ESTIMATING SOLAR PV POTENTIAL IN THE CLOUD JONATHAN COY GEOG 596A ADVISOR: JEFFREY BROWNSON.

Slides:



Advertisements
Similar presentations
AIP-2 Kickoff Workshop Energy SBA Scenario Answer Thierry Ranchin, Mines ParisTech, France Team: Mines ParisTech, DLR, JRC, NASA MeteoTest, Rutherford.
Advertisements

Photovoltaic Solar Energy
IMAGE SERVICES WHAT ARE THEY AND HOW DO YOU USE THEM?
Cloud Computing Mick Watson Director of ARK-Genomics The Roslin Institute.
EHarmony in Cloud Subtitle Brian Ko. eHarmony Online subscription-based matchmaking service Available in United States, Canada, Australia and United Kingdom.
 Solar energy is the result of thermonuclear fusion reactions deep within the sun.  Solar energy is the most abundant and most powerful energy source.
Solar Energy Analysis with ArcMap GIS Club at UWM Greg Latsch March 27, 2012.
Cloud Computing Brandon Hixon Jonathan Moore. Cloud Computing Brandon Hixon What is Cloud Computing? How does it work? Jonathan Moore What are the key.
Low Cost, Scalable Proteomics Data Analysis Using Amazon's Cloud Computing Services and Open Source Search Algorithms Brian D. Halligan, Ph.D. Medical.
1. Topics Is Cloud Computing the way to go? ARC ABM Review Configuration Basics Setting up the ARC Cloud-Based ABM Hardware Configuration Software Configuration.
Raster Based GIS Analysis
THE QUE GROUP WOULD LIKE TO THANK THE 2013 SPONSORS.
IS 466 ADVANCED TOPICS IN INFORMATION SYSTEMS LECTURER : NOUF ALMUJALLY 20 – 11 – 2011 College Of Computer Science and Information, Information Systems.
Raster Data. The Raster Data Model The Raster Data Model is used to model spatial phenomena that vary continuously over a surface and that do not have.
Solar Potential on the Middlebury College Campus using the ESRI Solar Analyst Tool Chris Rodgers May 10, 2007.
Matt Bertrand Building GIS Apps in the Cloud. Infrastructure - Provides computer infrastructure, typically a platform virtualization environment, as a.
A solar radiation model for photovoltaic and solar thermal
LiDAR Compressor 1.1. Compression Lossless 25% or smaller of the original size Some datasets can be compressed to 15% or smaller of their original size.
Esri International User Conference | San Diego, CA Technical Workshops | Lidar Solutions in ArcGIS Clayton Crawford July 2011.
CLOUD COMPUTING.
Rebecca Boger Earth and Environmental Sciences Brooklyn College.
LizardTech Geospatial Products April, LiDAR Compressor Compress point cloud data to MrSID Generation 4 (MG4) Lossless 25% of the original size.
Introduction to the course January 9, Points to Cover  What is GIS?  GIS and Geographic Information Science  Components of GIS Spatial data.
Cloud computing Tahani aljehani.
CHAPTER OVERVIEW SECTION 5.1 – MIS INFRASTRUCTURE
UNDERSTANDING LIDAR LIGHT DETECTION AND RANGING LIDAR is a remote sensing technique that can measure the distance to objects on and above the ground surface.
Solar Panel Site Suitability Study Greg Holland, Matt Lenox, and Tracy Ricker
Internet GIS. A vast network connecting computers throughout the world Computers on the Internet are physically connected Computers on the Internet use.
Sharing Geographic Content
Estimating Rooftop Solar Electricity Potential in Seattle from LiDAR Data Ryan M. Liddell Joseph A. Bishop, Ph.D. Photo Copyright H Brothers Inc; used.
Building Sustainable MIS Infrastuctures
Solar Photovoltaics. Solar Photovoltaics (PVs) Make electricity directly from sunlight without pollution, moving parts, or on site noise Sun covers the.
E8 / PPA Solar PV Design Implementation O&M Marshall Islands March 31-April 11, Solar Photovoltaic Theory 1-2. Potential assessment.
Cloud computing is the use of computing resources (hardware and software) that are delivered as a service over the Internet. Cloud is the metaphor for.
