Reducing intermittency with statistics.
This project applied modern portfolio theory to assist utilities in optimizing their portfolios of
renewable energy products, focusing on the long-term coincidence of production and
demand. Funding is provided by the National Science Foundation's small business grant
program (STTR Phase I).
Team:
Lead: Ted Ladd
Software: Alexis Sarthou
Statistics: Professor David Aadland
Data: Ross Manley
Title: Timing Is Everything: Matching Wind Production and Electricity Demand Using
Portfolio Theory
Purpose: The team is developing an algorithm to compare diurnal and seasonal patterns of
electricity production at wind sites with different profiles of electricity demand by regional
utilities. By employing statistical techniques from the field of security analysis (i.e. stock
trading), this tool helps reduce the impact of wind’s intermittency through diversification of
sites.
Abstract: Wind is unpredictably intermittent. By geographically diversifying its portfolio
of wind suppliers, a power purchaser can maximize the correlation of wind energy to its
own diurnal (daily) and seasonal (monthly) demand patterns while reducing the
variance (“firming”) of its wind power supply. Similarly, by conducting this analysis, wind
developers identify those utilities with coincidental demand curves to whom the wind
power will be most valuable. It also helps developers pre-package multiple sites to
increase their value.
This algorithm is being inserted into a web-based tool to perform these analyses,
comparisons, and recommendations in less than 5 seconds with no knowledge of
statistics necessary.
Because wind developers are notoriously secretive about releasing anemometer data,
we have mined public wind data from the U.S. National Climate Data Center and demand
data from the North American Electric Reliability Corporation. Users can upload their
own data while maintaining its confidentiality.
This topic is also important to transmission operators and policy-makers in order to
determine which areas of the nation have wind patterns that best match regional
demand, and thus where transmission construction should occur to optimize utilization
of wind energy. It may also be valuable to forecasters and dispatchers in order to
quickly define geographic areas for further, more detailed study.
This project is funded by the National Science Foundation in collaboration with the
Department of Finance at the University of Wyoming.
This project applied modern portfolio theory to assist utilities in optimizing their portfolios of
renewable energy products, focusing on the long-term coincidence of production and
demand. Funding is provided by the National Science Foundation's small business grant
program (STTR Phase I).
Team:
Lead: Ted Ladd
Software: Alexis Sarthou
Statistics: Professor David Aadland
Data: Ross Manley
Title: Timing Is Everything: Matching Wind Production and Electricity Demand Using
Portfolio Theory
Purpose: The team is developing an algorithm to compare diurnal and seasonal patterns of
electricity production at wind sites with different profiles of electricity demand by regional
utilities. By employing statistical techniques from the field of security analysis (i.e. stock
trading), this tool helps reduce the impact of wind’s intermittency through diversification of
sites.
Abstract: Wind is unpredictably intermittent. By geographically diversifying its portfolio
of wind suppliers, a power purchaser can maximize the correlation of wind energy to its
own diurnal (daily) and seasonal (monthly) demand patterns while reducing the
variance (“firming”) of its wind power supply. Similarly, by conducting this analysis, wind
developers identify those utilities with coincidental demand curves to whom the wind
power will be most valuable. It also helps developers pre-package multiple sites to
increase their value.
This algorithm is being inserted into a web-based tool to perform these analyses,
comparisons, and recommendations in less than 5 seconds with no knowledge of
statistics necessary.
Because wind developers are notoriously secretive about releasing anemometer data,
we have mined public wind data from the U.S. National Climate Data Center and demand
data from the North American Electric Reliability Corporation. Users can upload their
own data while maintaining its confidentiality.
This topic is also important to transmission operators and policy-makers in order to
determine which areas of the nation have wind patterns that best match regional
demand, and thus where transmission construction should occur to optimize utilization
of wind energy. It may also be valuable to forecasters and dispatchers in order to
quickly define geographic areas for further, more detailed study.
This project is funded by the National Science Foundation in collaboration with the
Department of Finance at the University of Wyoming.
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