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In this paper, the hybrid tabu search and simulated annealing (TSSA) method are modified for improving. The main point of modification is to apply sine value into the value of the temperature of simulated annealing. This modification aims to reduce the disadvantage of the original hybrid TSSA. This disadvantage means the value of each parameter is only increased to the maximum value of the temperature of simulated annealing. The optimal number of flexible alternating current transmission system (FACTS) controller determining method is used to determine optimal number of FACTS. SVC is used as FACTS controller in this paper. The split search space method is integrated to manage search space of static var compensator (SVC) operating point. The allocations of SVCs are used to enhance total transfer capability (TTC). Test results on the IEEE 118-bus system and the practical Electricity Generating Authority of Thailand (EGAT) 58-bus system show that the proposed improved hybrid TSSA with optimal number determining method of SVC give higher TTC and less number of SVC than test results from evolutionary programming (EP) and original hybrid TSSA.
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 R. Jomthong, P. Jirapong, and S. Chansareewittaya, “Optimal Choice and Allocation of Distributed Generations using Evolutionary Programming”, Proceeding of the CIGRE-AORC 2011, Chiang Mai, Thailand, October 2011.
 FACTS Terms & Definitions Task Force of the FACTS Working Group of the DC and FACTS Subcommittee, “Proposed Terms and Definitions for Flexible AC Transmission System (FACTS),” IEEE Transactions on Power Delivery, vol. 12, no. 4, Oct. 1997.
 H. Ren, D. Watts, Z. Mi, and J. Lu, “A review of FACTS’ practical Consideration and Economic Evaluation,” in Proc. Power and Energy Engineering Conference (APPEEC 2009), Asia-Pacific, 2009.
 P. V. d Oliveira and K. Yamanaka, “Image Segmentation Using Multilevel Thresholding and Genetic Algorithm: An Approach,” Proceeding of 2nd International Conference on Data Science and Business Analytics (ICDSBA), Sep 2018.
 P. A. Vikhar, “Evolutionary algorithms: A critical review and its future prospects,” Proceeding of the 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC).
 S. Chansareewittaya, “Enhancing ratio of TTC per fuel cost using evolutionary programming with UPFC,” Proceedings of 2018 5th International Conference on Business and Industrial Research: Smart Technology for Next Generation of Information, Engineering, Business and Social Science, Bangkok, Thailand, May 2018.
 J. S. de Souza et al., “Modified Particle Swarm Optimization Algorithm for Sizing Photovoltaic System,” IEEE Latin America Transactions, vol. 15, issue 2, pp.283 – 289, 2017.
 F. B. Abdelaziz and H. MirF, “An Optimization Model and Tabu Search Heuristic for Scheduling of Tasks on a Radar Sensor,” IEEE Sensors Journal, vol. 16 , issue 17, pp. 6694 – 6702, 2016.
 T. Assaf et al., “Fair and efficient energy consumption scheduling algorithm using tabu search for future smart grids,” IET Generation, Transmission & Distribution, vol. 12, issue 3, pp. 643 – 649, 2018.
 K. L. Lian and V. Andrean, “A new MPPT method for partially shaded PV system by combining modified INC and simulated annealing algorithm,” Proceeding of International Conference on High Voltage Engineering and Power Systems (ICHVEPS), Oct 2017.
 M. R. AlRashidi and M. E. El-Hawary, “Applications of computational intelligence techniques for solving the revived optimal power flow problem,” Electric Power Systems Research, vol. 79, issue 4, pp. 694-702, 2009.
 D. Chitara et al., “Cuckoo Search Optimization Algorithm for Designing of a Multimachine Power System Stabilizer,” IEEE Transactions on Industry Applications, vol. 54, issue 4, pp. 3056 – 3065, 2018.
 R. Sanjay et al., “Optimal Allocation of Distributed Generation Using Hybrid Grey Wolf Optimizer,” IEEE Access, vol. 5, pp. 14807 – 14818, 2017.
 S. Mirjalili, “Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems,” Neural Computing and Applications, Vol. 27, Issue 4, pp 1053–1073, May 2016.
