A new technique for solving the multi-objective optimization problem using hybrid approach

doi:10.1007/s11708-014-0311-0

Frontiers in Energy

2014, Vol. 8

Issue (4): 490-503 https://doi.org/10.1007/s11708-014-0311-0

本期目录

A new technique for solving the multi-objective optimization problem using hybrid approach

Mimoun YOUNES(

),Khodja FOUAD,Belabbes BAGDAD

Faculty of Technology, Djillali Liabes University, Sidi Bel Abbes 22000, Alegria

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Abstract：

Energy efficiency, which consists of using less energy or improving the level of service to energy consumers, refers to an effective way to provide overall energy. But its increasing pressure on the energy sector to control greenhouse gases and to reduce CO₂ emissions forced the power system operators to consider the emission problem as a consequential matter besides the economic problems. The economic power dispatch problem has, therefore, become a multi-objective optimization problem. Fuel cost, pollutant emissions, and system loss should be minimized simultaneously while satisfying certain system constraints. To achieve a good design with different solutions in a multi-objective optimization problem, fuel cost and pollutant emissions are converted into single optimization problem by introducing penalty factor. Now the power dispatch is formulated into a bi-objective optimization problem, two objectives with two algorithms, firefly algorithm for optimization the fuel cost, pollutant emissions and the real genetic algorithm for minimization of the transmission losses. In this paper the new approach (firefly algorithm-real genetic algorithm, FFA-RGA) has been applied to the standard IEEE 30-bus 6-generator. The effectiveness of the proposed approach is demonstrated by comparing its performance with other evolutionary multi-objective optimization algorithms. Simulation results show the validity and feasibility of the proposed method.

Key words： economic power dispatch (EPD) firefly algorithm (FFA) real genetic algorithm (RGA) hybrid method

收稿日期: 2013-11-02 出版日期: 2015-01-09

Corresponding Author(s): Mimoun YOUNES

引用本文:

. [J]. Frontiers in Energy, 2014, 8(4): 490-503.
Mimoun YOUNES,Khodja FOUAD,Belabbes BAGDAD. A new technique for solving the multi-objective optimization problem using hybrid approach. Front. Energy, 2014, 8(4): 490-503.

链接本文:

https://academic.hep.com.cn/fie/CN/10.1007/s11708-014-0311-0
https://academic.hep.com.cn/fie/CN/Y2014/V8/I4/490

