UCRL-CONF-229047
Data and Modeling Conflation Issues in Energy and Water Systems
N. Goldstein, R. Newmark, L. Burton, D. May, J. McMahon, C. D. Whitehead, G. Chatikar March 14, 2007
First Western Forum on Energy & Water Sustainability Santa Barbara, CA, United States March 22, 2007 through March 23, 2007
Disclaimer This document was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the University of California nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or the University of California, and shall not be used for advertising or product endorsement purposes.
Data and Modeling Conflation Issues in Energy and Water Systems Lawrence Livermore National Laboratory: Noah Goldstein, Robin Newmark, Liz Burton, Debbie May Lawrence Berkeley National Laboratory : Jim McMahon, Camilla Dunham Whitehead, Girish Ghatikar First Western Forum on Energy & Water Sustainability March 23, 2007
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Data and modeling protocols can facilitate or constitute barriers to integrated Energy-Water planning ¾
Integrated planning requires a clear understanding of the diversity of stakeholders and their specific data needs. Energy
¾
Water
Specific data sharing approaches need to be tailored to the unique Energy-Water situation. 3
New Awareness of the Energy-Water Nexus (EWN) ¾
Integrated Planning within the EWN: z z
¾
Different Communities Different Organizational Models
Maximizing buy-in: z z
Maintenance of organizational priorities Security of Data and Models
4
Our Study: Classify Entities and Relationships
Understand Data / Model Requirements Consider Appropriate Security Approaches
5
Energy Utilities and Water Providers – A complex world Investor Owned (and Privately Held)
1. z z
Regulated Unregulated
75% Energy
Federally Owned
2. z
Large-scale
Public
Private
Other Publicly Owned
3. z z
State Municipality / Regional
Cooperatively Owned
4. z
Water
80%
Local / Regional (Water) (EIA 1998)
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Diversity of Agencies Involved in Data Water
Energy
7
Placeholder for figure from Camilla
Energy
OPEC Resource Use / Forecasting
US Army Corp USBR Utilities / Fuel Companies
Policy Analysis
Supply by type
FERC
by time by region
States
Standards
EIA EPA States
Demand by sector
Regulation & Enforcement
by time
NERC ISOs
by region NERC Data Gathering
ISOs States Utilities
Ownership Utilities
Additional Participants in Planning ¾ ¾ ¾ ¾ ¾
State Utility Commissions State Environmental and Coastal Commissions Tribal Nations NGOs Watershed coordinating groups
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Relationships in the EWN Relationships between participants are shaped by their jurisdictional, organizational, and geographical structures:
Federal Water
Private
Private
Public
Public
Public
Private
Muni Power
State Water
Corporate Power 10
We need to translate: Obstacles to Data Sharing
Components of Information Security to… Confidentiality
Operational •Temporal •Spatial Logistical •Technology •Agency
Possession / Control
Availability
Proprietary •Corporations •3rd Parties
Information Security
Utility
Jurisdictional •Hierarchies •Boundaries
Cultural •Religion •Corporate
Integrity
Authenticity
Parker, 1995
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Lessons Learned from other industries ¾
US Census z
¾
Intelligence Community z
¾
Secure network protocols (e.g. passwords)
Department of Defense z
¾
Pedigree / provenance of data
Securities and Exchange Commission (SEC) z
¾
Summarization and Generalization
Stakeholder consensus of standards and methodology from the beginning
Food and Drug Administration z
Formalization of Data Standards, Confidentiality 12
Large-Scale Government Relationships ¾
TVA Example z
z
z
z
¾
Significant Stakeholders z
z
¾
Nation’s largest public power company 170B kWh in 2005 Hydro (10%), Fossil (62%) & Nuclear (28%) 7 states, 158 local distributors, 8.6M people, 48 dams TVA : electricity, water quality transportation Bureau of Reclamation, Army Corps of Engineers: dams and locks Southeastern Power Administration (SEPA): electricity administration
Confidentiality
Possession / Control
Availability
Information Security
Utility
Integrity
Authenticity
Potential solutions z z
Open, top-down data administration Leverage current infrastructure
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Grass-roots Relationships ¾
The Laramie River Station z z z
Cooperatively owned Feeds both E & W Interconnect 2004 drought necessitated purchase of water from 35 local wells
Confidentiality
Possession / Control
Availability
Information Security
Utility
Integrity
Authenticity
¾
Potential Solutions z z
z
Peer-to-peer network Guidelines and standards-based methodologies for data generalization Protection of proprietary nature of data 14
Third-party / Consultants ¾
New York City z
NYC’s “PlaNYC” 2030 • Water & Power among “Top 10 Goals” • Increase Population • NYState Reliability Council : 80% of power has to come from inside city
z z z
¾
NYPA (State) owns small-scale gas ConEdison Distribution Consulting companies vs. City Agencies
Potential Solutions z z
z z
Confidentiality
Possession / Control
Availability
Information Security
Utility
Integrity
Authenticity
City takes ownership Partner with State and consulting companies Centralized control Licenses data, use 15
Tribal Nations ¾
Black Mesa, Peabody Coal Mojave Generating Station z
z z z z
1971-1995 Coal sluiced 275 miles to Laughlin, NV Hopi: “Water is Sacred” Inequity of electric distribution History of distrust with Peabody, USA Lack of Energy or Water representation in Tribes
Confidentiality
Possession / Control
Availability
Information Security
Utility
¾
Integrity
Potential Solutions z z z z z z
Acknowledge cultural differences Clarity about ownership of resources Shared control between tribes Data transfer as currency Data Library (check in- check out) Secured network with distributed and documented control
Authenticity
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Conclusions ¾ ¾
Successful relationships are necessary in order to facilitate integrated planning Matching security needs for data necessitates understanding: z z z
Relationships Data needs Cultural context We welcome input! Examples and experiences of data sharing between Energy & Water stakeholders •Case Studies •Solutions •Lessons Learned Noah Goldstein
[email protected]
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Special Thanks ¾ ¾ ¾ ¾ ¾ ¾
Jay Casselberry, EIA Robert Goldstein, EPRI Bill Bourcier, Steve Grey, LLNL Brennan T. Smith, ORNL Kenya Crosser, BNL Craig Eaker, SoCal Edison
Work performed under the auspices of the U.S. Department of Energy by the University of California, Lawrence Livermore National Laboratory under Contract No. W-7405-Eng-48. UCRL-XXXX-220576
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