(2012). Improved Binary Particle Swarm Optimization Based TNEP Considering Network Losses, Voltage Level, and Uncertainty in Demand. Journal of Artificial Intelligence in Electrical Engineering, 1(2), 29-42.

. "Improved Binary Particle Swarm Optimization Based TNEP Considering Network Losses, Voltage Level, and Uncertainty in Demand". Journal of Artificial Intelligence in Electrical Engineering, 1, 2, 2012, 29-42.

(2012). 'Improved Binary Particle Swarm Optimization Based TNEP Considering Network Losses, Voltage Level, and Uncertainty in Demand', Journal of Artificial Intelligence in Electrical Engineering, 1(2), pp. 29-42.

Improved Binary Particle Swarm Optimization Based TNEP Considering Network Losses, Voltage Level, and Uncertainty in Demand. Journal of Artificial Intelligence in Electrical Engineering, 2012; 1(2): 29-42.

Improved Binary Particle Swarm Optimization Based TNEP Considering Network Losses, Voltage Level, and Uncertainty in Demand

Transmission network expansion planning (TNEP) is an important component of power system planning. It determines the characteristics and performance of the future electric power network and influences the power system operation directly. Different methods have been proposed for the solution of the static transmission network expansion planning (STNEP) problem till now. But in all of them, STNEP problem considering the network losses, voltage level and uncertainty in demand has not been solved by improved binary particle swarm optimization (IBPSO) algorithm. Binary particle swarm optimization (BPSO) is a new population-based intelligence algorithm and exhibits good performance on the solution of the large-scale and nonlinear optimization problems. However, it has been observed that standard BPSO algorithm has premature convergence when solving a complex optimization problem like STNEP. To resolve this problem, in this study, an IBPSO approach is proposed for the solution of the STNEP problem considering network losses, voltage level, and uncertainty in demand. The proposed algorithm has been tested on a real transmission network of the Azerbaijan regional electric company and compared with BPSO. The simulation results show that considering the losses even for transmission expansion planning of a network with low load growth is caused that operational costs decreases considerably and the network satisfies the requirement of delivering electric power more reliable to load centers. In addition, regarding the convergence curves of the two methods, it can be seen that precision of the proposed algorithm for the solution of the STNEP problem is more than BPSO.

[1] AR Abdelaziz, “Genetic algorithm-based power transmission expansion planning,” 7th IEEE Int Conf Electron Circuits and Syst, Lebanon, vol. 78, pp. 642-645, 2000. [2] VA Levi and MS Calovic, “Linearprogramming- based decomposition method for optimal planning of transmission network investments,” IEE Proc Gener Transm Distrib, vol. 140, pp. 516-522, 1993. [3] J Choi, TR Mount, “Thomas Transmission system expansion plans in view point of deterministic, probabilistic and security reliability criteria,” The 39th Hawaii Int Conf Syst Sci, vol. 10, pp.1-10, 2006. [4] IDJ Silva, MJ Rider, R Romero, CA Murari “Transmission network expansion planning considering uncertainness in demand,” IEEE Power Eng Soc Gen Meet, vol. 2, pp. 1424- 1429, 2005. [5] S Binato, MVF Periera, S Granville, “A new Benders decomposition approach to solve power transmission network design Problems,”

IEEE Trans Power Syst, vol. 16, pp. 235-240, 2001. [6] LL Garver, “Transmission network estimation using linear programming,” IEEE Trans Power Appar Syst, vol. PAS-89, pp.1688-1696, 1970. [7] IDJ Silva, MJ Rider, R Romero, CA Murari, “Transmission network expansion planning considering uncertainness in demand,” IEEE Power Eng Soc Gen Meet, vol. 2, pp. 1424- 1429, 2005. [8] P Maghouli, SH Hosseini, MO Buygi, M Shahidehpour, “A scenario-based multiobjective model for multi-stage transmission expansion planning,” IEEE Trans Power Syst, vol. 26, pp. 470-478, 2011. [9] AML Silva, LS Rezende, LAF Manso, LC Resende, “Reliability worth applied to transmission expansion planning based on ant colony system,” Int J Electr Power and Energy Syst, vol. 32, pp. 1077-10841, 2010 . [10] NH Sohtaoglu, “The effect of economic parameters on power transmission planning,” 9th Mediterr Electrotech Conf, vol. 2, pp. 941- 945, 1998. [11] B Graeber, “Generation and transmission expansion planning in southern Africa,” 1999 IEEE Africon, vol. 14, pp. 983-988, 1999. [12] MS Kandil, SM El-Debeiky, NE Hasanien, “Rule-based system for determining unit locations of a developed generation expansion plan for transmission planning,” IEE Proc Gener Transm Distrib, vol. 147, pp. 62-68, 2000. [13] RS Chanda, PK Bhattacharjee, “A reliability approach to transmission expansion planning using minimal cut theory,” Electr Power Syst Res, vol. 33, pp. 111-117, 1995. [14] RS Chanda, PK Bhattacharjee, “A reliability approach to transmission expansion planning using fuzzy fault-tree model,” Electr Power Syst Res, vol. 45, pp. 101-108, 1998. [15] S Granville, MVF Pereira, GB Dantzig, B Avi- Itzhak, M Avriel, A Monticelli, LMVG Pinto, “Mathematical decomposition techniques for power system expansion planning-analysis of the linearized power flow model using the Benders decomposition technique,” EPRI, Technical Report, RP, pp. 2473-6, 1988. [16] R Romero, A Monticelli, “A hierarchical decomposition approach for transmission network expansion planning,” IEEE Trans Power Syst, vol. 9, pp. 373-380, 1994. [17] S Binato, GC de Oliveira, Araujo JL, “A greedy randomized adaptive search procedure for transmission expansion planning,” IEEE Trans Power Syst, vol. 16, pp. 247-253, 2001. [18] STY Lee, KL Hocks, H Hnyilicza, “Transmission expansion by branch and bound integer programming with optimal cost

