Distributed generations planning in distribution networks using genetic algorithm-based multi-objective optimization

被引:0
作者
Mishra, Deependra Kumar [1 ]
Mukherjee, V. [1 ]
Singh, Bindeshwar [2 ]
机构
[1] Indian Inst Technol, Indian Sch Mines, Dhanbad, Jharkhand, India
[2] Kamla Nehru Inst Technol KNIT, Sultanpur, Uttar Pradesh, India
关键词
Distributed generations; Distribution networks; Different zip load models; Genetic algorithm; System performances indexes; LOAD MODELS; DG; PLACEMENT; RECONFIGURATION; FORMULATION; SYSTEM;
D O I
10.1007/s13198-024-02528-z
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The role of distributed generations (DGs) in a modern scenario is very useful for improving the system performances indexes like minimization of the total real and reactive power losses (ILP, and ILQ) of the system, voltage profile improvement (IVD), better voltage regulation (IVR), increasing the short circuit current capacity (ILC) and apparent power intake in the distribution networks. In this paper the novelty of the DGs are placed and sized with genetic algorithm (GA) in distribution network for improving system performance indexes. The system performance indexes such as ILP, ILQ, IVD, ILC, and IVR are considered for the planning of DGs. In this proposed work, 16-bus, 37-bus, 69-bus test systems is considered as a test systems, and constant impedance (Z), current (I), and power (P) load models is considered as a load. The proper placing of DGs in the distribution networks meets the challenge of more demand for electricity which can be achieved with enhanced load ability of the system with voltage stability and frequency stability also.
引用
收藏
页码:5246 / 5264
页数:19
相关论文
共 50 条
[41]   Practical Efficient Regional Land-Use Planning Using Constrained Multi-Objective Genetic Algorithm Optimization [J].
Pan, Tingting ;
Zhang, Yu ;
Su, Fenzhen ;
Lyne, Vincent ;
Cheng, Fei ;
Xiao, Han .
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2021, 10 (02)
[42]   A Multi-Objective Genetic Algorithm-Based Resource Scheduling in Mobile Cloud Computing [J].
Ramasubbareddy, Somula ;
Swetha, Evakattu ;
Luhach, Ashish Kumar ;
Srinivas, T. Aditya Sai .
INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE, 2021, 15 (03) :58-73
[43]   Multi-objective optimization for high recyclability material selection using genetic algorithm [J].
Sakundarini, Novita ;
Taha, Zahari ;
Abdul-Rashid, Salwa Hanim ;
Ghazilla, Raja Ariffin ;
Gonzales, Julirose .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2013, 68 (5-8) :1441-1451
[44]   Investigation of Distributed Generation Penetration Limits in Distribution Networks Using Multi-Objective Particle Swarm Optimization Technique [J].
Agajie, Takele Ferede ;
Gebru, Fsaha Mebrahtu ;
Salau, Ayodeji Olalekan ;
Aeggegn, Dessalegn Bitew .
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, 2023, 18 (06) :4025-4038
[45]   RESEARCH ON PLANNING AND PATH OPTIMIZATION OF LEISURE SPORTS ACTIVITIES BASED ON MULTI-OBJECTIVE GENETIC ALGORITHM [J].
Yang, Xu .
SCALABLE COMPUTING-PRACTICE AND EXPERIENCE, 2024, 25 (05) :3942-3951
[46]   A genetic algorithm for unconstrained multi-objective optimization [J].
Long, Qiang ;
Wu, Changzhi ;
Huang, Tingwen ;
Wang, Xiangyu .
SWARM AND EVOLUTIONARY COMPUTATION, 2015, 22 :1-14
[47]   Genetic algorithm for multi-objective experimental optimization [J].
Link, Hannes ;
Weuster-Botz, Dirk .
BIOPROCESS AND BIOSYSTEMS ENGINEERING, 2006, 29 (5-6) :385-390
[48]   Multi-Objective Genetic Algorithm-Based Autonomous Path Planning for Hinged-Tetro Reconfigurable Tiling Robot [J].
Cheng, Ku Ping ;
Elara, Mohan Rajesh ;
Nguyen Huu Khanh Nhan ;
Anh Vu Le .
IEEE ACCESS, 2020, 8 :121267-121284
[49]   Genetic algorithm for multi-objective experimental optimization [J].
Hannes Link ;
Dirk Weuster-Botz .
Bioprocess and Biosystems Engineering, 2006, 29 :385-390
[50]   Performance optimization of electric power steering based on multi-objective genetic algorithm [J].
Zhao Wan-zhong ;
Wang Chun-yan ;
Yu Lei-yan ;
Chen Tao .
JOURNAL OF CENTRAL SOUTH UNIVERSITY, 2013, 20 (01) :98-104