imperfect markets versus imperfect regulation in the u.s ... steve cicala background on electricity...

Imperfect Markets versus Imperfect Regulation

Imperfect Markets versus Imperfect

Regulation in the U.S. Electricity

Industry

Steve Cicala

University of Chicago

February 12, 2018

Steve Cicala February 12, 2018


Research Questions

Big Picture Question: How do markets performrelative to regulated allocation mechanisms, inclusiveof imperfections?

Application to electricity: How has transition tomarkets for determining production affected theallocative efficiency of electricity generation?

Gains from Trade. Market Power problems.

Steve Cicala Introduction February 12, 2018


Outline

Construct the universe costs, capacities, and hourlyoperations of nearly all electrical generating units inthe U.S. (1999-2012) ($1T).

Calculate measures of allocative efficiency Value of traded electricity Cost of out of merit generation.

Use staggered, discrete changes in how plants areselected to operate to estimate causal effect of marketintroduction on operations, net allocative efficiency.



Preview of Findings

Large improvements in allocative efficiency in marketareas.

Following Market Dispatch: Gains from trade increase 20% from a 10% increase in

traded volume. Plant availability increases 10%, reducing out of merit

costs by 20%.

More than offsets potential market power losses.



Outline of Talk

1. Background on Electricity Dispatch

2. Welfare Measurement

3. Data

4. Counterfactual Estimates

5. Conclusion



Outline



3. Data


5. Conclusion

Steve Cicala Background on Electricity Dispatch February 12, 2018


Demand Fluctuations Over the Week350

400

450

500

GW

h

0 24 48 72 96 120 144 168

Hour of Week



Demand Fluctuations Over the Year250

300

350

400

TW

h

Jan1998 Jan2000 Jan2002 Jan2004 Jan2006 Jan2008 Jan2010 Jan2012

MonthYear



Generation Units: Leontief, Capacity Contrained

Outputit = min {AiHeatit , Capacityi ,Laborit}



Agile Units for Peak Times: 50MW



High Capacity Units for Base Load: 1750MW



Supply Curves Order Generating Units0

200

400

600

800

$/M

Wh

0 10000 20000 30000Cumulative Nameplate

NEISO 1aug2005 0:00



Power Control Areas in 1997



A Brief History of the Electricity Sector

Was long believed to be a natural monopoly. Grew up as a balkanized patchwork of regulated,

vertically integrated utilities.

Rates were set by State Public Utility Commissions. Prudent cost recovery ensured regulation of

investments and operations.

Units dispatched based on engineering estimates ofcost within 100 Power Control Areas.



A Brief History of the Electricity Sector

Federal Energy Regulatory Commission (FERC)begins encouraging competitive generation as part ofbroader deregulatory push in late 1970s.

Late 1990s: Independent System Operators (ISO) /Regional Transmission Organizations (RTO) begintaking over dispatch in select areas areas.

ISO/RTOs consolidate Power Control Areas in to asingle dispatch algorithm.

Generating units are scheduled according to bids inday-ahead, uniform price auctions instead ofengineering estimates.

Real-time auctions determine who actually produceselectricity.



Balancing Authorities in 1999

NPCC

ERCOT

FRCC

SPP

MAPP

WECC

ECARMAIN

MAAC

SERC



Balancing Authorities Becoming Markets



Percent of Generation Capacity Dispatched by

Markets.1

.2.3

.4.5

.6S

har

e of

Nat

ional

Cap

acit

y

1998 2000 2002 2004 2006 2008 2010 2012Year



Number of 1999 Power Control Areas Under

Market Dispatch0

20

40

60

Count

1998 2000 2002 2004 2006 2008 2010 2012

Year



Existing Evidence on Restructuring and Markets

Restructuring papers focus on productivity changes: Fabrizio, Rose, and Wolfram (2007), Bushnell and

Wolfram (2005), Davis and Wolfram (2012), Chan,Fell, Lange, Li (2012), Cicala (2015).

Allocative efficiency losses in markets: Borenstein, Bushnell, and Wolak (2002), Borenstein

(2002), Bushnell, Mansur, and Savaria (2007), Joskowand Kahn (2002), Mansur (2001, 2008), Puller (2007),Ryan (2013), Wolfram (1999), among others.

