the influence of spatial effects on wind power …...compared to average monthly spot prices from...

BTU Cottbus – Lehrstuhl für Energiewirtschaft – Prof. Dr. Felix Müsgens

The Influence of Spatial Effects

on Wind Power Revenues

under Direct Marketing Rules by

Oliver Grothe, Felix Müsgens

Presented at the

Energy and Finance Seminar

LEF Lehrstuhl für Energiehandel und Finanzdienstleistungen

01.02.2012

2

Motivation

Historically, Renewable Energy Sources (RES) were subsidized with

fixed feed-in tariffs

Unit specific feed-in depending on technology, size, location, date of

commissioning, …

subsidy in EUR/MWh of electricity produced Incentive to

maximize total energy production over the lifetime of the plant

Guaranteed for 20 years

Successfully increased both relative and absolute share of RES

However, an increased price elasticity of supply (i.e. alignment of

production to market signals) is desirable


3

RESA2012

New version of the Renewable Energy Sources Act (RESA2012)

One major change: Improved (and more attractive) Direct-Marketing

– defined in §§33a to i (and appendix 4)

– units receive the monthly choice to either sell their electricity directly on the

wholesale market or receive feed-in tariff

– as market price is below feed-in tariff, an additional subsidy is paid (‘market

premium’)

– market premium depends on the revenue an average wind turbine would

have earned on the market. This means that any unit’s revenue depends on

generation from all other units (nationwide feed-in).

– Supposed to set incentive to increase price elasticity of supply

We quantify whether, where, and when the location of a plant

increases (or decreases) wind turbine operators’ revenues.


4

The Alternative: Feed-In tariff

A wind turbines profit contribution with the feed-in tariff can be written

as:

Where:

– 𝜋fit is the profit contribution with the feed-in tariff

– EV is the unit specific feed-in tariff (‘Einspeisevergütung’)

– et is the unit’s electricity generation in hour t

– t = 1,…,T are all hours of the respective month


T

t t

fit eEV1

5

Revenues under Direct Marketing

Key idea of direct marketing: unit receives revenues on market plus

additional subsidy.

Where:

– 𝜋MP is the profit contribution with the market premium

– pt is the wholesale price (i.e. spot price at the EPEX)

– et is (again) the unit’s electric output in hour t

– MP is the market premium (in €/MWh)


t tt tt

MP eMPep

6

Revenues under Direct Marketing

Appendix 4 in RESA2012 defines the market premium (in €/MWh):

Where:

– MW is the reference market value for wind power:

with NF being the (total) nationwide wind feed-in

– PM is a premium meant to cover additional costs under direct

marketing (such as market access, balancing group

management, risk premia). PM is set at 12 €/MWh in 2012 but

decreases to 7 €/MWh in 2015.


PMMWEVMP

t t

t tt

NF

NFpMW

7

Free monthly choice between feed-in and direct

marketing

Unit should opt for direct marketing if

(the inequality is strict if investors are risk averse and )

In this inequality, two things are unit specific:

1. c, the cost for direct marketing (e.g. economies of scale, …)

2. , the unit’s revenues

– while all units have an incentive to maximize their energy output,

– for any given output, the question when this output is generated is unit

specific (and depends on the location) – and influenced by the other

wind turbines.


cMPfit EE

MPfit varvar

cePM

NF

NFpEVepEEEeEVE t t

t t

t tt

t tt

MPfit

t t

t ttep

8

Market Value of Wind vs. Base Price


Monthly Average Revenue For Wind Turbines’ Nationwide Feed-In

(Market value ) in comparison to Base Price [€/MWh]

Compared to average monthly spot prices from 2010-07 to 2011-06.

The wind profile’s average price lies below the flat average price.

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Wind

Profile 44.26 44.73 53.26 51.20 54.00 46.89 43.23 39.79 44.58 47.70 44.89 49.37

Base

Price 50.13 50.86 54.46 51.57 56.85 52.29 45.81 39.80 45.88 50.30 48.53 55.55

t t

t tt

NF

NFpMW

9

Empirical Approach

Wind speed data from 37 German weather stations (DWD) in hourly

resolution from Jan 1st, 2001

– scale wind speed to hub height of modern wind turbines (80m)

– use scaled wind speeds to calculate the electricity a reference unit (GE

1.5 MW plant) would have generated at this spot

Wholesale electricity prices in hourly resolution from Jan 1st, 2001

Nationwide wind feed-in in hourly resolution

– available from Oct 29th, 2009

– used from Jul 1st, 2010 for 12 months (i.e. until Jun 30th, 2011)


