Development and testing of the genetic algorithm to select a scenario of distributed generation power supply system

被引:3
|
作者
Bugaeva, Tatyana [1 ]
Khabarov, Alexandr [1 ]
Novikova, Olga [1 ]
Plotkina, Uliyana [1 ]
机构
[1] Peter Great St Petersburg Polytech Univ, Politech Skaya St 29, St Petersburg 195251, Russia
关键词
MULTIOBJECTIVE OPTIMIZATION;
D O I
10.1088/1757-899X/497/1/012056
中图分类号
F [经济];
学科分类号
02 ;
摘要
The rapid development of small distributed energy economy requires the justification and design of methods and models for planning the development of energy systems using distributed generation sources. We have studied the possibility of using the genetic algorithm to solve the problem of selecting a scenario of distributed generation power supply system. To do this, a problem-solving mechanism was developed in the MATLAB environment based on the genetic algorithm and its program code was built. In order to test the created genetic algorithm for a hypothetical large consumer of electrical and heat power with typical load curves, an optimal scenario of a power supply system based on distributed generation was searched. The analysis of the calculation results carried out using the proposed algorithm, evaluation of the model's behavior when the initial data changes, indicate its efficiency and effectiveness.
引用
收藏
页数:6
相关论文
共 50 条
  • [21] Reactive power optimisation of distribution network with distributed generation based on genetic and immune algorithm
    Hao, Wenbo
    Liu, Boning
    Yao, Shujun
    Guo, Wanhua
    Huang, Wenerda
    JOURNAL OF ENGINEERING-JOE, 2019, (16): : 1280 - 1284
  • [22] Power Sharing in Distributed Power Generation System
    Singh, Alka
    Badoni, Manoj
    Singh, Bhim
    2012 IEEE 5TH INDIA INTERNATIONAL CONFERENCE ON POWER ELECTRONICS (IICPE 2012), 2012,
  • [23] Application of Metaheuristic Algorithms for Optimization of Recloser Placement in a Power Supply System with Distributed Generation
    N. N. Sergeev
    P. V. Matrenin
    Doklady Mathematics, 2024, 110 (Suppl 1) : S87 - S94
  • [24] Optimal power flow algorithm and analysis in distribution system considering distributed generation
    Sheng, Wanxing
    Liu, Ke-yan
    Cheng, Sheng
    IET GENERATION TRANSMISSION & DISTRIBUTION, 2014, 8 (02) : 261 - 272
  • [25] A distributed genetic algorithm for reactive power optimization
    Pan, Z.
    Zhang, B.
    Sun, H.
    Cheng, L.
    Dianli Xitong Zidonghue/Automation of Electric Power Systems, 2001, 25 (12): : 37 - 41
  • [26] A distributed power generation communication system
    Meng, J
    CCECE 2003: CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, VOLS 1-3, PROCEEDINGS: TOWARD A CARING AND HUMANE TECHNOLOGY, 2003, : 483 - 486
  • [27] Development of high-voltage power supply for electromagnetic immunity testing system
    Yu, Zhiyong
    Liu, Guangbin
    Wang, Xuemei
    Li, Zhongyi
    Gaodianya Jishu/High Voltage Engineering, 2000, 26 (05): : 41 - 42
  • [28] Optimal placement of Distributed Generation in power distribution systems using Neuro-genetic Algorithm
    Kazeem, Bakare
    Alor, Mike
    Okafor, E. N. C.
    2017 IEEE 3RD INTERNATIONAL CONFERENCE ON ELECTRO-TECHNOLOGY FOR NATIONAL DEVELOPMENT (NIGERCON), 2017, : 898 - 904
  • [29] Testing of a Distributed Generation System With a Virtual Grid
    Goyal, Sachin
    Ghosh, Arindam
    Ledwich, Gerard
    IEEE POWER AND ENERGY SOCIETY GENERAL MEETING 2010, 2010,
  • [30] The Location and Capacity of Distributed Generation Based on Genetic Algorithm
    Ye, Zezhou
    Lin, Rongheng
    Zou, Hua
    Wu, Budan
    Guo, Naiwang
    2017 5TH INTERNATIONAL CONFERENCE ON ENTERPRISE SYSTEMS (ES), 2017, : 1 - 6