Research Article | OPEN ACCESS
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
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,
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.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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