臺灣博碩士論文加值系統

本論文在此分為兩部分作討論。第一部分為多目標最佳虛功率調度問題，此問題是將電壓控制匯流排的火力發電機之發電量假設為已知的固定值，藉由控制變壓器有載分接頭、電容器注入的虛功率和參考匯流排與電壓控制匯流排的電壓，以達到降低傳輸線之總實功率損失及負載匯流排之總電壓偏移量的目的。為了求得更低的火力發電機之總發電成本，本論文討論的第二部分為，在上述的多目標最佳虛功率調度問題中加入最佳實功率調度及風力發電機，由於實際的負載需求及風速含有不確定性，所以將負載及風力發電不確定因素考慮其中。此部分的最佳實功率調度，是將多目標最佳虛功率調度後的控制變數與傳輸線之損耗係數設為固定值，再使用基於經濟調度的B-係數法求得各火力發電機之發電量，以達到最小化火力發電機之總發電成本的目的。

在此，本論文提出以含局部隨機搜索的增強型螢火蟲演算法(Enhanced Firefly Algorithm with Local Random Search, EFA-LRS)於多目標最佳虛功率調度及考慮負載及風力發電不確定因素之多目標最佳實功率與虛功率調度問題。此演算法是將原螢火蟲演算法的更新式作改良且修改參數並加入突變機制及局部隨機搜索，以增強演算法的開採與搜索能力，並加快收斂速度且不易陷入局部解。此外，本文使用模糊理論建立模糊歸屬函數，以解決多目標不同性質及目標函數「越小越好」這種不明確語意的問題。

為了驗證本論文所提出的方法對於多目標最佳虛功率調度及考慮負載及風力發電不確定因素之多目標最佳實功率與虛功率調度問題的有效性，本論文使用測試系統作實例測試，並與其它方法作比較。實驗結果證實本論文所提出的方法確實可以獲得較好的結果。

There are two parts of problem discussed in this thesis. The first part is multi-objective optimal reactive power dispatch problem. In this problem, the optimal solution is found under the condition which the active power of power generators of PV buses are assumed to be known and fixed. By controlling the load tap changer of transformers, reactive power output of capacitors, and voltage of slack bus and PV buses, the loss of transmission lines and the voltage deviation of load buses can be reduced. To make less cost of power generation, the second part that includes optimal active power dispatch and wind energy system. This problem also considers uncertainties which exist in practical load demands and wind speed. In optimal active power dispatch problem, the loss coefficients of transmission lines and control variables which are dispatched by multi-objective optimal reactive power dispatch are fixed. The B-coefficients based on economic dispatch are used to obtain active power of power generators for active power dispatch solution in order to make less cost of power generation.

This thesis presents enhanced firefly algorithm with local random search to multi-objective optimal active and reactive power dispatch with considering load and wind generation uncertainties problem. This algorithm is based on firefly algorithm which the update formula and parameters are modified and the mutation strategy and local random search are utilized to enhance the capabilities of exploring and searching. So the proposed algorithm can converge fast and the solution can avoid trapping in local minimum. Furthermore, in order to deal with the multi-objective problem and the linguistic expression such as “as little as possible”, the fuzzy theory is employed to establish the fuzzy membership functions.

To demonstrate the effectiveness of the proposed method for solving multi-objective optimal reactive power dispatch problem and multi-objective optimal active and reactive power dispatch with considering load and wind generation uncertainties problem, the test systems have been applied and the results of the proposed method are compared with those of other algorithms. The results show that the proposed method can get better solution.

