پیوندهای مفید
آمار
| تعداد نشریات | 26 |
| تعداد شماره‌ها | 305 |
| تعداد مقالات | 2,632 |
| تعداد مشاهده مقاله | 3,958,411 |
| تعداد دریافت فایل اصل مقاله | 2,666,033 |
Unit Commitment by a Fast and New Analytical Non-iterative Method Using IPPD Table and “λ-logic” Algorithm | ||
| Journal of Operation and Automation in Power Engineering | ||
| مقاله 3، دوره 7، شماره 1، مرداد 2019، صفحه 27-39 اصل مقاله (1.32 M) | ||
| نوع مقاله: Research paper | ||
| شناسه دیجیتال (DOI): 10.22098/joape.2019.4427.1351 | ||
| نویسندگان | ||
| R. Kazemzadeh* 1؛ M. Moazen2 | ||
| 1Department of Electrical Power Engineering, Sahand University of Technology, Tabriz, Iran. | ||
| 2Department of Electrical Engineering, Faculty of Engineering, University of Bonab, Bonab, Iran | ||
| چکیده | ||
| Many different methods have been presented to solve unit commitment (UC) problem in literature with different advantages and disadvantages. The need for multiple runs, huge computational burden and time, and poor convergence are some of the disadvantages, where are especially considerable in large scale systems. In this paper, a new analytical and non-iterative method is presented to solve UC problem. In the proposed method, improved pre-prepared power demand (IPPD) table is used to solve UC problem, and then analytical “λ-logic” algorithm is used to solve economic dispatch (ED) sub-problem. The analytical and non-iterative nature of the mentioned methods results in simplification of the UC problem solution. Obtaining minimum cost in very small time with only one run is the major advantage of the proposed method. The proposed method has been tested on 10 unit and 40-100 unit systems with consideration of different constraints, such as: power generation limit of units, reserve constraints, minimum up and down times of generating units. Comparing the simulation results of the proposed method with other methods in literature shows that in large scale systems, the proposed method achieves minimum operational cost within minimum computational time. | ||
| کلیدواژه‌ها | ||
| Unit commitment؛ Economic Dispatch؛ IPPD table؛ “λ-logic” algorithm | ||
| مراجع | ||
|
[1] R. Kerr, J. Scheidt, A. Fontanna and J. Wiley, “Unit commitment,” IEEE Trans. Power App. Syst., no. 5, pp. 417-21, 1966.
[2] A. Hatefi and R. Kazemzadeh, “Intelligent tuned harmony search for solving economic dispatch problem with valve-point effects and prohibited operating zones,” J. Oper. Autom. Power Eng.,vol. 1,no. 2, 2013.
[3] R. Sedaghati, F. Namdari, “An intelligent approach based on meta-heuristic algorithm for non-convex economic dispatch,” J. Oper. Autom. Power Eng.,vol. 3,no. 1, pp. 47-55, 2015.
[4] 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,no. 1, pp. 29-41, 2016.
[5] A. J. Wood and B. F. Wollenberg. Power generation, operation, and control, Wiley, 1996.
[6] R. Quan, J. Jian and L. Yang, “An improved priority list and neighborhood search method for unit commitment,” Int. J. Electr. Power Energy Syst., vol. 67,pp. 278-85, 2015.
[7] W. L. Snyder, H. D. Powell and J. C. Rayburn, “Dynamic programming approach to unit commitment,” IEEE Trans. Power Syst., vol. 2,no. 2, pp. 339-48, 1987.
[8] A. I. Cohen and M. Yoshimura, “A branch-and-bound algorithm for unit commitment,” IEEE Trans. Power App. Syst., no. 2, pp. 444-51, 1983.
[9] T. S. Dillon, K. W. Edwin, H. D. Kochs and R. Taud, “Integer programming approach to the problem of optimal unit commitment with probabilistic reserve determination,” IEEE Trans. Power App. Syst., no. 6, pp. 2154-66, 1978.
[10] M. Carrión and J. M. Arroyo, “A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem,” IEEE Trans. Power Syst., vol. 21,no. 3, pp. 1371-8, 2006.
[11] L. Yang, J. Jian, Y. Wang and Z. Dong, “Projected mixed integer programming formulations for unit commitment problem,” Int. J. Electr. Power Energy Syst., vol. 68,pp. 195-202, 2015.
[12] A. Viana and J. P. Pedroso, “A new MILP-based approach for unit commitment in power production planning,” Int. J. Electr. Power Energy Syst., vol. 44,no. 1, pp. 997-1005, 2013.
[13] S. Virmani, E. C. Adrian, K. Imhof and S. Mukherjee, “Implementation of a Lagrangian relaxation based unit commitment problem,” IEEE Trans. Power Syst., vol. 4,no. 4, pp. 1373-80, 1989.
