short run effects of carbon policy on u.s. electricity markets · 2019-11-06 · first solar...

First Solar Proprietary & Confidential - Finance/Accounting/RND

Steve Dahlke

1

Short run effects of carbon policy on U.S. electricity markets

U.S Association for Energy Economists Conference

Denver, CO, 11/5/2019

11Steve Dahlke Title and other information Other information


Overview

2

Conclusions:

• Carbon prices of $25 and $50 per ton reduce electricity emissions by 17% and 22%.

• State-level analysis:

• Fifteen states increase emissions after carbon price,

• Revenue rebate policy leads to wealth transfers.

Methods: Linear optimization model of U.S. electricity market dispatch

Contribution: First estimates of short run effects on U.S. electricity market since 2008. First national estimates with state-level analysis.

Steve Dahlke Ch 3: Short run effects of carbon policy on U.S. electricity markets Thesis defense


Background

3Steve Dahlke Ch 3: Short run effects of carbon policy on U.S. electricity markets Thesis defense

• Three carbon price bills were introduced in 2018 U.S. Congress (Kaufman, 2018).

• Lots of researchers studying effects of carbon policy on entire economy.• Jorgenson & Wilcoxen (1993); Goulder (1995); Rausch et al. (2011); Macaluso et al. (2018); Chen & Hafstead (2018).

• Electricity production is largest single contributing industry to climate change damages.• Contributed 32% of global anthropogenic emissions in 2010 (Nicholson et al., 2011) and 28% in U.S. (US EPA, 2015).

• Most economic research on effects of carbon policy on electricity sector studies limited to long run impacts. There are large uncertainties and disagreement within this literature.

• Murray et al. (2018); Paul et al. (2015); Caron et al. (2018); Mai et al. (2018).

• Literature gap: short term effects of carbon policy on U.S. electricity industry.• Voorspools & D’haeseleer (2006); Newcomer et al. (2007); Van den Bergh & Delarue (2015).


Model overview


• U.S. electricity industry with 10 market regions. Carbon price simulated as an increase in production costs.

• Minimize system production costs, subject to:

• Hourly regional supply = Hourly regional demand. Demand is exogenous and inelastic.

• Regional transmission constraints. No transmission constraints within regions.

• Plant-level capacity constraints.

• Linear program. Model is solved with LPSolve1 in the R computing environment.2

• Short run partial equilibrium market model. Capital stock, demand response, and general equilibrium effects are held fixed.

1Berkelaar et al. (2004). 2Berkelaar (2015); R Core Team (2018).


Model overview: Data


U.S. Energy Information Administration (US EIA):Power plant capacity limits & historic monthly generation (𝑄𝑝,𝑚), electricity production costs (𝑐𝑝,𝑡), demand (𝐷𝑟,𝑡), historic transmission flows (𝑡𝑥𝑟′,𝑟).

U.S. Environmental Protection Agency (US EPA): Historic power plant 𝐶𝑂2𝑒 emissions rates (𝐶𝑂2𝑝).

National Renewable Energy Laboratory (NREL):Technology-aggregated operations & maintenance costs (𝑐𝑝,𝑡).

North American Electric Reliability Corporation (NERC): Outage rates by technology and size-bin.

The World Bank: Baseline carbon costs

All data, code, and detailed results: https://osf.io/59pf6/

https://osf.io/59pf6/


Baseline validation: Emissions and prices


• Model-estimated annual 𝐶𝑂2𝑒 emissions from electricity production, 1.62 billion metric tons.

US EPA reported 1.78 billion tons in 2017 (US EPA, 2019). Difference likely from electricity plants < 1MW.

• Modeled-prices were compared with 2016-2018 average prices for regions that report data (LCG Consulting, 2019).

• Differences likely because California, New England, and New York have highest penetrations of distributed generation (<1MW) in the country (US EIA, 2015).

• Distributed generation will likely not be covered by carbon policy, and won’t affect short-run economics between covered coal and natural gas plants.


Baseline validation: Production


• Annual electricity production aggregated by dispatchable technology, GWh, modeled vs 2015-2017 average:

• Coal more in the model than in reality. Likely due to non-competitive forces and operational constraints not included in the model.

• Minimal impact on simulated results if these external factors remain constant after a carbon price.

Figure 22 Annual hourly generation in CAISO for baseline

model (top panel) and actual in 2016 (bottom panel).