中文摘要 ------------------------------------------------------------------------------- i

英文摘要 ------------------------------------------------------------------------------- iii

致謝 ------------------------------------------------------------------------------- v

目錄 ------------------------------------------------------------------------------- vi

表目錄 ------------------------------------------------------------------------------- viii

圖目錄 ------------------------------------------------------------------------------- x

第一章 緒論--------------------------------------------------------------------------- 1

1.1 研究背景與動機------------------------------------------------------------------- 1

1.2 研究方法與文獻回顧---------------------------------------------------------------- 2

1.3 論文大綱------------------------------------------------------------------------ 4



第二章 問題描述------------------------------------------------------------------------ 6

2.1 前言--------------------------------------------------------------------------- 6

2.2 傳統多目標最佳虛功率調度問題-------------------------------------------------------- 6

2.2.1 數學模型------------------------------------------------------------------------ 7

2.3 考慮負載及風速不確定因素之多目標最佳實功率與虛功率調度問題------------------------------ 11

2.3.1 實功率調度之數學模型-------------------------------------------------------------- 11

2.3.2 虛功率調度之數學模型-------------------------------------------------------------- 12



第三章 研究方法與理論------------------------------------------------------------------- 14

3.1 前言--------------------------------------------------------------------------- 14

3.2 不確定因素模型之建立-------------------------------------------------------------- 14

3.3 螢火蟲演算---------------------------------------------------------------------- 18

3.4 增強型螢火蟲演算法---------------------------------------------------------------- 20

3.5 局部隨機搜索--------------------------------------------------------------------- 24





第四章 含局部隨機搜索的增強型螢火蟲演算法作多目標最佳虛功率調度問題---------------------------- 26

4.1 前言-------------------------------------------------------------------------- 26

4.2 含局部隨機搜索的增強型螢火蟲演算法之能量函數的建立----------------------------------- 26

4.3 應用含局部隨機搜索的增強型螢火蟲演算法作多目標最佳虛功率調度問題之步驟------------------- 28

4.4 實例測試與分析------------------------------------------------------------------ 32

4.4.1 IEEE 30-Bus系統-------------------------------------------------------------- 32

4.4.2 IEEE 57-Bus系統-------------------------------------------------------------- 38

4.4.3 IEEE 118-Bus系統------------------------------------------------------------- 42

4.5 本章結論----------------------------------------------------------------------- 45



第五章 含局部隨機搜索的增強型螢火蟲演算法於考慮負載及風力發電不確定因素之多目標最佳實功率與虛功率調度問題-------- 46

5.1 前言-------------------------------------------------------------------------- 46

5.2 含局部隨機搜索的增強型螢火蟲演算法之能量函數的建立------------------------------------ 46

5.3 應用含局部隨機搜索的增強型螢火蟲演算法於考慮負載及風力發電不確定因素之多目標最佳實功率與虛功率調度問題之步驟------------------------------------ 49

5.4 實例測試與分析------------------------------------------------------------------ 55

5.4.1 IEEE 30-Bus系統-------------------------------------------------------------- 55

5.4.2 IEEE 118-Bus系統------------------------------------------------------------- 65

5.5 本章結論----------------------------------------------------------------------- 73



第六章 結論與未來展望------------------------------------------------------------------ 74

6.1 結論-------------------------------------------------------------------------- 74

6.2 未來展望----------------------------------------------------------------------- 75



參考文獻 ------------------------------------------------------------------------------ 76

作者簡介 ------------------------------------------------------------------------------ 78

[1]S. Granville, “Optimal reactive dispatch through interior point methods,” IEEE Transactions on Power Systems, Vol. 9, No. 1, pp. 136-146, February 1994.

[2]D. I. Sun, B. Ashley, B. Brewer, and A. Hughes, “Optimal power flow by Newton approach,” IEEE Transaction on Power Apparatus and Systems, Vol. PAS-103, No. 10, pp. 2864-2880, October 1984.

[3]K. Lba, H. Suzuki, and K. Suzuki, “Practical reactive power allocation/operation planning using successive linear programming,” IEEE Transactions on Power Systems, Vol. 3, No. 4, pp. 1741-1747, May 1988.

[4]N. Grudinin, “Reactive power optimization using successive quadratic programming method,” IEEE Transactions on Power Systems, Vol. 13, No. 4, pp. 1219-1225, November 1998.

[5]W. Yan, S. Lu, and D. C. Yu, “A novel optimal reactive power dispatch method based on an improved hybrid evolutionary programming technique,” IEEE Transactions on Power Systems, Vol. 19, No. 2, pp. 913-918, May 2004.

[6]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.

