unit commitment
The dataset contains IEEE 30-bus, 118-bus, 300-bus dataset we generated for learning for unit commitment. The dataset consists of data from both normal and extended time scales, with a total time span of one year. A data point is defined as the load demand for each period and the on/off status of the units at that moment. This dataset can be used to train a neural network to learn the mapping from load demand information to the on/off status of the units.
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Renewable energy is boosting the deployment of microgrids (MGs) with stochastic and low inertia nature. To improve the operational efficiency of MGs while guaranteeing frequency security, a three-stage stochastic unit commitment problem is proposed, where renewable energy can be deloaded. In the first stage, the diesel generators (DGs) are scheduled, responding to uncertainties of loads and photovoltaic generator output. In the second stage, the outputs of DGs are optimized to reduce the operational cost under uncertain disturbances.
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This data is associated to an optimization model for generation expansion planning considering energy-based and power-based formulations. The data contains the information used in the case studies related to demand and renewable time series, as well as the technical characteristic of generation and storage units.
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