Comparative Performance of Artificial Bee Colony Based Algorithms for Wind-Thermal Unit Commitment

Authors: P. K. Singhal, R. Naresh, V. Sharma

Abstract:

This paper presents the three optimization models, namely New Binary Artificial Bee Colony (NBABC) algorithm, NBABC with Local Search (NBABC-LS), and NBABC with Genetic Crossover (NBABC-GC) for solving the Wind-Thermal Unit Commitment (WTUC) problem. The uncertain nature of the wind power is incorporated using the Weibull probability density function, which is used to calculate the overestimation and underestimation costs associated with the wind power fluctuation. The NBABC algorithm utilizes a mechanism based on the dissimilarity measure between binary strings for generating the binary solutions in WTUC problem. In NBABC algorithm, an intelligent scout bee phase is proposed that replaces the abandoned solution with the global best solution. The local search operator exploits the neighboring region of the current solutions, whereas the integration of genetic crossover with the NBABC algorithm increases the diversity in the search space and thus avoids the problem of local trappings encountered with the NBABC algorithm. These models are then used to decide the units on/off status, whereas the lambda iteration method is used to dispatch the hourly load demand among the committed units. The effectiveness of the proposed models is validated on an IEEE 10-unit thermal system combined with a wind farm over the planning period of 24 hours.

Keywords: Artificial bee colony algorithm, economic dispatch, unit commitment, wind power.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1126285

References:

[1] R. Billinton, B. Karki, R. Karki, and G. Ramakrishna, “Unit commitment risk analysis of wind integrated power systems,” IEEE Trans. Power Syst., vol. 24, no. 2, pp. 930-939, 2009.
[2] X. Sun and C. Fang, “Interval mixed-integer programming for daily unit commitment and dispatch incorporating wind power,” in Proc. IEEE Int. Conf. on Power System Technology (POWERCON), pp. 1-6, 2010.
[3] J. J. Hargreaves and B. F. Hobbs, “Commitment and dispatch with uncertain wind generation by dynamic programming,” IEEE Trans. on Sustain. Energy, vol. 3, no. 4, pp. 724- 734, 2012.
[4] E. Delarue, D. Cattrysse, and W. D’haeseleer, “Enhanced priority list unit commitment method for power systems with a high share of renewable,” Electr. Power Syst. Res., vol. 105, pp. 115-123, 2013.
[5] G. J. Osorio, J. M. L. Rojas, J. C. O. Matias, and J. P. S. Catalão, “A new scenario generation-based method to solve the unit commitment problem with high penetration of renewable energies,” Int. J. Electric Power Energy Syst., vol.64, pp. 1063–1072, 2015.
[6] C. L. Chen, “Simulated annealing-based optimal wind-thermal coordination scheduling,” IET Genr. Transm. Distrib., vol. 1, no. 3, pp. 447-455, 2007.
[7] H. Siahkali and M. Vakilian, “Electricity generation scheduling with large-scale wind farms using particle swarm optimization,” Electr. Power Syst. Res., vol. 79, pp. 826–836, 2009.
[8] H. Siahkali and M. Vakilian, “Fuzzy generation scheduling for a generation company (GenCo) with large scale wind farms,” Energy Convers. Manag., vol. 51, pp. 1947-1957, 2010.
[9] Q. Wang, Y. Guan, and J. Wang, “A chance-constrained two-stagestochasticprogram for unit commitment withuncertain wind poweroutput,” IEEE Trans. Power Syst., vol. 27, no. 1, pp. 206-215, Feb.2012.
[10] C. Peng, H. Sun, J. Guo, and G. Liu, “Dynamic economic dispatch for wind-thermal power system using a novel bi-population chaotic differential evolution algorithm,” Int. J. Electric Power Energy Syst., vol.42, pp. 119-126, 2012.
[11] B. Ji, X. Yuan, Z. Chen, and H. Tian, “Improved gravitational search algorithm for unit commitment considering uncertainty of wind power,” Energy, vol. 67, pp. 52-62, 2014.
[12] B. Ji, X. Yuan, X. Li, Y. Huang, and W. Li, “Application of quantum-inspired binary gravitational search algorithm for thermal unit commitment with wind power integration,” Energy Convers. Manag., vol. 87, pp. 589–598, 2014.
[13] T. Niknam and H. R. Massrur, “Stochastic mid-term generation scheduling incorporated with wind power,” Int. J. Electric Power Energy Syst.,vol. 64, pp. 937–946, 2015.
[14] C. L. Chen, “Optimal wind–thermal generating unit commitment,” IEEE Trans. on Energy Convers., vol. 23, no. 1, pp. 273-280, 2008.
[15] B. Venkatesh, P. Yu, H. B. Gooi, and D. Choling, “Fuzzy MILP unit commitment incorporating wind generators,” IEEE Trans. Power Syst., vol. 23, no. 4, pp. 1738-1746, Nov. 2008.
[16] H. Siahkali and M. Vakilian, “Integrating large scale wind farms in fuzzy mid term unit commitment using PSO,” in Proc. 5thIEEE Int. Conf. on European Electricity Market (EEM 2008), pp. 1-6, 2008.
[17] B. Saravanan, S. Mishra, and D. Nag, “A solution to stochastic unit commitment problem for a wind-thermal system coordination,” Front. Energy, vol. 8, no. 2, pp. 192-200, 2014.
[18] Y. Zhang, F. Yao, H. H. C. Iu, T. Fernando, and H. Trinh, “Wind-thermal systems operation optimization considering emission problem,” Int. J Electric Power Energy Syst., vol. 65, pp. 238-245, 2015.
[19] M. R. Patel, Wind and Solar Power Systems: Design, Analysis, and Operation, 2nd ed. Boca Raton: CRC Press, 2006.
[20] D. Karaboga, and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” J. Glob. Optim., vol. 39, pp. 459–471, 2007.
[21] M. H. Kashan, N. Nahavandi, and A. H. Kashan, “DisABC: A new artificial bee colony algorithm for binary optimization,” Appl. Soft Comput., vol. 12, pp. 342-352, 2012.
[22] A. G. Abro, and J. M. Saleh, “Intelligent scout-bee based artificial bee colony optimization algorithm,” in Proc. IEEE Int. Conf. Control System, Computing and Engineering, Nov. 2012, pp. 380-385.
[23] P. K. Singhal, R. Naresh, and V. Sharma, “A modified binary artificial bee colony algorithm for ramp rate constrained unit commitment problem,” Int. Trans. Electr. Energ. Syst., vol. 25, pp. 3472-3491, 2015.
[24] P. Jain, Wind Energy Engineering, New York, USA: Tata McGraw Hill, 2011.
[25] C. Carrillo, J. Cidras, E. D. Dorada, and A. F. O. Montano, “An approach to determine the Weibull parameters for wind energy analysis: the case of Galicia (Spain),” Energies, vol. 7, pp. 2676-2700, 2014.
[26] A. Altunkaynak, T. Erdik, I. Dabanli, and Z. Sen, “Theoretical derivation of wind power probability distribution function and applications,” Appl. Energy, vol. 92, pp. 809-814, 2012.
[27] T. P. Chang, “Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application,” Appl. Energy, vol. 88, pp. 272-282, 2011.
[28] U.S. Bureau of Reclamation, USA. http://www.usbr.gov.