niknam T, Narimani MR, Aghaei J, Azizipanah-Abarghooee R. Improved particle swarm optimization for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index. IET Gener, Trans Distrib 2012;6:515–27.10.1049/iet-gtd.2011.0851Search in Google Scholar

25. Niknam T, Narimani MR, Jabbari M, Malekpour AR. A modified shuffle frog leaping algorithm for multi-objective optimal power flow. Energy 2011;36:6420–32.10.1016/j.energy.2011.09.027Search in Google Scholar

26. Daryani N, Hagh MT, Teimourzadeh S. Adaptive group search optimization algorithm for multi-objective optimal power flow problem. Appl Soft Comput 2016;38:1012–24.10.1016/j.asoc.2015.10.057Search in Google Scholar

27. Wang XF, W. L F, Du ZC. Modern power system analysis. Beijing: Science Press, 2003.Search in Google Scholar

28. Cao J, Yan Z, Fan X, Xu XY, Li JH, Cao L. Ac/dc power flow computation based on improved levenberg-marquardt method. Int J Emerg Elect Power Syst 2015;16:1–13.10.1515/ijeeps-2014-0121Search in Google Scholar

29. Cao J, Yan Z, Xu XY. “A modified levenebrg-marquardt approach to explore the limit operation state of ac/dc hybrid system,” IEEE Power & Energy Society General Meeting, Denver, pp. 1–5, 2015.Search in Google Scholar

Published Online: 2016-4-21
Published in Print: 2016-6-1

©2016 by De Gruyter

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