Find optimal capacity and location of distributed generation units in radial distribution networks by using enhanced coyote optimization algorithm

被引:42
作者
Thai Dinh Pham [1 ]
Thang Trung Nguyen [2 ]
Bach Hoang Dinh [2 ]
机构
[1] Duy Tan Univ, Inst Res & Dev, Danang 550000, Vietnam
[2] Ton Duc Thang Univ, Fac Elect & Elect Engn, Power Syst Optimizat Res Grp, Ho Chi Minh City, Vietnam
关键词
Coyote optimization algorithm; Distributed generation; Real power loss; Operation cost; PARTICLE SWARM OPTIMIZATION; POWER LOSS MINIMIZATION; OPTIMAL PLACEMENT; VOLTAGE STABILITY; DGS;
D O I
10.1007/s00521-020-05239-1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper proposes a novel effective optimization algorithm called enhanced coyote optimization algorithm (ECOA). This proposed method is applied to optimally select the position and capacity of distributed generators (DGs) in radial distribution networks. It is a multi-objective optimization problem where properly installing DGs should simultaneously reduce the power loss, operating costs as well as improve voltage stability. Based on the original coyote optimization algorithm (COA), ECOA is developed to be able to expand the search area and retain a good solution group in each generation. It includes two modifications to improve the efficiency of the original COA approach where the first one is replacing the central solution by the best current solution in the first new solution generation technique and the second focuses on reducing the computation burden and process time in the second new solution generation step. In this research, various experiments have been implemented by applying ECOA, COA as well as salp swarm algorithm (SSA), Sunflower optimization (SOA) for three IEEE radial distribution power networks with 33, 69 and 85 buses. Obtained results have been statistically analyzed to investigate the appropriate control parameters and to verify the performance of the proposed ECOA method. In addition, the performance of ECOA is also compared to various similar meta-heuristic methods such as genetic algorithm (GA), particle swarm optimization (PSO), hybrid genetic algorithm and particle swarm optimization (HGA-PSO), simulated annealing, bacterial foraging optimization algorithm, backtracking search optimization algorithm, harmony search algorithm, whale optimization algorithm (WOA) and combined power loss index-whale optimization algorithm (PLI-WOA). Detailed comparisons show that ECOA can determine more effective location and size of DGs with faster speed than other methods. Specifically, the improvement levels of the proposed method over compared to SFO, SSA, and COA can be up to 2.1978%, 0.7858% and 0.2348%. Furthermore, as compared to other existing methods in references, ECOA achieves the significant improvements which are up to 31.7491%, 20.2143% and 22.7213% for the three test systems, respectively. Thus, the proposed method is a favorable method in solving the optimal determination of DGs in radial distribution networks.
引用
收藏
页码:4343 / 4371
页数:29
相关论文
共 45 条
  • [21] Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems
    Mirjalili, Seyedali
    Gandomi, Amir H.
    Mirjalili, Seyedeh Zahra
    Saremi, Shahrzad
    Faris, Hossam
    Mirjalili, Seyed Mohammad
    [J]. ADVANCES IN ENGINEERING SOFTWARE, 2017, 114 : 163 - 191
  • [22] Mohamed IA, 2014, EVOLUT COMPUT, V57, P58, DOI [10.1016/j.swevo.2013.12.001, DOI 10.1016/J.SWEVO.2013.12.001]
  • [23] Optimal location and sizing of real power DG units to improve the voltage stability in the distribution system using ABC algorithm united with chaos
    Mohandas, N.
    Balamurugan, R.
    Lakshminarasimman, L.
    [J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2015, 66 : 41 - 52
  • [24] A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems
    Moradi, M. H.
    Abedini, M.
    [J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2012, 34 (01) : 66 - 74
  • [25] Morshidi MN., 2018, B ELECT ENG INFORM, V7, P442, DOI DOI 10.11591/EEI.V7I31276
  • [26] Nweke J. N., 2016, Niger. J. Technol, V35, P398, DOI [10.4314/njt.v35i2.22, DOI 10.4314/NJT.V35I2.22]
  • [27] Optimal Placement and Sizing of Distributed Generators in Unbalanced Distribution Systems Using Supervised Big Bang-Big Crunch Method
    Othman, M. M.
    El-Khattam, Walid
    Hegazy, Yasser G.
    Abdelaziz, Almoataz Y.
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2015, 30 (02) : 911 - 919
  • [28] Phonrattanasak Prakornchai, 2010, 2010 2nd International Conference on Mechanical and Electrical Technology (ICMET), P342, DOI 10.1109/ICMET.2010.5598377
  • [29] Coyote Optimization Algorithm: A new metaheuristic for global optimization problems
    Pierezan, Juliano
    Coelho, Leandro dos Santos
    [J]. 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2018, : 2633 - 2640
  • [30] Multiple DG placements in radial distribution system for multi objectives using Whale Optimization Algorithm
    Prakash, D. B.
    Lakshminarayana, C.
    [J]. ALEXANDRIA ENGINEERING JOURNAL, 2018, 57 (04) : 2797 - 2806