Multiple-distributed generation planning under load uncertainty and different penetration levels

被引:66
作者
Ugranli, Faruk [1 ]
Karatepe, Engin [1 ]
机构
[1] Ege Univ, Fac Engn, Dept Elect & Elect Engn, TR-35100 Izmir, Turkey
关键词
Distributed generation; Optimal size and location; Artificial neural network; Power loss; Load uncertainty; Penetration level; PARTICLE SWARM OPTIMIZATION; DISTRIBUTION NETWORKS; DG ALLOCATION; DISTRIBUTION-SYSTEMS; GENETIC ALGORITHM; PLACEMENT; RELIABILITY; LOSSES; IMPACT;
D O I
10.1016/j.ijepes.2012.10.043
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 penetration of distributed generation (DG) in power system is continually increasing. Hence, there is a need to investigate the potential benefits and drawbacks of DGs when integrating DG units in existing networks. The challenge of identifying the optimal locations and sizes has triggered research interest and many studies have been presented in this purpose. Different analytical techniques have been developed to minimize power losses for single-DG unit integration. If DG units are integrated at nonoptimal locations, the power losses increase, resulting in increased cost of energy. The novelty of this paper lies in studying the optimal placement of multiple-DG units in order to minimize power losses. In this study, an optimality criterion is investigated to minimize losses by including load uncertainty, different DG penetration levels and reactive power of multiple- DG concept. The simulation results show that it is not possible to form an analytical equation for optimum planning of DG in terms of load distribution, penetration level and reactive power. Due to the complexity of the multiple-DG concept, artificial neural network based optimal DG placement and size method is developed. The proposed method is implemented to the IEEE-30 bus test network and the results are presented and discussed. The results show that the proposed method can be applied to a power network for all possible scenarios. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:132 / 144
页数:13
相关论文
共 60 条
[41]   Effect of load models on assessment of energy losses in distributed generation planning [J].
Qian, Kejun ;
Zhou, Chengke ;
Allan, Malcolm ;
Yuan, Yue .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2011, 33 (06) :1243-1250
[42]   Assessment of energy distribution losses for increasing penetration of distributed generation [J].
Quezada, VHM ;
Abbad, JR ;
San Román, TG .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2006, 21 (02) :533-540
[43]   Two-stage distributed generation optimal sizing with clustering-based node selection [J].
Rotaru, Florina ;
Chicco, Gianfranco ;
Grigoras, Gheorghe ;
Cortina, Gheorghe .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2012, 40 (01) :120-129
[44]  
Saadat H., 2004, POWER SYSTEM ANAL
[45]   Cumulant-based stochastic nonlinear programming for variance constrained voltage stability analysis of power systems [J].
Schellenberg, A ;
Rosehart, W ;
Aguado, JA .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2006, 21 (02) :579-585
[46]   Application of ANN technique based on μ-synthesis gto load frequency control of interconnected power system [J].
Shayeghi, H. ;
Shayanfar, H. A. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2006, 28 (07) :503-511
[47]  
Shayeghi H, 2009, INT J ENERGY POWER E, V2, P144
[48]  
Sheidaei F., 2008, 2008 43 INT U POW EN, P1
[49]   Optimum allocation of distributed generations based on nodal pricing for profit, loss reduction, and voltage improvement including voltage rise issue [J].
Singh, R. K. ;
Goswami, S. K. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2010, 32 (06) :637-644
[50]   Fuzzy wavelet network identification of optimum operating point of non-crystalline silicon solar cells [J].
Syafaruddin ;
Karatepe, Engin ;
Hiyama, Takashi .
COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2012, 63 (01) :68-82