Quantum Inspired Evolutionary Algorithm with a Novel Elitist Local Search Method for Scheduling of Thermal Units
Article Sidebar
Main Article Content
Abstract
The unit commitment problem is a complex and essential problem in the power generation field, which is solved to obtain the schedule of a large number of generating units to minimize the operating cost and the fulfillment of consumer load demand. The present work solves the unit commitment problem using quantum-inspired evolutionary algorithms with a novel elitist local search method (QIEA-ELS). The proposed algorithm solves the unit commitment problem efficiently and its applicability is verified on various unit test systems. The constraints are satisfied efficiently to find a feasible solution, the novel elitist search method is used to locally explore the search area around the fittest individual to find a better solution in its vicinity in genotype space represent by qubits. The solution of the unit commitment is carried out considering two small population sizes as suggested in earlier work by other authors using QIEA, though it can be extended using larger population size also. The computational time is also reduced by using the suggested method with a novel elitist local search (ELS) method. The results obtained after applying the proposed algorithm are found to better as compared to other well-known solution techniques.
Article Details
References
N. P. Padhy, "Unit commitment-a bibliographical survey," in IEEE Transactions on Power Systems, vol. 19, no. 2, pp. 1196-1205, May 2004, DOI: 10.1109/TPWRS.2003.82161
Abdou, Idriss&Tkiouat, Mohamed. (2018). Unit Commitment Problem in Electrical Power System: A Literature Review. International Journal of Electrical and Computer Engineering (IJECE). 8. 1357. 10.11591/ijece.v8i3.pp1357-1372.
Mallipeddi, Rammohan & Suganthan, Ponnuthurai. (2014). Unit commitment - A survey and comparison of conventional and nature inspired algorithms. Int. J. of Bio-Inspired Computation. 6. 71 - 90. 10.1504/IJBIC.2014.060609.
Ananth, D V N & Vineela, K.S.T. (2019). A Review of Different Optimization Techniques for Solving Single and Multi-Objective Optimization Problem in Power System and Mostly Unit Commitment Problem. International Journal of Ambient Energy. 42. 1-27. 10.1080/01430750.2019.1611632.
Reddy, N. &Ramana, Nsgv& Reddy, K.. (2013). Detailed literature survey on different methodologies of unit commitment. Journal of Theoretical and Applied Information Technology. 53. 359-380.
Sivarajan, Ganesan & Subramanian, Sorimuthu. (2010). A novel hybrid method for thermal unit commitment problems. International Review on Modelling and Simulations. 3. 694-704.
Labbi, Yacine & ben attous, Djilani. (2014). A genetic algorithm to solve the thermal unit commitment problem. International Journal of Power and Energy Conversion. 5. 344-360. 10.1504/IJPEC.2014.065511.
Niknam, Taher & Khodaei, Amin & Fallahi, Farhad. (2009). A new decomposition approach for the thermal unit commitment problem. Applied Energy. 86. 1667-1674. 10.1016/j.apenergy.2009.01.022.
Morales-España, Germán & Latorre, Jesús & Ramos, Andres. (2013). Tight and Compact MILP Formulation for the Thermal Unit Commitment Problem. IEEE Transactions on Power Systems. 28. 10.1109/TPWRS.2013.2251373.
Thangavel, Venkatesan & Sanavullah, M.Y.. (2012). Implementation of modified SFLA for the thermal unit commitment problem. International Review on Modelling and Simulations. 5. 450-457.
Ba?aran Filik, Ümmühan & Kurban, Mehmet. (2010). Feasible Modified Subgradient Method for Solving the Thermal Unit Commitment Problem as a New Approach. Mathematical Problems in Engineering. 2010. 10.1155/2010/159429.
Navin, Nandan & Sharma, Rajneesh. (2019). A fuzzy reinforcement learning approach to thermal unit commitment problem. Neural Computing and Applications. 31. 10.1007/s00521-017-3106-5.
Carrion, Miguel & Arroyo, José. (2006). A Computationally Efficient Mixed-Integer Linear Formulation for the Thermal Unit Commitment Problem. Power Systems, IEEE Transactions on. 21. 1371 - 1378. 10.1109/TPWRS.2006.876672.
Yamashiro, Susumu & Asher, Ferix & Uchiyama, Toshiyuki & Nakamura, Youichi & Honma, Takeshi & Senzaki, Syoji. (1994). A Method for Weekly Thermal Unit Commitment Scheduling. IEEJ Transactions on Power and Energy. 114. 1236-1242. 10.1541/ieejpes1990.114.12_1236.
Yamashiro, Susumu & Konno, Tohru & Sato, Masahiro. (1995). A Monthly Thermal Unit Commitment Scheduling Method with Fuel Constraints. IEEJ Transactions on Power and Energy. 115. 1331-1336. 10.1541/ieejpes1990.115.11_1331.
Marcovecchio, Marian & Novais, Augusto & Grossmann, Ignacio. (2014). Deterministic Optimization of the Thermal Unit Commitment problem: a Branch and Cut Search. Computers & Chemical Engineering. 67. 10.1016/j.compchemeng.2014.03.009.
Virmani, Sudhir & Adrian, Eugene & Imhof, Karl & Mukherjee, Shishir. (1989). Implementation of Lagrangian Relaxation Based Unit Commitment Problem. Power Systems, IEEE Transactions on. 4. 1373 - 1380. 10.1109/59.41687.
Panwar, Lokesh & Reddy, Srikanth & Kumar, Rajesh. (2015). Binary Fireworks Algorithm Based Thermal Unit Commitment. International Journal of Swarm Intelligence and Evolutionary Computation. 6. 87-101. 10.4018/IJSIR.2015040104.
Kadam, D.P. & Wagh, S.S. & Patil, P.M.. (2008). Thermal Unit Commitment Problem by Using Genetic Algorithm, Fuzzy Logic and Priority List Method. Computational Intelligence and Multimedia Applications, International Conference on. 1. 468 - 472. 10.1109/ICCIMA.2007.338.
El-Sehiemy, Ragab & Kaddah, Sahar. (2014). Particle Swarm Optimization Algorithm for Unit Commitment Problem in Deregulated Environment.
Bisanovic, Smajo & Hajro, M. & Dlakic, M.. (2010). Mixed integer linear programming based thermal unit commitment problem in deregulated environment. Journal of Electrical Systems. 6. 466-479.
Nagata, Takeshi & Sasaki, Hiroshi & Duo, Hanzheng & Fujita, Hideki & Takayama, Toshiaki. (1999). Solution for unit commitment problem considering LNG fuel constraints. Electrical Engineering in Japan - ELEC ENG JPN. 125. 22-30. 10.1002/(SICI)1520-6416(19981130)125:33.0.CO;2-X.
Xie, Yu-Guang & Chiang, Hsiao-Dong. (2010). A Novel Solution Methodology for Solving Large-scale Thermal Unit Commitment Problems. Electric Power Components and Systems - ELECTR POWER COMPON SYST. 38. 1615-1634. 10.1080/15325008.2010.492453.
J. M. Alemany, F. Magnago and D. Moitre, "Benders Decomposition applied to Security Constrained Unit Commitment," in IEEE Latin America Transactions, vol. 11, no. 1, pp. 421-425, Feb. 2013, doi: 10.1109/TLA.2013.6502840.
H. Ahmadi and H. Ghasemi, "Security-Constrained Unit Commitment With Linearized System Frequency Limit Constraints," in IEEE Transactions on Power Systems, vol. 29, no. 4, pp. 1536-1545, July 2014, doi: 10.1109/TPWRS.2014.2297997.
Kumar, Vineet&Naresh, Ram & Sharma, Veena. (2022). Profit based Unit Commitment Problem Solution using Metaheuristic Optimization Approach. International Journal of Systems Science Operations & Logistics. 1-22. 10.1080/23302674.2022.2037026.
Kumar, Vineet&Naresh, Ram. (2021). Monarch Butterfly Optimization-Based Computational Methodology for Unit Commitment Problem. Electric Power Components and Systems. 48. 2181-2194. 10.1080/15325008.2021.1908458.
Kumar, Vineet&Naresh, Ram. (2020). Application of BARON Solver for Solution of Cost Based Unit Commitment Problem. International Journal on Electrical Engineering and Informatics. 12. 807-827. 10.15676/ijeei.2020.12.4.7.
Lau, T.W. & Chung, C.Y. & Wong, Kit & Chung, Tai-Shung & Ho, S.L.. (2009). Quantum-Inspired Evolutionary Algorithm Approach for Unit Commitment. Power Systems, IEEE Transactions on. 24. 1503 - 1512. 10.1109/TPWRS.2009.2021220.
Zhong-kai Feng, Shuai Liu, Wen-jing Niu, Shu-shan Li, Hui-jun Wu, Jia-yang Wang, Ecological operation of cascade hydropower reservoirs by elite-guide gravitational search algorithm with Lévy flight local search and mutation, Journal of Hydrology, Volume 581, 2020, 124425, ISSN 0022-1694, https://doi.org/10.1016/j.jhydrol.2019.124425.
Jatinder Singh Dhaliwal, J.S. Dhillon, Profit based unit commitment using memetic binary differential evolution algorithm, Applied Soft Computing, Volume 81, 2019, 105502, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2019.105502.
