An adaptive modified firefly algorithm to unit commitment problem for large-scale power systems

Rastgou, A.; Bahramara, S.

doi:10.22098/joape.2020.6423.1483

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	An adaptive modified firefly algorithm to unit commitment problem for large-scale power systems
Journal of Operation and Automation in Power Engineering
دوره 9، شماره 1، تیر 2021، صفحه 68-79 اصل مقاله (1.28 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2020.6423.1483
نویسندگان
A. Rastgou^* ¹؛ S. Bahramara²
¹Department of Electrical Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
²Department of Electrical Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
چکیده
Unit commitment (UC) problem tries to schedule output power of generation units to meet the system demand for the next several hours at minimum cost. UC adds a time dimension to the economic dispatch problem with the additional choice of turning generators to be on or off. In this paper, in order to improve both the exploitation and exploration abilities of the firefly algorithm (FA), a new modification approach based on the mutation and crossover operators as well as an adaptive formulation is applied as an adaptive modified firefly algorithm (AMFA). In this paper, it is shown that AMFA can solve the UC problem in a better manner compared to the other meta-heuristic methods. The method is applied on some case studies, a typical 10-unit test system, 12, 17, 26, and 38 generating unit systems, and IEEE 118-bus test system, all with a 24-hour scheduling horizon. Comparison of the obtained results with the other methods addressed in the literature shows the effectiveness and fastness of the applied method.
کلیدواژه‌ها
Adaptive modified firefly algorithm؛ Optimization in power system؛ Power generation scheduling؛ Unit commitment problem

مراجع
[1] S. Sadati et al., “Operational scheduling of a smart distribution system considering electric vehicles parking lot: A bi-level approach”, Int. J. Electr. Power Energy Syst., vol. 105, pp. 159-178, 2019. [2] E. Dehnavi, H. Abdi and F. Mohammadi, “Optimal emergency demand response program integrated with multi-objective dynamic economic emission dispatch problem”, J. Oper. Autom. Power Eng. vol. 4, pp. 29-41, 2016. [3] R. Sedaghati and F. Namdari, “An intelligent approach based on meta-heuristic algorithm for non-convex economic dispatch”, J. Oper. Autom. Power Eng., vol. 3, pp. 47-55, 2015. [4] A. Rastgou and J. Moshtagh, “Improved harmony search algorithm for transmission expansion planning with adequacy–security considerations in the deregulated power system”, Int. J. Electr. Power Energy Syst., vol. 60, pp. 153-164, 2014. [5] M. Shahbazitabar and H. Abdi, “A novel priority-based stochastic unit commitment considering renewable energy sources and parking lot cooperation”, Energy, vol. 161, pp. 308-324, 2018. [6] R. Kazemzadeh and M. Moazen, “Unit commitment by a fast and new analytical non-iterative method using IPPD table and “λ-logic” algorithm”, J. Oper. Autom. Power Eng., vol. 7, no. 1, pp. 27-39, 2019. [7] A. Viana and J. Pedroso, “A new MILP-based approach for unit commitment in power production planning”, Int. J. Electr. Power Energy Syst., vol. 44, pp. 997-1005, 2013. [8] M. Carrión and J. Arroyo, “A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem”, IEEE T. power syst., vol. 21, pp. 1371-1378, 2006. [9] H. Ma and S. Shahidehpour, “Transmission-constrained unit commitment based on Benders decomposition”, Int. J. Electr. Power Energy Syst., vol. 20, pp. 287-294, 