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A growing amount of variable generation is being connected to the distribution network, where it is not directly monitored or controlled by the system operator. This introduces a greater degree of uncertainty into bulk system operations. For short-term operational planning tasks, such as the prediction of network congestions, it is becoming increasingly necessary to forecast demand/generation profiles...
Wind power is uncertain and fluctuating. To address its impact on system, a jointed scheduling model of wind power/thermal generation/ energy storage system (ESS) is established in this paper, based on bi-level programming (BLP). Operations of different types of power sources are optimized in the upper-level, while unit commitment of thermal generation is optimized in the lower-level. These two layers...
Quantification of uncertainties associated with solar photovoltaic (PV) power generation forecasts is essential for optimal management of solar PV farms and their successful integration into the grid. These uncertainties can be appropriately quantified and represented in the form of probabilistic rather than deterministic. This paper introduces bootstrap confidence intervals (CIs) to quantify uncertainty...
There are many uncertainties associated with forecasting electric vehicle charging and discharging capacity due to the stochastic nature of human behavior surrounding usage and intermittent travel patterns. This uncertainty if unmanaged has the potential to radically change traditional load profiles. Therefore optimal capacity forecasting methods are important for large-scale electric vehicle integration...
In this paper, three individual indices, as well as a new comprehensive index, are introduced to evaluate prediction intervals. Then, two practical methods, namely, Interval Extension Method and Optimal Scalar Method are proposed to build the prediction intervals based on an ensemble of Extreme Learning Machines. Case studies on hour-ahead load interval forecasting with respect to Chicago Metro Area...
The future power grid will need to incorporate systems and processes with a higher degree of variability and randomness due to the penetration of renewable energy resources and the increase of energy demand. Forecasting variables in a more uncertain environment poses new challenges and revisions of the existing forecasting methodologies will have to be made to maintain forecasting accuracy. This paper...
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