Overall results: Emissions


• Annual U.S. electricity sector 𝐶𝑂2𝑒 emissions for three scenarios:

• 4.9% and 6.3% of total U.S. emissions

• 16% and 21% of U.S. commitment under Paris Climate Accord.

• Suggests significant short run emissions reductions likely from a U.S. carbon price on electricity sector.

• The policies generate $33.5B and $63.0B in government revenue.


State-level results: Emissions and generation


• Most net emissions reductions from coal heavy states.

• Cost-minimizing response involves emissions increasing in some states.

Decrease in coal generation (top) and increase in natural gas generation (bottom) from $50/ton carbon price.


Overall results: Prices


• Largest price increases in coal-heavy markets: Central, Mid-Atlantic, Midwest.

• Short run, first-order price effects.

• Short run price increases will be partially offset by reduced demand and new investment in supply in the long run:

• Electricity supply takes several years to build.

• Most customers insulated from short run price changes through long run contracts and regulated prices


Overall results: Transmission flows


• Flows adjust so that regions with larger production cost impacts import from regions with lower impacts.

• Biggest impact in Northwest region, flows reverse as Northwest drastically reduced its coal generation.

• Made possible by ample transmission capacity and available natural gas capacity across the Western U.S.

• Model does not consider impacts of hydro and administrative market structures on western electricity trade (Dahlke, 2019).


State-level results: Production costs


Changes in production cost and CO2 emissions by state after $50/ton price.

State production cost changes are a function of changes in 1) costs incurred by plants and 2) in-state production.


State-level results: Revenue rebates


• Two of the three policies recently introduced in Congress return revenue to citizens.

• $50/ton carbon tax on electricity sector raises $63 billion in short run.

• Approximately $194 per U.S. citizen.

• Flat per capita rebate combined with varying per capita revenue leads to wealth transfers.

• Per capita revenue from CA: $82, NY: $62, WV: $1,077.


Conclusions


• This research studies short term effects of a carbon price on U.S. electricity markets.

• CO2 prices of $25 and $50 per ton cause 17% and 22% reductions from present levels.

• Most reductions from high coal states.

• Analysis captures switch in production from coal to natural gas among existing U.S. plants.

• A flat per capita rebate of tax revenue involves $194 per person in $50/ton scenario, involves wealth transfers across states.

• Model does not consider investment or demand response to changes in prices from policy.


References


Caron, Justin, Stuart M. Cohen, Maxwell Brown, and John M. Reilly. “Exploring the Impacts of a National U.S. CO2 Tax and Revenue Recycling Options with a Coupled Electricity-Economy Model.” Climate Change Economics 09, no. 01 (February 1, 2018): 1840015. https://doi.org/10.1142/S2010007818400158.

Chen, Yunguang, and Marc a. C. Hafstead. “Using a Carbon Tax to Meet U.S. International Climate Pledges.” Climate Change Economics 10, no. 01 (November 6, 2018): 1950002. https://doi.org/10.1142/S2010007819500027.

Dahlke, Steve. “Integrating Electricity Markets: Impacts of Increasing Trade on Prices and Emissions in the Western United States.” ArXiv Preprint ArXiv:1810.04759, 2019.

Goulder, Lawrence H. “Effects of Carbon Taxes in an Economy with Prior Tax Distortions: An Intertemporal General Equilibrium Analysis.” Journal of Environmental Economics and Management 29, no. 3 (November 1, 1995): 271–97. https://doi.org/10.1006/jeem.1995.1047.

Jorgenson, Dale W., and Peter J. Wilcoxen. “Reducing US Carbon Emissions: An Econometric General Equilibrium Assessment.” Resource and Energy Economics 15, no. 1 (1993): 7–25.

Kaufman, Noah. “How the Bipartisan Energy Innovation and Carbon Dividend Act Compares to Other Carbon Tax Proposals.” Commentary. Columbia SIPA Center on Global Energy Policy, 2018. https://energypolicy.columbia.edu/research/commentary/how-bipartisan-energy-innovation-and-carbon-dividend-act-compares-other-carbon-tax-proposals.

Macaluso, Nick, Sugandha Tuladhar, Jared Woollacott, James R. Mcfarland, Jared Creason, and Jefferson Cole. “The Impact of Carbon Taxation and Revenue Recycling on U.S. Industries.” Climate Change Economics 09, no. 01 (February 1, 2018): 1840005. https://doi.org/10.1142/S2010007818400055.

