This paper presents a new hybrid approach that combines Modified Priority List (MPL) with Charged System Search (CSS), termed MPL-CSS, to solve one of the most crucial power system’s operational optimization problems, known as unit commitment (UC) scheduling. The UC scheduling problem is a mixed-integer nonlinear problem, highly-dimensional and extremely constrained. Existing meta-heuristic UC solution methods have the problems of stopping at a local optimum and slow convergence when applied to large-scale, heavily-constrained UC applications. In the first step of the proposed method, initial hourly optimum solutions of UC are obtained by Modified Priority List (MPL); however, the obtained UC solution may still be possible to be further improved. Therefore, in the second step, the CSS is utilized to achieve higher quality solutions. The UC is formulated as mixed integer linear programming to ensure the tractability of the results. The proposed method is successfully applied to a popular test system up to 100 units generators for both 24-hr and 168-hr system. Computational results show that both solution cost and execution time are superior to those of published methods.
References
[1]
Shahidehpour, M., Yamin, H. and Li, Z. (2002) Market Operations in Electric Power Systems. Wiley, New York. https://doi.org/10.1002/047122412X
[2]
Padhy, N.P. (2004) Unit Commitment—A Bibliographical Survey. IEEE Transactions on Power Systems, 19, 1196-1205.
https://doi.org/10.1109/TPWRS.2003.821611
[3]
Jeong, Y.W., Park, J.B., Jang, S.H. and Lee, K.Y. (2010) A New Quantum-Inspired Binary PSO: Application to Unit Commitment Problems for Power Systems. IEEE Transactions on Power Systems, 25, 1486-1495.
https://doi.org/10.1109/TPWRS.2010.2042472
[4]
Delarue, E., Cattrysse, D. and D’haeseleer, W. (2013) Enhanced Priority List Unit Commitment Method for Power Systems with a High Share of Renewable. Electric Power Systems Research, 10, 115-123. https://doi.org/10.1016/j.epsr.2013.07.014
[5]
Sheble, G.B. (1990) Solution of the Unit Commitment Problem by the Method of Unit Periods. IEEE Transactions on Power Systems, 5, 257-260.
https://doi.org/10.1109/59.49114
[6]
Senjyu, T., Shimabukuro, K., Uezato, K. and Funabashi, Y.A. (2003) Fast Technique for Unit Commitment Problem by Extended Priority List. IEEE Transactions on Power Systems, 18, 882-888. https://doi.org/10.1109/TPWRS.2003.811000
[7]
Kumar, S.S. and Palanisamy, V. (2007) A Dynamic Programming Based Fast Computation Hopfield Neural Network for Unit Commitment and Economic Dispatch. Electric Power Systems Research, 77, 917-925.
https://doi.org/10.1016/j.epsr.2006.08.005
[8]
Pang, C.K., Sheble, G.B. and Albuyeh, F. (1981) Evaluation of Dynamic Programming Based Methods and Multiple Area Representation for Thermal Unit Commitments. IEEE Transactions on Power Apparatus and Systems, PAS-100, 1212-1218.
https://doi.org/10.1109/TPAS.1981.316592
[9]
Benhamida, F. and Abdelbar, B. (2010) Enhanced Lagrangian Relaxation Solution to the Generation Scheduling Problem. International Journal of Electrical Power & Energy Systems, 32, 1099-1105. https://doi.org/10.1016/j.ijepes.2010.06.007
[10]
Ongsakul, W. and Petcharaks, N. (2004) Unit Commitment by Enhanced Adaptive Lagrangian Relaxation. IEEE Transactions on Power Systems, 19, 620-628.
https://doi.org/10.1109/TPWRS.2003.820707
[11]
Virmani, S., Adrian, E.C., Imhof, K. and Muhherjee, S. (1989) Implementation of a Lagrangian Based Unit Commitment Problem. IEEE Transactions on Power Systems, 4, 1373-1380. https://doi.org/10.1109/59.41687
[12]
Cohen, A.I. amd Yoshimura, M. (1983) A Branch-and-Bound Algorithm for Unit Commitment. IEEE Transactions on Power Apparatus and Systems, PAS-102, 444-451. https://doi.org/10.1109/TPAS.1983.317714
[13]
Ohuch, A. and Kaji, I. (1975) A Branch-and-Bound Algorithm for Start-Up and Shutdown Problem of Thermal Generating Units. Institute of Electrical Engineering, Jpn. 95-B, 461-468.
