本文輔以水滴流動群聚智能之概念於電力系統可調裝置之規劃控制，此方法乃模擬自然界水滴聚流尋找最佳路徑，並經由流動過程中之速度變數與路徑阻礙之影響關係，進而推導水滴尋優捷徑之模式建立，並將其應用至電力系統運轉之最佳規劃控制。此外為使尋優邁向最佳解之機率增加，本文並嵌入環境變化因子於演算模型，進而提出改良型水滴演算法之應用，期能更加優質提高電力系統運轉效能。而為驗證該方法之規劃協調可行性，本文已將其測試於電力系統可控設備之規劃協調，並與部分方法相較，測試結果顯示本文所提方法不僅提昇水滴演算法之尋優能力，亦兼顧減少輸電線路損失及電壓偏移量之目標，期有助於電力規劃應用參考之需。

This thesis proposes to apply the concept of water drop to assist in the planning and control of adjustable devices in a power system. The method mimics the nature of water drop flowing towards the optimal path by considering the speed variables as well as the path impediment, hence forming an optimization scheme that is applicable to power system planning and control. In order to increase the probability of finding the optimal points, the method has further included the environmental variation factors for the paradigm modeling, anticipating improving the optimization performance in a more significant manner. This method has been simulated through several systems with comparisons to other methods. Test results indicate that the proposed approach not only enhances the capability of optimization, but also reaches the goal of the decrement of transmission losses and voltage deviations. The outcome gained from this thesis is expected to be beneficial for power system planning applications.

[1]F. Li, H. Sun, H. Wan, J. Wang, Y. Xia, Z. Xu and P. Zhang, “Smart transmission grid: vision and framework,” IEEE Transactions on Smart Grid, Vol. 1, No. 2, pp. 168-177, August 2010.
[2]G. K. Venayagamoorthy and R. G. Harley, “Swarm intelligence for transmission system control,” IEEE Power Enginerring Society, General Meeting, Tampa, FL, pp. 1-4, June 2007.
[3]A. Khorsandi, A. Alimardani, B. Vahidi and S. H. Hosseinian, “Hybrid shuffled frog leaping algorithm and nelder-mead simplex search for optimal reactive power dispatch,” IET Proceedings: Generation, Transmission and Distribution, Vol. 5, No. 2, pp. 249-256, February 2011.
[4]A. A. Abou El-Ela and M. A. Abido, “Optimal operation strategy for reactive power control modeling,” Modeling, Simulation, and Control, A, AMSE Press, Vol. 41, No. 3, pp. 19-40, 1992.
[5]R. Motapalomino and V. H. Quintana, “Sparse reactive power scheduling by a penalty-function linear programming technique,” IEEE Transactions on Power Systems, Vol. 1, No. 3, pp. 31-39, August 1986.
[6]S. S. Sachdeva and R. Billinto, “Optimal network var planning by nonlinear-programming,” IEEE Transactions on Power Appartus and Systems, Vol. PA92, No. 4, pp. 1217-1225, 1973.
[7]V. H. Quintana and M. Santosnieto, “Reactive power dispatch by successive quadratic programming,” IEEE Transactions on Energy Conversion, Vol. 4, No. 3, pp. 425-435, September 1989.
[8]Q. H. Wu, Y, J. Cao and J. Y, Wen, “Optimal reactive power dispatch using an adaptive genetic algorithm,” International Journal of Electrical Power and Energy Systems, Vol. 20, No. 8, pp. 563-569, November 1988.
[9]F. Li, J. D. Pilgrim, C. Dabeedin, A. Chebbo and R. K. Aggarwal, “Genetic algorithms for optimal reactive power compensation on the national grid system,” IEEE Transactions on Power Systems, pp. 493-500, February 2005.
[10]D. Devaraj and B. Yegnanarayana, “Genetic-algorithm-based optimal power flow for security enhancement,” IET Proceedings: Generation, Transmission and Distribution, Vol. 152, No. 6, pp. 899-905, November 2005.
[11]L. L. Lai and J. T. Ma, “Application of evolutionary programming to reactive power planning comparison with nonlinear programming approach,” IEEE Transactions on Power Systems, Vol. 12, No. 1, pp. 198-206, February 1997.
[12]Q. H. Wu and J. T. Ma, “Power system optimal reactive power dispatch using evolutionary porgramming,” IEEE Transactions on Power Systems, Vol. 10, No. 3, pp. 1243-1249, August 1995.