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1	Carpentier J. Contribution to the study of economic dispatch. Bulletin of the French Society of Electricians, 1962, 3: 431–447
2	Vanderbei J R, Shanno F D. An interior-point algorithm for nonconvex nonlinear programming. Computational Optimization and Applications, 1999, 13(1–3): 231–252 https://doi.org/10.1023/A:1008677427361
3	Bottero M H, Caliana E D, Fahmideh-Vojdani A R. Economic dispatch using the reduced hessian. IEEE Transactions on Power Apparatus and Systems, 1982, 101(10): 3679–3688 https://doi.org/10.1109/TPAS.1982.317053
4	Reid G E, Hasdorf L. Economic dispatch using quadratic programming. IEEE Transactions on Power Apparatus and Systems, 1973, 92(6): 2015–2023 https://doi.org/10.1109/TPAS.1973.293582
5	Stott B, Hobson E. Power system security control calculation using linear programming. IEEE Transactions on Power Apparatus and Systems, 1978, 97(5): 1713–1720 https://doi.org/10.1109/TPAS.1978.354664
6	Stott B, Hobson E. Power system security control calculation using linear programming. IEEE Transactions on Power Apparatus and Systems, 1978, 97(5): 1721–1731 https://doi.org/10.1109/TPAS.1978.354665
7	Momoh J A, Zhu J Z. Improved interior point method for OPF problems. IEEE Transactions on Power Systems, 1999, 14(3): 1114–1120 https://doi.org/10.1109/59.780938
8	Sun D I, Ashley B, Brewer B, Hughes A, Tinney W F. Optimal power flow by Newton approach. IEEE Transactions on Power Apparatus and Systems, 1984, PAS-103(10): 2864–2880 https://doi.org/10.1109/TPAS.1984.318284
9	Bahiense L, Oliveira G C, Pereira M, Granville S. A mixed integer disjunctive model for transmission network expansion. IEEE Transactions on Power Systems, 2001, 16(3): 560–565 https://doi.org/10.1109/59.932295
10	Dusonchet Y P, El-Abiad A H. Transmission planning using discrete dynamic optimization. IEEE Transactions on Power Apparatus and Systems, 1997, 92: 1358–1371
11	Haffner S, Monticelli A, Garcia A, Romero R. Specialised branch and bound algorithm for transmission network expansion planning. IEE Proceedings-Generation, Transmission and Distribution, 2001, 148(5): 482–488 https://doi.org/10.1049/ip-gtd:20010502
12	Glover F. Tabu search—Part I. ORSA Journal on Computing, 1986, 1(3): 190–206
13	Kirkpatrick S, Gelatt C D, Vecchi M P. Optimisation by simulated annealing. Science, 1983, 220(4598): 671–680 https://doi.org/10.1126/science.220.4598.671
14	Lai L L, Ma J T, Yokoyama R, Zhao M. Improved genetic algorithms for optimal power flow under both normal and contingent operation states. International Journal of Electrical Power & Energy Systems, 1997, 19(5): 287–292 https://doi.org/10.1016/S0142-0615(96)00051-8
15	Yuryevich J, Wong K P. Evolutionary programming based optimal power flow algorithm. IEEE Transactions on Power Systems, 1999, 14(4): 1245–1250 https://doi.org/10.1109/59.801880
16	Mousavian S, Valenzuela J, Wang J. Real-time data reassurance in electrical power systems based on artificial neural networks. Electric Power Systems Research, 2013, 96: 285–295 https://doi.org/10.1016/j.epsr.2012.11.015
17	Abido M A. Optimal power flow using particle swarm optimization. International Journal of Electrical Power & Energy Systems, 2002, 24(7): 563–571 https://doi.org/10.1016/S0142-0615(01)00067-9
18	Song Y H, Chou C S, Stonham T J. Combined heat and power economic dispatch by improved ant colony search algorithm. Electric Power Systems Research, 1999, 52(2): 115–121 https://doi.org/10.1016/S0378-7796(99)00011-5
19	Fesanghary M, Mahdavi M, Minary-Jolandan M, Alizadeh Y. Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Computer Methods Applied Mechanics and Engineering, 2008, 197(33–40): 3080–3091
20	Abido M A. A novel multiobjective evolutionary algorithm for environmental/economic power dispatch. Electric Power Systems Research, 2003, 65(1): 71–81 https://doi.org/10.1016/S0378-7796(02)00221-3
21	Abido M A. Multiobjective evolutionary algorithms for electric power dispatch problem. IEEE Transactions on Evolutionary Computation, 2006, 10(3): 315–329 https://doi.org/10.1109/TEVC.2005.857073
22	Yang X S. Review of meta-heuristic and generalized evolutionary walk algorithm. International Journal of Bio-Inspired Computation, 2011, 3(2): 77–84 https://doi.org/10.1504/IJBIC.2011.039907
23	Amiri B, Hossain L, Crawford J W, Wigand R T. Community detection in complex networks: multi-objective enhanced firefly algorithm. Knowledge-Based Systems, 2013, 46: 1–11 https://doi.org/10.1016/j.knosys.2013.01.004
24	Gandomi A H, Yang X S, Talatahari S, Alavi A H. Firefly algorithm with chaos. Communications in Nonlinear Science and Numerical Simulation, 2013, 18(1): 89–98 https://doi.org/10.1016/j.cnsns.2012.06.009
