Optimization of real power generation plants for power loss minimization and voltage profile improvement using Krill herd algorithm

Reduction of generation cost and transmission losses is one of the most important issues of today's power systems. In this paper, a multi- objective optimal power flow using a biologically inspired, Krill herd (KH) Search Algorithm has been proposed. Unified Power Flow Controller (UPFC) is a multilateral device in the Flexible Alternating Current Transmission System (FACTS) family which has the capability of controlling power system parameters like line reactance, voltage magnitude and phase angle either independently or collectively. The effectiveness of Krill herd (KH) algorithm is tested on IEEE 14 bus test system with the presence of UPFC for real power losses minimization and voltage deviation minimization as objective function and the results are presented and analyzed. The results of Krill herd algorithm are also compared with genetic algorithm with and without UPFC to establish the efficiency of the proposed algorithm.

[1]  Yoke Lin Tan,et al.  Dynamic characteristic study of UPFC based on a detailed simulation model , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).

[2]  T. J. Hammons,et al.  Flexible AC transmission systems (FACTS) , 1997 .

[3]  M. Surya Kalavathi,et al.  UPFC placement: A new self adaptive firefly algorithm , 2013 .

[4]  Abhay Singh,et al.  Comparative Study of Krill Herd, Firefly and Cuckoo Search Algorithms for Unimodal and Multimodal Optimization , 2014 .

[5]  Ernest Nkusi.  Flexible AC Transmission System (Facts) Devices , 2015 .

[6]  Hari Om Gupta,et al.  Location of unified power flow controller for congestion management , 2001 .

[7]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[8]  Vassilios Petridis,et al.  Optimal power flow by enhanced genetic algorithm , 2002 .

[9]  P. K. Chattopadhyay,et al.  Application of bio-inspired krill herd algorithm to combined heat and power economic dispatch , 2014, 2014 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA).

[10]  Abdelazeem A. Abdelsalam,et al.  A novel facts compensation scheme for power quality improvement in wind Smart Grid , 2012, 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).

[11]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[12]  S. Gerbex,et al.  Optimal Location of Multi-Type FACTS Devices in a Power System by Means of Genetic Algorithms , 2001, IEEE Power Engineering Review.

[13]  Xin-She Yang,et al.  Firefly Algorithm, Lévy Flights and Global Optimization , 2010, SGAI Conf..

[14]  Innocent Kamwa,et al.  Optimal placement of multiple-type FACTS devices to maximize power system loadability using a generic graphical user interface , 2013, IEEE Transactions on Power Systems.

[15]  Zhang Boming,et al.  Optimal active OPF with FACTS devices by an innovative load-equivalent approach , 2000 .

[16]  Y. H. Song,et al.  Power flow control approach to power systems with embedded FACTS devices , 2002 .

[17]  N.P. Padhy,et al.  Optimal power flow incorporating FACTS devices - bibliography and survey , 2003, 2003 IEEE PES Transmission and Distribution Conference and Exposition (IEEE Cat. No.03CH37495).

[18]  Narain G. Hingorani.  Flexible AC Transmission System (FACTS) , 1996 .

[19]  D. Povh.  Modeling of FACTS in power system studies , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).

[20]  T. K. Saha,et al.  Mixed-integer method for optimal UPFC placement based on line flow-based equations , 2010, 2010 20th Australasian Universities Power Engineering Conference.