CHAPTER FIVE INFRASTRUCTURES: SUSTAINABLE TECHNOLOGIES
GIS and Cloud Computing. Flickr  Upload and manage your photos online  Share your photos with your family and friends  Post your photos everywhere.
Accessing the Amazon Elastic Compute Cloud (EC2) Angadh Singh Jerome Braun.
Optimization of solar panel installation using remote sensing technique Dr. Kakoli Saha (Ph.D.) Department of Planning, SPA Bhopal
OF THE IMPACT OF PARTIAL SHADING ON THE PERFORMANCE OF A GRID-TIED PHOTOVOLTAIC SYSTEM K. Hurayb, Y. Moumouni, F. A. da Silva,Y. Baghzouz Electrical &
, Increasing Discoverability and Accessibility of NASA Atmospheric Science Data Center (ASDC) Data Products with GIS Technology ASDC Introduction The Atmospheric.
, Implementing GIS for Expanded Data Accessibility and Discoverability ASDC Introduction The Atmospheric Science Data Center (ASDC) at NASA Langley Research.
IPlant Collaborative Tools and Services Workshop iPlant Collaborative Tools and Services Workshop Overview of Atmosphere.
Introduction to Cloud Computing
SOLAR ENERGY Daniel Khan 607. Solar energy is the sun’s rays (or solar radiation) that reaches the Earth. For millions of years the radiant energy from.
A Framework for Elastic Execution of Existing MPI Programs Aarthi Raveendran Tekin Bicer Gagan Agrawal 1.
1 Nassau Community CollegeProf. Vincent Costa Session 7 Infrastructures Sustainable Technologies CMP 117 Business Computing: Concepts &Applications.
Presented by: Mostafa Magdi. Contents Introduction. Cloud Computing Definition. Cloud Computing Characteristics. Cloud Computing Key features. Cost Virtualization.
Power Generation from Renewable Energy Sources Fall 2012 Instructor: Xiaodong Chu : Office Tel.:
Investigating Renewable Energy Data from Photovoltaic (PV) Solar Panels In Petersham, MA.
What is the cloud ? IT as a service Cloud allows access to services without user technical knowledge or control of supporting infrastructure Best described.
The New Zealand Institute for Plant & Food Research Limited Use of Cloud computing in impact assessment of climate change Kwang Soo Kim and Doug MacKenzie.
Deploying a VGI application in one day Tom Brenneman.
Lecture 3 The Digital Image – Part I - Single Channel Data 12 September
Mapping Rooftop Solar Power Potential in Greater Cincinnati Brandon Flessner GIS Analyst OKI Regional Council of Governments 2015 Ohio GIS Conference September.
Cloud Computing Project By:Jessica, Fadiah, and Bill.
Using GIS to Find Suitable Locations for Solar Power Plants Submitted By: Scott Peterson May 12, 2005 Texas A&M University Department of Civil Engineering.
Solar energy - one of the renewable energy; the conversion of sunlight into electricity. Concentrated solar power systems use lenses or mirrors and tracking.
Steve Kopp Esri ArcGIS 10 and Beyond Arc Hydro River Workshop, Austin, Texas, December 1, 2010.
WFM 6202: Remote Sensing and GIS in Water Management © Dr. Akm Saiful IslamDr. Akm Saiful Islam WFM 6202: Remote Sensing and GIS in Water Management Dr.
Solar Energy Ashley Valera & Edrick Moreno Period 6.
Cloud Computing Andrew Stromme and Colin Schimmelfing.
Solar on Grid Systems 123 Zero Energy. If you need an affordable solar energy solution for transforming your home into a Zero Energy Home, the solar Grid.
KAASHIV INFOTECH – A SOFTWARE CUM RESEARCH COMPANY IN ELECTRONICS, ELECTRICAL, CIVIL AND MECHANICAL AREAS
© 2015 MetricStream, Inc. All Rights Reserved. AWS server provisioning © 2015 MetricStream, Inc. All Rights Reserved. By, Srikanth K & Rohit.
Best Practices for Managing and Serving Lidar and Elevation Data Cody Benkelman.
Chapter 6: Securing the Cloud
Modeling Renewable Energy Potential Using ArcGIS
CHAPTER OVERVIEW SECTION 5.1 – MIS INFRASTRUCTURE
Jerald Overstreet, GISP Server Portal SQL Manager Admin
Presentation transcript:

ESTIMATING SOLAR PV POTENTIAL IN THE CLOUD JONATHAN COY GEOG 596A ADVISOR: JEFFREY BROWNSON

PROJECT BACKGROUND Concept started during GEOG586 – Geographical Information Analysis capstone project Concept started during GEOG586 – Geographical Information Analysis capstone project Solar rooftop potential project for a subdivision in Flagstaff, AZ Solar rooftop potential project for a subdivision in Flagstaff, AZ Processing time too intensive Processing time too intensive Classification of results inaccurate Classification of results inaccurate Experimentation with cloud processing during capstone project for GEOG897C – Cloud Server and GIS Experimentation with cloud processing during capstone project for GEOG897C – Cloud Server and GIS LAS Dataset to Raster (Digital Surface Model, DSM) LAS Dataset to Raster (Digital Surface Model, DSM) 9 hours processing time to 6 minutes 9 hours processing time to 6 minutes

OVERVIEW Photovoltaics (PV) is a method of generating electricity by collecting solar radiation from solar panels Photovoltaics (PV) is a method of generating electricity by collecting solar radiation from solar panels Represents a sustainable energy resource Represents a sustainable energy resource Increasingly important with rising energy costs, global warming, recent increases in technology and events such as the Fukushima nuclear disaster in Japan Increasingly important with rising energy costs, global warming, recent increases in technology and events such as the Fukushima nuclear disaster in Japan

ARCGIS “AREA SOLAR RADIATION” Irradiation: is a measure of solar radiation received on a given surface and recorded for a specific time period (energy density units of kWh/m 2 ) Irradiation: is a measure of solar radiation received on a given surface and recorded for a specific time period (energy density units of kWh/m 2 ) Tool derives incoming insolation from a raster surface Tool derives incoming insolation from a raster surface Located within ArcToolbox Spatial Analyst Tools Located within ArcToolbox Spatial Analyst Tools Extremely processing intensive. Calculations can take hours of even days to run for large scale areas depending on the input parameters Extremely processing intensive. Calculations can take hours of even days to run for large scale areas depending on the input parameters Documentation of input parameters is poor Documentation of input parameters is poor

THE AMAZON CLOUD ADVANTAGE Amazon Web Services offers an easy to use and affordable Infrastructure as a Service (IaaS) called Amazon Elastic Compute Cloud (EC2) Amazon Web Services offers an easy to use and affordable Infrastructure as a Service (IaaS) called Amazon Elastic Compute Cloud (EC2) Amazon houses, administers and maintains the hardware, the client pays on a per-use basis Amazon houses, administers and maintains the hardware, the client pays on a per-use basis Setup can take minutes and uses ESRI’s pre-configured Amazon Machine Image (AMI). Services can be started and stopped as needed all over a web connection Setup can take minutes and uses ESRI’s pre-configured Amazon Machine Image (AMI). Services can be started and stopped as needed all over a web connection Allows access to powerful server processing as needed Allows access to powerful server processing as needed 9 hours versus 5 minutes 9 hours versus 5 minutes High-Memory Double Extra Large Instance High-Memory Double Extra Large Instance 34 GB memory, 13 processors, 64-bit, 850 GB storage, Windows 34 GB memory, 13 processors, 64-bit, 850 GB storage, Windows $1.02 per hour $1.02 per hour

THE AMAZON CLOUD DISADVANTAGE Upload and download time of large geospatial datasets Upload and download time of large geospatial datasets >30 hours upload time >30 hours upload time Restrictions with storing datasets offsite Restrictions with storing datasets offsite Administration and setup Administration and setup Remote Desktop access denied through firewall Remote Desktop access denied through firewall

ARCGIS “AREA SOLAR RADIATION” DEFICIENCIES With the help of Brownson group several potential deficiencies have been identified within current methodology that the Area Solar Radiation Tool uses to calculate irradiation compared to the “solar community” With the help of Brownson group several potential deficiencies have been identified within current methodology that the Area Solar Radiation Tool uses to calculate irradiation compared to the “solar community” Community uses “irradiation” units of J/m 2 not kWh/m 2 Community uses “irradiation” units of J/m 2 not kWh/m 2 Area Solar Tool does not calculate what is happening in the real sky day to day; uses fixed global estimations from the user instead of local meteorological data Area Solar Tool does not calculate what is happening in the real sky day to day; uses fixed global estimations from the user instead of local meteorological data Results will show how much sun an area receives but will not show what you actually get Results will show how much sun an area receives but will not show what you actually get