[13]P. Gardel, B. Baran, H. Estigarribia, U. Fernandez and S. Duarte, “Multiobjective reactive power compensation with an ant colony optimization algorithm,” IEE International Conference on AC and DC Power Transmission, London, UK, No. 28-31, pp. 276-280, March 2006.
[14]T. Niknam, “A new approach based on ant colony optimization for daily volt/var control in distribution networks considering distributed generators,” Energy Conversion and Management, Vol. 49, No. 12, pp. 3417-3424, December 2008.
[15]M. Hiroyuki and K. Yubun, “Power network decomposition with new ant colony optimization,” International Conference on Probabilistic Methods Applied to Power Systems, Stockholm, Sweden, No. 11-15, pp. 1-6, June 2006.
[16]J. G. Vlachogiannis, N. D. Hatziargyriou and K. Y. Lee, “Ant colony system-based algorithm for constrained load flow problem,” IEEE Transactions on Power Systems, Vol. 20, No. 3, pp. 1241-1249, August 2005.
[17]K. Y. Lee and J. G. Vlachogiannis, “Ant colony system-based algorithm for constrained load flow problem,” International Conference on Intelligent Systems Application to Power Systems, University Park, PA, No. 6-10, pp. 22-35, November 2005.
[18]T. Sum-im, “Economic dispatch by ant colony search algorithm,” IEEE Conference on Cybernetics and Intelligent Systems, Singapore, No. 1-3, pp. 416-421, December 2004.
[19]Y. D. Valle, J. G. Vlachogiannis, S. Mohagheghi, J. C. Hernandez and R. G. Harley, “Particle swarm optimization: basic concepts, variants and applications in power systems,” IEEE Transactions on Evolutionary Computation, Vol. 12, No. 2, pp. 171-195, April 2008.
[20]H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama and Nakanishi, “A particle swarm optimization for reactive power voltage control considering voltage security assessment,” IEEE Transactions on Power Systems, Vol. 15, No. 4, pp. 1232-1239, November 2001.
[21]H. Sharifzadeh and M. Jazaeri, “Optimal reactive power dispatch based on particle swarm optimization considering FACTS devices,” International Conference on Sustainable Power Generation and Supply, pp. 1-7, April 2009.
[22]B. Zhao, C. X. Guo and Y. J. Cao, “A multiagent-based particle swarm optimization approach for optimal reactive power dispatch,” IEEE Transactions on Power Systems, Vol. 20, No. 2, pp. 1070-1078, May 2005.
[23]J. G. Vlachogiannis and K. Y. Lee, “A comparative study on particle swarm optimization for optimal steady-state performance of power systems,” IEEE Transactions on Power Systems, Vol. 21, No. 4, pp. 1718-1728, November 2006.
[24]P. Subbaraj and P. N. Rajnarayanan, “Hybrid particle swarm optimization based optimal reactive power dispatch,” International Journal of Computer Applications, Vol. 1, No. 5, pp. 65-70, January 2010.
[25]M. R. Alrashidi and M. E. El-Hawary, “Hybrid particle swarm optimization approach for solving the discrete OPF problem considering the valve loading effects,” IEEE Transactions on Power Systems, Vol. 22, No. 4, pp. 2030-2038, November 2007.
[26]S. H. Hamed, “Intelligent water drops algorithm : A new optimization method for solving the multiple knapsack problem,” International Journal of Intelligent Computing and Cybernetics , Vol. 1, No. 2, pp. 193-212, March 2008.
[27]S. H. Hamed, “The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm,” International Journal of Bio-Inspired Computatuion , Vol. 1, No. 1/2, pp. 71-79, January 2009.
[28]S. R. Rayapudi, “An intelligent water drop algorithm for solving economic load dispatch problem,” International Journal of Electrical and Electronics Engineering , Vol. 5, No. 1, pp. 43-50, 2011.
[29]C. L. Wadhwa, “Electrical power systems,” Wiley Eastern Limited, pp. 222-245, March 1991.
[30]K. R. C. Mamandur and R. D. Chenoweth, “Optimal control of reactive power flow for improvements in voltage profiles and for real power loss minimization,” IEEE Transactions on Power Apparatus and Systems, Vol. PAS-100, No. 7, pp. 3185-3194, July 1981.
[31]C. Dai, W. Chen, Y. Zhu and X. Zhang, “Seeker optimization algorithm for optimal reactive power dispatch,” IEEE Transactions on Power Systems, Vol. 24, No. 3, pp. 1218-1231, August 2009.