[14]
Carrión, M. and Arroyo, J.M. (2006) A Computationally Efficient Mixed-Integer Linear Formulation for the Thermal Unit Commitment Problem. IEEE Transactions on Power Systems, 21, 1371-1378.
https://doi.org/10.1109/TPWRS.2006.876672
[15]
Norouzi, M.R., Ahmadi, A., Nezhad, A.E. and Amir, G. (2014) Mixed Integer Programming of Multi-Objective Security-Constrained Hydro/Thermal Unit Commitment. Renewable and Sustainable Energy Reviews, 33, 585-593.
[16]
Appala, V.S. and Erlich, I. (2008) A New Approach for Solving the Unit Commitment Problem by Adaptive Particle Swarm Optimization. Proceedings of the 2008 IEEE Power & Energy Society General Meeting, Pittsburgh, 20-24 July 2008, 1-6.
https://doi.org/10.1109/PES.2008.4596390
[17]
Kazarlis, S.A., Bakirtzis, A.G. and Petridis, V.A (1996) Genetic Algorithm Solution to the Unit Commitment Problem. IEEE Transactions on Power Systems, 11, 83-92.
https://doi.org/10.1109/59.485989
[18]
Swarup, K.S. and Yamashiro, S. (2002) Unit Commitment Solution Methodology Using Genetic Algorithm. IEEE Transactions on Power Systems, 17, 87-91.
https://doi.org/10.1109/59.982197
[19]
Senjyu, T., Yamashiro, H., Shimabukuro, K., Uezato, K. and Funabashi, T. (2003) Fast Solution Technique for Large-Scale Unit Commitment Problem Using Genetic Algorithm. IEE Proceedings—Generation, Transmission and Distribution, 150, 753-760. https://doi.org/10.1049/ip-gtd:20030939
[20]
Christober, A.R.C. and Mohan, M.R. (2007) An Evolutionary Programming Based Simulated Annealing Method for Solving the Unit Commitment Problem. International Journal of Electrical Power & Energy Systems, 29, 540-550.
https://doi.org/10.1016/j.ijepes.2006.12.001
[21]
Venkatesh, P., Gnanadass, R. and Padhy, N.P. (2003) Comparision and Application of Evolutionary Programming Techniques to Combined Economic Emission Dispatch with Line Flow Constraints. IEEE Transactions on Power Systems, 18, 688- 692. https://doi.org/10.1109/TPWRS.2003.811008
[22]
Dudek, G. (2010) Adaptive Simulated Annealing Schedule to the Unit Commitment Problem. Electric Power Systems Research, 80, 465-472.
https://doi.org/10.1016/j.epsr.2009.10.019
[23]
Simopoulos, D.N., Kavatza, S.D. and Vournas, C.D. (2006) Unit Commitment by an Enhanced Simulated Annealing Algorithm. IEEE Transactions on Power Systems, 21, 68-76. https://doi.org/10.1109/TPWRS.2005.860922
[24]
Zhao, B., Guo, C.X., Bai, B.R. and Cao, Y.J. (2006) An Improved Particle Swarm Optimization Algorithm for Unit Commitment. International Journal of Electrical Power & Energy Systems, 28, 482-490. https://doi.org/10.1016/j.ijepes.2006.02.011
[25]
Pappala, V.S. and Erlich, I.A. (2008) New Approach for Solving the Unit Commitment Problem by Adaptive Particle Swarm Optimization. Proceedings of IEEE Power & Energy Society General Meeting, Pittsburgh, 20-24 July 2008, 1-6.
https://doi.org/10.1109/pes.2008.4596390
[26]
Ting, T.O., 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, 411-418.
