Abstract
TNEP is one of the important parts of power system planning which determines the number, time, and location of new lines for adding to transmission network. It is a hard, large-scale and highly nonlinear combinatorial optimization problem that can be solved by classic, nonclassic or heuristic methods. Classic methods like linear programming and Bender decomposition are only based on mathematical principles, but their difficulty is that if the scale of problem is large, it is very difficult to find accurate and reasonable solutions. Contrary to classic methods, nonclassic ones such as evolutionary algorithms like GAs are not based on mathematical rules and simply can be applied for solution of complex problems. GA is a random search method that has demonstrated the ability to deal with nonconvex, nonlinear, integer-mixed optimization problems like the STNEP problem. Although global optimization techniques like GA to be good methods for the solution of TNEP problem, however, when the system has a highly epistatic objective function and number of parameters to be optimized is large, then they have degraded efficiency to obtain global optimum solution and also simulation process use a lot of computing time. Heuristic methods like PSO can improve speed and accuracy of the solution program. PSO is a novel population-based heuristic that is a useful tool for engineering optimization. Unlike the other heuristic techniques, it has a flexible and well-balanced mechanism to enhance the global and local exploration abilities. In this chapter, first we review some research in the field of TNEP. Then, the method of mathematical modeling for TNEP problem is presented. Afterward, GA and PSO algorithms are described completely. Finally, effective parameters on network losses with a few examples are introduced.
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Notes
- 1.
Benders hierarchical decomposition approach.
References
Abdelaziz AR (2000) Genetic algorithm based power transmission expansion planning. 7th IEEE Int Conf Electron Circuits and Syst 2:642–645
Levi VA, Calovic MS (1993) Linear-programming-based decomposition method for optimal planning of transmission network investments. IEE Proc Gener Transm Distrib 140:516–522
Binato S, de Oliveira GC, Araujo JL (2001) A greedy randomized adaptive search procedure for transmission expansion planning. IEEE Trans Power Syst 16:247–253
Choi J, Mount T, Thomas R (2006) Transmission system expansion plans in view point of deterministic, probabilistic and security reliability criteria. The 39th Hawaii Int Conf Syst Sci 10:1–10
Silva IDJ, Rider MJ, Romero R, Murari CA (2005) Transmission network expansion planning considering uncertainness in demand. Power Eng Soc Gen Meet 2:1424–1429
Binato S, Periera MVF, Granville S (2001) A new Benders decomposition approach to solve power transmission network design problems. IEEE Trans Power Syst 16:235–240
Escobar AH, Gallego RA, Romero R (2004) Multistage and coordinated planning of the expansion of transmission systems. IEEE Trans Power Syst 19:735–744
Dodu JC, Merlin A (1981) Dynamic model for long-term expansion planning studies of power transmission systems: The Ortie model. Int J Electr Power & Energy Syst 3:2–16
Garver LL (1970) Transmission net estimation using linear programming. IEEE Trans Power Appar Syst PAS-89:1688–1696
Chanda RS, Bhattacharjee PK (1995) A reliability approach to transmission expansion planning using minimal cut theory. Electr Power Syst Res 33:111–117
Chanda RS, Bhattacharjee PK (1998) A reliability approach to transmission expansion planning using fuzzy fault-tree model. Electr Power Syst Res 45:101–108
Sohtaoglu NH (1998) The effect of economic parameters on power transmission planning. 9th Mediterr Electrotech Conf 2:941–945
Granville S, Pereira MVF et al (1988) 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 2473–2476
Romero R, Monticelli A (1994) A hierarchical decomposition approach for transmission network expansion planning. IEEE Trans Power Syst 9:373–380
Lee STY, Hocks KL, Hnyilicza H (1970) Transmission expansion of branch and bound integer programming with optimal cost capacity curves. IEEE Trans Power Appar Syst PAS-93:1390–1400
Periera MVF, Pinto L (1985) Application of sensitivity analysis of load supplying capacity to interactive transmission expansion planning. IEEE Trans Power Appar Syst PAS-104:381-389
Romero R, Gallego RA, Monticelli A (1996) Transmission system expansion planning by simulated annealing. IEEE Trans Power Syst 11:364–369
