State-Level Energy and Emissions Projections from GCAM-USAState-Level Energy and Emissions...

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State-Level Energy and Emissions Projections from GCAM-USA Steven J. Smith*, Catherine Ledna Joint Global Change Research Institute College Park, MD *Also Department of Atmospheric and Oceanic Science University of Maryland [email protected] .

USEPA 2017 International Emission Inventory Conference August 19, 2017

With contributions from:

Wenjing Shi, Yang Ou, Christopher G. Nolte, Daniel H. Loughlin, Gokul Iyer

Outline

  Introduction to Integrated Assessment Models   Wide variety of IAMs

  GCAM Overview

  Flexible, technologically detailed (for an IAM), global -> regional model

  GCAM-USA Overview

  US State-level detail   Example applications (that don’t involve air pollutants)

  Air Pollutant Emissions in GCAM-USA

  Methodology and Data Sources   Comparison to EPA Inventories   Example analysis - RPS

  Questions Please ask clarifying questions during the talk!

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What is an Integrated Assessment Model (IAM)?

IAMs are research tools that integrate human and natural systems   IAMs provide insights that would be otherwise unavailable from disciplinary research

  IAMs focus on interactions between complex and nonlinear systems

  IAMs are not substitutes for disciplinary research or more detailed modeling

IAMs are also science-based decision support tools   IAMs support national, international, regional, and private-sector decisions

Human Systems

Natural Earth Systems

ENERGY

Economy Security

Settlements

Food

Health

Managed Ecosystems

TechnologyScience

TransportPopulation

Sea Ice Carbon Cycle

Earth System Models

EcosystemsOceans

Atmospheric Chemistry

Hydrology

Coastal Zones

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IAMs – “Big Picture” Analysis With “Just Enough” Detail   IAMs were designed to provide strategic insights

  Not designed to model very fine details (unemployment, electrical grid operation, daily oil market price paths)

  IAMs are:   Global in scope   Generally include all anthropogenic sources of emissions   Include some representation of the climate system

  However, there is significant variation across models as to their:   Spatial resolution (countries to regions to global)   Inclusion of gases and substances   Energy system detail   Representation of agriculture and land-use   Economic assumptions and technological change   Degree of foresight   Sophistication of the climate model component

  There is a big difference between highly-aggregated IAMs used for cost-benefit analysis (and social-cost of carbon estimates) and higher-resolution IAMs used for analysis of system dynamics such as GCAM (which does not produce a social-cost of carbon 4

The Global Change Assessment Model (GCAM)

The Global Change Assessment Model


GCAM is an open-‐source, global integrated assessment model

GCAM links Economic, Energy, Land-‐use, and Climate systems (and now Water) Typically used to examine the effect of socioeconomic scenarios, technology, and policy on the complex system that links economy, energy, agriculture, land-‐use, and climate

Technology-‐rich model (for an IAM) Emissions of carbonaceous aerosols, reacDve gases, sulfur dioxide, ozone precursors, and 16 greenhouse gases Runs through 2100 in 5-‐year <me-‐steps DocumentaDon available at: wiki.umd.edu/gcam

Also a GCAM Community Listserve and an annual GCAM community meeDng

32 Region Energy/Economy Model

283 Agriculture and Land Use Regions

7 233 Water Basins

Technology Competition: Logit Choice Model

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  Economic competition among technologies takes place at many sectors and levels.

  Assumes a distribution of realized costs due to heterogeneous conditions.

  Market share based on probability that a technology has the least cost for an application.   Avoids a “winner take all” result.   “Logit” specification.

ª Relative and absolute cost technology choice models implemented.

A Probabilistic Approach

`

Median CostTechnology 1



Market Price

sharei =αi costi

σ

α j cost jσ

j∑


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Example Detail: Transportation

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  The choice among modes of transportation in the passenger sector is a function of the cost of travel, the time it takes, and income. –  Vehicles are also vintaged

Similar level of detail for freight transport

GCAM USA

GCAM-USA: Overview

  GCAM-USA is a version of GCAM with sub-regional detail in the United States.

