TDET  >> Vol. 2 No. 4 (December 2013)

    Multi-Objective Optimal Reactive Power Dispatch with Considering Load Uncertainty

  • 全文下载: PDF(386KB) HTML    PP.68-77   DOI: 10.12677/TDET.2013.24012  
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电容器增强型萤火虫演算法模糊理论负载不确定性虚功率调度Capacitor; Enhanced Firefly Algorithm; Fuzzy Theory; Load Uncertainty; Reactive Power Dispatch

由于传统的最佳虚功率调度都是将负载需求假设为已知的固定值,以求得负载在确定状态下的最佳解,但实际的负载需求含有不确定性。为了使调度结果更适用于实际情形,本文探讨考虑负载不确定性的多目标最佳虚功率调度问题。此外,本文提出以增强型萤火虫演算法(Enhanced Firefly Algorithm, EFA)应用于此问题,增强型萤火虫演算法是将原萤火虫演算法的更新式作改良且修改参数并加入突变机制,以增强演算法的开采与搜索能力,并加快收敛速度且不易陷入局部解。另外,本文使用模糊理论建立模糊归属函数,以解决多目标不同性质及目标函数“越小越好”这种不明确语意的问题。为了验证本文所使用的方法对于考虑负载不确定性的多目标最佳虚功率调度问题的有效性,本文使用IEEE57-Bus系统作为测试系统,并与其它演算法作比较。实验结果证实本文所提出的方法确实可以获得较好的结果。
 In traditional optimal reactive power dispatch problem, the optimal solution is found under the condition that the load demands are assumed to be known and fixed, but the practical load demands have uncertainty. This paper investigates the multi-objective optimal reactive power dispatch with considering load uncertainty to make the dispatch results more suitable for real situation. In this paper, an enhanced firefly algorithm is presented to solve the problem. Enhanced firefly algorithm is based on firefly algorithm that the update formula and parameters are modified and the mutation strategy is 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 ambiguous 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 with considering load uncertainty problem, the IEEE 57-Bus system has been applied to the reactive power dispatching 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.

梁瑞勋, 王嘉庆. 有考虑负载不确定性的多目标最佳虚功率调度[J]. 输配电工程与技术, 2013, 2(4): 68-77.


[1] Granville, S. (1994) Optimal reactive dispatch through interior point methods. IEEE Transactions on Power Systems, 9, 136- 146.
[2] Sun, D.I., Ashley, B., Brewer, B. and Hughes, A. (1984) Optimal power flow by Newton approach. IEEE Transaction on Power Apparatus and Systems, PAS-103, 2864-2880.
[3] Lba, K., Suzuki, H. and Suzuki, K. (1988) Practical reactive power allocation/operation planning using successive linear programming. IEEE Transactions on Power Systems, 3, 1741- 1747.
[4] Grudinin, N. (1998) Reactive power optimization using successive quadratic programming method. IEEE Transactions on Power Systems, 13, 1219-1225.
[5] Yan, W., Lu, S. and Yu, D.C. (2004) A novel optimal reactive power dispatch method based on an improved hybrid evolutionary programming technique. IEEE Transactions on Power Systems, 19, 913-918.
[6] Zhao, B., Guo, C.X. and Cao, Y.J. (2005) A multiagent-based particle swarm optimization approach for optimalreactive power dispatch. IEEE Transactions on Power Systems, 20, 1070-1078.
[7] Zhang, Y.J. and Ren, Z. (2005) Optimal reactive power dispatch considering costs of adjusting the control devices. IEEE Transactions on Power Systems, 20, 1349-1356.
[8] Dai, C., Chen, W., Zhu, Y. and Zhang, X. (2009) Seeker optimization algorithm for optimal reactive power dispatch. IEEE Transactions on Power Systems, 24, 1218-1231.
[9] Lee, Z.H., Lin, Y.H. and Duan, X.Z. (2010) Non-dominated sorting genetic algorithm-II for robust multi-objective optimal reactive power dispatch. IET Generation, Transmission & Distribution, 4, 1000-1008.
[10] Khorsandi, A., Alimardani, A., Vahidi, B. and Hosseinian, S.H. Hybrid shuffled frog leaping algorithm and Nelder-Mead simplex search for optimal reactive power dispatch. IET Generation, Transmission & Distribution, 5, 249-256.
[11] Duman, S. (2012) Optimal reactive power dispatch using a gravitational search algorithm. IET Generation, Transmission & Distribution, 6, 563-576.
[12] Li, M.S., Ji, T.Y. and Wu, Q.H. (2010) Stochastic optimal power flow using a paired-bacteria optimizer. IEEE Conference on Power and Energy Society General Meeting, Minneapolis, 25-29 July 2010, 1-7.
[13] Krishnanand, K.N. and Ghose, D. (2005) Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. IEEE Conference on Swarm Intelligence Symposium, 8-10 June 2005, 84-91.
[14] Taher, N., Rasoul, A.A. and Alireza, R. (2012) Reserve constrained dynamic economic dispatch: A new fast self-adaptive modified firefly algorithm. IEEE Systems Journal, 6, 635-646.
[15] Manju, A. and Nigam, M.J. (2012) Firefly algorithm with fireflies having quantum behavior,” International Conference on Radar, Communication and Computing, Tiruvannamalai, 21-22 December 2012, 117-119.
[16] Ross, T.J. (2010) Fuzzy logic with engineering applications. Wiley, Hoboken.
[17] Gaing Z.L. and Chang, R.F. (2006) Security-constrained optimal power flow by mixed-integer genetic algorithm with arithmetic operators. IEEE Conference on Power Engineering Society General Meeting, Montreal, 18-22 June 2006, 1-8.