Optimal Siting and Sizing of Intermittent Distributed Generators in Distribution System

被引:45
作者
Zhang, Shenxi [1 ]
Cheng, Haozhong [1 ]
Li, Ke [1 ]
Bazargan, Masoud [1 ]
Yao, Liangzhong [1 ]
机构
[1] Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai 200240, Peoples R China
基金
中国国家自然科学基金; 英国工程与自然科学研究理事会;
关键词
intermittent distributed generators; siting and sizing; chance-constrained programming; correlation; Monte Carlo simulation; multi-population differential evolution algorithm; DIFFERENTIAL EVOLUTION; INPUT VARIABLES; POWER-SYSTEM; UNCERTAINTIES; FLOW; PENETRATION; CAPABILITY; PLACEMENT;
D O I
10.1002/tee.22129
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];
摘要
Intermittent distributed generators (IDGs), such as distributed wind turbine generator (WTG) and photovoltaic generator (PVG), have been developing rapidly in recent years. The output power of WTG and PVG highly depends on the wind speed and illumination intensity, respectively. There always exist correlations among the wind speed, illumination intensity, and bus load, which could have significant influence on the determination of siting and sizing of IDGs in distribution system. Given this background, a chance-constrained-programming-based IDGs planning model, which can take into account the correlations, is developed in this paper. Latin hypercube sampling technique and Cholesky decomposition are introduced to handle the correlations. A Monte Carlo simulation-embedded multi-population differential evolution algorithm is employed to solve the developed model. Case studies carried out on the Baran & Wu 33-bus distribution system verify the feasibility of the developed model and effectiveness of the proposed solving methodology. (C) 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
引用
收藏
页码:628 / 635
页数:8
相关论文
共 26 条
[1]
Probabilistic approach for optimal allocation of wind-based distributed generation in distribution systems [J].
Atwa, Y. M. ;
El-Saadany, E. F. .
IET RENEWABLE POWER GENERATION, 2011, 5 (01) :79-88
[2]
NETWORK RECONFIGURATION IN DISTRIBUTION-SYSTEMS FOR LOSS REDUCTION AND LOAD BALANCING [J].
BARAN, ME ;
WU, FF .
IEEE TRANSACTIONS ON POWER DELIVERY, 1989, 4 (02) :1401-1407
[3]
Application of differential evolution algorithm for transient stability constrained optimal power flow [J].
Cai, H. R. ;
Chung, C. Y. ;
Wong, K. P. .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2008, 23 (02) :719-728
[4]
Probabilistic Load Flow Method Based on Nataf Transformation and Latin Hypercube Sampling [J].
Chen, Yan ;
Wen, Jinyu ;
Cheng, Shijie .
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2013, 4 (02) :294-301
[5]
Ant direction hybrid differential evolution for solving large capacitor placement problems [J].
Chiou, JP ;
Chang, CF ;
Su, CT .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2004, 19 (04) :1794-1800
[6]
A Framework for Optimal Placement of Energy Storage Units Within a Power System With High Wind Penetration [J].
Ghofrani, Mahmoud ;
Arabali, Amirsaman ;
Etezadi-Amoli, Mehdi ;
Fadali, Mohammed Sami .
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2013, 4 (02) :434-442
[7]
Guo J., 2012, IEEE MTT S INT MICRO, P1
[8]
Probabilistic Power Flow by Monte Carlo Simulation With Latin Supercube Sampling [J].
Hajian, Mahdi ;
Rosehart, William D. ;
Zareipour, Hamidreza .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2013, 28 (02) :1550-1559
[9]
Helen JC, 2003, RELIAB ENG SYST SAFE, V81, P23
[10]
A DISTRIBUTION-FREE APPROACH TO INDUCING RANK CORRELATION AMONG INPUT VARIABLES [J].
IMAN, RL ;
CONOVER, WJ .
COMMUNICATIONS IN STATISTICS PART B-SIMULATION AND COMPUTATION, 1982, 11 (03) :311-334