1998. [10] T. Senjyu et al., “Emerging solution of large-scale unit commitment problem by stochastic priority list”, Electr. Power Syst. Res., vol. 76, pp. 283-292, 2006. [11] X. Yu and X. Zhang, “Unit commitment using Lagrangian relaxation and particle swarm optimization”, Int. J. Electr. Power Energy Syst., vol. 61, pp. 510-522, 2014. [12] W. Ongsakul and N. Petcharaks, “Unit commitment by enhanced adaptive Lagrangian relaxation”, IEEE T. Power Syst., vol. 19, pp. 620-628, 2004. [13] T. Li and M. Shahidehpour, “Price-based unit commitment: A case of Lagrangian relaxation versus mixed integer programming”, IEEE T. power syst., vol. 20, pp. 2015-2025, 2005. [14] T. Senjyu et al., “A fast technique for unit commitment problem by extended priority list”, IEEE T. Power Syst., vol. 18, no. 2, pp. 882-888, 2003. [15] H. Nikzad and H. Abdi, “A robust unit commitment based on GA-PL strategy by applying TOAT and considering emission costs and energy storage systems”, Electr. Power Syst. Res., vol. 180, p. 106154, 2020. [16] S. A. Kazarlis, A. Bakirtzis and V. Petridis, “A genetic algorithm solution to the unit commitment problem”, IEEE T. Power Syst., vol. 11, pp. 83-92, 1996. [17] V. Kumar and D. Kumar, “Binary whale optimization algorithm and its application to unit commitment problem”, Neural Comput. Appl., pp. 1-29, 2018. [18] C. Dang and M. Li, “A floating-point genetic algorithm for solving the unit commitment problem”, Europ. J. Oper. Res., vol. 181, pp. 1370-1395, 2007. [19] L. Sun, Y. Zhang, and C. Jiang, “A matrix real-coded genetic algorithm to the unit commitment problem”, Electr. Power Syst. Res., vol. 76, pp. 716-728, 2006. [20] T. Senjyu, H. Yamashiro, K. Uezato and T. Funabashi, “A unit commitment problem by using genetic algorithm based on unit characteristic classification”, IEEE Power Eng. Soc. Winter Meet. Conf. Proc., pp. 58-63, 2002. [21] I. Damousis, A. Bakirtzis and P. Dokopoulos, “A solution to the unit-commitment problem using integer-coded genetic algorithm”, IEEE T. Power syst., vol. 19, pp. 1165-72, 2004. [22] C. Columbus, K. Chandrasekaran and S. Simon, “Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs”, Appl. Soft Comput., vol. 12, pp. 145-160, 2012. [23] T. Victoire and A. Jeyakumar, “A tabu search based hybrid optimization approach for a fuzzy modelled unit commitment problem”, Electr. Power Syst. Res., vol. 76, pp. 413-425, 2006. [24] T. Victoire and A. Jeyakumar, “Unit commitment by a tabu-search-based hybrid-optimisation technique”, IEEE Proc. Gener. Transm. Distrib., vol. 152, pp. 563-74, 2005. [25] Y. Jeong, J. Park, S. Jang and K. Lee, “A new quantum-inspired binary PSO: application to unit commitment problems for power systems”, IEEE T. Power Syst., vol. 25, pp. 1486-1495, 2010. [26] T. Ting, M. Rao and C. Loo, “A novel approach for unit commitment problem via an effective hybrid particle swarm optimization”, IEEE T. Power Syst., vol. 21, pp. 411-418, 2006. [27] Z. Gaing, “Discrete particle swarm optimization algorithm for unit commitment”, IEEE Power Eng. Soc. Gener. Meet. pp. 418-424, 2003. [28] B. Zhao, C. Guo, B. Bai and Y. Cao, “An improved particle swarm optimization algorithm for unit commitment”, Int. J. Electr. Power Energy Syst., vol. 28, pp. 482-490, 2006. [29] D. Simopoulos, S. Kavatza and C. Vournas, “Unit commitment by an enhanced simulated annealing algorithm”, IEEE T. Power Syst., vol. 21, pp. 68-76, 2006. [30] P. Roy, “Solution of unit commitment problem using gravitational search algorithm”, Int. J. Electr. Power Energy Syst., vol. 53, pp. 85-94, 