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Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index
Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index
- Author(s): T. Niknam ; M.R. Narimani ; J. Aghaei ; R. Azizipanah-Abarghooee
- DOI: 10.1049/iet-gtd.2011.0851
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- Author(s): T. Niknam 1 ; M.R. Narimani 1 ; J. Aghaei 1 ; R. Azizipanah-Abarghooee 1
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View affiliations
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Affiliations:
1: Department of Electronic and Electrical Engineering, Shiraz University of Technology, Shiraz, Iran
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Affiliations:
1: Department of Electronic and Electrical Engineering, Shiraz University of Technology, Shiraz, Iran
- Source:
Volume 6, Issue 6,
June 2012,
p.
515 – 527
DOI: 10.1049/iet-gtd.2011.0851 , Print ISSN 1751-8687, Online ISSN 1751-8695
© The Institution of Engineering and Technology
Published
The study presents an improved particle swarm optimisation (IPSO) method for the multi-objective optimal power flow (OPF) problem. The proposed multi-objective OPF considers the cost, loss, voltage stability and emission impacts as the objective functions. A fuzzy decision-based mechanism is used to select the best compromise solution of Pareto set obtained by the proposed algorithm. Furthermore, to improve the quality of the solution, particularly to avoid being trapped in local optima, this study presents an IPSO that profits from chaos queues and self-adaptive concepts to adjust the particle swarm optimisation (PSO) parameters. Also, a new mutation is applied to increase the search ability of the proposed algorithm. The 30-bus IEEE test system is presented to illustrate the application of the proposed problem. The obtained results are compared with those in the literatures and the superiority of the proposed approach over other methods is demonstrated.
Inspec keywords: particle swarm optimisation; power system stability; fuzzy set theory; Pareto optimisation; air pollution control; decision theory; load flow
Other keywords:
Subjects: Pollution detection and control; Power system control; Combinatorial mathematics; Game theory; Optimisation techniques
References
-
-
1)
- M. SailajaKumari , S. Maheswarapu . Enhanced genetic algorithm based computation technique for multi-objective optimal power flow solution. Int. J. Electr. Power Energy Syst. , 6 , 736 - 742
-
2)
- S. Jeyadevi , S. Baskar , C.K. Babulal , M. Willjuice Iruthayarajan . Solving multi objective optimal reactive power dispatch using modified NSGA-II. Electr. Power Energy Syst. , 2 , 219 - 228
-
3)
- Rosales Hernandez, Y., Hiyama, T.: `Minimization of voltage deviations, power losses and control actions in a transmission power system', 15thInt. Conf. on Intelligent System Applications to Power Systems (ISAP), 8–12 November 2009, p. 1–5.
-
4)
- T. Niknam , H.Z. Meymand , H.D. Mojarrad . An efficient algorithm for multi-objective optimal operation management of distribution network considering fuel cell power plants. Energy , 1 , 119 - 132
-
5)
- R. Caponetto , L. Fortuna , S. Fazzino , M.G. Xibilia . Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Trans. Evol. Comput. , 3 , 289 - 304
-
6)
- J. Kennedy , R. Eberhart . Particle swarm optimization. IEEE Int. Conf. Neural Netw. , 1942 - 1948
-
7)
- E. Elbeltagi , T. Hegazy , D. Grierson . Comparison among five evolutionary-based optimization algorithms. Adv. Eng. Inf. , 1 , 43 - 53
-
8)
- M.A. Abido . A novel multi objective evolutionary algorithm for environmental/economic power dispatch. Electr. Power Syst. Res. , 1 , 71 - 81
-
9)
- B.K. Panigrahi , V. RavikumarPandi , S. Das , S. Das . Multiobjective fuzzy dominance based bacterial foraging algorithm to solve economic emission dispatch problem. Energy , 12 , 4761 - 4770
-
10)
- T. Niknam , M.R. Narimani , J. Aghaie , M. Nayeripour . Modified honey bee mating optimization to solve dynamic optimal power flow with considering generator constraints. IET Gener. Transm. Distrib. , 10 , 989 - 1002
-
11)
- T. Niknam . An efficient hybrid evolutionary algorithm based on PSO and HBMO algorithms for multi-objective distribution feeder reconfiguration. Energy Convers. Manage. , 8 , 2074 - 2082
-
12)
- A. Al-Muhawesh Tareq , S. Qamber Isa . The established mega watt linear programming-based optimal power flowmodel applied to the real power 56-bus system in eastern province of Saudi Arabia. Energy , 1 , 12 - 21
-
13)
- T. Bouktir , L. Slimani , B. Mahdad . Optimal power dispatch for large-scale power system using stochastic search algorithms. Int. J. Power Energy Syst. , 1 , 1 - 10
-
14)
- J. Yuryevich , K.P. Wong . Evolutionary programming based optimal power flow algorithm. IEEE Trans. Power Syst. , 4 , 1245 - 1250
-
15)
- T. Niknam , E. Azad-Farsani . A hybrid self-adaptive particle swarm optimization and modified shuffled frog leaping algorithm for distribution feeder reconfiguration. Eng. Appl. Artif. Intell. , 8 , 1340 - 1349
-
16)
- Van Veldhuizen, D.A., Lamont, G.B.: `Multiobjective evolutionary algorithm research: a history and analysis', TR-98-03, Technical, 1998.