Mai, Trieu, John Bistline, Yinong Sun, Wesley Cole, Cara Marcy, Chris Namovicz, and David Young. “The Role of Input Assumptions and Model Structures in Projections of Variable Renewable Energy: A Multi-Model Perspective of the U.S. Electricity System.” Energy Economics 76 (October 1, 2018): 313–24. https://doi.org/10.1016/j.eneco.2018.10.019.

Murray, Brian C., John Bistline, Jared Creason, Evelyn Wright, Amit Kanudia, and Francisco de la Chesnaye. “The EMF 32 Study on Technology and Climate Policy Strategies for Greenhouse Gas Reductions in the U.S. Electric Power Sector: An Overview.” Energy Economics 73 (June 1, 2018): 286–89. https://doi.org/10.1016/j.eneco.2018.03.007.

National Renewable Energy Laboratory (NREL). “Annual Technology Baseline (ATB),” 2018. https://atb.nrel.gov/.

Newcomer, Adam, Seth A. Blumsack, Jay Apt, Lester B. Lave, and M. Granger Morgan. “Short Run Effects of a Price on Carbon Dioxide Emissions from U.S. Electric Generators.” Environmental Science & Technology 42, no. 9 (May 1, 2008): 3139–44. https://doi.org/10.1021/es071749d.

Nicholson, Martin, Tom Biegler, and Barry W. Brook. “How Carbon Pricing Changes the Relative Competitiveness of Low-Carbon Baseload Generating Technologies.” Energy 36, no. 1 (January 1, 2011): 305–13. https://doi.org/10.1016/j.energy.2010.10.039.

North American Electric Reliability Corporation (NERC). “Generating Unit Statistical Brochures,” 2017. https://www.nerc.com/pa/RAPA/gads/Pages/Reports.aspx.

https://doi.org/10.1142/S2010007818400158

https://doi.org/10.1142/S2010007819500027

https://doi.org/10.1006/jeem.1995.1047

https://energypolicy.columbia.edu/research/commentary/how-bipartisan-energy-innovation-and-carbon-dividend-act-compares-other-carbon-tax-proposals

https://doi.org/10.1142/S2010007818400055

https://doi.org/10.1016/j.eneco.2018.10.019


https://atb.nrel.gov/

https://doi.org/10.1021/es071749d

https://doi.org/10.1016/j.energy.2010.10.039

https://www.nerc.com/pa/RAPA/gads/Pages/Reports.aspx


References


Paul, Anthony, Karen Palmer, and Matthew Woerman. “Incentives, Margins, and Cost Effectiveness in Comprehensive Climate Policy for the Power Sector.” Climate Change Economics 06, no. 04 (October 18, 2015): 1550016. https://doi.org/10.1142/S2010007815500165.

Rausch, Sebastian, Gilbert E. Metcalf, and John M. Reilly. “Distributional Impacts of Carbon Pricing: A General Equilibrium Approach with Micro-Data for Households.” Energy Economics, Supplemental Issue: Fourth Atlantic Workshop in Energy and Environmental Economics, 33 (December 1, 2011): S20–33. https://doi.org/10.1016/j.eneco.2011.07.023.

The World Bank. “Carbon Pricing Dashboard.” Up-to-date overview of carbon pricing initiatives, 2019. https://carbonpricingdashboard.worldbank.org/map_data.

United States Energy Information Administration (US EIA). “EIA Electricity Data Now Include Estimated Small-Scale Solar PV Capacity and Generation - Today in Energy,” 2015. https://www.eia.gov/todayinenergy/detail.php?id=23972#.

———. “Form EIA-860 Detailed Data with Previous Form Data (EIA-860A/860B),” 2017. https://www.eia.gov/electricity/data/eia860/.

———. “Form EIA-923 Detailed Data with Previous Form Data (EIA-906/920),” 2018. https://www.eia.gov/electricity/data/eia923/.

———. “U.S. Electric System Operating Data,” 2019. https://www.eia.gov/realtime_grid/#/status?end=20190418T15.

United States Environmental Protection Agency (US EPA). “Emissions & Generation Resource Integrated Database (EGRID),” 2016. https://www.epa.gov/energy/emissions-generation-resource-integrated-database-egrid.

———. “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2017.” Reports and Assessments, 2019. https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2017.

———. “Sources of Greenhouse Gas Emissions.” Overviews and Factsheets. US EPA, December 29, 2015. https://www.epa.gov/ghgemissions/sources-greenhouse-gas-emissions.