[7]Y. J. Zhang and Z. Ren, “Optimal reactive power dispatch considering costs of adjusting the control devices,” IEEE Transactions on Power Systems, Vol. 20, No. 3, pp. 1349-1356, August 2005.

[8]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.

[9]Z. H. Lee, Y. H. Lin, and X. Z. Duan, “Non-dominated sorting genetic algorithm-II for robust multi-objective optimal reactive power dispatch,” IET Gener. Transm. Distrib., Vol. 4, No. 9, pp. 1000-1008, September 2010.

[10]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 Gener. Transm. Distrib., Vol. 5, No. 2, pp. 249-256, February 2011.

[11]S. Duman, Y. Son ̈mez, U. Gu ̈venc,and N. Yo ̈ru ̈keren, “Optimal reactive power dispatch using a gravitational search algorithm,” IET Gener. Transm. Distrib, Vol. 6, No. 6, pp. 563–576, June 2012.

[12]M. S. Li, T. Y. Ji, and Q. H. Wu, “Stochastic optimal power flow using a paired-bacteria optimizer,” IEEE Conf. on Power and Energy Society General Meeting, pp. 1-7, 2010.

[13]X. S. Yang, Nature-Inspired Metaheuristic Algorithms, Luniver, 2008.

[14]R. H. Liang and J. C. Wang, ” Application of modified firefly algorithm for multiobjective optimal reactive power dispatch,” The 8th Intelligent Living Technology Conf. on Institute for Energy Research, pp. 857-863, 2013.

[15]H. V. Haghi and M. T. Bina, “Improvement of load bus voltages considering the optimal dispatch of active and reactive powers,” The 43rd International Conf. on Universities Power Engineering, pp. 1-8, 2008.

[16]H. Saadat, Power System Analysis, McGraw-Hill Book Companies, Inc., New York, 1999.

[17]Z. Liu, F. Wen, and G. Ledwich, ”Optimal siting and sizing of distributed generators in distribution systems considering uncertainties,” IEEE Transactions on Power Delivery, Vol. 26, No. 4, pp. 2541–2551, October 2011.

[18]G. Papaefthymiou, A. Tsanakas, M. Reza, P. H. Schavemaker, and L. van der Sluis, “Stochastic modelling and analysis of horizontally operated power systems with a high wind energy penetration,” St. Peters-burg PowerTech Conference, June 2005.

[19]N. Taher, A. A. Rasoul, and R. Alireza, “Reserve constrained dynamic economic dispatch: a new fast self-adaptive modified firefly algorithm,” IEEE Systems Journal, Vol. 6, No. 4, pp. 635-646, December 2012.

[20]A. Manju and M. J. Nigam, “Firefly algorithm with fireflies having quantum behavior,” International Conf. on Radar, Communication and Computing, pp. 117-119, 2012.

[21]R. Y. Xiao and J. H. Yu, “A newly self-adaptive strategy for the PSO,” Fourth International Conf. on Natural Computation, pp. 396-400. 2008.

[22]A. I. Selvakumar and K. Thanushkodi, “A new particle swarm optimization solution to nonconvex economic dispatch problems,” IEEE Transactions on Power Systems, Vol. 22, No. 1, pp. 42-51, February 2007.

[23]T. J. Ross, Fuzzy Logic with Engineering Applications, John Wiley & Sons, Icn., New York, 2010.

[24]K. Y. Lee, Y. M. Park, and J. L. Ortiz, “A united approach to optimal real and reactive power dispatch,” IEEE Transactions on Power Apparatus and Systems, Vol. PAS-104, No. 5, pp. 1147-1153, May 1985.

[25]Z. L. Gaing and R. F. Chang, “Security-constrained optimal power flow by mixed-integer genetic algorithm with arithmetic operators,” IEEE Conf. on Power Engineering Society General Meeting, pp. 1-8, 2006.

[26]S. Anantasate, C. Chokpanyasuwan, and P. Bhasaputra, “Optimal power flow by using bees algorithm,” International Conf. on Electrical Engineering/Electronics Computer Telecommunications and Information Technology, pp. 430-434, 2010.