A novel approach for unit commitment problem via an effective hybrid particle swarm optimization
This paper presents a new approach via hybrid particle swarm optimization (HPSO) scheme to solve the unit commitment (UC) problem. HPSO proposed in this paper is a blend of binary particle swarm optimization (BPSO) and real coded particle swarm optimization (RCPSO). The UC problem is handled by BPSO...
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Institute of Electrical and Electronics Engineers (IEEE)
2006
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my.um.eprints.51942017-07-08T04:02:15Z http://eprints.um.edu.my/5194/ A novel approach for unit commitment problem via an effective hybrid particle swarm optimization Ting, T.O. Rao, M.V.C. Loo, C.K. T Technology (General) This paper presents a new approach via hybrid particle swarm optimization (HPSO) scheme to solve the unit commitment (UC) problem. HPSO proposed in this paper is a blend of binary particle swarm optimization (BPSO) and real coded particle swarm optimization (RCPSO). The UC problem is handled by BPSO, while RCPSO solves the economic load dispatch problem. Both algorithms are run simultaneously, adjusting their solutions in search of a better solution. Problem formulation of the UC takes into consideration the minimum up and down time constraints, start-up cost, and spinning reserve and is defined as the minimization of the total objective function while satisfying all the associated constraints. Problem formulation, representation, and the simulation results for a ten generator-scheduling problem are presented. Results clearly show that HPSO is very competent in solving the UC problem in comparison to other existing methods. Institute of Electrical and Electronics Engineers (IEEE) 2006 Article PeerReviewed Ting, T.O. and Rao, M.V.C. and Loo, C.K. (2006) A novel approach for unit commitment problem via an effective hybrid particle swarm optimization. IEEE Transactions on Power Systems, 21 (1). pp. 411-418. ISSN 0885-8950 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1583740 |
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T Technology (General) Ting, T.O. Rao, M.V.C. Loo, C.K. A novel approach for unit commitment problem via an effective hybrid particle swarm optimization |
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This paper presents a new approach via hybrid particle swarm optimization (HPSO) scheme to solve the unit commitment (UC) problem. HPSO proposed in this paper is a blend of binary particle swarm optimization (BPSO) and real coded particle swarm optimization (RCPSO). The UC problem is handled by BPSO, while RCPSO solves the economic load dispatch problem. Both algorithms are run simultaneously, adjusting their solutions in search of a better solution. Problem formulation of the UC takes into consideration the minimum up and down time constraints, start-up cost, and spinning reserve and is defined as the minimization of the total objective function while satisfying all the associated constraints. Problem formulation, representation, and the simulation results for a ten generator-scheduling problem are presented. Results clearly show that HPSO is very competent in solving the UC problem in comparison to other existing methods. |
| format |
Article |
| author |
Ting, T.O. Rao, M.V.C. Loo, C.K. |
| author_facet |
Ting, T.O. Rao, M.V.C. Loo, C.K. |
| author_sort |
Ting, T.O. |
| title |
A novel approach for unit commitment problem via an effective hybrid particle swarm optimization |
| title_short |
A novel approach for unit commitment problem via an effective hybrid particle swarm optimization |
| title_full |
A novel approach for unit commitment problem via an effective hybrid particle swarm optimization |
| title_fullStr |
A novel approach for unit commitment problem via an effective hybrid particle swarm optimization |
| title_full_unstemmed |
A novel approach for unit commitment problem via an effective hybrid particle swarm optimization |
| title_sort |
novel approach for unit commitment problem via an effective hybrid particle swarm optimization |
| publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
| publishDate |
2006 |
| url |
http://eprints.um.edu.my/5194/ http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1583740 |
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1643687510395584512 |
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13.211869 |