-
17)
- M.R. AlRashidi , M.E. El-Hawary . Hybrid particle swarm optimization approach for solving the discrete OPF problem considering the valve loading effects. IEEE Trans. Power Syst. , 4 , 2030 - 2038
-
18)
- A.L. Costa , A. Simões Costa . Energy and ancillary service dispatch through dynamic optimal power flow. Electr. Power Syst. Res. , 8 , 1047 - 1055
-
19)
- L. Slimani , T. Bouktir . Economic power dispatch of power system with pollution control using multi objective ant colony optimization. Int. J. Comput. Intell. Res. , 2 , 145 - 153
-
20)
- T. Niknam . A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem. Appl. Energy , 1 , 327 - 339
-
21)
- R.C. Burchet , H.H. Happ , D.R. Vierath . Quadratically convergent optimal power flow. IEEE Trans. Power Appar. Syst. , 11 , 3267 - 3276
-
22)
- M.A. Abido . Optimal power flow using tabu search algorithm. Electr. Power Compon. Syst. , 5 , 469 - 483
-
23)
- O. Alsac , B. Stott . Optimal load flow with steady-state security. IEEE Trans. Power Appar. Syst. , 3 , 745 - 751
-
24)
- K. Andrew , Z. Haiyang . Optimization of wind turbine energy and power factor with an evolutionary computation algorithm. Energy , 3 , 1324 - 1332
-
25)
- X. Yan , V.H. Quintana . Improving an interior point based OPF by dynamic adjustments of step sizes and tolerances. IEEE Trans. Power Syst. , 2 , 709 - 717
-
26)
- S. Samir , Z. Khaled . Modified differential evolution algorithm for optimal power flow with non-smooth cost functions. Energy Convers. Manag. , 11 , 3036 - 3042
-
27)
- W. Zhang , Y. Liu . Multi-objective reactive power and voltage control based on fuzzy optimization strategy and fuzzy adaptive particle swarm. Int. J. Electr. Power Energy Syst. , 525 - 532
-
28)
- R.A. Jabr , B.C. Pal . Intermittent wind generation in optimal power flow dispatching. IEE Proc. Gener. Transm. Distrib. , 1 , 66 - 74
-
29)
- T. Niknam , H. Zeinoddini-Meymand , H. Doagou-Mojarrad . A Practical multi-objective PSO algorithm for optimal operation management of distribution network with regard to fuel cell power plants. Renew. Energy , 1529 - 1544
-
30)
- K. Vaisakh , L.R. Srinivas . Genetic evolving ant direction HDE for OPF with non-smooth cost functionsand statistical analysis. Expert Syst. Appl. , 3 , 2046 - 2062
-
31)
- T. Niknam , H. DoagouMojarrad , H. ZeinoddiniMeymand . A novel hybrid particle swarm optimization for economic dispatch with valve-point loading effects. Energy Convers. Manage. , 4 , 1800 - 1809
-
32)
- C.A. Coello Coello , G. Toscano Pulido , M. Salazar Lechuga . Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. , 3 , 256 - 279
-
33)
- T. Niknam , M.R. Narimani , M. Jabbari , A.R. Malekpour . A modified shuffle frog leaping algorithm for multi-objective optimal power flow. Energy , 6420 - 6432
-
34)
- A.G. Bakistzis , P.N. Biskas , C.E. Zoumas , V. Petridis . Optimal power flow by enhanced genetic algorithm. IEEE Trans. Power Syst. , 2 , 229 - 236
-
35)
- J.B. Park , Y.W. Jeong , H.H. Kim , J.R. Shin . An improved particle swarm optimization for economic dispatch with valve-point effect. Int. J. Innov. Energy Syst. Power , 1 , 1 - 7
-
36)
- A.A. El-Keib , H. Ma , J.L. Hart . Economic dispatch in view of the clean air act of 1990. IEEE Trans. Power Syst. , 2 , 972 - 778
-
37)
- A. Saini , D.K. Chaturvedi , A.K. Saxena . Optimal power flow solution: a GA-fuzzy system approach. Int. J. Emerging Electr. Power Syst. , 2 , 1 - 21
-
38)
- H. Habiabollahzadeh , G. Luo , A. Semlyen . Hydrothermal optimal power flow based on combined linear and nonlinear programming methodology. IEEE Trans. Power Syst. , 2 , 530 - 537
-
1)
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