Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Valuation and Optimal Operation of Electric Power Plants in Competitive Markets
Operations Research
A Stochastic Programming Approach to Power Portfolio Optimization
Operations Research
A two-stage stochastic programming model for electric energy producers
Computers and Operations Research
Applied Artificial Intelligence
Discrete Optimization
Proceedings of the Winter Simulation Conference
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The electric power industry is going through deregulation. As a result, the load on the generating units of a utility is becoming increasingly unpredictable. Furthermore, electric utilities may need to buy power or sell their production to a power pool that serves as a spot market for electricity. These trading activities expose utilities to volatile electricity prices. In this paper, we present a stochastic model for the unit commitment that incorporates power trading into the picture. Our model also accounts for fuel constraints and prices that may vary with electricity prices and demand. The resulting model is a mixed-integer program that is solved using Lagrangian relaxation and Bender's decomposition. Using this solution approach, we solve problems with 729 demand scenarios on a single processor to within 0.1% of the optimal solution in less than 10 minutes. Our numerical results indicate that significant savings can be achieved when the spot market is entered into the problem and when stochastic policy is adopted instead of a deterministic one.