[14] X. Feng and Y. Liao, “A new Lagrangian multiplier update approach for Lagrangian relaxation based unit commitment,” Electr. Power Compon. Syst., vol. 34,no. 8, pp. 857-66, 2006.
[15] X. Sun, P. Luh, M. Bragin, Y. Chen, J. Wan and F. Wang, “A novel decomposition and coordination approach for large day-ahead unit commitment with combined cycle units,” IEEE Trans. Power Syst., 2018.
[16] V. S. Pappala and I. Erlich, “A new approach for solving the unit commitment problem by adaptive particle swarm optimization,” Proc. of IEEE PES Gen. Meeting, Pittsburgh, 2008, pp. 1-6.
[17] G. V. S. Reddy and V. Ganesh, C. S. Rao, “Implementation of clustering based unit commitment employing imperialistic competition algorithm,” Int. J. Electr. Power Energy Syst., vol. 82,pp. 621-8, 2016.
[18] H. Sasaki, M. Watanabe, J. Kubokawa, N. Yorino and R. Yokoyama, “A solution method of unit commitment by artificial neural networks,” IEEE Trans. Power Syst., vol. 7,no. 3, pp. 974-81, 1992.
[19] V. N. Dieu and W. Ongsakul, “Ramp rate constrained unit commitment by improved priority list and augmented Lagrange Hopfield network,” Electr. Power Syst. Res., vol. 78,no. 3, pp. 291-301, 2008.
[20] S. A. Kazarlis, A. Bakirtzis and V. Petridis, “A genetic algorithm solution to the unit commitment problem,” IEEE Trans. Power Syst., vol. 11,no. 1, pp. 83-92, 1996.
[21] J. M. Arroyo and A. J. Conejo, “A parallel repair genetic algorithm to solve the unit commitment problem,” IEEE Trans. Power Syst., vol. 17,no. 4, pp. 1216-24, 2002.
[22] D. Simopoulos and G. Contaxis, “Unit commitment with ramp rate constraints using the simulated annealing algorithm,” Proc. IEE Meditraneh Electrotech. Conf., 2004, pp. 845-9.
[23] D. N. Simopoulos, S. D. Kavatza and C. D. Vournas, “Unit commitment by an enhanced simulated annealing algorithm,” IEEE Trans. Power Syst., vol. 21,no. 1, pp. 68-76, 2006.
[24] K. Srikanth, L. K. Panwar, B. Panigrahi, E. Herrera-Viedma, A. K. Sangaiah and G.-G. Wang, “Meta-heuristic framework: quantum inspired binary grey wolf optimizer for unit commitment problem,” Comput. Electr. Eng., 2017.
[25] L. K. Panwar, S. Reddy K, A. Verma, B. K. Panigrahi and R. Kumar, “Binary Grey wolf optimizer for large scale unit commitment problem,” Swarm Evol. Comput., vol. 38,pp. 251-66, 2018.
[26] K. Juste, H. Kita, E. Tanaka and J. Hasegawa, “An evolutionary programming solution to the unit commitment problem,” IEEE Trans. Power Syst., vol. 14,no. 4, pp. 1452-9, 1999.
[27] Y. W. Jeong, J. B. Park, J. R. Shin and K. Y. Lee, “A thermal unit commitment approach using an improved quantum evolutionary algorithm,” Electr. Power Compon. Syst., vol. 37,no. 7, pp. 770-86, 2009.
[28] A. Trivedi, D. Srinivasan, K. Pal, C. Saha and T. Reindl, “Enhanced multiobjective evolutionary algorithm based on decomposition for solving the unit commitment problem,” IEEE Trans. Ind. Inf., vol. 11,no. 6, pp. 1346-57, 2015.
[29] 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,no. 7, pp. 482-90, 2006.
[30] T. Ting, M. Rao and C. Loo, “A novel approach for unit commitment problem via an effective hybrid particle swarm optimization,” IEEE Trans. Power Syst., vol. 21,no. 1, pp. 411-8, 2006.
[31] G. Xiong, X. Liu, D. Chen, J. Zhang and T. Hashiyama, “PSO algorithm‐based scenario reduction method for stochastic unit commitment problem,” IEEJ Trans. Electr. Electron. Eng., vol. 12,no. 2, pp. 206-13, 2017.
[32] S. P. Simon, N. P. Padhy and R. Anand, “An ant colony system approach for unit commitment problem,” Int. J. Electr. Power Energy Syst., vol. 28,no. 5, pp. 315-23, 2006.
[33] C. C. A. Rajan and M. Mohan, “An evolutionary programming-based tabu search method for solving the unit commitment problem,” IEEE Trans. Power Syst., vol. 19,no. 1, pp. 577-85, 2004.
