Profit-based Thermal Unit Maintenance Scheduling under Price Volatility in Competitive Enivornment

J. Sugimoto, H. Tajima, S. Machi, R. Yokoyama, and V.V.R. Silva (Japan)


Power Generation Maintenance, Tabu Search, Electricity Market, Electricity Price Forecasting, Artificial Neural Network.


This paper presents an improved profit-based maintenance scheduling approach by using Reactive Tabu search (RTS) in competitive environment. In competitive power markets, electricity prices are determined by biddings in electric power exchanges or bilateral contracts among suppliers and customers. So 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 Artificial Neural Network (ANN). 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.

Important Links:

Go Back