DocumentCode :
3101857
Title :
Profit-oriented Thermal Unit Maintenance Scheduling under Competitive Environment
Author :
Sugimoto, Junjiro ; Isa, Aishah Mohd ; Yokoyama, Ryuichi
Author_Institution :
Electr. Eng. Dept., Tokyo Metropolitan Univ., Tokyo
fYear :
2007
fDate :
24-28 June 2007
Firstpage :
1
Lastpage :
6
Abstract :
In this paper, an improved maintenance scheduling approach suitable for the competitive environment is proposed by taking account of profits and costs of generation companies and the formulated combinatorial optimization problem is solved by using Reactive Tabu search (RTS) In competitive power markets, electricity prices are determined by balance between demand and supply in electric power exchanges or bilateral contracts. Therefore it is essential for system operation planners and market participants to take the volatility of electricity price into consideration. In the proposed maintenance scheduling method, firstly, electricity prices are forecasted for the targeted period using proposed aggregated bidding model. Secondly, the optimal combinatorial maintenance-scheduling problem is solved by using Reactive Tabu Search in the light of the electricity prices forecasted. This method proposes a new objective function by which the most profitable maintenance schedule would be attained. As an objective function, Opportunity Loss of Maintenance (OLM) is adopted to maximize the profit of Generation Companies (GENCOS). Finally, the proposed maintenance scheduling is applied to a practical power system test model to verify the advantages and effectiveness of the method.
Keywords :
power generation scheduling; power markets; power system economics; power system management; Reactive Tabu search; bilateral contracts; competitive environment; demand and supply; electric power exchanges; electricity prices; maintenance scheduling; objective function; opportunity loss of maintenance; power markets; profit oriented; thermal unit; Economic forecasting; Electricity supply industry; Fuels; Job shop scheduling; Power generation; Power markets; Power system modeling; Power system planning; Predictive models; Preventive maintenance; Artificial Neural Network; Electricity Market; Electricity Price Forecasting; Power Generation Maintenance; Tabu Search;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Engineering Society General Meeting, 2007. IEEE
Conference_Location :
Tampa, FL
ISSN :
1932-5517
Print_ISBN :
1-4244-1296-X
Electronic_ISBN :
1932-5517
Type :
conf
DOI :
10.1109/PES.2007.386099
Filename :
4275865
Link To Document :
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