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Conference Presentations by Anneliese Alexander

Research paper thumbnail of The National Energy with Weather System (NEWS) Simulator Shows that up to 80% Reduction in Carbon Dioxide Emissions from the US Electric Sector is Only Cost Effective at Large Geographic Scales

The importance of weather-driven renewable energies for the United States energy portfolio is gro... more The importance of weather-driven renewable energies for the United States energy portfolio is growing. The main perceived
problems with weather-driven renewable energies are their intermittent nature, low power density, and high costs. In 2009, we
began a large-scale investigation into the characteristics of weather-driven renewables. The project utilized the best available
weather data assimilation model to compute high spatial and temporal resolution power datasets for the renewable resources
of wind and solar PV. The weather model used is the Rapid Update Cycle for the years of 2006-2008. The team also collated a detailed electrical load dataset for the contiguous USA from the Federal Energy Regulatory Commission for the same three-year
period. The coincident time series of electrical load and weather data allows the possibility of temporally correlated computations
for optimal design over large geographic areas.
In the past two years, the team have designed and built a sophisticated mathematical optimization tool that is based upon linear
programming (clack et al.) with an economic objective. The tool has been constructed to include salient features of the electrical
grid, such as; transmission, construction and siting constraints, reserve requirements, electrical losses due to transmission,
asynchronous regions, “reliability” enforcement, capital costs, fuel costs, and many others.
The NEWS tool has been used to study different configurations of the US electric power systems. We performed a simplified test
where the US electric system consisted of wind, solar PV, nuclear, hydroelectric and natural gas only with the addition of HVDC
bulk transmission. The test shows that if the US meets its goals in price reduction of variable generation the US would only have
dramatic reductions of carbon dioxide emissions that is cost effective with a national-scale interconnected system. The smaller the
system, the higher the carbon emissions and steeper the cost.

CTM Clack, Y Fu, AE MacDonald, Linear programming techniques for developing an optimal electrical system including high voltage
direct-current transmission and storage, International Journal of Electric Power and Energy Systems 68, 103-114

Research paper thumbnail of Modeling Solar Irradiance and Solar PV Power Output to Create a Resource Assessment using Linear Multiple Multivariate Regression

The increased use of solar photovoltaic (PV) cells as energy sources on electric grids has create... more The increased use of solar photovoltaic (PV) cells as energy sources on electric grids has created the need for accurate solar irradiance and power production estimations. In the present paper, we establish a novel technique for solar irradiance estimations and creating a resource assessment dataset by utilizing numerical weather model variables, satellite data, and the surface radiation budget (SURFRAD) network and the Integrated Surface Irradiance Study (ISIS) network measurements. The solar irradiance outputs are global horizontal (GHI), direct normal (DNI), and diffuse horizontal (DHI). The technique is developed over the United States by training a linear multiple multivariate regression scheme at ten locations and then applying the coefficients to independent variables over the whole geographic domain of study.
The irradiance estimations are used as inputs for a solar PV power-modeling algorithm to compute power production estimations at every grid cell across the domain. The dataset is analyzed to predict the capacity factors for solar resource sites around the USA for the three years of 2006-2008. Statistics are shown to validate the skill of the scheme at geographic sites independent of the training set. In addition, we show that more high quality (geographically dispersed) observation sites increase the skill of the scheme.

Research paper thumbnail of Modeling Solar Irradiance and Solar PV Power Output to Create a Resource Dataset Using Linear Multiple Multivariate Regression

The increased use of solar photovoltaic (PV) cells as energy sources on electric grids has create... more The increased use of solar photovoltaic (PV) cells as energy sources on electric grids has created the need for more solar
irradiance and power production estimates. To do so over a large geographic region is done readily with the inclusion of satellite
and numerical weather prediction (NWP) models.
In the present paper, we establish a technique for solar irradiance estimates and creating a resource dataset by NWP variables,
satellite data, and surface measurements. The solar irradiance outputs are global horizontal (GHI), direct normal (DNI), and
diffuse horizontal (DHI). The technique is developed over the United States by training a linear multiple multivariate regression
scheme at ground-based network locations and then applying the coefficients to independent variables over the whole geographic
domain of study.
The irradiance estimates are used as inputs for a solar PV power-modeling algorithm to compute power production estimates at
every grid cell across the domain. The dataset is analyzed to predict the capacity factors for solar resource sites around the US for the years of 2006 – 2012. Statistics are shown to validate the skill of the scheme at geographic sites independent of the training
set as well as against some other available products.

Research paper thumbnail of Co-optimization of Generation Expansion and HVDC Transmission Overlay

Research paper thumbnail of National Energy with Weather System (NEWS) Simulator Results

Papers by Anneliese Alexander

Research paper thumbnail of Advances in Optimizing Weather Driven Electric Power Systems

Research paper thumbnail of Modeling Solar Irradiance and Solar PV Power Output to Create a Resource Assessment Using Linear Multiple Multivariate Regression

Journal of Applied Meteorology and Climatology, 2017

The increased use of solar photovoltaic (PV) cells as energy sources on electric grids has create... more The increased use of solar photovoltaic (PV) cells as energy sources on electric grids has created the need for more accessible solar irradiance and power production estimates for use in power modeling software. In the present paper, a novel technique for creating solar irradiance estimates is introduced. A solar PV resource dataset created by combining numerical weather prediction assimilation model variables, satellite data, and high-resolution ground-based measurements is also presented. The dataset contains ≈152 000 geographic locations each with ≈26 000 hourly time steps. The solar irradiance outputs are global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DIF). The technique is developed over the United States by training a linear multiple multivariate regression scheme at 10 locations. The technique is then applied to independent locations over the whole geographic domain. The irradiance estimates are input into a solar PV pow...

Research paper thumbnail of Future cost-competitive electricity systems and their impact on US CO2 emissions

Nature Climate Change, 2016

Carbon dioxide emissions from electricity generation are a major cause of anthropogenic climate c... more Carbon dioxide emissions from electricity generation are a major cause of anthropogenic climate change. The deployment of wind and solar power reduces these emissions, but is subject to the variability of the weather. In the present study, we calculate the cost-optimized configuration of variable electrical power generators using weather data with high spatial (13-km) and temporal (60-min) resolution over the contiguous US. Our results show that when using future anticipated costs for wind and solar, carbon dioxide emissions from the US electricity sector can be reduced by up to 80% relative to 1990 levels, without an increase in the levelized cost of electricity. The reductions are possible with current technologies and without electrical storage. Wind and solar power increase their share of electricity production as the system grows to encompass large-scale weather patterns. This reduction in carbon emissions is achieved by moving away from a regionally divided electricity sector to a national system enabled by high-voltage direct-current transmission.

Research paper thumbnail of Modeling Solar Irradiance and Wind Speeds to Create an Accurate Resource Assessment for Renewable Energy

Research paper thumbnail of National Energy with Weather System (NEWS) Simulator Results

Research paper thumbnail of The Variability and Intermittency of Wind and Solar Power Can Be Overcome Without Storage By Using the National Energy With Weather System (NEWS) Simulator To Design A National US Electric (and Energy) Sector

Research paper thumbnail of Demonstrating the effect of vertical and directional shear for resource mapping of wind power

Research paper thumbnail of Multivariate linear regression technique for computing solar irradiance estimations using the SURFRAD and ISIS networks

The increased use of solar photovoltaic cells as energy sources on electrical grids has created t... more The increased use of solar photovoltaic cells as energy sources on electrical grids has created the need for accurate solar irradiance assessment over continental scales. In the present paper, we discuss a technique for computing solar irradiance estimations that utilizes numerical weather model variables, satellite data, and SURFRAD and ISIS network measurements. The numerical weather model used is the Rapid Update Cycle. The solar irradiance estimations found are more accurate than the solar irradiance fields provided by the satellites alone. Moreover, estimations are provided for the global horizontal, direct normal, and diffuse horizontal irradiance fields. The multivariate regression implemented allows accurate estimations of solar irradiance, but relies on high quality solar measurements at the surface over a geographically diverse domain. The technique developed in the present paper is also applicable to solar irradiance forecasts.

Research paper thumbnail of Future cost-competitive electricity systems and their impact on US CO2 emissions

Carbon dioxide emissions from electricity generation are a major cause of anthropogenic climate c... more Carbon dioxide emissions from electricity generation are a major cause of anthropogenic climate change. The deployment of wind and solar power reduces these emissions, but is subject to the variability of the weather. In the present study, we calculate the cost-optimized configuration of variable electrical power generators using weather data with high spatial (13-km) and temporal (60-min) resolution over the contiguous US. Our results show that when using future anticipated costs for wind and solar, carbon dioxide emissions from the US electricity sector can be reduced by up to 80% relative to 1990 levels, without an increase in the levelized cost of electricity. The reductions are possible with current technologies and without electrical storage. Wind and solar power increase their share of electricity production as the system grows to encompass large-scale weather patterns. This reduction in carbon emissions is achieved by moving away from a regionally divided electricity sector to a national system enabled by high-voltage direct-current transmission.

Research paper thumbnail of The National Energy with Weather System (NEWS) Simulator Shows that up to 80% Reduction in Carbon Dioxide Emissions from the US Electric Sector is Only Cost Effective at Large Geographic Scales

The importance of weather-driven renewable energies for the United States energy portfolio is gro... more The importance of weather-driven renewable energies for the United States energy portfolio is growing. The main perceived
problems with weather-driven renewable energies are their intermittent nature, low power density, and high costs. In 2009, we
began a large-scale investigation into the characteristics of weather-driven renewables. The project utilized the best available
weather data assimilation model to compute high spatial and temporal resolution power datasets for the renewable resources
of wind and solar PV. The weather model used is the Rapid Update Cycle for the years of 2006-2008. The team also collated a detailed electrical load dataset for the contiguous USA from the Federal Energy Regulatory Commission for the same three-year
period. The coincident time series of electrical load and weather data allows the possibility of temporally correlated computations
for optimal design over large geographic areas.
In the past two years, the team have designed and built a sophisticated mathematical optimization tool that is based upon linear
programming (clack et al.) with an economic objective. The tool has been constructed to include salient features of the electrical
grid, such as; transmission, construction and siting constraints, reserve requirements, electrical losses due to transmission,
asynchronous regions, “reliability” enforcement, capital costs, fuel costs, and many others.
The NEWS tool has been used to study different configurations of the US electric power systems. We performed a simplified test
where the US electric system consisted of wind, solar PV, nuclear, hydroelectric and natural gas only with the addition of HVDC
bulk transmission. The test shows that if the US meets its goals in price reduction of variable generation the US would only have
dramatic reductions of carbon dioxide emissions that is cost effective with a national-scale interconnected system. The smaller the
system, the higher the carbon emissions and steeper the cost.

CTM Clack, Y Fu, AE MacDonald, Linear programming techniques for developing an optimal electrical system including high voltage
direct-current transmission and storage, International Journal of Electric Power and Energy Systems 68, 103-114

Research paper thumbnail of Modeling Solar Irradiance and Solar PV Power Output to Create a Resource Assessment using Linear Multiple Multivariate Regression

The increased use of solar photovoltaic (PV) cells as energy sources on electric grids has create... more The increased use of solar photovoltaic (PV) cells as energy sources on electric grids has created the need for accurate solar irradiance and power production estimations. In the present paper, we establish a novel technique for solar irradiance estimations and creating a resource assessment dataset by utilizing numerical weather model variables, satellite data, and the surface radiation budget (SURFRAD) network and the Integrated Surface Irradiance Study (ISIS) network measurements. The solar irradiance outputs are global horizontal (GHI), direct normal (DNI), and diffuse horizontal (DHI). The technique is developed over the United States by training a linear multiple multivariate regression scheme at ten locations and then applying the coefficients to independent variables over the whole geographic domain of study.
The irradiance estimations are used as inputs for a solar PV power-modeling algorithm to compute power production estimations at every grid cell across the domain. The dataset is analyzed to predict the capacity factors for solar resource sites around the USA for the three years of 2006-2008. Statistics are shown to validate the skill of the scheme at geographic sites independent of the training set. In addition, we show that more high quality (geographically dispersed) observation sites increase the skill of the scheme.

Research paper thumbnail of Modeling Solar Irradiance and Solar PV Power Output to Create a Resource Dataset Using Linear Multiple Multivariate Regression

The increased use of solar photovoltaic (PV) cells as energy sources on electric grids has create... more The increased use of solar photovoltaic (PV) cells as energy sources on electric grids has created the need for more solar
irradiance and power production estimates. To do so over a large geographic region is done readily with the inclusion of satellite
and numerical weather prediction (NWP) models.
In the present paper, we establish a technique for solar irradiance estimates and creating a resource dataset by NWP variables,
satellite data, and surface measurements. The solar irradiance outputs are global horizontal (GHI), direct normal (DNI), and
diffuse horizontal (DHI). The technique is developed over the United States by training a linear multiple multivariate regression
scheme at ground-based network locations and then applying the coefficients to independent variables over the whole geographic
domain of study.
The irradiance estimates are used as inputs for a solar PV power-modeling algorithm to compute power production estimates at
every grid cell across the domain. The dataset is analyzed to predict the capacity factors for solar resource sites around the US for the years of 2006 – 2012. Statistics are shown to validate the skill of the scheme at geographic sites independent of the training
set as well as against some other available products.

Research paper thumbnail of Co-optimization of Generation Expansion and HVDC Transmission Overlay

Research paper thumbnail of National Energy with Weather System (NEWS) Simulator Results

Research paper thumbnail of Advances in Optimizing Weather Driven Electric Power Systems

Research paper thumbnail of Modeling Solar Irradiance and Solar PV Power Output to Create a Resource Assessment Using Linear Multiple Multivariate Regression

Journal of Applied Meteorology and Climatology, 2017

The increased use of solar photovoltaic (PV) cells as energy sources on electric grids has create... more The increased use of solar photovoltaic (PV) cells as energy sources on electric grids has created the need for more accessible solar irradiance and power production estimates for use in power modeling software. In the present paper, a novel technique for creating solar irradiance estimates is introduced. A solar PV resource dataset created by combining numerical weather prediction assimilation model variables, satellite data, and high-resolution ground-based measurements is also presented. The dataset contains ≈152 000 geographic locations each with ≈26 000 hourly time steps. The solar irradiance outputs are global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DIF). The technique is developed over the United States by training a linear multiple multivariate regression scheme at 10 locations. The technique is then applied to independent locations over the whole geographic domain. The irradiance estimates are input into a solar PV pow...

Research paper thumbnail of Future cost-competitive electricity systems and their impact on US CO2 emissions

Nature Climate Change, 2016

Carbon dioxide emissions from electricity generation are a major cause of anthropogenic climate c... more Carbon dioxide emissions from electricity generation are a major cause of anthropogenic climate change. The deployment of wind and solar power reduces these emissions, but is subject to the variability of the weather. In the present study, we calculate the cost-optimized configuration of variable electrical power generators using weather data with high spatial (13-km) and temporal (60-min) resolution over the contiguous US. Our results show that when using future anticipated costs for wind and solar, carbon dioxide emissions from the US electricity sector can be reduced by up to 80% relative to 1990 levels, without an increase in the levelized cost of electricity. The reductions are possible with current technologies and without electrical storage. Wind and solar power increase their share of electricity production as the system grows to encompass large-scale weather patterns. This reduction in carbon emissions is achieved by moving away from a regionally divided electricity sector to a national system enabled by high-voltage direct-current transmission.

Research paper thumbnail of Modeling Solar Irradiance and Wind Speeds to Create an Accurate Resource Assessment for Renewable Energy

Research paper thumbnail of National Energy with Weather System (NEWS) Simulator Results

Research paper thumbnail of The Variability and Intermittency of Wind and Solar Power Can Be Overcome Without Storage By Using the National Energy With Weather System (NEWS) Simulator To Design A National US Electric (and Energy) Sector

Research paper thumbnail of Demonstrating the effect of vertical and directional shear for resource mapping of wind power

Research paper thumbnail of Multivariate linear regression technique for computing solar irradiance estimations using the SURFRAD and ISIS networks

The increased use of solar photovoltaic cells as energy sources on electrical grids has created t... more The increased use of solar photovoltaic cells as energy sources on electrical grids has created the need for accurate solar irradiance assessment over continental scales. In the present paper, we discuss a technique for computing solar irradiance estimations that utilizes numerical weather model variables, satellite data, and SURFRAD and ISIS network measurements. The numerical weather model used is the Rapid Update Cycle. The solar irradiance estimations found are more accurate than the solar irradiance fields provided by the satellites alone. Moreover, estimations are provided for the global horizontal, direct normal, and diffuse horizontal irradiance fields. The multivariate regression implemented allows accurate estimations of solar irradiance, but relies on high quality solar measurements at the surface over a geographically diverse domain. The technique developed in the present paper is also applicable to solar irradiance forecasts.

Research paper thumbnail of Future cost-competitive electricity systems and their impact on US CO2 emissions

Carbon dioxide emissions from electricity generation are a major cause of anthropogenic climate c... more Carbon dioxide emissions from electricity generation are a major cause of anthropogenic climate change. The deployment of wind and solar power reduces these emissions, but is subject to the variability of the weather. In the present study, we calculate the cost-optimized configuration of variable electrical power generators using weather data with high spatial (13-km) and temporal (60-min) resolution over the contiguous US. Our results show that when using future anticipated costs for wind and solar, carbon dioxide emissions from the US electricity sector can be reduced by up to 80% relative to 1990 levels, without an increase in the levelized cost of electricity. The reductions are possible with current technologies and without electrical storage. Wind and solar power increase their share of electricity production as the system grows to encompass large-scale weather patterns. This reduction in carbon emissions is achieved by moving away from a regionally divided electricity sector to a national system enabled by high-voltage direct-current transmission.