Allocative efficiency gains of market dispatch versusbilateral trades:

Mansur and White (2012)




Even the Cato Institute has lost patience withcompetitive reforms in electricity and appears tosee merit in returning to the good old days ofregulated vertically integrated utilities. -Joskow(2006)




...there has been a significant decline in coalusage at Dairylands facilities due to decreasedoperating hours since joining the MISO.-Dairyland 2011 Annual Report



Outline



3. Data


5. Conclusion

Steve Cicala Welfare Measurement February 12, 2018


A Merit Order Example0

200

400

600

800

$/M

Wh


NEISO 1aug2005 0:00




200

400

600

800

$/M

Wh


Installed Capacity Operating

Net Generation: 11783

NEISO 1aug2005 0:00




200

400

600

800

$/M

Wh


Installed Capacity In Merit Order

Out of Merit

Net Generation: 11783

NEISO 1aug2005 0:00



What are Out of Merit Costs? Cost of Production, Npt MW capacity:

Cpt(Qpt) =

Npti=0

cpt(i)qpt(i)

where Qpt =

Npti=0

qpt(i); qpt(i) [0, 1] i

Merit Order Cost of Production:

C pt(Qpt) =

Qpti=0

cpt(i)

Out of Merit Costs:

Cpt(Qpt) C pt(Qpt)



Losses from Out-of-Merit Generation



Trade: Fixed-Factor Hecksher-Ohlin Model



Production Cost Decomposition Total Costs:

p

Cpt (Qpt) =p

[Cpt (Qpt) C pt (Qpt)

]

Within PCAs

+ ...

+p

{C t (Lpt)

[C pt (Lpt) C pt (Qpt)

]}

Across PCAs

Gains from Trade:p

C t (Lpt) C pt (Qpt) + cpt (i = Qpt) [Qpt Lpt ] + ...

p

[cpt (i = Qpt) ct (i = Qt)] [Qpt Lpt ]



Local Gains from Imports



Local Gains from Exports



Outline



3. Data


5. Conclusion

Steve Cicala Data February 12, 2018


Data

Hourly Demand for 1999 footprints (FERC-714,Regional Markets and Authorities)

Merit Order / Supply Curve Generator Nameplate (EIA-860) Monthly Unit-Level Heat Rates using Net Generation

(EIA-767/923) Combined Cycle Heat Rates, Hydro Output (EIA-906) Market Price of Fuel (Daily Regional Gas Hub prices,

FERC/EIA-423/923) Emissions Prices



Data

Hourly Operational Supply Curve EPA CEMS Gross Load, Emissions Boiler-Generator Crosswalks (EIA-767/860) Combined Cycle Scaling for Missing Cycles (EIA-906) NRC Power Reactor Status Reports for Nuclear USGS Stream flows, USBR, TVA

Power Control Area Configuration as of 1999 (EPAseGRID, FERC Form 714)



Hourly Operations at Brayton Point0

50

100

150

200

250

MW

h

30dec2004 04:26:40 24apr2005 22:13:20 18aug2005 16:00:00 12dec2005 09:46:40Time

Nameplate Capacity: 243 MW (1963)

Brayton Point Unit 1



Supply and Demand Data0

100

200

300

400

TW

h

Jan1998 Jan2000 Jan2002 Jan2004 Jan2006 Jan2008 Jan2010 Jan2012MonthYear

Nuclear Hydro

Wind CEMS Hourly

Monthly Load



Imports and Exports20

25

30

35

40

TW

h /

Month

Jan1998 Jan2000 Jan2002 Jan2004 Jan2006 Jan2008 Jan2010 Jan2012MonthYear

Sum of Hourly PCA Imports Sum of Hourly PCA Exports



050

100

150

200

250

$/M

Wh

0 5000 10000 15000 20000 25000Cumulative Nameplate

Out of Merit Cost Gains From Trade

Load Generation

20aug2010 12:00:00

Measuring Welfare from Hourly Operations



Hourly Operations Animation



Outline



3. Data


5. Conclusion

Steve Cicala Counterfactual Estimates February 12, 2018


Estimation Strategy

Difference-in-Differences Design:

ypt = Dpt + p + tr + pt

Treatment on the Treated:

ypt(0) Dpt |Xi



Potential Confounding with DD Approach



Fuel Prices Over Time0

510

15

20

25

$/M

MB

TU

1998 2000 2002 2004 2006 2008 2010 2012Year

Gas

01

23

45

$/M

MB

TU

1998 2000 2002 2004 2006 2008 2010 2012Year

Coal



Policy Function Approach

A Policy maps states to (probabilistic) actions:

py (i ,Xipt ,Dpt) = Pr [qpt(i) = 1|Xipt ,Dpt ]

Counterfactual outcomes based on Behavior predicted by policy under control Observed production costs



Policy Function Approach: Machine Learning

Random Forest: nonparametric regression withcross-validated tuning parameters

Predict behavior in year t based on policy estimated inyear t 1

Y 0pt = F(q0pt,Xpt

)= F

(0p,y1,Xpt

)+ F

(q0pt,Xpt

) F

(0p,y,Xpt

) Contemporaneous Error

+ ...

