Modeling of bidding Strategies for Power Suppliers and Large Consumers in Electricity Market with Risk Analysis
K. Asokan1, R. Ashok Kumar2

1.K.Asokan, Department of Electrical Egg, Annamalai University, Annamalai Nagar, India.
2R.Ashok kumar, Department of Electrical Engg, Annamalai University, Annamalai Nagar, India.
Manuscript received on April 03, 2013. | Revised Manuscript received on April 29, 2013. | Manuscript published on May 05, 2013. | PP: 271-276 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1524053213/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In the competitive electricity market, Generation companies and large consumers are participating in bidding methodologies for their own benefits. In oligopoly market structure, GENCOs tries to maximize their profit and minimize the risk factor. So it is very essential and important for the GENCOs to formulate optimal bidding strategies with risk terminology before entering into the electricity market to achieve a maximum profit , since the market clearing price (MRP) are variable in nature. In this paper an optimal bidding strategy associated with risk management is devised as a multi objective stochastic optimization problem and solved by Quantum inspired PSO. The impact of risk on the GENCOs is analyzed by introducing the factor λ. The proposed Quantum inspired PSO effectively maximize the GENCOs profit and benefit of large consumers. A numerical example with six suppliers and two large consumers is considered to illustrate the essential features of the proposed method and test results are tabulated. The simulation result shows that these approaches effectively maximize the Profit and Benefit of Power suppliers and Large Consumers, converge much faster and more reliable when compared with existing methods.
Keywords: Electricity market, Optimal bidding, Profit maximization, Risk analysis, Quantum inspired PSO.

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