J. Bae, S. Jeong, Y. Kim and H. Lee. Local optimal search algorithm for unit commitment problem, 2011 International Conference on Advanced Power System Automation and Protection, Beijing, China, 2011, pp. 1317-1323, doi: 10.1109/APAP.2011.6180583.
L. Fei and L. Jinghua, "A Solution to the Unit Commitment Problem Based on Local Search Method," 2009 International Conference on Energy and Environment Technology, Guilin, China, 2009, pp. 51-56, doi: 10.1109/ICEET.2009.249.
A. Mousa and K. Kotb, "A Hybrid Optimization Technique Coupling an Evolutionary and a Local Search Algorithm for Economic Emission Load Dispatch Problem," Applied Mathematics, Vol. 2 No. 7, 2011, pp. 890-898. doi: 10.4236/am.2011.27119.
Zhang W, Lan Y. A Novel Memetic Algorithm Based on Multiparent Evolution and Adaptive Local Search for Large-Scale Global Optimization. Comput Intell Neurosci. 2022 Mar 24;2022:3558385. doi: 10.1155/2022/3558385. PMID: 35371240; PMCID: PMC8970930.
Changshou Deng, Xiaogang Dong, Yucheng Tan, Hu Peng, "Enhanced Differential Evolution Algorithm with Local Search Based on Hadamard Matrix", Computational Intelligence and Neuroscience, vol. 2021, Article ID 8930980, 20 pages, 2021. https://doi.org/10.1155/2021/8930980
Adubi, S.A.; Oladipupo, O.O.; Olugbara, O.O. Evolutionary Algorithm-Based Iterated Local Search Hyper-Heuristic for Combinatorial Optimization Problems. Algorithms 2022, 15, 405. https://doi.org/10.3390/a15110405.
Kramer, Oliver. (2010). A Review of Constraint-Handling Techniques for Evolution Strategies. Applied Comp. Int. Soft Computing. 2010. 10.1155/2010/185063.
Mallipeddi, Rammohan&Suganthan, Ponnuthurai. (2010). Ensemble of Constraint Handling Techniques. IEEE Trans. Evolutionary Computation. 14. 561-579. 10.1109/TEVC.2009.2033582.
Liang, Ximing& Long, Wen &Haoyu, Qin & Li, Shanchun. (2009). A Novel Constraint-Handling Method Based on Evolutionary Algorithm. 10.1109/ICICTA.2009.40.
Mezura-Montes, Efrén&Coello, Carlos. (2011). Constraint-handling in nature-inspired numerical optimization: Past, present, and future. Swarm and Evolutionary Computation. 1. 173-194. 10.1016/j.swevo.2011.10.001.
Ashish Mani and C. Patvardhan, “A novel hybrid constraint handling Technique for Evolutionary Optimization”, 978-1-4244-2959-2/09/$25.00© 2009 IEEE.
Kutschera, Ulrich & Niklas, Karl. (2004). The modern theory of biological evolution: An expanded synthesis. Die Naturwissenschaften. 91. 255-76. 10.1007/s00114-004-0515-y.
Gexiang Zhang, “Quantum-inspired evolutionary algorithms: a survey and empirical study” J Heuristics (2011) 17: 303–351, DOI 10.1007/s10732-010-9136-0.
Ashish Mani and C. Patvardhan, “An Adaptive Quantum Evolutionary Algorithm for Engineering Optimization Problems” ©2010International Journal of Computer Applications (0975 - 8887) Volume 1 – No. 22.
Kuk-Hyun Han and Jong-Hwan Kim, “Introduction of Quantum-inspired Evolutionary Algorithm”, 2002 FIRA Robot Congress Seoul, Korea.
C. Y. Chung, H. Yu and K. P. Wong, "An Advanced Quantum-Inspired Evolutionary Algorithm for Unit Commitment," in IEEE Transactions on Power Systems, vol. 26, no. 2, pp. 847-854, May 2011, doi 10.1109/TPWRS.2010.2059716.
Jitender Singh, Ashish Mani, H. P. Singh, and D. S. Rana. "Towards Multipartite Adaptive Binary & Real Coded Quantum-Inspired Evolutionary Algorithm for Solving Multi-Objective Unit Commitment Problem with Thermal Units and Wind Farm." In 2021 4th International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE), pp. 592-597. IEEE, 2021. DOI: 10.1109/RDCAPE52977.2021.9633584.
Jitender Singh, Ashish Mani, H. P. Singh, and D. S. Rana. Towards multipartite adaptive binary-real quantum inspired evolutionary algorithm for scheduling wind-thermal units. In AIP Conference Proceedings 2494,020004 (2022) https://doi.org/10. 1063/5.0107169