10.1186/2251-6832-4-7

A simple and fast approach for allocation and size evaluation of distributed generation

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  • Partha Kayal*1,
  • Chandan Kumar Chanda1,
  1. Department of Electrical Engineering, Bengal Engineering & Science University, Shibpur, Howrah, 711103, IN
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Published in Issue 2013-01-22

How to Cite

Kayal, P., & Chanda, C. K. (2013). A simple and fast approach for allocation and size evaluation of distributed generation. International Journal of Energy and Environmental Engineering, 4(1 (December 2013). https://doi.org/10.1186/2251-6832-4-7
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Abstract

Abstract Penetration of distributed generation (DG) units in distribution network has increased rapidly stimulated by reduced network power loss, improved bus voltage profile, and better power quality. Appropriate size and allocation of DG units play a significant role to get beneficial effects. The objective of this study is to demonstrate a simple and fast technique to determine appropriate location and size of DG units. A voltage stability indicator (VSI) is derived which can quantify the voltage stability conditions of buses in distribution network. According to VSI, vulnerable buses of the network are arranged rank-wise to form a priority list for allocation of DG units. To determine the size of DG units, a feed forward artificial neural network is prepared in MATLAB environment (The MathWorks, Inc., Massachusetts, USA). The effectiveness of the proposed methodology has been tested on a 52-bus radial distribution network. After appropriate allocation of DG units, voltage profiles of most of the buses are increased significantly. The results also indicated that the total loss of the distribution network has reduced by nearly 76.39%, and voltage stability conditions of buses are improved considerably. Voltage stability conditions of bus-13, bus-36, and bus-44 are raised by 23.16%, 29.23%, and 37.64% respectively.

Keywords

  • Voltage stability condition,
  • Distributed generation units,
  • Priority list,
  • Allocation,
  • ANN
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References

  1. Lal and Laturi (2012) Techno-economic analysis of a hybrid mini-grid system for Fiji islands https://doi.org/10.1186/2251-6832-3-10
  2. Hedayati et al. (2008) A method for placement of DG units in distribution networks 23(3) (pp. 1620-1628) https://doi.org/10.1109/TPWRD.2007.916106
  3. Kashem and Ledwich (2005) Multiple distributed generators for distribution feeder voltage support 20(3) (pp. 676-684) https://doi.org/10.1109/TEC.2004.832090
  4. Rafidah and Rahim (2010) In the 4th international power engineering and optimization conference
  5. Islam (2012) Increasing wind energy penetration level using pumped hydro storage in island micro-grid system https://doi.org/10.1186/2251-6832-3-9
  6. Acharya et al. (2006) An analytical approach for DG allocation in primary distribution network (pp. 669-678) https://doi.org/10.1016/j.ijepes.2006.02.013
  7. Singh et al. (2008) GA based energy loss minimization approach for optimal sizing and placement of distributed generation (pp. 147-156)
  8. Caisheng and Nehrir (2004) Analytical approaches for optimal placement of distributed generation sources in power system 19(4) (pp. 2068-2076) https://doi.org/10.1109/TPWRS.2004.836189
  9. Oyedepo (2012) Efficient energy utilization as a tool for sustainable development in Nigeria https://doi.org/10.1186/2251-6832-3-11
  10. Dey et al. (2004) Development of global voltage security indicator (VSI) and role of SVC on it in longitudinal power supply (LPS) system (pp. 1-9) https://doi.org/10.1016/S0378-7796(03)00125-1
  11. Gubina and Strmcnik (1997) A simple approach to voltage stability assessment in radial network 12(3) (pp. 1121-1128) https://doi.org/10.1109/59.630451
  12. Shin (2007) A new optimal routing algorithm for loss minimization and voltage stability improvement in radial power systems 22(2) (pp. 648-657) https://doi.org/10.1109/TPWRS.2006.889112
  13. Ismail (2009) Neural network based load balancing and reactive power control by static VAR compensator 1(1) (pp. 1793-8198)
  14. Thukaram et al. (2005) Artificial neural network and support vector machine approach for locating faults in radial distribution system 20(2) (pp. 710-721) https://doi.org/10.1109/TPWRD.2005.844307