Van den Bergh, Kenneth, and Erik Delarue. “Quantifying CO2 Abatement Costs in the Power Sector.” Energy Policy 80 (May 1, 2015): 88–97. https://doi.org/10.1016/j.enpol.2015.01.034.

Voorspools, Kris R., and William D. D’haeseleer. “Modelling of Electricity Generation of Large Interconnected Power Systems: How Can a CO2 Tax Influence the European Generation Mix.” Energy Conversion and Management 47, no. 11 (July 1, 2006): 1338–58. https://doi.org/10.1016/j.enconman.2005.08.022.

https://doi.org/10.1142/S2010007815500165


https://carbonpricingdashboard.worldbank.org/map_data

https://www.eia.gov/todayinenergy/detail.php?id=23972

https://www.eia.gov/electricity/data/eia860/

https://www.eia.gov/electricity/data/eia923/

https://www.eia.gov/realtime_grid/#/status?end=20190418T15

https://www.epa.gov/energy/emissions-generation-resource-integrated-database-egrid

https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2017

https://www.epa.gov/ghgemissions/sources-greenhouse-gas-emissions

https://doi.org/10.1016/j.enpol.2015.01.034

https://doi.org/10.1016/j.enconman.2005.08.022


Appendix: Model overview: Parameter definitions


The model includes the following parameters:

𝑄𝑝,𝑚 maximum operating capacity of plant 𝑝 during month 𝑚 in megawatts (𝑀𝑊)

𝑐𝑝,𝑡 production cost of plant 𝑝 in hour 𝑡, in dollars per megawatt-hour ($/𝑀𝑊ℎ)

𝐶𝑂2𝑝 carbon dioxide emissions rate for plant 𝑝, in 𝑡𝑜𝑛𝑠 𝐶𝑂2

𝑀𝑊ℎ

𝐷𝑟,𝑡 demand in market region 𝑟 during hour 𝑡, in 𝑀𝑊ℎ

𝑂𝑟,𝑡 hourly operating reserves in region 𝑟, in 𝑀𝑊ℎ

𝑡𝑥𝑟′,𝑟 transmission capacity from region 𝑟′ to region 𝑟, measured in 𝑀𝑊ℎ

𝑖𝑚𝑝𝑟,𝑚 average net international imports into market region 𝑟 for month 𝑚

𝑓𝑒𝑒 carbon price imposed by policy, in $/𝑀𝑊ℎ

The set of choice variables are the level of production from each plant for each hour, 𝑞𝑝,𝑡, and the levels of power

transferred between each region, 𝑞𝑡𝑥𝑟′,𝑟,𝑡. The model assumes operators for each market regionally coordinate to

minimize the cost of dispatching power plants subject to demand levels.


Model overview: Algebra


The optimization problem is formulated as follows:

(1) minimize𝑞𝑟,𝑝,𝑡, 𝑞𝑡𝑥𝑟′,𝑟,𝑡

𝑟

𝑝∈𝑟

𝑡

𝑞𝑟,𝑝,𝑡(𝑐𝑝,𝑡 + 𝐶𝑂2𝑝𝑓𝑒𝑒𝑟) , ∀ 𝑡

Subject to the following sets of constraints:

2 σ𝑝∈𝑟 𝑞𝑝∈𝑟,𝑡 + σ 𝑟′≠𝑟(𝑞𝑡𝑥𝑟′,𝑟,𝑡 − 𝑞𝑡𝑥𝑟,𝑟′,𝑡) + 𝑖𝑚𝑝𝑟,𝑚 ≥ 𝐷𝑟,𝑡 + 𝑂𝑟,𝑡, ∀ 𝑟,𝑚, 𝑡 ∈ 𝑚

3 0 ≤ 𝑞𝑝,𝑡 ≤ 𝑄𝑝,𝑚, ∀𝑝,𝑚, 𝑡 ∈ 𝑚

4 − 𝑡𝑥𝑟′,𝑟 ≤ 𝑞𝑡𝑥𝑟′,𝑟,𝑡 ≤ 𝑡𝑥𝑟′,𝑟, ∀ 𝑟, 𝑟′ ≠ 𝑟, 𝑡

The objective function in equation (1) minimizes production costs, including the carbon price. Equation (2) requires that production plus net imports from all other regions meets demand plus operating reserves in region r, for all hours. Equations (3) limits production from each plant to be less than or equal to its total capacity and non-negative. Equation (5) limits energy transfers across market regions to the available transmission capacity available between each pair of market regions.