25	Senapati M R, Dash P K. Local linear wavelet neural network based breast tumor classification using firefly algorithm. Neural Computing & Applications, 2013, 22(7–8): 1591–1598 https://doi.org/10.1007/s00521-012-0927-0
26	Fister I, Yang X S, Brest J, Fister I Jr. Modified firefly algorithm using quaternion representation. Expert Systems with Applications, 2013, 40(18): 7220–7230 https://doi.org/10.1016/j.eswa.2013.06.070
27	Kazem A, Sharifi E, Hussain F K, Saberi M, Hussain O K. Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Applied Soft Computing, 2013, 13(2): 947–958 https://doi.org/10.1016/j.asoc.2012.09.024
28	Yang X S. Multiobjective firefly algorithm for continuous optimization. Engineering with Computers, 2013, 29(2): 175–184 https://doi.org/10.1007/s00366-012-0254-1
29	Yang X S. Nature-Inspired Metaheuristic Algorithms. Bristol: Luniver Press, 2008
30	Yang X S. Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, 2010: 221–230
31	Basu B, Mahanti G K. Firefly and artificial bees colony algorithm for synthesis of scanned and broadside linear array antenna. Progress in Electromagnetic Research B, 2011, 32: 169–190 https://doi.org/10.2528/PIERB11053108
32	Yazdani A, Jayabarathi T, Ramesh V, Raghunathan T. Combined heat and power economic dispatch problem using firefly algorithm. Frontiers in Energy, 2013, 7(2): 133–139 https://doi.org/10.1007/s11708-013-0248-8
33	Holland J. Adaptation in Natural and Artificial Systems. Ann Arbor, USA: The University of Michigan Press, 1975
34	Goldberg D E. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Educational Publishers Inc, 1989
35	Younes M, Rahli M, Koridak L A. Optimal power flow based on hybrid genetic algorithm. Journal of Information Science and Engineering, 2007, 23: 1801–1816
36	Michalewicz Z. A survey of constraint handling techniques in evolutionary computation methods. In: Mcdonnell J R, Renolds R G, Fogel D B, eds. Proceedings of the 4th Annual Conference on Evolutionary Programming, MIT Press, 1995, 135–155
37	Caorsi S, Massa A, Pastorino M. A computational technique based on a real-coded genetic algorithm for microwave imaging purposes. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(4): 1697–1708 https://doi.org/10.1109/36.851968
38	Oyama A, Obayashi S, Nakamura T. Real-coded adaptive range genetic algorithm applied to transonic wing optimization. Applied Soft Computing, 2001, 1(3): 179–187 https://doi.org/10.1016/S1568-4946(01)00017-5
39	Niknam T, Narimani M R, Jabbari M, Malekpour A R. A modified shuffle frog leaping algorithm for multi-objective optimal power flow. Energy, 2011, 36(11): 6420–6432 https://doi.org/10.1016/j.energy.2011.09.027
40	Saini A, Chaturvedi D K, Saxena A K. Optimal power flow solution: a GA-fuzzy system approach. International Journal of Emerging Electric Power Systems, 2006, 5(2): 1–21 https://doi.org/10.2202/1553-779X.1091
41	Ongsakul W, Tantimaporn T. Optimal power flow by improved evolutionary programming. Electric Power Components and Systems, 2006, 34(1): 79–95 https://doi.org/10.1080/15325000691001458
42	Abido M A. Optimal power flow using tabu search algorithm. Electric Power Components and Systems, 2002, 30(5): 469–483 https://doi.org/10.1080/15325000252888425
43	Yuryevich J, Wong K P. Evolutionary programming based optimal power flow algorithm. IEEE Transactions on Power Systems, 1999, 14(4): 1245–1250 https://doi.org/10.1109/59.801880
44	Narimani M R, Azizipanah-Abarghooee R, Zoghdar-Moghadam-Shahrekohne B, Gholami K. A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type. Energy, 2013, 49: 119–136 https://doi.org/10.1016/j.energy.2012.09.031
45	Sailaja Kunari M, Maheswarapu S. Enhanced genetic algorithm based computation technique for multi-objective optimal power flow. International Journal of Electrical Power & Energy Systems, 2010, 32(6): 736–742 https://doi.org/10.1016/j.ijepes.2010.01.010
46	Vaisakh K, Srinivas L R. A genetic evolving ant direction DE for OPF with non-smooth cost functions and statistical analysis. Energy, 2010, 35(8): 3155–3171 https://doi.org/10.1016/j.energy.2010.03.051
47	Rezaei Adaryani M, Karami A. Artificial bee colony algorithm for solving multi-objective optimal power flow problem. International Journal of Electrical Power & Energy Systems, 2013, 53: 219– 230 https://doi.org/10.1016/j.ijepes.2013.04.021
48	Perez-Guerrero R E, Cedefio-Maldonado J R. Differential evolution based economic environmental power dispatch. In: Proceedings of the 37th Annual North American Power Symposium. Piscataway, USA: NJ IEEE Service Center, 2005, 191–197
49	Wang L, Singh C. Balancing risk and cost in fuzzy economic dispatch including wind power penetration based on particle swarm optimization. Electric Power Systems Research, 2008, 78(8): 1361–1368 https://doi.org/10.1016/j.epsr.2007.12.005

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