Transmission Expansion Planning Using Bacterial Foraging Optimization Algorithm
محورهای موضوعی : Majlesi Journal of Telecommunication DevicesMehdi Tabasi 1 , Aliza Bakhshinejad 2 , Hosein Shaddel 3
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2 - Department of electrical engineering, Sowmesara branch, Islamic Azad University, Sowmesara, Iran
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کلید واژه: Optimization, Power System, Transmission expansion planning, Bacterial Foraging Optimization Algorithm,
چکیده مقاله :
Transmission expansion planning (TEP) refers to specifying the place, time, and number of new transmission lines that should be established, so that given the network available, one can fulfill the potential demand of the power system in the future in terms of both operation and economic aspects (given the system constraints). Nevertheless, TEP is intrinsically a large-scale, mixed integer, nonlinear, and non-convex problem, which basically has several local optima. Solving this problem is very difficult and its computation is very time-consuming. To solve such a problem, a powerful optimization method is needed. In this paper, to solve the TEP problem, a new optimization algorithm called bacterial foraging optimization algorithm (BFOA) has been used. The proposed method has been studied on a 6-bus network for different scenarios, with the results indicating efficiency of BFOA.
Transmission expansion planning (TEP) refers to specifying the place, time, and number of new transmission lines that should be established, so that given the network available, one can fulfill the potential demand of the power system in the future in terms of both operation and economic aspects (given the system constraints). Nevertheless, TEP is intrinsically a large-scale, mixed integer, nonlinear, and non-convex problem, which basically has several local optima. Solving this problem is very difficult and its computation is very time-consuming. To solve such a problem, a powerful optimization method is needed. In this paper, to solve the TEP problem, a new optimization algorithm called bacterial foraging optimization algorithm (BFOA) has been used. The proposed method has been studied on a 6-bus network for different scenarios, with the results indicating efficiency of BFOA.
[1] H. Seifi and M. S. Sepasian, Electric Power System Planning Issues, Algorithms and Solutions, vol. I. Springer-Verlag Berlin Heidelberg, 2011.
[2] R. Hemmati, R. Hooshmand and A. Khodabakhshian, “State-of-the-art of transmission expansion planning: Comprehensive review,” Renewable and Sustainable Energy Reviews, vol. 23, pp. 312-319, July 2013.
[3] C. Ruiz and A. J. Conejo, “Robust transmission expansion planning,” European Journal of Operational Research, vol. 242, pp. 390-401, April 2015.
[4] S. Lumbreras and A. Ramos, “The new challenges to transmission expansion planning. Survey of recent practice and literature review,” Electric Power Systems Research, vol. 134, pp. 19-29, May 2016.
[5] A. Mills, R. Wisera and K. Porter, “The cost of transmission for wind energy in the United States: A review of transmission planning studies,” Renewable and Sustainable Energy Reviews, vol. 16, pp. 1-19, January 2012.
[6] M. Mahdavi, H. Shayeghi and A. Kazemi, “DCGA based evaluating role of bundle lines in TTEP considering expansion of substations from voltage level point of view,” Energy Convers Manage., vol. 50, pp. 2067-2073, 2009.
[7] R. Gallego, R. Romero and A. J. Monticelli, “Tabu search algorithm for network synthesis,” IEEE Trans. Power Systems, vol. 15, pp. 490-495, 2000.
[8] H. Shayeghi, M. Mahdavi and A. Kazemi, “Discrete Particle Swarm Optimization Algorithm Used for TNEP Considering Network Adequacy Restriction,” International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, vol. 3, pp. 521-528, 2009.
[9] M.S. El-bages and W.T. Elsayed, “Social spider algorithm for solving the transmission expansion planning problem,” Electric Power Systems Research, vol. 143, pp. 235–243, Feb. 2017.
[10] A. H. Dominguez, A. H. Escobar and R. A. Gallego, “An MILP model for the static transmission expansion planning problem including HVAC/HVDC links, security constraints and power losses with a reduced search space,” Electric Power Systems Research, vol. 143, pp. 611–623, Feb. 2017.
[11] M. Moradi, H. Abdi, S. Lumbreras, A. Ramos and S. Karimi, “Transmission Expansion Planning in the presence of wind farms with a mixed AC and DC power flow model using an Imperialist Competitive Algorithm,” Electric Power Systems Research, vol. 140, pp. 493–506, November 2016.
[12] C. A. G. MacRae, A.T. Ernst and M. Ozlen, “A Benders decomposition approach to transmission expansion planning considering energy storage,” Energy, vol. 112, pp. 795–803, October 2016.
[13] B. Alizadeh, S. Dehghan, N. Amjady, S. Jadid and A. Kazemi, “Robust transmission system expansion considering planning uncertainties,” IET Gener. Transm. Distrib., vol. 7, pp. 1318–1331, 2013.
[14] D. Pozo, E.E. Sauma, and J. Contreras, “A three-level static MILP model for generation and transmission expansion planning,” IEEE Trans. Power Systems, vol. 28, pp. 202-210, 2013.
[15] A. Khodaei, M. Shahidehpour, L. Wu and Z. Li, “Coordination of short-term operation constraints in multi-area expansion planning,” IEEE Trans. Power Systems, vol. 24, pp. 2242-2250, 2012.
[16] T. Akbari, A. Rahimi-Kian and M. Tavakoli Bin, “Security-constrained transmission expansion planning: A stochastic multi-objective approach,” Electric Power Systems Research, vol. 43, pp. 444–453, Feb. 2012.
[17] K. Sathish Kumar and T. Jayabarathi, “Power system reconfiguration and loss minimization for an distribution systems using bacterial foraging optimization algorithm,” Electrical Power and Energy Systems, vol. 36, pp. 13-17, Feb. 2012.
[18] J. Zheng, F. Wen, G. Ledwich and J. Huang, “Risk control in transmission system expansion planning with wind generators,” IEEE Trans. Electrical Energy Systems, vol. 24, pp. 227–245, 2014.
[19] N. Jhankal and D. Adhyaru, “Bacterial foraging optimization algorithm: A derivative free technique,” presented at the Nirma University International Conference on Engineering (NUiCONE), Gujarat, India, 2012.
[20] A. Grey and A. Sekar, “Unified solution of security-constrained unit commitment problem using a linear programming methodology,” IET Gener. Transm. Distrib., vol. 2, pp. 856-867, 2008.