2013. [31] M. Hadji and B. Vahidi, “A solution to the unit commitment problem using imperialistic competition algorithm”, IEEE T. Power Syst., vol. 27, pp. 117-124, 2011. [32] J. Ebrahimi, S. Hosseinian and G. Gharehpetian, “Unit commitment problem solution using shuffled frog leaping algorithm”, IEEE T. Power Syst., vol. 26, pp. 573-581, 2010. [33] M. Eslamian, S. Hosseinian and B. Vahidi, “Bacterial foraging-based solution to the unit-commitment problem”, IEEE T. Power Syst., vol. 24, pp. 1478-88, 2009. [34] D. Datta and S. Dutta, “A binary-real-coded differential evolution for unit commitment problem”, Int. J. Electr. Power Energy Syst., vol. 42, pp. 517-524, 2012. [35] K. Juste, H. Kita, E. Tanaka and J. Hasegawa, “An evolutionary programming solution to the unit commitment problem”, IEEE T. Power Syst., vol. 14, pp. 1452-1459, 1999. [36] J. Valenzuela and A. Smith, “A seeded memetic algorithm for large unit commitment problems”, J. Heuristics, vol. 8, pp. 173-195, 2002. [37] A. Saber, T. Senjyu, A. Yona and T. Funabashi, “Unit commitment computation by fuzzy adaptive particle swarm optimisation”, IET Gener. Transm. Distrib., vol. 1, pp. 456-465, 2007. [38] M. Samiee, N. Amjady and H. Sharifzadeh, “Security constrained unit commitment of power systems by a new combinatorial solution strategy composed of enhanced harmony search algorithm and numerical optimization”, Int. J. Electr. Power Energy Syst., vol. 44, pp. 471-481, 2013. [39] C. Su and Y. Hsu, “Fuzzy dynamic programming: an application to unit commitment”, IEEE T. Power Syst., vol. 6, pp. 1231-1237, 1991. [40] C. Huang, “Application of genetic-based neural networks to thermal unit commitment”, IEEE T. Power Syst., vol. 12, pp. 654-660, 1997. [41] C. Cheng, C. Liu and C. Liu, “Unit commitment by Lagrangian relaxation and genetic algorithms”, IEEE T. Power Syst., vol. 15, pp. 707-714, 2000. [42] H. Balci and J. Valenzuela, “Scheduling electric power generators using particle swarm optimization combined with the Lagrangian relaxation method”, Int. J. Appl. Math. Comput. Sci., vol. 14, pp. 411-421, 2004. [43] A. Mantawy, Y. Abdel-Magid and S. Selim, “Integrating genetic algorithms, tabu search, and simulated annealing for the unit commitment problem”, IEEE T. Power Syst., vol. 14, pp. 829-836, 1999. [44] D. Srinivasan and J. Chazelas, “A priority list-based evolutionary algorithm to solve large scale unit commitment problem”, Int. Conf. Power Syst. Tech., pp. 1746-1751, 2004. [45] Y. Yang, Y. Mao, P. Yang and Y. Jiang, “The unit commitment problem based on an improved firefly and particle swarm optimization hybrid algorithm”, Chinese Autom. Cong., pp. 718-722, 2013. [46] B. Koodalsamy et al., “Firefly algorithm with multiple workers for the power system unit commitment problem”, Turkish J. Electr. Eng. Comput. Sci., vol. 24, pp. 4773-4789, 2016. [47] K. Chandrasekaran, S. Simon and N. Padhy, “Binary real coded firefly algorithm for solving unit commitment problem”, Inf. Sci., vol. 249, pp. 67-84, 2013. [48] K. Chandrasekaran and S. Simon, “Network and reliability constrained unit commitment problem using binary real coded firefly algorithm”, Int. J. Electr. Power Energy Syst., vol. 43, pp. 921-932, 2012. [49] B. Rampriya, K. Mahadevan and S. Kannan, “Unit commitment in deregulated power system using Lagrangian firefly algorithm”, Int. Conf. Commun. Control Comput. Tech., pp. 389-393, 2010. [50] P. Balachennaiah, M. Suryakalavathi and P. Nagendra, “Firefly algorithm based solution to minimize the real power loss in a power system”, Ain Shams Eng. J., vol. 