capacity curves,” IEEE Trans Power Appar Syst, vol. PAS-93, pp. 1390-1400, 1974. [19] MVF Periera, LMVG Pinto, “Application of sensitivity analysis of load supplying capability to interactive transmission expansion planning,” IEEE Trans Power Appar Syst, vol. PAS-104, pp. 381 -389, 1985. [20] R Romero, RA Gallego, A Monticelli, “Transmission system expansion planning by simulated annealing,” IEEE Trans Power Syst, vol. 11, pp. 364-369, 1996. [21] RA Gallego, AB Alves, A Monticelli, R Romero, “Parallel simulated annealing applied to long term transmission network expansion planning,” IEEE Trans Power Syst, vol. 12, pp. 181-188, 1997. [22] T Al-Saba, I El-Amin, “The application of artificial intelligent tools to the transmission expansion problem,” Electr Power Syst Res, vol. 62, pp. 117-126, 2002. [23] J Contreras, FF Wu, “A kernel-oriented algorithm for transmission expansion planning,” IEEE Trans Power Syst, vol. 15, pp. 1434-1440, 2000. [24] ASD Braga, JT Saraiva, “A multiyear dynamic approach for transmission expansion planning and long-term marginal costs computation,” IEEE Trans Power Syst, vol. 20, pp. 1631- 1639, 2005. [25] EL Silva, HA Gil, JM Areiza, “Transmission network expansion planning under an improved genetic algorithm,” IEEE Trans Power Syst, vol. 15, pp. 1168-1174, 2000. [26] EL Silva, JMA Oritz, GC Oleveria, S Binato, “Transmission network expansion planning under a Tabu search approach,” IEEE Trans Power Syst, vol. 16, pp. 62-68, 2001. [27] S Jalilzadeh, A Kazemi, H Shayeghi, M Mahdavi, “Technical and economic evaluation of voltage level in transmission network expansion planning using GA,” Energy Convers Manag, vol. 49, pp. 1119-1125, 2008. [28] H Shayeghi, S Jalilzadeh, M Mahdavi, H Haddadian, “Studying influence of two effective parameters on network losses in transmission expansion planning using DCGA,” Energy Convers Manag, vol. 49, pp. 3017-3024, 2008. [29] H Shayeghi, M Mahdavi, “Studying the effect of losses coefficient on transmission expansion planning using decimal codification based GA,” Int J Tech Phys Probl Eng, vol. 1, pp. 58- 64, 2009. [30] H Shayeghi, M Mahdavi, “Genetic algorithm based studying of bundle lines effect on network losses in transmission network expansion planning,” J Electr Eng, vol. 60, pp. 237-245, 2009. [31] JH Zhao, J Foster, ZY Dong, KP Wong,

“Flexible transmission network planning considering distributed generation impacts,” IEEE Trans Power Syst, vol. 26, pp. 1434- 1443, 2011. [32] M Mahdavi, H Shayeghi, A Kazemi, “DCGA based evaluating role of bundle lines in TNEP considering expansion of substations from voltage level point of view,” Energy Convers Manag, vol. 50, pp. 2067-2073, 2009. [33] H Shayeghi, M Mahdavi, A Kazemi, HA Shayanfar, “Studying effect of bundle lines on TNEP considering network losses using decimal codification genetic algorithm,” Energy Convers Manag, vol. 51, pp. 2685- 2691, 2010. [34] H Shayeghi, M Mahdavi, HA Shayanfar, A Bagheri, “Application of binary particle swarm optimization for transmission expansion planning considering lines loading,” In proceedings of the 2009 Int Conf Artif Intell, USA, pp. 653-659, 2009. [35] H Shayeghi, A Jalili, HA Shayanfar, “Multistage fuzzy load frequency control using PSO,” Energy Convers Manag, vol. 49, pp. 2570- 2580, 2008. [36] M Clerc, J Kennedy, “The particle swarmexplosion, stability, and convergence in a multidimensional complex space,” IEEE Trans Evol Comput, vol. 6, pp. 58-73, 2002. [37] N Jin, YR Samii, “Advances in particle swarm optimization for antenna designs: real-number, binary, single-objective and multiobjective implementations,” IEEE Trans Antennas Propag, vol. 55, pp. 556-567, 2007. [38] AAA Esmin, GL Torres, ACZ de Souza, “A hybrid particle swarm optimization applied to loss power minimization,” IEEE Trans Power Syst, vol. 20, pp. 859-866, 2005.