  GCAM-USA is a full, global integrated assessment model (IAM).

  It is actively being used to explore energy-water-land interactions

See bibliography at end of this talk

GCAM

GCAM - USA

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Part of an overall trend in our work to add greater

spatial and sectoral detail (where needed)

GCAM-USA Detail

  Socioeconomic projections are input at state level   Population   GDP (as labor productivity growth)

  Energy transformation at state level   Electricity generation & Refining by state   Full electricity (and CO2 storage) trade within modified NERC regions

  Renewable and carbon storage resources at state level   Wind, Solar (central PV, rooftop PV, solar thermal), geothermal   Carbon storage

  Energy final demands at state level   Buildings: representative commercial & residential building in each state   Transportation: passenger & freight with detailed technologies   Industry: aggregate energy demands (process model in progress)

Not modeled at the state level   Fossil Resources/Fossil resource production   Agricultural demand (USA total) & supply (10 agro-economic zones AEZ)

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Future Water Consumption

Water demands, integrated into the model, allow analysis of sensitivity to assumptions including policy options. Work incorporating water supply and demand in prep.

Liu et al. (2015) “Water demands for electricity generaDon in the U.S.: Modeling different scenarios for the water–energy nexus" Technological ForecasDng & Social Change 94 (2015) 318–334. doi: 10.1016/j.techfore.2014.11.004

the results of the RCP 4.5_NucCCS and reference scenarios, andthen compare RCP 4.5_NucCCS with RCP 4.5_RE.

In the RCP 4.5_NucCCS scenario, nuclear power plantsbecome the dominant cooling water users by 2095 in Centraland Eastern U.S. — regions that remain predominantly coal-burning in the reference scenario (see Figs. S10 and S11).Florida and Texas stand out in a comparison of water demands,since both states are highly populated with significant energydemand, but their electric-sector water demands exhibitdifferent responses in the reference and RCP 4.5_NucCCSscenarios.

While oil-fired power plants in Florida account for over 50%of the total waterwithdrawal in 2005, natural gas power plantsdominate water withdrawal under the reference scenario by

2095, and nuclear power plants are the main users under RCP4.5_NucCCS (Fig. 4(b)). Overall, Florida withdraws 67% lesswater under climatemitigation (Fig. S10(c)), mainly because ofthe dramatic decrease in withdrawal requirements of naturalgas power plants. These results point to an interesting trade-off.On one hand, climate mitigation favors nuclear, solar, andbiomass power plants over fossil fuel power plants — forexample, generation from natural gas plants is 46% less underRCP 4.5_NucCCS than under the reference scenario in 2095(Fig. 4(a)). On the other hand, under RCP 4.5_NucCCS, genericsteam natural gas plants are gradually replaced by natural gascombined-cycle (NGCC) and NGCC with carbon capture andsequestration (CCS). NGCC plants are less water-intensive thanconventional steam plants and NGCC with CCS is limited to

Fig. 3. (a) State level electric-sector water withdrawal by cooling technology for the year 2095 under the reference scenario. (b) State level electric-sector waterwithdrawal by cooling technology for the year 2095 under the frozen scenario. (c) The difference in water withdrawal between frozen and reference scenarios for theyear 2095.

325L. Liu et al. / Technological Forecasting & Social Change 94 (2015) 318–334

Ø  Reference scenario water withdraw where the prevalence of closed-loop cooling increases over time for electric generation.

Ø  Withdraw rates are up

to ~ 10 km3/yr larger at the state level under a “frozen cooling technology” scenario.

2095 Water withdraw for Electric Generation

Future Water Consumption

Liu et al. (2015) “Water demands for electricity generaDon in the U.S.: Modeling different scenarios for the water–energy nexus" Technological ForecasDng & Social Change 94 (2015) 318–334. doi: 10.1016/j.techfore.2014.11.004

Ø  Population is not the only factor driving water consumption.

