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     Research Journal of Applied Sciences, Engineering and Technology


Optimal Bidding Strategy in Power Market before and after Congestion Management Using Invasive Weed Optimization

1Mohsen Khalilpour, 2Navid Razmjooy, 1Akbar Danandeh and 1Mehdi Rostamzadeh
1Young Researchers Club, Ardabil Branch, Islamic Azad University, Ardabil, Iran
2Young Researchers Club, Majlesi Branch, Islamic Azad University, Isfahan, Iran
Research Journal of Applied Sciences, Engineering and Technology  2013  4:1330-1338
http://dx.doi.org/10.19026/rjaset.5.4869  |  © The Author(s) 2013
Received: July 05, 2012  |  Accepted: August 08, 2012  |  Published: February 01, 2013

Abstract

Power companies world-wide have been restructuring their electric power systems from a vertically integrated entity to a deregulated, open-market environment. Previously, electric utilities usually sought to maximize the social welfare of the system with distributional equity as its main operational criterion. The operating paradigm was based on achieving the least-cost system solution while meeting reliability and security margins. This often resulted in investments in generating capacity operating at very low capacity factors. Decommissioning of this type of generating capacity was a natural outcome when the vertically integrated utilities moved over to deregulated market operations. This study proposes an optimizing base and load demand relative binding strategy for generating power apprises of different units in the investigated system. Afterwards, congestion effect in this biding strategy is investigated. The described systems analysis is implemented on 5 and 9 bus systems and optimizing technique in this issue is the Invasive Weed Optimization algorithm; the results are then compared by GA. Finally, examined systems is simulated by using the Power World software; experimental results show that the proposed technique (Invasive Weed Optimization) is a high performance by compared GA for the congestion management purposes.

Keywords:

Congestion management, genetic algorithm, invasive weeds optimization, local marginal price, power flow, power market, power world software,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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