Energy and Climate Cluster Summer Meeting Abstracts

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Energy and Climate

ESIP Summer Meeting 2011 (Santa Fe, NM)

July 12-15, 2011
Energy-Climate Cluster – Agenda & Abstracts
(Co-chairs: Shailendra Kumar, Northrop Grumman, and Richard S. Eckman, NASA HQ)

Contents

GEOSS Architecture Implementation Pilot (AIP-3) Energy Scenario: Use case of the environmental impact assessment of the production, transportation and use of energy for the photovoltaic (PV) sector

Lionel Menard, MINES ParisTech, Paris

During Phase 3 of the Architecture Implementation Pilot (AIP3) of GEOSS (Global Earth Observation System of Systems) MINES ParisTech with the help of two FP7 European funded project (GENESIS and EnerGEO) developed a scenario called "environmental impact assessment of the production, transportation and use of energy for the photovoltaic (PV) sector through Life Cycle Assessment (LCA)". This scenario aims at providing decision-makers and policy-planners with reliable and precise knowledge of several impacts induced by the various technologies used in the Photovoltaic (PV) sector, and consequently at helping them in selecting the most appropriate technologies or identifying the most relevant locations for PV installations.

Several components have been provided with respect to the GCI (GEOSS Common Infrastructure) architecture to perform the scenario. These components allow users to access, search and use the data, information, tools and services. They include:

- An Energy Community Portal (www.webservice-energy.org) where several OGC (Open Geospatial Consortium) Web Services including WMS (Web Map Service) and WPS (Web Processing Service) have been deployed. These Web Services give access to data that are needed to assess environmental impacts along the complete PV supply chain, such as the HelioClim-3 solar radiation database from MINES ParisTech and Life Cycle Inventories (LCI) information made available by Ecoinvent.

- A GEOSS compliant OGC CSW (Catalogue Service for the Web) Catalogue, provided by the FP7 EnerGEO project, enabling the Web Services to be searched and discovered by GEO Portal end-users (energeo.researchstudio.at).

- A Geographic WebGIS client provided through the FP7 GENESIS project platform (gppf.genesis-fp7.eu) that allows end-users to run environmental impact assessment scenarios by means of the above-cited WPS. The resulting impact calculations are visualized as maps (using WMS) and in tabular and graph form (using data encoded in OGCs Geography Markup Language -GML)

The focus of the presentation will be put on the architecture that must be built to address GEOSS recommendation for interoperability from a data provider perspective.


Wind Energy Resource Assessment

Daran L. Rife, National Center for Atmospheric Research, Boulder, Colorado

Wind resource assessment is the formal process for estimating the wind power production potential at a prospective wind farm. There are many uncertainties associated with resource assessments, including measurement uncertainty, and estimating the expected long-term energy yield, which is typically based on a single year of measurements. Financiers require clearly quantified uncertainties to assess their risk, and ultimately determine whether they will underwrite development of a wind farm. Thus, very precise wind resource assessments are crucial, since even a small amount of inaccuracy in the assessment can amount to a loss of over $1 million in revenue per year for a typical 250-megawatt wind farm. This talk will describe how wind resource assessments are performed and how they are used to finance a wind farm. The presenter will then describe how a team at the National Center for Atmospheric Research (NCAR) is using NASA earth science datasets and innovative statistical techniques to develop a more accurate and economical wind resource assessment tool that can be used by the renewable energy industry.

NASA’s Modern Era Retrospective-analysis for Research and Applications (MERRA) is the cornerstone to this effort. MERRA is a 3D global record of weather every 6 hours over the past 30 years, and incorporates a wide variety of surface- and satellite-based measurements important to the development of wind energy applications. Using NCAR’s innovative statistical sampling techniques, the presenter will demonstrate that only a 2-3% sample of the 30-year MERRA record is needed to build an accurate estimate of wind power potential. This result strongly suggests that the large cost of producing wind resource assessments can be drastically reduced, yet still produce a full wind characterization at a prospective wind farm site. Additionally, results from NCAR’s project partner, V-bar, LLC, demonstrates that the long-term data records from MERRA are very valuable in remote areas of developing countries, were little or no long-term wind measurements are available. For a typical wind farm with no suitable long-term reference station, this can amount to savings of several million dollars for the project developer.