 H. Chen, “Artificial Bee Colony Optimizer Based on Bee Life-Cycle for Stationary and Dynamic Optimization,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, issue 2, pp. 327 – 346, 2017.
 S. Chansareewittaya and P. Jirapong, “Power Transfer Capability Enhancement with Multitype FACTS Controllers using Hybrid Particle Swarm Optimization,” Electrical Engineering, Vol. 97, Issue 2 (2015), pp. 119-127, Springer Publishing
 H. Binol et al., “Hybrid evolutionary search method for complex function optimisation problems,” Electronics Letters, vol. 54, Issue 24, pp. 1377 – 1379, 2018.
 P. Bhasaputra and W. Ongsakul, “Optimal power flow with multitype FACTS devices by hybrid TS/SA approach,” Proceeding of the IEEE International Conference on Industrial Technology 2002, pp. 285-290, Bangkok, Thailand, 2002
 G. C. Ejebe, J. G. Waight, S. N. Manuel, and W. F. Tinney, “Fast calculation of linear available transfer capability,” IEEE Transactions on Power Systems, vol. 15, no. 3, Aug. 2000.
 G. C. Ejebe, “Available transfer capability calculations,” IEEE Transactions on Power Systems, vol. 13, no. 4, Nov. 1998.
 M. H. Gravener and C. Nwankpa, “Available transfer capability and first order sensitivity,” IEEE Transactions on Power Systems, vol. 14, May 1999.
 Y. Ou and C. Singh, “Assessment of available transfer capability and margins,” IEEE Transactions on Power Systems, vol. 17, May 2002.
 M. A. Abdel-Moamen and N. P. Padhy, “Optimal power flow incorporating FACTS devices-bibliography and survey,” in Proc. IEEE PES Transmission and Distribution Conference and Exposition 2003, vol. 2, pp. 669-676, Sep. 2003.
 P. Bhasaputra and W. Ongsakul, “Optimal power flow with FACTS devices by hybrid TS/SA approach,” International Journal of Electrical Power & Energy Systems, Vol. 24, Issue 10, pp. 851-857, December 2002.
 M. R. AlRashidi and M. E. El-Hawary, “Applications of computational intelligence techniques for solving the revived optimal power flow problem,” Electric Power Systems Research, vol. 79, issue 4, pp. 694-702, Apr. 2009.
 S. Chansareewittaya and P. Jirapong, “Total Transfer Capability Enhancement with Optimal Number of UPFC using Hybrid TSSA,” Proceeding of the IEEE ECTI-CON 2012, Cha-am, Phetchaburi, Thailand, May 2012.
 S. Chansareewittaya and P. Jirapong, “Power transfer capability Enhancement with Optimal Number of FACTS Controllers using hybrid TSSA,” Proceeding of the IEEE SouthEastCon 2012-IEEE Region3 Conference, Orlando, Florida, USA., March 2012.
 S. Chansareewittaya, “Optimal Allocations of FACTS Controllers for Economic Dispatch using Evolutionary Programming,” Proceeding of the ICSEC 2017 - 21st International Computer Science and Engineering Conference 2017, Bangkok, Thailand, November 2017.
 P. Srisathian and P. Jirapong, “Optimal capacitor allocation for power transfer capability and power loss improvements in power transmission systems using evolutionary programming,” Proceeding of the IEEE ECTI-CON 2011, Khon Kaen, Thailand, May 2011.
 S. Chansareewittaya and P. Jirapong, “Power Transfer Capability enhancement with Optimal Maximum Number of FACTS Controllers using Evolutionary Programming,” Proceeding of the 37rd Annual Conference of the IEEE Industrial Electronics Society (IEEE-IECON), November 2011.
 D. B. Fogel, “The Advantages of Evolutionary Computation,” Proceeding of Biocomputing and emergent computation: Proceedings of BCEC97.
 P. Bhasaputra and W. Ongsakul, “Optimal placement of multi-type FACTS devices by hybrid TS/SA approach,” Proceeding of the 2003 International Symposium on Circuits and Systems, Bangkok, Thailand, June 2003.
 Power Systems Engineering Research Center (PSERC). Available: https://www.eng.nsf.gov/iucrc/