Inaugural Workshop

Vision Session

Vision Session Details

Criteria for Selection of Vision Recommendations
Five criteria were presented to guide the development of overarching and crosscutting collaborative analysis opportunities under the following areas:
- Electricity
- Transportation
- Energy Efficiency and Demand-Side Generation

• On what energy issues/analysis activities do the needs and interests of decision makers and analysts intersect?
• What energy issues/analysis activities were self-identified as important, yet not addressed to a critical mass of participating organizations (i.e., which attracted the most diverse groups of decision makers and/or analysts)?
• Are current information, tools, and models inadequate to address this issue?
• Is there a discrete, actionable activity to address this issue?
• Can a collaborative effort of multiple organizations provide additional value through 1) collaboration on scope (to identify specific activities for different parties to perform), 2) leveraging of resources and capabilities, and 3) sharing of information and results?

I. Electricity

A. Electricity Analysis Collaboration Vision Recommendations

• Develop a better understanding/better approach to uncertainty analysis of constraints to energy options.
  • E.g., how does waste disposal affect the role of nuclear power?
  • How does uncertainty around fossil fuel prices and carbon markets impact the technology choices and cost of electricity?
  • How should we model regulatory uncertainties?
  • How will potential constraints on water supply and use affect current and new technology options?
  • How might uncertainty about transmission capacity and fuel transportation (such as natural gas pipelines and rail transport for coal) affect generation options?
    
    
• Develop ways to model or reflect the complex and patchwork nature of the regulatory and policy environment and improve the links between policy decisions and technology choices.
  • Analysis tools are needed to assess how to simulate the complex and sometimes disjointed policy framework, e.g., the disconnect between wholesale and retail restructuring and the state-by-state regulatory environments.
  • Better tracking and forecasting of changes in regulations and policies are needed.
  • Analysis tools are needed to determine how changes in policy will affect the deployment of technologies in the electricity industry, e.g. what are the longer-term impacts of deregulation, how will industry meet accelerate RPS requirements in some states?
  • How much new renewable energy is the result of policies such as standardized interconnection and net metering or how much do standby rates deter renewable energy development?
  • What incentive levels are required to make a project viable?
    
    
• Improve analysis of transmission system operations and constraints
  • E.g. improve the simulation of contractual transactions flows and constraints which do not always align with physical flows or constraints.
  • Improve analysis to help inform decisions about the costs and benefits of generation options that can interconnect with existing transmission versus generation that requires new transmission construction.
    
    
• Expand analysis to capture the full range of value of electricity alternatives in consistent metrics (apples to apples) to help guide policy that can maximize multiple attributes.
  • E.g. evaluate alternatives in a way that allows direct comparison of cost, reliability, equity, environmental impacts, contribution to peak or energy demand, economic development impacts.)

B. 2nd Tier Electricity Recommendations

• Improved portfolio analysis tools at the state level to assist with regional planning
• More dynamic analysis to better reflect the impact of renewables on the transmission system.
• Better data on the potential for electricity storage
• Analysis to quantify the financial returns to encourage new investment in transmission and distribution system
• How should we measure or analyze security risks on the electricity system?

II. Transportation

A. Transportation Analysis Collaboration Vision Recommendations

• Incorporate behavioral factors in models to better capture how consumers make decisions. A suite of tools are available:
  • Penetration models
  • Agent-based modeling where individual or organizations are modeled
  • NEMS approach to modeling manufacturer behavior
    
    
• Make data collection more complete, transparent, standardized and centralized.
  • More data on current preferences and use drivers. Participants noted that in some cases, funding for important data collection is being cut (e.g. DOT- National Household Travel Survey and DOT- Vehicle Inventory and Use Survey data gathering).
  • Different tools for collecting data on behavioral factors
  • New data on future technologies is needed in order to support analysis of potential market and environmental impacts. E.g. need to better understand the off-peak electricity requirements of plug-in hybrid vehicles (PHEV).
  • Improved database on biomass resources – current, potential and land requirements
  • Need data on workforce/ skills resource requirement to support implementation analysis
  • Detailed distribution of miles traveled by type of consumer rather than averages
    
    
• Develop more comprehensive modeling tools
  • Making explicit links with models like NEMS
  • Incorporating full supply chain, cross–sector analysis that looks at the impacts between transportation fuels and chemical, fertilizers, and agriculture/food industry.
  • Expanding environmental impacts to include water (e.g. in production of ethanol and tar sands)
  • DOE's EERE is developing a transition model that looks at the competing use of biomass, investor behavior and manufacturer behavior. A possible opportunity for collaboration is between DOE and USDA which is evaluating the efficacy of the 60 billion gallon ethanol target driving the analysis.

B. 2nd Tier Transportation Issues

• Develop better ways to account for uncertainty in modeling results.
  • E.g. NEMS includes multiple cases, but need to understand how to better interpret the probability of those scenarios. Risk analysis and better characterization of risk factors would help.
    
    
• Greater analysis of infrastructure needs and implications of alternative technologies

III. Energy Efficiency and Demand-Side Generation

A. Key Policy Drivers

• How do we save energy in a hurry and maintain the savings longer?
• Can models be both simple enough to be understood by decision makers and robust enough to be trusted and accurate?
• Can we overcome the failure of models to represent the fundamental dynamics of energy-related decisions?
• What is needed to create and energy efficiency ethic and which approaches are most effective – laws/regulations, market incentives, education, etc.?
• How can we model the vision of expanded distributed generation?

B. Decision Makers

• Government policy makers
• Congressional leaders and staff – different needs and budget considerations
• Technology manufacturers and distributors
• Modelers and analysts
• Energy consumers

C. EE & DG Collaborative Analysis Recommendations

• Better understanding of consumers' behavior and non-economic decision factors
  • There may be empirical data that could help inform analysis about consumer behavior, but analysts are aware of it, therefore, we need better mechanisms to synthesize and share information on how consumers behave.
  • Manufacturer and utility data on consumer behavior could be enlightening; challenge is that it is often treated as proprietary information.
    
    
• Better understanding of supplier decisions; how do new technologies and practices get introduced and how can this be influenced by policy?
  • E.g. Better understanding of utility decisions, such as how they are planning for carbon caps. May not be as analytically rigorous as we think; regulatory environment plays an important role.
  • Models tend to assume that technologies and services just appear in response to demand without a clear understanding of how entrepreneurs weigh risk, market positioning, etc.
  • Need to understand the critical pathways to achieving successful technology deployment.
    
    
• Better understanding of programs, policies, and R&D portfolio management and what has worked. Why or why not?
  
  
• Better representation the infrastructure needs of new technologies and the new relationships between suppliers and consumers.
  • Should also understand the constraints of existing infrastructure, including human capital infrastructure, and how it needs to evolve.
    
    
• Better models and linkages between models.
  • Most macro economic models are designed to show negative economic impacts with any change from Business-as-Usual because they do not adequately capture the fundamental dynamics of energy-related decisions.
  • E.g. we need better linkages between detailed tools like building simulation models and macro tools like demand analysis models, to analyze system interactions.
  • Need to rely less on over simplified characterization through averages and prototypes and more on probabilistic and agent based modeling that better capture a distribution of responses.

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