An Improved Particle Swarm Optimization Algorithm forOptimal Allocation of Distributed Generation Units in Radial Power Systems

被引:16
作者
Hantash, Neda [1 ]
Khatib, Tamer [2 ]
Khammash, Maher [3 ]
机构
[1] An Najah Natl Univ, Fac Grad Studies, Nablus 97300, Palestine
[2] An Najah Natl Univ, Dept Energy Engn & Environm, Nablus 97300, Palestine
[3] An Najah Natl Univ, Dept Elect Engn, Nablus 97300, Palestine
关键词
DISTRIBUTION NETWORKS; OPTIMAL PLACEMENT; MODELS; MINIMIZATION;
D O I
10.1155/2020/8824988
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, an improved particle swarm optimization method (PSO) is proposed to optimally size and place a DG unit in an electrical power system so as to improve voltage profile and reduce active power losses in the system. An IEEE 34 distribution bus system is used as a case study for this research. A new equation of weight inertia is proposed so as to improve the performance of the PSO conventional algorithm. This development is done by controlling the inertia weight which affects the updating velocity of particles in the algorithm. Matlab codes are developed for the adapted electrical power system and the improved PSO algorithm. Results show that the proposed PSO algorithm successfully finds the optimal size and location of the desired DG unit with a capacity of 1.6722 MW at bus number 10. This makes the voltage magnitude of the selected bus equal to 1.0055 pu and improves the status of the electrical power system in general. The minimum value of fitness losses using the applied algorithm is found to be 0.0.0406 while the average elapsed time is 62.2325 s. In addition to that, the proposed PSO algorithm reduces the active power losses by 31.6%. This means that the average elapsed time is reduced by 21% by using the proposed PSO algorithm as compared to the conventional PSO algorithm that is based on the liner inertia weight equation.
引用
收藏
页数:8
相关论文
共 36 条
[1]   Multiobjective particle swarm optimization for environmental/economic dispatch problem [J].
Abido, M. A. .
ELECTRIC POWER SYSTEMS RESEARCH, 2009, 79 (07) :1105-1113
[2]   Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony Algorithm [J].
Abu-Mouti, Fahad S. ;
El-Hawary, M. E. .
IEEE TRANSACTIONS ON POWER DELIVERY, 2011, 26 (04) :2090-2101
[3]   Particle Swarm Optimization for the Minimization of Power Losses in Distribution Networks [J].
Abugri, Joseph B. ;
Karam, Marc .
2015 12TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY - NEW GENERATIONS, 2015, :73-78
[4]   A new approach for optimum DG placement and sizing based on voltage stability maximization and minimization of power losses [J].
Aman, M. M. ;
Jasmon, G. B. ;
Bakar, A. H. A. ;
Mokhlis, H. .
ENERGY CONVERSION AND MANAGEMENT, 2013, 70 :202-210
[5]   Distributed generation planning using differential evolution accounting voltage stability consideration [J].
Arya, L. D. ;
Koshti, Atul ;
Choube, S. C. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2012, 42 (01) :196-207
[6]  
Ben Ghalia M, 2008, MIDWEST SYMP CIRCUIT, P759, DOI 10.1109/MWSCAS.2008.4616910
[7]  
Bollen M.H. J., 2011, Integration of Distributed Generation in the Power Systems
[8]   A comparison of heuristic optimization techniques for optimal placement and sizing of photovoltaic based distributed generation in a distribution system [J].
Daud, S. ;
Kadir, A. F. A. ;
Gan, C. K. ;
Mohamed, A. ;
Khatib, Tamer .
SOLAR ENERGY, 2016, 140 :219-226
[9]   Facing classification problems with Particle Swarm Optimization [J].
De Falco, I. ;
Della Cioppa, A. ;
Tarantino, E. .
APPLIED SOFT COMPUTING, 2007, 7 (03) :652-658
[10]   Optimal placement of multi-distributed generation units including different load models using particle swarm optimisation [J].
El-Zonkoly, A. M. .
IET GENERATION TRANSMISSION & DISTRIBUTION, 2011, 5 (07) :760-771