 |
Inaugural Workshop
Breakout Sessions
This section summarizes the activities resulting from the topic-specific breakout sessions, including the goals, current related activities, next steps identified, the lead and the other participants. Additional details on topic activities can be accessed by linking on the session title below.
Session attendees:
Brian Card (DOE-EERE)
Charles Drummond (NETL)
Stephen Dunn (EPA)
Skip Laitner (ACEEE)
Dan Loughlin (EPA)
Denise Mulholland (EPA)
Catherine Morris (Keystone)
David Shen (TMS)
Dave Sire (USDA)
Topic clarification:
- Evaluation impact includes prospective and retrospective back. Focus currently is on prospective,
- Need to address risk of achieving technical goals,
- Need analytical efforts, models, tools that look at resources (from fossil to efficiency) that looks at interaction.
Important Decision Makers Identified:
- States — governors, agency heads, legislators,
- DOE — Secretary — funding and program,
- Office of Management and Budget and Congress,
- EPA,
- ACEEE — state and industry leaders (for example DOW 25% reduction by 2015 in energy-intensity goal),
- Consumers in a limited fashion.
Data needs to improve evaluation tools
- Need to know what is out there — Create a thorough inventory of existing models, explaining model outputs and methodology differences. Identify best practices.
- Michael Leifman's renewable energy modeling workshop exercise a good approach,
- Update 1990 DOE document (Sec. Watkins) addressing the current state of modeling — under Office of Policy? (Skip Laitner to provide correct citation).
- Consistent data in multiple models. How to link two models, or ensure appropriate transfer of data (e.g., link MARKAL with REMI model to share assumptions).
Performing benefits analysis
- Economic parameters and their relationships are not modeled properly — can often yield negative results to a new policy in modeling tools
- One example is price signal from a carbon charge — prices goes up; can only have negative impact on GDP; anyway — should model any case that leads to negative GDP,
- Another example: NEMS uses global insight feedback model, which looks at increase in consumer product as inflationary,
- Another example: combined heat and power is being put into building through a third party — where the incentive is energy savings percentage, not initial costs.
- Models cannot deal with wide variation of fossil prices we are currently experiencing and should be revised to be able to deal with the 1) high prices 2) wide variation/fluctuation.
- Calibrate forecasting method with historical data. EX NEMS should look back at previous forecasts with current data to see variance and adjust projection accordingly. Quantities were about right, but the energy prices were wrong.
Collaboration process
- Need to validate models using organizations outside of the model operator; great opportunity to collaborate.
- Disaggregation of input and output data to state level to see the national impact as well as impact of national policy on a state/region.
- Develop consistent method and tools for dealing with technology optimism. Looking at the same types of risk across different technologies.
- Suggestion of looking at GWU site using Delphi approach — is there a way to integrate a Delphi approach with time series data?
- Full account of all impacts — most models only cover investment cost, energy savings.
- Four categories of costs (R&D, program, investment/labor to get installed, and transaction costs — like training).
- Four categories of benefits (energy savings; non-energy savings like productivity gains; health/environmental benefits and less need for control technologies; and spill over beyond original targeted area like DG coming from jets).
- Collaborate on model development. Example AMIGA model looks at petroleum refiner information — this portion could be used by EIA in NEMS.
Activities decided by the group:
Activity No. 1 — Full Accounting of Impacts — assume ACEEE/NREL lead
- Data Tools: White Paper, workshop
- Participating Organizations: ACEEE, NREL, Univ of CA, EPA, DOT, DOE, Ag, FERC, RTOs, HUD, PMAs,
- Next Steps: Identify potential participants/sponsors, event in fall/winter 2006, collective Web sites (wikipedia)
Activity No. 2 — Appropriate specification of relationship between economic factors (econometric data.)
- Data Tools:NEMS, EMF models, MARKAL-REMI
- Participating Organizations: same
- Next Steps:
- Assessment of critical factors;
- Key relationship of ?;
- Model inventory;
- Best practices workshop.
Activity No. 3 — Validation of results of evaluations tools
- Data Tools: White paper on validation method
- Participating Organizations: Same with additional modelers/users as appropriate
- Next Steps: Succeeds model inventory; Develop appropriate Validation method for each model; Id the models that need validation; develop white paper on validation methods; conduct validation exercise
Activity No. 4 — Model inventory and best practices and identify strengths/limitations
|
|
