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Multi-stage decision making, a fundamental tenet of stochastic programming, resonates well with the practice of the electricity markets. The day-ahead market, used to commit the generators, bears uncertainty in the power demand and physical conditions of the generators and transmission lines. The situation becomes less uncertain in the real-time market, where the dispatch is decided. Although traditional...
In this study, unit commitment (UC) problem is solved for an optimum schedule of generating units based on the load data forecasted by using artificial neural network (ANN) model and ANN model with autoregressive (AR). Low-cost generation is important in power system analysis. Under forecasting or over forecasting will result in the requirement of purchasing power from spot market or an unnecessary...
Wind energy on a power system alters the unit commitment and dispatch problem, as it adds a stochastic element due to the uncertainty of wind power forecasts. By explicitly taking into account the stochastic nature of wind power, it is expected that better schedules should be produced, thereby reducing costs on the system. This paper compares a stochastically optimised unit commitment and dispatch...
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