[34] F. Barani, M. Mirhosseini, H. Nezamabadi-pour and M. M. Farsangi, “Unit commitment by an improved binary quantum GSA,” Appl. Soft Comput., vol. 60,pp. 180-9, 2017.
[35] S. Reddy, L. K. Panwar, B. Panigrahi and R. Kumar, “Solution to unit commitment in power system operation planning using binary coded modified moth flame optimization algorithm (BMMFOA): A flame selection based computational technique,” J. Comput. Sci., 2017.
[36] M. Xie, Y. Zhu, S. Ke, Y. Du and M. Liu, “Ordinal optimization theory to solve large‐scale power system unit commitment,” IEEJ Trans. Electr. Electron. Eng., 2018.
[37] S. Reddy K, L. Panwar, B. Panigrahi and R. Kumar, “Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets,” Eng. Optim., pp. 1-21, 2018.
[38] L. Yang, C. Zhang, J. Jian, K. Meng, Y. Xu and Z. Dong, “A novel projected two-binary-variable formulation for unit commitment in power systems,” Appl. Energy, vol. 187,pp. 732-45, 2017.
[39] A. Y. Saber, T. Senjyu, T. Miyagi, N. Urasaki and T. Funabashi, “Absolute stochastic simulated annealing approach to unit commitment problem,” Proc. 13th Int. Conf. Intell. Syst. Appl. Power Syst., Arlington, 2005.
[40] X. Yuan, H. Nie, A. Su, L. Wang and Y. Yuan, “An improved binary particle swarm optimization for unit commitment problem,” Expert Syst. Appl., vol. 36,no. 4, pp. 8049-55, 2009.
[41] C. P. Cheng, C. W. Liu, C. C. Liu, “Unit commitment by Lagrangian relaxation and genetic algorithms,” IEEE Trans. Power Syst., vol. 15,no. 2, pp. 707-14, 2000.
[42] P. Sriyanyong and Y. Song, “Unit commitment using particle swarm optimization combined with Lagrange relaxation,” Proc. IEEE PES Gen. Meeting,, 2005, pp. 2752-9.
[43] F. Rouhi and R. Effatnejad, “Unit commitment in power system by combination of dynamic programming, genetic algorithm and particle swarm optimization (PSO),” Indian J. Sci. Technol., vol. 8,no. 2, p. 134, 2015.
[44] S. Halilcevic and I. Softic, “Degree of optimality as a measure of distance of power system operation from optimal operation,” J. Oper. Autom. Power Eng.,vol. 6,no. 1, pp. 69-79, 2018.
[45] T. Victoire and A. Jeyakumar, “Unit commitment by a tabu-search-based hybrid-optimisation technique,” IET Gener. Transm. Distrib., pp. 563-74, 2005.
[46] P. Attaviriyanupap, H. Kita, E. Tanaka and J. Hasegawa, “A hybrid LR-EP for solving new profit-based UC problem under competitive environment,” IEEE Trans. Power Syst., vol. 18,no. 1, pp. 229-37, 2003.
[47] T. Logenthiran and W. L. Woo, “Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling,” Int. J. Electr. Power Energy Syst., vol. 64,pp. 356-64, 2015.
[48] C. S. Sundaram, M. Sudhakaran and P. A. D. V. Raj, “Tabu search-enhanced artificial bee colony algorithm to solve profit-based unit commitment problem with emission limitations in deregulated electricity market,” Int. J. Metaheuristics, vol. 6,no. 1-2, pp. 107-32, 2017.
[49] V. K. Kamboj, “A novel hybrid PSO–GWO approach for unit commitment problem,” Neural Comput. Appl., vol. 27,no. 6, pp. 1643-55, 2016.
[50] Z. Yang, K. Li, Q. Niu and Y. Xue, “A novel parallel-series hybrid meta-heuristic method for solving a hybrid unit commitment problem,” Knowledge Based Syst., vol. 134,pp. 13-30, 2017.
[51] M. Nemati, M. Braun and S. Tenbohlen, “Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming,” Appl. Energy, vol. 210,pp. 944-63, 2018.
[52] H. Anand, N. Narang and J. Dhillon, “Profit based unit commitment using hybrid optimization technique,” Energy, vol. 148,pp. 701-15, 2018.
[53] S. Prabakaran and S. Tamilselvi, P. A. D. V. Raj, M. Sudhakaran, S. Rajasekar, "Solution for multi-area unit commitment problem using PSO-based modified firefly algorithm", Adv. Syst. Control Autom., Springer; 2018. pp. 625-36.
[54] S. Hemamalini and S. P. Simon, “Dynamic economic dispatch using maclaurin series based lagrangian method,” Energy Convers. Manage., vol. 51,no. 11, pp. 2212-9, 2010.
