The Cannibalization Effect of Wind and Solar inthe California Wholesale Electricity Market

Javier López Prol*ˆ, Karl W. Steiningerˆ, David Zilberman’

*DK Climate ChangeˆWegener Center for Climate and Global Change, University of Graz, Austria’Agricultural and Resource Economics, University of California Berkeley, USA

September 2017

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Outline

I The rise of variable renewablesI The merit order and the cannibalization effectI Absolute and relative cannibalization effectI ConclusionsI Further research: demand and supply side cannibalization effect

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The rise of variable renewables in California

0

5

10

15

2013 2014 2015 2016 2017date

Pen

etra

tion

(%)

sourcesolar_sh_fctwind_sh_fct

Figure 1: Smoothed daily solar and wind penetration3 / 22

The merit-order effectI Renewable energies pressure down electricity prices

Figure 2: The merit order effect. Source: CLEW 2016 4 / 22

The cannibalization effectThe higher solar/wind electricity penetration, the lower its value

I Absolute cannibalization: (solar) daily unit revenues (psd):

psd =

∑24t=1 ptqs

t∑24t=1 qs

t

I Relative cannibalization: value factor (VF):unit revenue (ps

d) divided by daily avg. wholesale price (pd)

VF sd = ps

dpd

=

∑24t=1 ptqs

t∑24t=1 qs

t∑24t=1 pt24

p = price; q = quantity ;s : solar ; t : time(hour); d : day

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Unit revenue and value factor visualized

VFd > 1 ⇔ psd > pd

VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35VF = 1.35

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Normalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generationNormalized solar generation

0

30

60

90

00 02 04 06 08 10 12 14 16 18 20 22 00hour

$/M

Wh

2013−06−30

VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5VF = 0.5

0

10

20

30

40

50

00 02 04 06 08 10 12 14 16 18 20 22 00hour

$/M

Wh

2016−03−29

Figure 3: Calculation of daily value factors from hourly data 6 / 22

Modeling the cannibalization effect

I Absolute:

psd = α+ β1qs

d + β2qwd + β3pg

d + β4dd + γ′Dd + εd

I Relative:

VF sd = α+ β1(qs

d/dd) + β2(qwd /dd) + β3pg

d + γ′Dd + εd

q = quantity ; d = demand ; p = price; D = vector of time dummies

s : solar ; w : wind ; g : gas

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Absolute cannibalization: Unit Revenuessolar wind

2013 2014 2015 2016 2017 2013 2014 2015 2016 2017

0

20

40

60

date

Uni

t rev

enue

s ($

/MW

h)

sourcesolarwind

Figure 4: Daily solar and wind unit revenues8 / 22

Electricity prices

0

20

40

60

2013 2014 2015 2016 2017date

Ele

ctric

ity p

rice

($/M

Wh)

Figure 5: Daily electricity prices9 / 22

Relative cannibalization: Value Factors

−0.5

0.0

0.5

1.0

1.5

2013 2014 2015 2016 2017factor(year)

VF

sourcesolarwind

Figure 6: Wind and solar Value Factors descriptive statistics over time10 / 22

Relative cannibalization: Value Factorssolar wind

0 5 10 15 0 5 10 15

0.0

0.5

1.0

1.5

penetration

VF

sourcesolarwind

Figure 7: Solar and wind cannibalization effect11 / 22

Implications for PV competitivenessValue-adjusted PV LCOE = LCOE/VF

Figure 8: PV cannibalization and competitiveness

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Conclusions

I Increasing generation tends to lower the unit revenues of bothsolar and wind

I Increasing penetration rapidly undermines the value of solarI Increasing penetration increases the variability of the solar

value factorI The value factor of wind, however, seems to be insensitive to

its penetrationI The cannibalization effect could jeopardize PV competitiveness.

Mitigation?I Market structureI Storage optionsI Interconnections

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Further research: supply vs. demand-side cannibalization

How to disentangle demand-side (distributed self-consumption PV)from supply-side (centralized utility-scale) cannibalization?

I Estimate demand-side generation from installed capacityI And then re-estimate the model as

VF sd = α+β1[(qss

d + qsdd )/(dd + qsd

d )]+β2[qw

d /(dd + qsdd )] + β3pg

d + γ′Dd + εd

qsdd : daily quantity solar demand-side (distributed)

qssd : daily quantity solar supply-side (centralized)

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Figure 9: that’s me

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US electricity system

Figure 10: US interconnections16 / 22

US electricity system

Figure 11: US electricity markets. Source: FERC 17 / 22

Appendix Solar power in California

Figure 12: PV penetration. Source: IEA

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Appendix Solar power in California

Figure 13: Installed capacity. Source: SEIA and EIA19 / 22

Appendix: California electricity mix

Figure 14: California electriciy mix, 2011 - 2016. Source: IEA

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Appendix: Actual vs. forecasted demand

20000

30000

40000

20000 30000 40000demand_act

dem

and_

fct

Figure 15: Demand forecast error21 / 22

Appendix: Actual vs. forecasted generation

0

2500

5000

7500

10000

0 2500 5000 7500 10000vre_act

vre_

fct

Figure 16: Generation forecast error22 / 22