Optimal Allocation of FACTS Devices by Using Multi-Objective Optimal Power Flow and Genetic Algorithms

被引:0
|
作者
Ippolito, Lucio [1 ]
La Cortiglia, Antonio [1 ]
Petrocelli, Michele [1 ]
机构
[1] Univ Salerno, Dept Elect Engn, Salerno, Italy
来源
INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS | 2006年 / 7卷 / 02期
关键词
FACTS; UPFC; transmission systems; multi-objective; genetic algorithms;
D O I
10.2202/1553-779X.1099
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The increases in power flows and environmental constraints are forcing electricity utilities to install new equipment to enhance network operation. Some application of Flexible AC Transmission System (FACTS) technologies to existing high-voltage power systems has proved the use of FACTS technology may be a cost-effective option for power delivery system enhancements. Amongst various power electronic devices, the unified power flow controller (UPFC) device has captured the interest of researchers for its capability of regulating the power flow and minimizing the power losses simultaneously. Since for a cost-effective application of FACTS technology a proper selection of the number and placement of these devices is required, the scope of this paper is to propose a methodology, based on a genetic algorithm, able to identify the optimal number and location of UPFC devices in an assigned power system network for maximizing system capabilities, social welfare and to satisfy contractual requirements in an open market power. In order to validate the usefulness of the approach suggested herein, a case study using a IEEE 30-bus power system is presented and discussed.
引用
收藏
页码:1 / 19
页数:20
相关论文
共 50 条
  • [31] Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms
    Ahmed, Faez
    Deb, Kalyanmoy
    SOFT COMPUTING, 2013, 17 (07) : 1283 - 1299
  • [32] Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms
    Faez Ahmed
    Kalyanmoy Deb
    Soft Computing, 2013, 17 : 1283 - 1299
  • [33] Design of microvascular flow networks using multi-objective genetic algorithms
    Aragon, Alejandro M.
    Wayer, Jessica K.
    Geubelle, Philippe H.
    Goldberg, David E.
    White, Scott R.
    COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2008, 197 (49-50) : 4399 - 4410
  • [34] Optimization of Optimal Power Flow Problems with FACTS Devices Using PSO Technique
    Metweely, Khaled M.
    Morsy, Gamal A.
    Amer, Ragab A.
    2017 NINETEENTH INTERNATIONAL MIDDLE-EAST POWER SYSTEMS CONFERENCE (MEPCON), 2017, : 181 - 189
  • [35] Multi-Objective Genetic Algorithm for voltage stability enhancement using rescheduling and FACTS devices
    Roselyn, J. Preetha
    Devaraj, D.
    Dash, Subhransu Sekhar
    AIN SHAMS ENGINEERING JOURNAL, 2014, 5 (03) : 789 - 801
  • [36] Differential search algorithm for solving multi-objective optimal power flow problem
    Abaci, Kadir
    Yamacli, Volkan
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2016, 79 : 1 - 10
  • [37] Adaptive multiple evolutionary algorithms search for multi-objective optimal reactive power dispatch
    Li Hongxin
    Li Yinhong
    Chen Jinfu
    INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2014, 24 (06): : 780 - 795
  • [38] Optimal power flow with a versatile FACTS controller by genetic algorithm approach
    Leung, HC
    Chung, TS
    APSCOM - 2000: 5TH INTERNATIONAL CONFERENCE ON ADVANCES IN POWER SYSTEM CONTROL, OPERATION & MANAGEMENT, VOLS 1 AND 2, 2000, : 178 - 183
  • [39] Optimal power flow with a versatile FACTS controller by genetic algorithm approach
    Leung, HC
    Chung, TS
    2000 IEEE POWER ENGINEERING SOCIETY WINTER MEETING - VOLS 1-4, CONFERENCE PROCEEDINGS, 2000, : 2806 - 2811
  • [40] Coordinated control of FACTS devices based on optimal power flow
    Glanzmann, G
    Andersson, G
    37TH NORTH AMERICAN POWER SYMPOSIUM, PROCEEDINGS, 2005, : 141 - 148