[55] K. Chandram and N. Subrahmanyam, M. Sydulu, “Unit commitment by improved pre-prepared power demand table and muller method,” Int. J. Electr. Power Energy Syst., vol. 33,no. 1, pp. 106-14, 2011.
[56] Q. P. Zheng, J. Wang and A. L. Liu, “Stochastic optimization for unit commitment-A review,” IEEE Trans. Power Syst., vol. 30,no. 4, pp. 1913-24, 2015.
[57] B. Saravanan, S. Das, S. Sikri and D. Kothari, “A solution to the unit commitment problem—a review,” Front. Energy, vol. 7,no. 2, pp. 223-36, 2013.
[58] I. Abdou and M. Tkiouat, “Unit commitment problem in electrical power system: a literature review,” Int. J. Electr. Comput. Eng., vol. 8,no. 3, 2018.
[59] A. Theerthamalai and S. Maheswarapu, “An effective non-iterative “ λ-logic based” algorithm for economic dispatch of generators with cubic fuel cost function,” Int. J. Electr. Power Energy Syst., vol. 32,no. 5, pp. 539-42, 2010.
[60] S. Sajjadi and R. Kazemzadeh, “A new analytical maclaurin series based λ-logic algorithm to solve the non-convex economic dispatch problem considering valve-point effect,” Iran. J. Electr. Comput. Eng., vol. 12,no. 1-2, pp. 63-9, 2013.
[61] V. K. Kamboj, S. Bath and J. Dhillon, “A novel hybrid DE–random search approach for unit commitment problem,” Neural Comput. .Appl., vol. 28,no. 7, pp. 1559-81, 2017.
[62] C. Chung, H. Yu and K. P. Wong, “An advanced quantum-inspired evolutionary algorithm for unit commitment,” IEEE Trans. Power Syst., vol. 26,no. 2, pp. 847-54, 2011.
[63] W. Ongsakul and N. Petcharaks, “Unit commitment by enhanced adaptive Lagrangian relaxation,” IEEE Trans. Power Syst., vol. 19,no. 1, pp. 620-8, 2004.
[64] K.-i. Tokoro, Y. Masuda and H. Nishino, “Soving unit commitment problem by combining of continuous relaxation method and genetic algorithm,” Proc. SICE Annual Conf., 2008, pp. 3474-8.
[65] I. G. Damousis, A. G. Bakirtzis and P. S. Dokopoulos, “A solution to the unit-commitment problem using integer-coded genetic algorithm,” IEEE Trans. Power Syst., vol. 19,no. 2, pp. 1165-72, 2004.
[66] T. Senjyu, H. Yamashiro, K. Shimabukuro, K. Uezato and T. Funabashi, “Fast solution technique for large-scale unit commitment problem using genetic algorithm,” IET Gener. Transm. Distrib., vol. 150,no. 6, pp. 753-60, 2003.
[67] T. Senjyu, H. Yamashiro, K. Uezato and T. Funabashi, “A unit commitment problem by using genetic algorithm based on unit characteristic classification,” Proc. IEEE PES Winter Meeting, 2002, pp. 58-63.
[68] S. B. A. Bukhari, A. Ahmad, S. A. Raza and M. N. Siddique, “A ring crossover genetic algorithm for the unit commitment problem,” Turk. J. Electr. Eng. Comput. Sci., vol. 24,no. 5, pp. 3862-76, 2016.
[69] A. Trivedi, D. Srinivasan, S. Biswas and T. Reindl, “A genetic algorithm–differential evolution based hybrid framework: case study on unit commitment scheduling problem,” Inf. Sci., vol. 354,pp. 275-300, 2016.
[70] Y. W. Jeong and W. N. Lee, H. H. Kim, J. B. Park, J. R. Shin, “Thermal unit commitment using binary differential evolution,” J. Electr. Eng. Technol., vol. 4,no. 3, pp. 323-9, 2009.
[71] L. Fei and L. Jinghua, “A solution to the unit commitment problem based on local search method,” Proc. Int. conf. ICEET, 2009, pp. 51-6.
[72] Y. W. Jeong, J. B. Park, S. H. Jang and K. Y. Lee, “A new quantum-inspired binary PSO for thermal unit commitment problems,” Proc. Int. conf. ISAP, 2009, pp. 1-6.
[73] B. Wang, Y. Li and J. Watada, “Re-scheduling the unit commitment problem in fuzzy environment,” Proc. IEEE Int. conf. Fuzz. Syst., 2011, pp. 1090-5. | ||
|
آمار تعداد مشاهده مقاله: 867 تعداد دریافت فایل اصل مقاله: 775 |
||