9, pp. 89-100, 2018. [51] M. Singh, R. Patel and D. Neema, “Robust tuning of excitation controller for stability enhancement using multi-objective metaheuristic Firefly algorithm”, Swarm Evolutionary comput., vol. 44, pp. 136-147, 2019. [52] M. Zile, “Routine test analysis in power transformers by using firefly algorithm and computer program”, IEEE Access, vol. 7, pp. 132033-40, 2019. [53] A. Rastgou and J. Moshtagh, “Application of firefly algorithm for multi-stage transmission expansion planning with adequacy-security considerations in deregulated environments”, Appl. Soft Comput., vol. 41, pp. 373-389, 2016. [54] S. Sanajaoba, “Optimal sizing of off-grid hybrid energy system based on minimum cost of energy and reliability criteria using firefly algorithm”, Solar Energy, vol. 188, pp. 655-666, 2019. [55] X. Yang, S. Hosseini and A. Gandomi, “Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect”, Appl. soft comput., vol. 12, pp. 1180-86, 2012. [56] M. Sundari, M. Rajaram and S. Balaraman, “Application of improved firefly algorithm for programmed PWM in multilevel inverter with adjustable DC sources”, Appl. Soft Comput., vol. 41, pp. 169-179, 2016. [57] S. Singh, N. Sinha, A. Goswami and N. Sinha, “Robust estimation of power system harmonics using a hybrid firefly based recursive least square algorithm”, Int. J. Electr. Power Energy Syst., vol. 80, pp. 287-296, 2016. [58] G. Sekhar, R. Sahu, A. Baliarsingh and S. Panda, “Load frequency control of power system under deregulated environment using optimal firefly algorithm”, Int. J. Electr. Power Energy Syst., vol. 74, pp. 195-211, 2016. [59] G. Manoranjitham and A. Shunmugalatha, “Retracted: application of firefly algorithm on optimal power flow control incorporating simplified impedance UPFC model”, Int. J. Electr. Power Energy Syst., vol. 71, pp. 358-363, 2015. [60] R. Sahu, S. Panda and P. Pradhan, “Design and analysis of hybrid firefly algorithm-pattern search based fuzzy PID controller for LFC of multi area power systems”, Int. J. Electr. Power Energy Syst., vol. 69, pp. 200-212, 2015. [61] L. Xiao, W. Shao, T. Liang and C. Wang, “A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting”, Appl. Energy, vol. 167, pp. 135-153, 2016. [62] N. Saxena and S. Ganguli, “Solar and wind power estimation and economic load dispatch using firefly algorithm”, Procedia Computer Sci., vol. 70, pp. 688-700, 2015. [63] S. Mahapatra, S. Panda and S. Swain, “A hybrid firefly algorithm and pattern search technique for SSSC based power oscillation damping controller design”, Ain Shams Eng. J., vol. 5, pp. 1177-1188, 2014. [64] K. Naidu et al., “Application of firefly algorithm with online wavelet filter in automatic generation control of an interconnected reheat thermal power system”, Int. J. Electr. Power Energy Syst., vol. 63, pp. 401-413, 2014. [65] M. Younes, F. Khodja and R. Kherfane, “Multi-objective economic emission dispatch solution using hybrid FFA (firefly algorithm) and considering wind power penetration”, Energy, vol. 67, pp. 595-606, 2014. [66] A. Rastgou, J. Moshtagh and S. Bahramara, “Improved harmony search algorithm for electrical distribution network expansion planning in the presence of distributed generators”, Energy, vol. 151, pp. 178-202, 2018. [67] A. Kavousi-Fard, H. Samet and F. Marzbani, “A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting”, Expert Syst. Appl., vol. 41, pp. 6047-56, 2014.
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An adaptive modified firefly algorithm to unit commitment problem for large-scale power systems