Ø  Increases in the

prevalence of closed-loop cooling technologies reduce water demands over time relative to a frozen tech scenario.

Ø  Shifts in electric generation technologies also change water demands

2095 Water Consumption vs Population Change

of 85.2% in scenarios 4 (Macknick et al., 2012a) and 28.5% inU.S. WET-L1S (Blanc et al., 2014). The different signs ofchanges result primarily from more aggressive deploymentof renewable energies by 2050 in the latter two studies.

4.4. Key drivers of electricity water demand at the state level

Next, we focus on state-level results based on the improvedgeographic and technological resolution of the GCAM-USAframework, and its provision of a full energy system model,including electricity demands and non-US regions. Analysis ofthe five key drivers in our scenarios also provides insight intothe regional response of U.S. water sector to socioeconomicchanges, future technological transitions, and climate mitiga-tion policies.

4.4.1. Population growthPopulation growth is one of the key drivers of changes

in future U.S. electric-sector water demand. As the totalpopulation increases, demands for goods and services thatuse energy also increase, which puts upward pressure onelectricity demand. Further, with more people relocating tothe South and West and smaller increases of population inthe Midwest and Northeast U.S. (Fig. S8(b)), the changes inlocal energy demand and their associated water demandsreflect U.S. population migration dynamics. Fig. 2 shows thatpopulation growth partially explains the simulated increase inwater consumption, particularly in the Southern U.S. However,population growth effects on water consumption in the West,Midwest, andNortheast are reducedby other factors,which arediscussed in the following sections.

4.4.2. Cooling technology mix (reference vs. frozen)Because water resources are more abundant in the Eastern

U.S., the majority of the power plants in the Eastern U.S. useonce-through cooling (Averyt et al., 2011). Spatial results in thereference scenario show a complete phase-out of once-through power plants in many states (Fig. 3(a)). However,under the frozen scenario, once-through systems remaindominant across the U.S. except in some western states suchas California, which is abandoning once-through coolingtechnology in power plants due to stringent state regula-tions (http://www.waterboards.ca.gov/water_issues/programs/ocean/cwa316/policy.shtml) (Fig. 3(b)). Thus, a result of thecooling system conversions is the reduction ofwaterwithdrawalacross all states except California (Fig. 3(c)).

In terms of water consumption, once-through cooling allbut disappears under the reference scenario, and closed-loopand non-cooling related water uses (namely, evaporation fromhydro power plants and solar panel washing water) drive upconsumptive water use in the U.S. (Fig. S9(a)). Under thefrozen scenario, in contrast, there is still considerable con-sumptive use from once-through cooling scattered across theU.S. (Fig. S9(b)); therefore, the majority of states consume lesswater than under the reference scenario (Fig. S9(c)).

4.4.3. Fuel portfolio (Ref vs. RCP4.5_NucCCS and RCP4.5_RE)Future fuel mix under the reference scenario is described in

Clarke et al. (2009). The two climate mitigation scenariosdisplay different trajectories to meet the end-of-century mitiga-tion goal (Fig. S5(c) and (d)). To clarify the implications ofclimate mitigation policy on electricity water demand and theconsequences of different stabilization paths, we first compare

Fig. 2. Population percent change in 2095 from 2005 vs. water consumption percent change in 2095 from 2005.

324 L. Liu et al. / Technological Forecasting & Social Change 94 (2015) 318–334

Future Building Energy Consumption

A more detailed representation of the U.S. at the 50 state level, embedded within the global model allows for improved modeling of issues such as the impact of changing climate on US building energy consumption.