10

Historical analysis July 2010 to June 2011

We compute for each location:

Answering the question how much more or less (in € per MWh of

output) a turbine at that specific location would have earned due to

the difference between the location‘s feed-in profile and the average

nationwide profile.


t t

t tt

t t

t tt

NF

NFp

e

ep

11

Results for the twelve months July 2010 to June 2011


Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecAachen 3,35 -4,80 -1,04 2,13 0,84 -1,45 8,24 1,06 -0,49 8,24 1,06 -0,49Augsburg 5,45 -5,22 3,23 2,80 3,53 1,67 3,25 5,21 5,83 3,25 5,21 5,83Bamberg 5,13 -5,38 2,24 1,84 0,66 3,58 4,41 7,04 5,23 4,41 7,04 5,23Berlin-Tempelhof -2,10 0,50 0,15 -0,25 1,38 4,89 -1,73 4,36 4,27 -1,73 4,36 4,27Bremen -0,33 0,07 -0,07 1,48 1,59 2,27 5,47 2,54 1,59 5,47 2,54 1,59Dresden-Klotzsche -0,90 -0,93 -0,45 -0,23 1,58 2,55 0,73 2,28 4,73 0,73 2,28 4,73Duesseldorf 5,25 -0,62 0,66 0,25 0,04 2,01 6,00 2,77 1,14 6,00 2,77 1,14Emden 2,29 2,28 0,91 1,32 2,08 1,79 5,88 3,52 1,08 5,88 3,52 1,08Erfurt-Weimar 0,69 -2,92 0,49 1,54 1,45 1,40 1,92 3,33 2,46 1,92 3,33 2,46Fichtelberg 4,33 5,53 1,17 0,08 0,74 2,26 -1,55 -0,44 0,95 -1,55 -0,44 0,95Frankfurt/Main 4,85 -6,20 1,27 1,14 1,99 2,36 5,51 2,47 2,39 5,51 2,47 2,39Goerlitz -1,58 -1,38 -0,87 -0,58 2,15 4,19 -0,07 2,71 4,07 -0,07 2,71 4,07Greifswald -5,07 3,04 0,90 0,20 -0,09 9,45 0,68 4,99 4,66 0,68 4,99 4,66Hamburg-Fuhlsbuettel -1,02 2,21 0,63 1,41 2,31 1,76 4,13 1,36 0,80 4,13 1,36 0,80Hannover 1,75 -1,57 -0,03 1,04 1,68 3,32 4,76 3,19 2,14 4,76 3,19 2,14Helgoland 3,46 7,12 0,93 0,39 0,49 1,77 1,43 0,20 1,00 1,43 0,20 1,00Hof 2,21 -7,06 1,16 1,52 3,04 2,15 2,20 5,10 2,79 2,20 5,10 2,79Hohenpeissenberg 2,47 -6,66 1,86 0,18 1,67 -3,86 1,50 3,73 -2,03 1,50 3,73 -2,03Kahler Asten 2,02 1,75 0,88 -0,22 0,42 1,74 1,38 0,18 -0,74 1,38 0,18 -0,74Kempten 0,94 -8,85 2,28 2,43 4,30 1,53 4,26 4,94 4,73 4,26 4,94 4,73Konstanz 5,81 -17,75 3,07 1,02 5,41 -2,63 3,04 5,06 3,63 3,04 5,06 3,63Leipzig/Halle -1,25 -0,29 -0,21 1,25 2,30 1,48 1,48 1,20 2,45 1,48 1,20 2,45Lindenberg -4,45 -3,30 -0,90 0,77 3,27 -0,86 -3,33 1,20 1,95 -3,33 1,20 1,95Magdeburg -7,16 -9,44 -1,11 0,82 3,98 -0,90 2,83 4,27 2,54 2,83 4,27 2,54Meiningen 6,47 -8,54 1,25 -0,44 1,99 4,21 2,39 3,54 2,02 2,39 3,54 2,02Neuruppin -2,16 1,02 -1,32 -0,74 1,09 3,80 3,42 4,34 4,23 3,42 4,34 4,23Nuernberg 5,09 -2,17 3,23 3,00 4,43 1,22 4,12 5,92 5,05 4,12 5,92 5,05Potsdam -5,53 -2,94 -0,72 0,40 0,95 -1,16 -3,72 0,61 0,92 -3,72 0,61 0,92Rostock-Warnemuende -2,82 1,45 0,15 0,65 1,84 4,19 -2,95 3,08 3,79 -2,95 3,08 3,79Saarbruecken-Ensheim 4,62 -5,89 0,97 -0,46 2,63 2,47 8,02 3,05 1,38 8,02 3,05 1,38Schleswig -5,05 1,35 0,31 1,16 3,74 6,77 3,78 3,36 2,24 3,78 3,36 2,24Schwerin -6,23 -1,68 -0,32 1,52 2,79 1,31 1,82 0,96 0,86 1,82 0,96 0,86Straubing 5,26 0,82 1,56 0,89 6,32 2,06 1,75 5,72 6,07 1,75 5,72 6,07Stuttgart-Echterdingen 7,80 1,14 3,02 2,92 3,32 2,17 5,17 5,31 5,16 5,17 5,31 5,16Westermarkelsdorf 1,96 5,82 0,78 0,30 1,36 4,39 -0,41 1,14 0,79 -0,41 1,14 0,79Wuerzburg 5,26 -1,84 2,55 1,43 3,88 1,81 6,63 3,87 1,81 6,63 3,87 1,81Zugspitze 4,44 4,73 1,17 1,36 1,63 2,47 0,57 -0,11 -0,11 0,57 -0,11 -0,11

