DocumentCode :
2062395
Title :
Profit maximization of a generation company based on Biogeography based Optimization
Author :
Jain, P. ; Agarwal, A. ; Gupta, N. ; Sharma, R. ; Paliwal, U. ; Bhakar, R.
Author_Institution :
Electr. Eng. Dept., Malaviya Nat. Inst. of Technol. Jaipur, Jaipur, India
fYear :
2012
fDate :
22-26 July 2012
Firstpage :
1
Lastpage :
6
Abstract :
In a deregulated electricity market, generating companies aim to maximize their profit, by bidding optimally in the day-ahead market, under incomplete information of the competing generators. This paper develops an optimal bidding strategy for a thermal generator, considering a nonlinear operating cost function. Each generating company offers block bid as price and quantity pairs and sealed auction with a pay-as-bid is employed. Rival bidding behavior is described using normal probability distribution function, and the optimal bidding strategy for a generation company is formulated as a stochastic optimization problem. This is solved using Monte Carlo Simulations with Biogeography based Optimization (BBO) approach. BBO is a new heuristic algorithm that retains the properties of all good solutions and improves the quality of poor solutions, in the entire population of feasible solutions. The effectiveness of the proposed method is tested on a sample system, and optimal bid quantities and prices are obtained.
Keywords :
Monte Carlo methods; electric generators; normal distribution; optimisation; power generation economics; power markets; profitability; stochastic processes; tendering; thermal power stations; BBO; Monte Carlo simulation; biogeography based optimization; day-ahead market; electricity market deregulation; generation company; heuristic algorithm; nonlinear operating cost function; normal probability distribution function; optimal bidding strategy; profit maximization; stochastic optimization problem; thermal generator; Biogeography; Companies; Mathematical model; Monte Carlo methods; Optimization; Sociology; Bidding Strategy; Biogeography based optimization; Day Ahead Market; Monte Carlo Simulation; Normal Distributions;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Society General Meeting, 2012 IEEE
Conference_Location :
San Diego, CA
ISSN :
1944-9925
Print_ISBN :
978-1-4673-2727-5
Electronic_ISBN :
1944-9925
Type :
conf
DOI :
10.1109/PESGM.2012.6345445
Filename :
6345445
Link To Document :
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