16 Zhou Y, et al. 2014. "Modeling the effect of climate change on U.S. state-‐level buildings energy demands in an integrated assessment framework." Applied Energy 113:1077-‐1088. doi:10.1016/j.apenergy.2013.08.034

Recent and In-Progress Improvements

  Harmonization to EIA’s Annual Energy Outlook   Follows similar trend other than overestimate in 2015   Will be corrected w/ calibration year is updated to 2015

  Air pollutant emissions updates   (see next slides)

  Electricity load segments   Generation segments: peak, sub-peak, intermediate, baseload

  Detailed Industrial Energy Model   ~12 sectors and & ~8 services (boilers, process heat, electro-chemical, feedstocks, etc.)   Brings industry sectors into similar level of detail as buildings and transportation

  Natural gas supply and infrastructure   State supply curves. Natural gas trade/transport between states

  More detailed regional representation of land-use change   Better aligned with water basins/states

  Energy-Water-Land interactions   Water demand, supply and associated markets

  New Technologies   Offshore Wind (collaboration with Z. Cramer) 17

3000

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2010 2020 2030 2040 2050

Electricity generation [Billion kWh/yr]

GCAM-USA AEO-2015 AEO-2016 AEO-2017

Air-Pollutant Emissions in GCAM-USA

State-level criteria pollutant emissions

In collaboration with EPA ORD, air pollutant emissions in GCAM-USA have been updated.

Calibrated to NEI 2011 emissions at the state-level Emission factors (EFs) incorporate impact of on-the books regulations (CSAPR), new source performance standards (NSPS2), MACT requirements, consent decrees, etc. for new capital stock Data sources: ü  Electric generation: IPM version 5.13, GREET 2014, with technology-specific New

Source Performance Standards (NSPSs). ü  Industrial fuel consumption: derived from EPAUS9r2014 MARKAL energy modeling

framework

ü  Refineries: GREET (which was developed from EPA inventories and approximates the effects of NSPSs)

ü  Light- and heavy-duty vehicle: Mobile Vehicle Emissions Simulator (MOVES)

Add emission factors for fuel/technology combinations not represented separately in NEI or other inventory data

Part of this process have been evaluating/updating other GCAM assumptions so as to better model energy-system dynamics over 1-3 decade time horizons.

19 Funding for this work provided by the US EPA Office of Research and Development.

Comparison With EPA Inventories

In general, GCAM-USA projections are now similar to EPA regulatory inventories.

Solid Lines – GCAM-USA projections Because these projections includes only policies currently in place, some emissions can ultimately increase in the long-term as energy demands or other drivers increase over time. 20

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21 Shi, W. et al. 2017. "ProjecDng state-‐level air pollutant emissions using an integrated assessment model: GCAM-‐USA" Submiced, in Review

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State level comparison

Figure shows a “quality metric” that is equal to 2 when both base-year and 2025 values match EPA’s inventories for that state.

There are larger differences at the state level. State level energy projections have not been harmonized. Emissions agree better in CSAPR states since electric sector emissions are required to match IPM modeled values through an emissions market within the model.


Analysis: Air Pollution Co-benefit of Efficiency and Renewable Energy Measures

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Analysis examining the impact on state pollutant emissions due to a renewable energy standard requiring that specified percentage of new generation be supplied by renewable power (wind, solar, biomass, geothermal).

This leads to various analysis set-up choices and additional questions.

Applied RPS constraint at the grid region level (roughly equal to NERC regions). —  From a modeling standpoint, this is a clean set-up since model freely trades

electricity within a grid-region.

—  Question: what would differing state level RPS standards mean in the context of interconnected AC grids where net state-level electricity import/export is generally not zero?

Might renewable portfolio standards ultimately be set high enough that existing capacity would be either prematurely retired or under-utilized? (Set-up in this analysis assumes not.) Results are often in a context of relatively low projected growth in electricity demand —  Often this implies relatively low levels of new capacity addition.

Electricity System Projections

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System size varies between states.

Under a region-wide RPS, generation shifts (although shifts

are modest in this case).

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Preliminary Results

Renewable Generation

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0.04


Tg

PM2.5.NoInd.Tg 2030

●●

● ●

●

0.00

0.02

0.04


Tg

PM10.NoInd.Tg 2030

●●

●

●

●

0.00

0.02

0.04


Tg

scenario● P2040

R2000

NMVOC.NoInd.Tg 2030

Criteria pollutant emissions decrease,

although change is small in this case

Preliminary Results

Assumptions for biomass (both transformation side and end-use) can impact

these results.

