Abstract
This paper presents a day-based stochastic unit maintenance schedule (UMS) model for power producers to optimize their payoffs, considering the uncertainty of market prices. In the proposed model, the producer's benefits include the expected energy-selling profits in energy market and maintenance costs in each period. An effective Monte Carlo simulation based on latin hypercube sampling (LHS-MC) is adopted to evaluate the related risk associated with uncertain energy prices. Then, the proposed UMS issue can be solved via a combination of genetic algorithms and linear programmings. Finally, numerical examples on a four-unit producer are utilized to illustrate the usefulness of the presented scheme.
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