(1) Minimize system costs by choosing hourly generation and transmission levels.

(2) Supply equals demand.

(3) Plant capacity constraints.

(4) Regional transmission constraints.

Subject to:


Model overview: Linear optimization


Recipe for a linear optimization model:

𝑚𝑎𝑥𝑥

𝒄𝑇𝒙

𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝑨𝒙 ≤ 𝒃

𝑐ℎ𝑜𝑜𝑠𝑒 𝑥′𝑠 𝑡𝑜 𝑚𝑎𝑥𝑖𝑚𝑖𝑧𝑒 𝑜𝑟 𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒:

𝑐1𝑥1 + 𝑐2𝑥2 +⋯+ 𝑐𝑛𝑥𝑛

𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜:

𝑎11𝑥1 + 𝑎12𝑥2 +⋯+ 𝑎1𝑛𝑥𝑛 ≤ 𝑏1

𝑎21𝑥1 + 𝑎22𝑥2 +⋯+ 𝑎2𝑛𝑥𝑛 ≤ 𝑏2⋮

𝑎𝑚1𝑥1 + 𝑎𝑚2𝑥2 +⋯+ 𝑎𝑚𝑛𝑥𝑛 ≤ 𝑏𝑚

𝑚𝑎𝑥𝑥

𝑐1 𝑐2 … 𝑐𝑛

𝑥1𝑥2⋮𝑥𝑛

𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜:

𝑎11 𝑎12 ⋯ 𝑎1𝑛𝑎21 𝑎22 ⋯ 𝑎2𝑛⋮

𝑎𝑚1

⋮𝑎𝑚2

⋱⋯

⋮𝑎𝑚𝑛

𝑥1𝑥2⋮𝑥𝑛

≤

𝑏1𝑏2⋮𝑏𝑚

⇒


Model overview: Data objects


𝑚𝑎𝑥𝑥

𝒄𝑇𝒙


𝒙 ∈ ℝ+𝑛 vector of power plant production decisions (𝒒) and transmission levels (𝒒𝒕𝒙).

𝒄 ∈ ℝ+𝑛 vector of power plant production costs and transmission costs.

𝒃 ∈ ℝ+𝑚 vector of power plant and transmission capacity constraints, and regional demand constraints.

𝑨 ∈ ℝ+𝑚×𝑛 constraint coefficients.

Dimensions:𝑃 8,377 power plants𝑇𝑋 90 transmission constraints𝑅 10 market regions

𝑛 = 𝑃 + 𝑇𝑋 = 8,467𝑚 = 𝑃 + 𝑇𝑋 + 𝑅 = 8,477

Definition of R demand constraints:

𝑑𝑟,𝑝 = ቊ10

𝑖𝑓 𝑝 ∈ 𝑟𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

𝑡𝑟,𝑡𝑥 = ቊ10

𝑖𝑓 𝑡𝑥 ∈ 𝑟𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒


Model overview: Solver


Lp_solve is an open-sourced linear program solver written in ANSI C. http://lpsolve.sourceforge.net/5.5/

Package “lpSolve” provides interface with R. https://cran.r-project.org/web/packages/lpSolve/

direction “min” or “max”

objective.in Vector of objective function coefficients (𝒄)

const.mat Coefficient constraint matrix (𝑨)

const.dir Vector of constraint directions (" ≤, " " ≥ ", " == “)

const.rhs Vector of values for right side of constraints (𝒃)

int.vec index of integer-constrained variables

binary.vec index of binary-constrained variables

𝑚𝑎𝑥 𝒄𝑇𝒙


lp(direction, objective.in, const.mat, const.dir, const.rhs, int.vec, binary.vec,…)

http://lpsolve.sourceforge.net/5.5/

https://cran.r-project.org/web/packages/lpSolve/


Model overview: Treatment of wind, solar, hydro


• Capacity limits are based off 2015-2017 plant-level monthly averages.

• This replicates aggregate output while abstracting from hourly variability in wind and solar, and reservoir-driven production constraints of hydro operators.

• These plants have low production costs, zero greenhouse gas emissions, and are rarely on the margin in U.S. markets.

• Thus, their production will be relatively unaffected in the short run from a carbon price, and this model abstraction is ok.

short run effects of carbon policy on u.s. electricity markets · 2019-11-06 · first solar...

Documents