https://doi.org/10.1109/TPWRS.2005.860907
[27]
Shukla, A. and Singh, S.N. (2016) Advanced Three-Stage Pseudo-Inspired Weight- Improved Crazy Particle Swarm Optimization for Unit Commitment Problem. Energy, 96, 23-36. https://doi.org/10.1016/j.energy.2015.12.046
[28]
Eslamian, M., Hosseinian, S.H. and Vahidi, B. (2009) Bacterial Foraging-Based Solution to the Unit-Commitment Problem. IEEE Transactions on Power Systems, 24, 1478-1488. https://doi.org/10.1109/TPWRS.2009.2021216
[29]
Ebrahimi, J., Hosseinian, S.H. and Gharehpetian, G.B. (2011) Unit Commitment Problem Solution Using Shuffled Frog Leaping Algorithm. IEEE Transactions on Power Systems, 26, 573-581. https://doi.org/10.1109/TPWRS.2010.2052639
[30]
Barati, M. and Farsangi, M.M. (2014) Solving Unit Commitment Problem by a Binary Shuffled Frog Leaping Algorithm. IET Generation, Transmission & Distribution, 8, 1050-1060. https://doi.org/10.1049/iet-gtd.2013.0436
[31]
Jeong, Y.W., Park, J.B., Shin, J.R. and Lee, K.Y. (2009) A Thermal Unit Commitment Approach Using an Improved Quantum Evolutionary Algorithm. Electric Power Components and Systems, 37, 770-786.
https://doi.org/10.1080/15325000902762331
[32]
Xiaohui, Y., Anjun, S., Hao, N., Yanbin, Y. and Liang, W. (2011) Unit Commitment Problem Using Enhanced Particle Swarm Optimization Algorithm. Soft Computing, 15, 139-148. https://doi.org/10.1007/s00500-010-0541-y
[33]
Datta, D. (2013) Unit Commitment Problem with Ramp Rate Constraint Using a Binary-Real-Coded Genetic Algorithm. Applied Soft Computing, 13, 3873-3883.
https://doi.org/10.1016/j.asoc.2013.05.002
[34]
Singhal, P.K., Naresh, R. and Sharma, V. (2013) Binary Fish Swarm Algorithm for Profit-Based Unit Commitment Problem in Competitive Electricity Market with Ramp Rate Constraints. IET Generation, Transmission & Distribution, 7, 298-308
[35]
.
Wu, Z. and Chow, T.W.S. (2015) Binary Neighbourhood Field Optimisation for Unit Commitment Problems. IET Generation, Transmission & Distribution, 9, 1697-1707.
[36]
Kamboj, V.K., Bath, S.K. and Dhillon, J.S. (2016) Implementation of Hybrid Harmony Search/Random Search Algorithm for Single Area Unit Commitment Problem. International Journal of Electrical Power & Energy Systems, 82, 228-249.
https://doi.org/10.1016/j.ijepes.2015.11.045
[37]
Reddy, G.V.S., Ganesh, V. and Rao, C.S. (2016) Implementation of Clustering Based Unit Commitment Employing Imperialistic Competition Algorithm. International Journal of Electrical Power & Energy Systems, 82, 621-628.
https://doi.org/10.1016/j.ijepes.2016.04.043
[38]
Kaveh, A. and Talatahari, S. (2010) A Novel Heuristic Optimization Method: Charged System Search. Acta Mechanica, 213, 267-289.
https://doi.org/10.1007/s00707-009-0270-4
[39]
Sishaj, P.S., Narayana, P.P. and Anand, R.S. (2006) An Ant Colony System App- roach for Unit Commitment Problem. International Journal of Electrical Power & Energy Systems, 28, 315-323. https://doi.org/10.1016/j.ijepes.2005.12.004
[40]
Chu, C.C. and Tsai, M.S. (2013) Application of Novel Charged System Search with Real Number String for Distribution System Loss Minimization. IEEE Transactions on Power Systems, 28, 3600-3609. https://doi.org/10.1109/TPWRS.2013.2264836