Application of Statistical Correlations of Sub-Hourly Irradiance Measurements and Hourly SUNY Data to Photovoltaic Array Performance

Marissa Hummon, National Renewable Energy Laboratory, Golden, Colorado

Photovoltaic power plant operators have observed changes in power output of up to 80% over two to five minutes. Electric system operators, who balance generation and demand on a second-to-second basis, continually plan for changes in demand and generation anywhere from five minutes to one week. They require accurate solar forecasts, on multiple time scales, if photovoltaic power contributes substantial generation capacity to their system. The field of sub-hourly irradiance analysis is emerging, in part to assist utilities and transmission planners, however there are few sub-hourly irradiance sensors on the earth’s surface. One possible analysis path, and the subject of this presentation, is to analyze the statistical correlations between a group of hourly satellite-based reflectance measurements (SUNY, gridded at 0.1 degrees) and minutely ground-based point measurements of irradiance. We develop probability distributions between multipoint satellite data and short-term (less than ten-minutes) cloud events occurring at nearby point locations on multiple timescales. We apply this analysis to the development of an algorithm to synthesize 1-min power output data, from hourly satellite data, for 1488 locations throughout the southwest.


Facilitating Responsible Siting of Renewable Energy

Alison LaBonte, Office of Science and Technology Policy, Washington DC
Taber Allison, American Wind & Wildlife Institute, Washington DC

Renewable energy development is an important element to the Nation achieving energy security, independence, and a sustainable energy future. The benefits are clear: renewable energy is a viable, locally sourced, and clean energy replacement to traditional energy resources that are being depleted. The community (federal and state governments, industry consortiums, academia, and environmental non-profits) is looking to identify the available science and technology, and conduct the additional research required, to minimize the potential negative impacts of development. Access to the relevant existing data sets and associated tools for decision making is key to enabling responsible siting of renewable energy. Land management agencies may pre-designate zones suitable for development based on the predicted technology impact to the environment using information and decision resources. For example, under the Department of Interior’s Smart from the Start Initiative, compilation of baseline data has informed the location of Wind Energy Areas on the Atlantic Outer Continental Shelf. In turn, a wind energy developer will assess project risk to the environment to determine a feasible location for project siting by using available information and decision resources at local scales. New data collection will be required to evaluate the performance of, and iteratively improve decision tools, especially as technologies evolve and scale up. There are still improvements that can be made in the near-term as a greater understanding of the impacts at multiple scales, and in various types of ecosystems, can be gained through meta-analysis of existing data. Data and decision tool management is therefore an essential need to advance scientific understanding, and inform decision processes. A selection of current efforts for facilitating responsible siting of renewable energy through data and decision tool management will be presented.

Open Energy Information

Debbie Brodt-Giles
National Renewable Energy Laboratory, Golden, Colorado

Open Energy Information (OpenEI - openei.org) is a platform designed to be the world's most comprehensive, open, and collaborative energy information network—supplying powerful data to decision makers and supporting a global energy transformation. The platform is developed by the National Renewable Energy Laboratory (NREL), but is intended for the world’s contribution, collaboration, and participation. The platform provides a means for DOE and its laboratories to share energy data and information while addressing the White House directive to be open, participatory, and collaborative with open government data. For more information, access the OpenEI video and summary featured on the White House Open Government Featured Innovations Web site: http://www.whitehouse.gov/open/innovations/OpenEnergyInformation. Although much of the world’s energy-related information and data are available as resources on the Internet, they are dispersed among innumerable individuals and organizations, available in widely disparate formats, and highly variable in quality and usefulness. This creates a major challenge for:

  • Researchers, who need to share data to accelerate innovation
  • Consumers, who need to have timely, accessible data to make day-to-day decisions
  • Policy makers, who need to research effective solutions based on technology capabilities, resource availability, market needs and effective incentives
  • Entrepreneurs, who need to perform due diligence and market assessments based on real data