+ F(0p,y,Xpt

) F

(0p,y1,Xpt

) Change in Policy Function

= F(0p,y1,Xpt

)+ tr + p + pt



Policy Function Approach: Machine Learning

DD on the fit of the predicted outcome:

Ypt F (0p,y1,Xpt) = Dpt + tr + p + pt

All predictions are out of sample: How well does last years operations predict this

years operations? Avoids in-sample/out-of-sample changes in fit.



Relative Perfomance of Random Forest.6

.7.8

.91

RS

S R

elat

ive

to S

imple

OL

S

0 20 40 60 80 100Percentile of Merit Order

Flexible OLS Random Forest



Intuitive View of Estimation Strategy3

.54

4.5

55

.56

Gen

erat

ion

(G

W)

01apr2004 01oct2004 01apr2005 01oct2005 01apr2006Date

Northern States Power

33

.54

4.5

Gen

erat

ion

(G

W)

01apr2004 01oct2004 01apr2005 01oct2005 01apr2006Date

WAPA ColoradoMissouri



A Sampling of Issues / Limitations

SUTVA Measurement Error

No Labor, O&M

Heterogeneous institutions Short-run (infrastructure limitations) Perfectly Inelastic Demand: No Demand-Side Loses

(Gains)

No Pre-period for Markets before 1999 (Early CAISO,PJM).



DD by Months to Market:

Log(Gains from Trade)

1

.50

.51

20 10 0 10 20

Ave. Treatment on the Treated

95% Confidence IntervalSteve Cicala Counterfactual Estimates February 12, 2018


Policy Function by Months to Market:


.50

.51

20 10 0 10 20


95% Confidence Interval



Average Effect of Market Dispatch:Log(Gains from Trade)

(1) (2) (3) (4)Market Dispatch 0.312*** 0.350*** 0.258*** 0.191***

(0.045) (0.042) (0.041) (0.048)Log(Load) Yes Yes YesPCA Trend Yes YesPolicy Function YesClusters 16424 16424 16424 15814PCAs 98 98 98 98R2 0.494 0.579 0.617 0.125Obs. 8671235 8671235 8671235 8098935

Note: All specifications include PCA and Region-Date-Hour FixedEffects. Demand controls are PCA-specific. Standard errors clusteredby PCA-Month in parentheses. * p


DD by Months to Market:

Log(Out of Merit Costs)

.50

.5

20 10 0 10 20





Policy Function by Months to Market:


.50

.51

20 10 0 10 20





Average Effect of Market Dispatch:Log(Out of Merit Costs)

(1) (2) (3) (4)Market Dispatch 0.186*** 0.164*** 0.009 0.179***




Average Effect of Market Dispatch:Log(MWh Traded)

(1) (2) (3) (4)Market Dispatch 0.125*** 0.118*** 0.095*** 0.098***




Average Effect of Market Dispatch:Log(MWh Out of Merit)

(1) (2) (3) (4)Market Dispatch 0.111*** 0.113*** 0.061*** 0.117***




Treatment Effects by Day of Year:


1

.50

.51

0 100 200 300 400

95% Confidence Interval Ave. Treatment on the Treated



Treatment Effects by Day of Year:


.8

.6

.4

.20

.2

0 100 200 300 400

95% Confidence Interval Ave. Treatment on the Treated



Summary of Causal Estimates

Short run estimates show substantial netimprovements in allocative efficiency:

Gains from trade in market areas: 20% ($1B/year) Traded Quantities: 10% Out of Merit Quantities: 10% Out of Merit Costs: 20% ($3B/year)

Increased availability has swamped withholding.



Results: Discussion

Large for an institutional change Smaller relative to longer-run changes in sector Fuel price variance reduces precision, creates

heterogeneous effects



Outline



3. Data


5. Conclusion

Steve Cicala Conclusion February 12, 2018


Conclusion

Electricity markets currently dispatch about 60% ofgenerating capacity

Short run estimates show substantial netimprovements in allocative efficiency

Steve Cicala Conclusion February 12, 2018

Background on Electricity DispatchWelfare MeasurementDataCounterfactual EstimatesConclusion

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imperfect markets versus imperfect regulation in the u.s ... steve cicala background on electricity...

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