SOLAR COMMUNITY METHODS National Renewable Energy Laboratory (NREL) National Renewable Energy Laboratory (NREL) System Advisor Model (SAM) System Advisor Model (SAM) Simulate many locations as points Simulate many locations as points Rooftop orientation and slope (obtained from slope and aspect) Rooftop orientation and slope (obtained from slope and aspect) Shading factors (tree heights from LiDAR data) Shading factors (tree heights from LiDAR data) Financials Financials Typical Meteorological Year (TMY) data Typical Meteorological Year (TMY) data Hourly values of solar radiation and meteorological elements for an entire year Hourly values of solar radiation and meteorological elements for an entire year kWh/m 2 or J/m 2 kWh/m 2 or J/m 2

METEOROLOGICAL DATA EXAMPLE Flagstaff hourly data per month for Beam Normal (amount of solar radiation from the direction of the sun) and Global Horizontal (the sum of direct and diffuse radiation) Flagstaff hourly data per month for Beam Normal (amount of solar radiation from the direction of the sun) and Global Horizontal (the sum of direct and diffuse radiation)

LOCALE: FLAGSTAFF Flagstaff offers an ideal setting to discover shortages of the ArcGIS Solar Radiation tool due to: Flagstaff offers an ideal setting to discover shortages of the ArcGIS Solar Radiation tool due to: Clear skies Clear skies Meteorology is likely fairly stable, not complex Meteorology is likely fairly stable, not complex

PROJECT CONCEPT In the cloud process a digital surface model (DSM) from a LiDAR dataset which covers a portion of NE Flagstaff, Arizona In the cloud process a digital surface model (DSM) from a LiDAR dataset which covers a portion of NE Flagstaff, Arizona LiDAR dataset has a 2.38 foot point spacing LiDAR dataset has a 2.38 foot point spacing Use the DSM as the input raster to calculate incoming insolation for three residential building rooftops in the cloud using “ArcGIS Area Solar Radiation” tool Use the DSM as the input raster to calculate incoming insolation for three residential building rooftops in the cloud using “ArcGIS Area Solar Radiation” tool Outputs will be compared to an energy analysis of the same rooftops using SAM to investigate the differences between Outputs will be compared to an energy analysis of the same rooftops using SAM to investigate the differences between

HYPOTHESIS Total calculated irradiation for the test rooftops using the ArcGIS Area Solar tool will greatly differ from the results produced using SAM and therefore should not be used as a tool by GIS professionals to determine rooftop solar potentials. Total calculated irradiation for the test rooftops using the ArcGIS Area Solar tool will greatly differ from the results produced using SAM and therefore should not be used as a tool by GIS professionals to determine rooftop solar potentials.

RESULTS If true, recommendations will be made to ESRI regarding how to improve results and why the shortcomings with the tool exist If true, recommendations will be made to ESRI regarding how to improve results and why the shortcomings with the tool exist If false, results will be compared to industry standard practices with explanations as to why the tool produces accurate results If false, results will be compared to industry standard practices with explanations as to why the tool produces accurate results

POTENTIAL IMPACT TO GIS COMMUNITY As more communities and cities use this tool to determine solar potential and share it with the public to push green initiatives it is important the results are accurate or the limitations of the tool are better understood As more communities and cities use this tool to determine solar potential and share it with the public to push green initiatives it is important the results are accurate or the limitations of the tool are better understood CH2M Hill Solar Portals – Los Angeles CH2M Hill Solar Portals – Los Angeles Risk with small communities with small budgets with access to LiDAR data and ArcGIS start producing such maps on a large scale Risk with small communities with small budgets with access to LiDAR data and ArcGIS start producing such maps on a large scale Results are not vetted due to processing time and lack of understanding by GIS professionals Results are not vetted due to processing time and lack of understanding by GIS professionals

FINAL RESULTS Professional Conference Professional Conference Solar Solar GIS/Geography GIS/Geography Journal Article Journal Article

QUESTIONS?