Optimum placement of Distributed Generation Sources to Minimize Losses and to Improvement Voltage Profile of Distribute Network Using Data Envelopment Analysis Method
الموضوعات : International Journal of Data Envelopment Analysis
1 - هیئت علمی
الکلمات المفتاحية: Distributed generation Sources, Minimum losses, Improvement of voltage profile, Data envelopment analysis (DEA),
ملخص المقالة :
one of the novel solutions for reduction of power loss and improvement of voltage profile in the distribute network is distributed generation sources. To this purpose, capacity and install location of generation sources in the distribute network are especially important. In this paper, a new algorithm using data envelopment analysis (DEA) method is presented for optimum placement and capacity determination of distributed generation sources for reducing of power loss and modifying of voltage profile. Proposed algorithm is tested on the 33 and 69 buses standard systems. Simulation results show effectiveness of proposed algorithm for optimum placement and capacity determination of distributed generation sources considering boundary conditions such as limitation of voltage and capacity of network feeders.
[1] Golshan M, Arefifar S. Optimal allocation of distributed generation and reactive sources considering tap positions of voltage regulators as control variables. European Transactions on Electrical Power. 2007 Nov; 17(3): 219-239.
[2] Atwa Y, El-Saadany E. Probabilistic approach for optimal allocation of wind-based distributed generation in distribution systems. IET Renewable Power Generation. 2011 Feb; 5(1): 79-88.
[3] Prommee W, Ongsakul W. Optimal multiple distributed generation placement in microgrid system by improved reinitialized social structures particle swarm optimization. European Transactions on Electrical Power. 2011 Jan; 21(1): 489-504.
[4] Rau N, Wan Y. Optimum Location of Resources in Distributed Planning. IEEE Transaction on Power System. 1994 Nov; 9(4): 2014-2020.
[5] Nara K, Hayashi Y, Ikeda K. Ashizawa K. Application of tabu search to optimal placement of distributed generators. Proceedings of IEEE Power Engineering Society Winter Meeting. 2001 Feb; 2(1): 918–923.
[6] El-Khattam W, Bhattacharya K, Hegazy Y, Salama M. Optimal investment planning for distributed generation in a competitive electricity market. . IEEE Transaction on Power System. 2004 Jan;19(3): 1674–1684.
[7] Soroudi A, Ehsan M, Caire R, Hadjsaid N. Hybrid immune- genetic algorithm method for benefit maximization of distribution network operators and distributed generation owners in a deregulated environment. IET Renewable Power Generation. 2011 Oct; 5(9): 961-972.
[8] Acharya N, Mahat P, Mithulananthan N. An analytical approach for DG allocation in primary distribution network. International Journal of Electric Power Energy System. 2006 Dec; 28(10): 669–678.
[9] Banker R.D, Charnes A, Cooper W.W. Some models for estimiating technical and scale inefficies in data envelopment analysis. Management Science. 1984 Sep;30(9): 1078-1092.
[10] Charnes A, Cooper W.W, Golany B, Seiford L, Stutz J. Foundations of data envelopment analysis and pareto– koopmans empirical production function. Journal of Econometrics. 1985 Nov; 30(1): 91–107.
[11] Jahanshahloo G.R, Hosseinzadeh Lotfi F, Shoja N, Tohidi G, Razavyan S. Ranking using l1 -norm in data envelopment analysis. Elsevier, Applied Mathematics and computation. 2004 May; 153(1): 215-224.
