Interaction of a GHG Emissions Cap With Energy Technologies and Markets USAEE Annual Conference – Washington DCOctober 11, 2011

Donald Hanson and David SchmalzerArgonne National Laboratory

Acknowledgements

We want to thank the National Energy Technology Laboratory for support. Particularly we want to thank Tim Fout, NETL Project Manager, and John Marano, JJM Energy Consulting.

The runs shown here are preliminary results.

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Background on Carbon Capture for Existing Fossil Energy Power Plants and Sequestration

Source: Ciferno, J., CO2 Capture from Existing Coal Fired Power Plants, NETL, December 2007

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Deployment Barriers for CO2 Capture on Coal Power Plants

Source: Ciferno, J., The U.S. Department of Energy’s Carbon Dioxide Capture RD&D Program, Pittsburgh, August 2011

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Our Scenario Results Show Need for Existing Plant CCS under a Wide Range of Conditions

High Gas Shale Production, Slower Electricity Demand Growth (hG-rD) High Gas Shale Production, Higher Electricity Demand Growth (hG-HD) Lower Gas Shale Production, Slower Electricity Demand Growth (LG-rD) Lower Gas Shale Production, Higher Electricity Demand Growth (LG-HD)

In the results, we refer to Slower Electricity Demand Growth as “Reference Electricity Demand” and Lower Gas Shale Production as “Reference Gas Shale Production .“

We assume a CO2 emissions reduction requirement by 2050

We also assume continued R&D related cost reductions in post-combustion CO2 capture technology

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All Modular Integrated Growth Assessment (AMIGA ) Systemsimplified

Resource Supply and Productiono Coal, biomasso Petroleumo Gas shales

Electric Capacity and Generationo 21 technologieso Power plant unit inventoryo Dispatch on load curve

Economic Modelo 90 sectorso End-use energy demando Transportation

technologies, vehicles and energy use

Fuels Refining and Blending Modelo 26 processeso Includes biofuels

Resource demand

Q

Fuels demand

Q

Electricdemand

Q

Resourcedemand

Q

The AMIGA model system converges on a supply and demand balance for major energy carriers.

Relationship Between Demand and Generation in the Electricity Market

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Solve for Supply and Demand Balance in Gas Market.

Simple, dynamic gas supply scenario model where current production depends on wellhead gas price, state of technical progress in extraction costs, and cumulative production.

Downward sloping demand functions for gas in enduse sectors Higher gas prices reduce investments in NGCC and increase coal

use:– Existing coal units are more likely to continue to operate longer rather

than retire.– In the midterm, an additional increment of new IGCC with CCS

operating at full capacity factors becomes economic relative to more NGCC operating at shoulder-load capacity factors.

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Reference Demand and Reference Gas Shale Scenario – Total Generation (Electric Power Sector & End Use)

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High Gas Shale Scenario: Gas Incrementally Displaces Most Other Generation Sources

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Gas CC & CT

Higher Electricity Demand due to More Electric Vehicles; Reference Gas Scenario

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Gas – CC & CT

We find that we need to do about the same number of PC plant retrofits in the High Shale Gas Scenario. The need for this technology is robust, given emission reductions.Compare the two shale gas scenarios: High and Low. With more gas:

1.Some older, existing coal plants will be repowered with gas, reducing CO 2 emissions.

2.Some near-zero generation (i.e., renewables, IGCC with CCS, nuclear) will be displaced on the margin with gas, increasing CO2 emissions.

Balancing 1 & 2, keeping the amount of CO2 emissions reduction unchanged, we will need to get about the same amount of CO2 reduction from retrofitting existing PC plants in both scenarios.

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Increasing Capacity Factors results in a “rebound” effect on generation. Assume a parasitic load of 25% based on improved retrofit technology. From example below, (1 – 0.25)*(0.654)/0.544, yielding 10% reduced generation. Further, non-retrofitted units will phase down the load curve and finally retire.

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UNIT CAPACITY FACTOR RESULTS Low Gas Shale High Gas Shale

RetroUnit non-retro RetroUnit non-retro

Ref Elec Dmd 65.2 51.8 65.4 54.4

High Elec Dmd 67.2 52.5 67.4 54.5

Margins at Which Economic Decisions Apply Existing coal-fired units in the unit inventory are sorted by the model in

order of highest return for refurbishment and CCS retrofit. NETL studies show great diversity in the population of existing PC units. This is a classic case for obtaining economic efficiency gains through emission trading, relative to command and control regulation.

Units which are not retrofitted continue to operate until no longer profitable, given the price of electricity and the cost of tradable CO2 emission allowances.

New NGCC capacity will enter in the loading order after base-load units with lower variable costs. The (competitive market or regulated) price of electricity must be sufficient to cover full investment costs of new capacity.

New base-load advanced coal units (e.g., IGCC with CCS) compete with shoulder load NGCC, where IGCC also gets a system cost reduction credit for displacing a portion of higher cost generation, and, through learning, lowering present value cost of future capacity needs (see Hanson references on dynamic programming optimization).

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Future Planned Work

Examine the savings in scarce investment dollars that could be obtained from retrofitting existing power plants compared with building new plants.

Examine impacts of proposed EPA regulations

Examine regional power pool impacts in US

Include the energy security benefits of maintaining the existing coal-fired fleet (e.g., reduce oil imports through increased vehicle electrification)

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References

1. M.G.Shelby, A.Fawcett, E.Smith, D.Hanson, and R.Sands, “Representing technology in CGE Models: A Comparison of SGM and AMIGA for Electricity sector CO2 Mitigation,” Int. J. Energy Technology and Policy, Vol 6, No. 4, 2008, pp. 323-342.

2. D.A.Hanson, Y.Kryukov, S.Leyffer, and T.Munson, “Optimal Control Model of Technology Transition,” Int. J. Global Energy Issues, Vol. 33, Nos. 3-4, 2010, pp.154-175.

3. D.A.Hanson, “Optimizing the Penetration of Advanced Low-Carbon Energy Technologies,” USAEE Annual Conference, Houston TX, September 16-19, 2007.

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