Reactive Power Compensation of the Distribution Power System with Distributed Generation Using Improved Tabu Search Algorithm

Abstract:

Taking account of the mutual impacts of distributed generation and reactive power, to determine the optimal position and capacity of the compensation device to be installed, the paper proposed an improved Tabu search algorithm for reactive power optimization. The voltage quality is considered of the model using minimum network active power loss as objective Function. It is achieved by maintaining the whole system power loss as minimum thereby reducing cost allocation. On the basis of general Tabu search algorithm, the algorithm used memory guidance search strategy to focus on searching for a local optimum value, avoid a global search blindness. To deal with the neighborhood solution set properly and save algorithm storage space , some corresponding improvements are made, thus, it is easily to stop the iteration of partial optimization and it is more probable to achieve the global optimization by use of the improved algorithm. Simulations are carried out on standard IEEE 33 test system and results are presented.

• Periodical: Advanced Materials Research (Volumes 765-767)
• Pages: 2503-2508
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.765-767.2503
• Online since: September 2013
• Authors: Xiang Lei, Yan Li, Shao Rong Wang, Hong Zhao, Fen Zhou, Zeng Sheng Chen
• Keywords: Distributed Generation (DG), Improved Tabu Search Algorithm, Network Active Power Loss, Power Distribution System, Reactive Power Optimization

References

[1] C. Wang, M.H. Nehrir, Analytical approaches for optimal placement of DG sources in power systems, IEEE Trans. Power Syst. 19(November(4)(2004) 2068-(2076).

DOI: 10.1109/tpwrs.2004.836189

[2] ZHANG Tingchang, GENG Guangfei. Reactive Power Optimization for Medium Voltage Distribution NetworkBased on Improved Particle Swarm Optimization[J]. Power System Technology, 2012, 36(2): 158-162(inChinese).

[3] M. Huneault and F. D. Galiana, A survey of the optimal powerflow literature, IEEE Trans. Power Systems, Vol. 6, No. 2, pp.762-770.

DOI: 10.1109/59.76723

[4] A. Santos Jr., and G.R.M. da Costa, Optimal-power-flow solutionby Newton's method applied to an augmented Lagrangianfunction, IEE Proc. -Gener. Transm. Distrib, vol. 142, no. 1, pp.33-36, Jan. (1995).

DOI: 10.1049/ip-gtd:19951586

[5] O. Alsac, J. Bright, M. Prais, and B. Stott, Further Developments in LP-Based Optimal Power Flow, IEEE Transactions on Power Systems, vol. 5, no. 3, pp.697-711, Aug. (1990).

DOI: 10.1109/59.65896

[6] N. P. Padhy, M. A. R. Abdel-Moamen, P. K. Trivedi, and B. Das, A hybrid model for optimal power flow incorporating facts devices, in Proc. IEEE Power Engineering Society winter meeting, vol. 2, pp.510-515, (2001).

DOI: 10.1109/pesw.2001.916900

[7] H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama, Y. Nakanishi, A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Security Assessment, IEEE Trans. on Power Systems Vo. 15, No. 4, pp.1232-1239, Nov, 2000.

DOI: 10.1109/59.898095

[8] H. Aoki,Y. Mizutani, Voltage and Reactive Power Control in Power Systems by Genetic Algorithm Considering System Decompositionand Control Process, IEEJ Trans. B Vol. 122, No. 11. pp.1145-1156, 2002(in Japanese).

DOI: 10.1541/ieejpes1990.122.11_1145

[9] Dorigo M, Maniezzo V, Colorni A. Ant System: Optimization by a Colony oCooperating Agents. IEEE Transactions on Power Systems. 1996, 26(1): 29~41.

DOI: 10.1109/3477.484436

[10] LIU Yutian, MA Li. Reactive power optimization based on Tabu search approach. Automation of Electric Power Systems, 2000, 24 (2): 61264.

[11] Mesut E B, Wu Felix F. Network reconfiguration in distributionsystems for loss reduction and load balancing[J]. IEEE Trans onPower Delivery, 1989, 4(2): 1401-1407.

DOI: 10.1109/61.25627

[12] SD 325-1989, the power system voltage and reactive power technical guide (Trial) [S].