.png){kind=icon}
Return
Efficient Uncertainty Quantification in Stochastic Economic Dispatch
.png){kind=icon}
OA
AI
DOI：10.1109/TPWRS.2016.2615334.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Stochastic economic dispatch models address uncertainties in forecasts of renewable generation output by considering a finite number of realizations drawn from a stochastic process model, typically via Monte Carlo sampling. Accurate evaluations of expectations or higher order moments for quantities of interest, e.g., generating cost, can require a prohibitively large number of samples. We propose an alternative to Monte Carlo sampling based on polynomial chaos expansions. These representations enable efficient and accurate propagation of uncertainties in model parameters, using sparse quadrature methods. We also use Karhunen-Lo` eve expansions for efficient representation of uncertain renewable energy generation that follows geographical and temporal correlations derived from historical data at each wind farm. Considering expected production cost, we demonstrate that the proposed approach can yield several orders of magnitude reduction in computational cost for solving stochastic economic dispatch relative toMonte Carlo sampling, for a given target error threshold.
Keywords:
Karhunen-Loeve expansion
Monte Carlo sampling
polynomial chaos expansion
stochastic economic dispatch
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.2
Papers:
1.1W
Citations:
5.0W
Organization
Cited Papers
Topiramate attenuates withdrawal signs after chronic intermittent ethanol in rats.png)
NeuroReport
NeuroReportIF0
PREAI
ShareSaveShareSaveShareSave
ShareSaveShareSaveShareSave
View more