Gallego RA, Alves AB, Monticelli A, Romero R (1997) Parallel simulated annealing applied to long term transmission network expansion planning. IEEE Trans Power Syst 12:181–188
Al-Saba T, El-Amin I (2002) The application of artificial intelligent tools to the transmission expansion problem. Electr Power Syst Res 62:117–126
Contreras J, Wu FF (2000) A kernel-oriented algorithm for transmission expansion planning. IEEE Trans Power Syst 15:1434–1440
Silva EL, Gil HA, Areiza JM (2000) Transmission network expansion planning under an improved genetic algorithm. IEEE Trans Power Syst 15:1168–1174
Silva EL, Oritz JMA, Oleveria GC, Binato S (2001) Transmission network expansion planning under a Tabu search approach. IEEE Trans Power Syst 16:62–68
Jalilzadeh S, Kazemi A, Shayeghi H, Mahdavi M (2008) Technical and economic evaluation of voltage level in transmission network expansion planning using GA. Energy Convers Manag 49:1119–1125
Shayeghi H, Jalilzadeh S, Mahdavi M, Haddadian H (2008) Studying influence of two effective parameters on network losses in transmission expansion planning using DCGA. Energy Convers Manag 49:3017–3024
Shayeghi H, Mahdavi M, Bagheri A (2010) Discrete PSO algorithm based optimization of transmission lines loading in TNEP problem. Energy Convers Manag 51:112–121
Shayeghi H, Mahdavi M, Bagheri A (2010) An improved DPSO with mutation based on similarity algorithm for optimization of transmission lines loading. Energy Convers Manag 51:2715–2723
Contreras J, Wu FF (2000) A kernel-oriented algorithm for transmission expansion planning. IEEE Trans Power Syst 15:1434–1440
Braga ASD, Saraiva JT (2005) A multiyear dynamic approach for transmission expansion planning and long-term marginal costs computation. IEEE Trans Power Syst 20:1631–1639
Sepasian MS, Seifi H, Foroud AA, Hatami AR (2009) A multiyear security constrained hybrid generation-transmission expansion planning algorithm including fuel supply costs. IEEE Trans Power Syst 24:1609–1618
Bulent Tor O, Guven AN, Shahidehpour M (2008) Congestion-driven transmission planning considering the impact of generator expansion. IEEE Trans Power Syst 23:781–789
Mahdavi M, Shayeghi H, Kazemi A (2009) DCGA based evaluating role of bundle lines in TNEP considering expansion of substations from voltage level point of view. Energy Convers Manag 50:2067–2073
Sum-Im T, Taylor GA, Irving MR, Song YH (2009) Differential evolution algorithm for static and multistage transmission expansion planning. IET Gener Transm Distrib 3:365–384
Shayeghi H, Mahdavi M (2009) Genetic algorithm based studying of bundle lines effect on network losses in transmission network expansion planning. J Electr Eng 60:237–245
Eldershaw C (2000) A brief Survey of genetic algorithms as applied to non-linear optimization. Technical Report, University of Queensland, pp 1–14
Chipperfield A, Fleming P, Pohlheim H, Fonseca C (1994) Genetic algorithms toolbox for use with MATLAB®. Users Guide, University of Sheffield, p 1–94
Mitchell M (1995) Genetic algorithms: an overview, Technical Report, vol. 1. Santa Fe Institute, pp 31–39
Townsend AAR (2003) Genetic algorithms, A tutorial, pp 1–52
Shayeghi H, Jalili A, Shayanfar HA (2008) Multi-stage fuzzy load frequency control using PSO. Energy Convers Manag 49:2570–2580
Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6:58–73
Jin YX, Cheng HZ, Yan JYM, Zhang L (2007) New discrete method for particle swarm optimization and its application in transmission network expansion planning. Electr Power Syst Res 77:227–233
Jin N, Samii YR (2007) Advances in particle swarm optimization for antenna designs: real-number, binary, single-objective and multiobjective implementations. IEEE Trans Antennas Propag 55:556–567
Mahdavi M, Monsef H, Bagheri A (2012) Lines loading optimization in transmission expansion planning based on binary PSO algorithm. i-manag J Inf Technol 1:24–32
Liu J, Fan X, Qu Z (2007) An improved particle swarm optimization with mutation based on similarity. Third Int Conf Nat Comput 4:824–828
Trelea IC (2003) The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett 85:317–325
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Shayeghi, H., Mahdavi, M. (2013). Application of PSO and GA for Transmission Network Expansion Planning. In: Bizon, N., Shayeghi, H., Mahdavi Tabatabaei, N. (eds) Analysis, Control and Optimal Operations in Hybrid Power Systems. Green Energy and Technology. Springer, London. https://doi.org/10.1007/978-1-4471-5538-6_6
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