Summary

GCAM-USA is a flexible modeling tool that can now be applied to energy and air pollution analysis at the state level.

At the national level, air pollution projections agree well with regulatory inventories. As with any model, results at finer scales are increasingly sensitive to assumptions and model behavior.

State level air pollutant emissions show larger differences at this level.

This tool does not replace the need for more detailed modeling Regulatory impact analysis often requires more detailed tools that consider the system “as it is now” and might evolve in the near-term.

GCAM-USA can be a useful tool to allow flexible analysis with multiple scenarios with different driver (e.g. Population, Shi et al. in prep), technology (Ou et al. in prep), or policy assumptions.

27

GCAM-USA Bibliography

Zhou Y, LE Clarke, J Eom, GP Kyle, PL Patel, SH Kim, J Dirks, E Jensen, Y Liu, J Rice, L Schmidt, and T Seiple. 2014. Modeling the effect of climate change on U.S. state-level buildings energy demands in an integrated assessment framework. Applied Energy, 113, 1077–1088.

Scott MJ, DS Daly, Y Zhou, JS Rice, PL Patel, HC McJeon, GP Kyle, SH Kim, J Eom, and LE Clarke. 2014. "Evaluating sub-national building-energy efficiency policy options under uncertainty: Efficient sensitivity testing of alternative climate, technological, and socioeconomic futures in a regional integrated-assessment model. ." Energy Economics 43(2014):22-33. doi:10.1016/j.eneco.2014.01.012

Scott MJ, DS Daly, JE Hathaway, CS Lansing, Y Liu, HC McJeon, RH Moss, PL Patel, MJ Peterson, JS Rice, and Y Zhou. 2015. "Calculating Impacts of Energy Standards on Energy Demand in U.S. Buildings with Uncertainty in an Integrated Assessment Model." Energy 90(Part 2):1682-1694. doi:10.1016/j.energy.2015.06.127

Liu L, M Hejazi, P Patel, P Kyle, E Davies, Y Zhou, L Clarke, and J Edmonds (2015) “Water demands for electricity generation in the U.S.: Modeling different scenarios for the water–energy nexus" Technological Forecasting & Social Change 94 (2015) 318–334. doi: 10.1016/j.techfore.2014.11.004.

McFarland J, Y Zhou, LE Clarke, P Sullivan, J Colman, W Jaglom, M Colley, PL Patel, J Eom, SH Kim, GP Kyle, P Schultz, B Venkatesh, J Haydel, C Mack, and J Creason. 2015. "Impacts of rising air temperatures and emissions mitigation on electricity demand and supply in the United States: a multi-model comparison." Climatic Change 131(1):111-125. doi:10.1007/s10584-015-1380-8

Hejazi MI, N Voisin, L Liu, LM Bramer, DC Fortin, JE Hathaway, M Huang, GP Kyle, LYR Leung, H Li, Y Liu, PL Patel, TC Pulsipher, JS Rice, TK Tesfa, CR Vernon, and Y Zhou. 2015. "21st Century United States Emissions Mitigation Could Increase Water Stress more than the Climate Change it is Mitigating." Proceedings of the National Academy of Sciences of the United States of America 112(34):10635-10640. doi:10.1073/pnas.1421675112

Iyer, G., Ledna, C., Clarke, L., McJeon, H., Edmonds, J., & Wise, M. (2017). GCAM-USA Analysis of US Electric Power Sector Transitions (PNNL-26174).

Iyer G., C Ledna, LE Clarke, JA Edmonds, HC McJeon, P Kyle and J Williams. Measuring Progress from Nationally Determined Contributions to Mid-Century Strategies. Nature Climate Change (accepted in principle).

28

The End

Contributions from: Wenjing Shi, Yang Ou, Christopher G. Nolte, Daniel H. Loughlin

Date post:	18-Mar-2020
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