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2013 (Vol. 5, Issue: 04)
Article Information:

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

Mohsen Khalilpour, Navid Razmjooy, Akbar Danandeh and Mehdi Rostamzadeh
Corresponding Author:  Navid Razmjooy 

Key words:  Congestion management, genetic algorithm, invasive weeds optimization, local marginal price, power flow, power market, power world software
Vol. 5 , (04): 1330-1338
Submitted Accepted Published
July 05, 2012 August 08, 2012 February 01, 2013

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.
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  Cite this Reference:
Mohsen Khalilpour, Navid Razmjooy, Akbar Danandeh and Mehdi Rostamzadeh, 2013. Optimal Bidding Strategy in Power Market before and after Congestion Management Using Invasive Weed Optimization.  Research Journal of Applied Sciences, Engineering and Technology, 5(04): 1330-1338.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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