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Data-driven modeling for effective integration of wind power
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Authors
Arrieta Prieto, Mario, Enrique
Issue Date
2021-05
Type
Electronic thesis
Thesis
Thesis
Language
en_US
Keywords
Decision sciences and engineering systems
Alternative Title
Abstract
Stochastic methods to explicitly model the uncertainty of wind power output are essentialfor the effective integration of this renewable energy source into the power supply. Recent
years have seen several advances in forecasting models for wind power output, though these
models typically focused on a single wind farm that has already been built. A result of
this dissertation is a novel forecasting framework that builds probabilistic forecasts for existing
farms. This spatio-temporal framework exploits their asymmetric association in time
and space via an ARIMA (Auto-Regressive Integrated Moving Average) -DVINE (Drawable
Vine) copula model hybridized with support vector regression (SVR). A second result of this
dissertation is a new optimization model that, ex ante, finds the best placement of a new
wind farm, based on its overall positive impact for society. Utilizing historical data of wind
power output for a set of established wind farms belonging to the Energy Reliability Council
of Texas (ERCOT), this data-driven optimization problem explicitly considers the temporal
and spatial dynamics of the wind power generation process via a predictive model built
upon hybrid implementations of spatio-temporal random-effects models and random forests.
The predictive model for wind power output is considered, per se, a significant contribution
because it allows to conduct prospective analysis of the potential of a given location where
no records are available and no farms have been installed. In total, three new models have
been developed to better forecast and predict wind power production, in order to increase
the effective integration of wind power in the electricity system.
Description
May2021
School of Engineering
School of Engineering
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY