CARTO Case Studies Advancing Renewable Energy through Spatial Analysis and Visualization: A Case Study of NREL

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	Advancing Renewable Energy through Spatial Analysis and Visualization: A Case Study of NREL CARTO

Advancing Renewable Energy through Spatial Analysis and Visualization: A Case Study of NREL

CARTO

Technology Category	Analytics & Modeling - Big Data Analytics Application Infrastructure & Middleware - Data Visualization
Applicable Industries	Electrical Grids Renewable Energy
Applicable Functions	Product Research & Development
Use Cases	Microgrid Object Detection
Services	Data Science Services
Challenge	The National Renewable Energy Laboratory (NREL) is the only federal laboratory in the United States that focuses solely on renewable energy, commercialization, development, and research. The challenge NREL faces is how to realize high penetrations of renewable energy while achieving broad goals of reliability, resilience, and affordability. The complexity of the energy grid, with its numerous generators and variable load, requires sophisticated tools and visualizations to understand and manage. A fundamental challenge with renewable energy is its variability and continuity in both space and time, which poses challenges to traditional models. The question is how to take a phenomenon that’s inherently continuous and variable, and fit it into a discrete model space, whether it’s nodal or regional. The biggest question is how to ensure that the resource is properly characterized and preserved. Read More
About Customer	The customer in this case study is the National Renewable Energy Laboratory (NREL), a federal laboratory in the United States that focuses solely on renewable energy, commercialization, development, and research. NREL is committed to advancing reliable, resilient, and affordable clean energy. To achieve this, they have to think big and consider the big picture, which involves realizing high penetrations of renewable energy while simultaneously achieving broad goals. NREL is dedicated to overcoming the challenges posed by the variability and continuity of renewable energy in both space and time. They are committed to ensuring that renewable energy resources are properly characterized and preserved. Read More
Solution	NREL has embraced spatial analysis and geospatial data science to address these challenges. This approach combines traditional GIS, geospatial big data, geo statistical analysis, and spatial temporal and multivariate modeling and visualization. NREL's spatial data science team has a broad research focus, represented in a pyramid that applies technological and economical constraints to whittle down the resource until the developable renewable energy potential is found. The team uses spatial analysis to understand barriers and regulations, transmission analysis, wind farm characterizations, and resource analysis. They also use generator modeling to understand the performance of different photovoltaic technologies across broad geographies. All these disparate pieces of analysis and research are combined in a spatial temporal techno-economic model called the Renewable Energy Potential Model or ‘reV’. This model combines resource information, systems engineering modeling, economics, technical spatial constraints, and transmission analysis to come up with geospatial supply curves. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Big Data Analytics

Application Infrastructure & Middleware - Data Visualization

Applicable Industries

Electrical Grids

Renewable Energy

Applicable Functions

Product Research & Development

Use Cases

Microgrid

Object Detection

Services

Data Science Services

Challenge

The National Renewable Energy Laboratory (NREL) is the only federal laboratory in the United States that focuses solely on renewable energy, commercialization, development, and research. The challenge NREL faces is how to realize high penetrations of renewable energy while achieving broad goals of reliability, resilience, and affordability. The complexity of the energy grid, with its numerous generators and variable load, requires sophisticated tools and visualizations to understand and manage. A fundamental challenge with renewable energy is its variability and continuity in both space and time, which poses challenges to traditional models. The question is how to take a phenomenon that’s inherently continuous and variable, and fit it into a discrete model space, whether it’s nodal or regional. The biggest question is how to ensure that the resource is properly characterized and preserved.

About Customer

The customer in this case study is the National Renewable Energy Laboratory (NREL), a federal laboratory in the United States that focuses solely on renewable energy, commercialization, development, and research. NREL is committed to advancing reliable, resilient, and affordable clean energy. To achieve this, they have to think big and consider the big picture, which involves realizing high penetrations of renewable energy while simultaneously achieving broad goals. NREL is dedicated to overcoming the challenges posed by the variability and continuity of renewable energy in both space and time. They are committed to ensuring that renewable energy resources are properly characterized and preserved.

Solution

NREL has embraced spatial analysis and geospatial data science to address these challenges. This approach combines traditional GIS, geospatial big data, geo statistical analysis, and spatial temporal and multivariate modeling and visualization. NREL's spatial data science team has a broad research focus, represented in a pyramid that applies technological and economical constraints to whittle down the resource until the developable renewable energy potential is found. The team uses spatial analysis to understand barriers and regulations, transmission analysis, wind farm characterizations, and resource analysis. They also use generator modeling to understand the performance of different photovoltaic technologies across broad geographies. All these disparate pieces of analysis and research are combined in a spatial temporal techno-economic model called the Renewable Energy Potential Model or ‘reV’. This model combines resource information, systems engineering modeling, economics, technical spatial constraints, and transmission analysis to come up with geospatial supply curves.

Impact #1	The use of spatial analysis and geospatial data science has allowed NREL to better understand and manage the complexities of the energy grid. The use of the Renewable Energy Potential Model or ‘reV’ has enabled them to combine disparate pieces of analysis and research into a comprehensive model that takes into account resource information, systems engineering modeling, economics, technical spatial constraints, and transmission analysis. This has resulted in geospatial supply curves that illuminate the results of technological advances, constraints, and barriers. The results of the spatial analysis are fundamental and foundational to all downstream analysis when it comes to the electric grid, whether it’s unit commitment, resource planning, distributed generation adoption, or policymaking.

Impact #1

The use of spatial analysis and geospatial data science has allowed NREL to better understand and manage the complexities of the energy grid. The use of the Renewable Energy Potential Model or ‘reV’ has enabled them to combine disparate pieces of analysis and research into a comprehensive model that takes into account resource information, systems engineering modeling, economics, technical spatial constraints, and transmission analysis. This has resulted in geospatial supply curves that illuminate the results of technological advances, constraints, and barriers. The results of the spatial analysis are fundamental and foundational to all downstream analysis when it comes to the electric grid, whether it’s unit commitment, resource planning, distributed generation adoption, or policymaking.

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Advancing Renewable Energy through Spatial Analysis and Visualization: A Case Study of NREL

Operational Impact