OpenEI provides a solution using its open-source Web platform—similar to the one used by Wikipedia. The platform provides large amounts of energy-related data and information which can be easily searched, accessed, and used both by people and automated machine processes. NREL developed OpenEI using the standards and practices of the Linked Open Data community, which makes the platform much more robust and powerful than typical Web sites and databases. As an open platform, all users can search, edit, add, and access data in OpenEI —for free. The user community contributes the content and ensures its accuracy and relevance; as the community expands, so does the comprehensiveness and quality of the content. The data are structured and tagged with descriptors to enable cross-linking among related data sets, advanced search functionality, and consistent, usable formatting. Data input protocols and quality standards help ensure the content is structured and described properly and derived from a credible source. Although DOE/NREL is developing OpenEI and seeding it with initial data, it is designed to become a true community model with millions of users, a large core of active contributors, and numerous sponsors. The linked open data within OpenEI will have countless benefits because the platform links energy communities and decision makers (including policymakers, researchers, technology investors, venture capitalists, and market professionals) with valuable energy data, information, analyses, tools, images, maps, and other resources. By providing access to the best available data, OpenEI may help decision makers reduce missteps and save time and money. Through this improved sharing of energy information, we also can benefit from the acceleration of energy technology research and a transformation to a clean, secure energy future.

Presentation Overview:

  • Provide background on OpenEI and how/when it was developed
  • Present an overview of the need that OpenEI is filling
  • Describe the elements of the Web site platform
  • Wiki (using Semantic Media Wiki)
  • Data sets contribution (using Drupal)
  • Crowdsourcing (provide examples such as the Utility Rate Database, Incentives, Clean Energy Companies)
  • Explain the approach and benefits of Linked Open Data (examples will show this data in action and demonstrate ties to other federally funded Web sites and resources)


The New Peer-To-Peer Architecture of the Earth System Grid Federation

Luca Cinquini, Amy Braverman, Dan Crichton, Chris Mattmann
NASA Jet Propulsion Laboratory, Pasadena, California

The Earth System Grid Federation (ESGF) is a spontaneous collaboration of groups and institutions working together to setup a global, distributed infrastructure for the management, access and analysis of climate data. Most noticeably, the ESGF is in the process of archiving and serving the enormous amount of model output produced as part of the Climate Model Intercomparison Project phase 5 (CMIP5), as well as selected observational datasets from several agencies that can be used to validate the models. These data collections will provide the basis for the upcoming 5th Assessment Report of the International Panel on Climate Change (IPCC-AR5). This talk will describe the new ESGF software architecture, which is evolving towards a peer-to-peer system where each Node is composed of open source, modular and configurable components. We will also show how the ESGF infrastructure is starting to connect several data processing and analysis services into a cohesive system that is allowing users to request server-side products both through browsers (via a Thredds Data Server or Live Access Server), or through rich desktop clients (such as UV-CDAT and the Climate Data Exchange toolkit). Finally, we will also investigate the process established by the ESGF to validate and quality-assurance the datasets published to the system, both for model output and observations.


Impact of Climate Change on Energy Demand and the Optimal Site Selection of Wind and Solar Farms

Glenn Higgins, Randall Alliss, Duane Apling, Heather Kiley, Kremena Darmenova, Phillip Hayes and Michael Mason, Environmental Science and Engineering Department, Northrop Grumman Information Systems, Chantilly, Virginia

Energy security is a major concern in the United States and abroad. Sustainable energy implies a high degree of certainty in the ability to meet current and future energy demands in light of future political, demographic, regulatory, resource abundance, economic, climatic, and other uncertainties. Renewable energy is a part of many energy sustainability solutions because the “resources”, e.g. solar and wind energy are essentially free and readily available. However, if solar and wind energy are to be major contributors to sustainable energy solutions, then the issue of spatiotemporal energy variability must be addressed. On the other hand, climate change (increasing temperatures and changes in precipitation patterns worldwide) accompanied by demographic and other changes may have significant impact on energy demand (and supply). This paper describes two capabilities developed by Northrop Grumman: (1) Downscaled decision aids and empirical energy consumption models that were fitted to end-user supplied energy consumption records. These decision aids can support decision-making for understanding the requirements of enduring energy infrastructure, short- and long-term energy production and conservation policy alternatives. The energy models were rigorously validated via additional data quality control and model parameter uncertainty analysis; (2) The MORE Power application provides optimum site selection of the wind and solar farms within a “network” in order to maximize the total output and minimize the variability of power produced by the network. The impact of the quality of source data is also addressed.

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