12

Extension of Observation Period

Based on this limited data set, results may be reliable – or might

have happened by chance. Questions remains: are results robust,

i.e. significant

First idea (easy but impracticable): take ten years of data

– Problems:

• nationwide feed-in data in hourly resolution not publicly available

• key interest: making recommendations for the future, hence data from ten

years back would not be representative

– far less installed wind capacity

– other fundamentals (merit order etc.) have changed as well


13

Extension of Observation Period

Instead, we:

– Use the DWD wind speed data for 37 locations to estimate how

nationwide wind feed-in would have been given todays installed

capacities

• NFt = β0 + β1et(1) + · · · + β37et

(37) + εt

– Decided against adjusting prices (would add other modeling errors)

– Interpret results as lower bounds (in absolute terms):

• the reaction of prices to nationwide wind feed-in is underestimated when not

adjusting the data

• hence, both benefit of negative and loss of positive dependencies between

specific location and nationwide feed-in should be underestimated.


14

Actual and Model Estimated Production for July 2010


15

Historical analysis

based on data from January 2001 to June 2011


Station Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Aachen -1,02 -1,22 -1,61 1,13 1,04 3,78 4,17*** 3,28 0,84 -2,37*** -1,51 -3,55

Augsburg -0,52 0,30 0,67 2,35* 2,73*** 4,60*** 5,41 3,99*** 3,40 3,87*** 1,66 -1,35

Bamberg 0,66 -0,49 0,43 2,29** 2,69*** 4,24*** 6,04*** 5,71*** 5,58*** 4,34*** -0,68 -1,32

Berlin-Tempelhof -1,22 0,23 0,51 1,85 1,82 2,82*** 2,77 3,36*** 3,02 1,73* 0,04 -2,38

Bremen -0,81 -0,31 0,27 1,03 2,22* 3,12 4,04*** 3,40*** 2,46** 0,51 -0,49 -2,62

Dresden-Klotzsche -0,56 -0,19 0,32 1,10 1,54 2,24 1,52 2,27 2,05 0,89 -0,80 -2,03

Duesseldorf -0,10 -0,66 0,57 1,49 1,97 3,24 4,70* 3,38 2,98 1,41 0,57 -1,40

Emden -0,19 0,36 0,51 1,39 2,52** 3,31*** 4,70 3,02** 2,81 0,48 -0,59 -1,41

Erfurt-Weimar -1,26 -0,68 -0,14 1,00 1,58 1,96 4,39* 2,22*** 2,11 -0,21 -1,01 -2,00

Fichtelberg 1,98** 0,94 1,26 -0,23 -0,96 -1,60 -2,08** -0,99 -0,34 0,92 1,47 4,17***

Frankfurt/Main 0,23 0,09 0,84 2,15 2,49*** 3,48 4,92 3,61** 3,10** 3,69*** 2,18 -1,63

Goerlitz 0,20 -0,26 0,41 2,18 3,12 4,11* 2,95 4,66** 4,16 1,10 -0,29 -1,37

Greifswald -0,84∗ ∗ ∗ 1,02 0,34 2,06 2,80*** 5,23 7,85*** 3,86* 3,42*** 2,99*** 0,24*** -3,62

Hamburg-Fuhlsbuettel 0,17 -0,65*** 0,50 1,63*** 2,78 3,61*** 5,02 3,29*** 3,04 0,55 -0,21 -2,87***

Hannover -1,07*** -0,37 0,26 1,47** 2,68*** 3,90 4,03*** 3,86*** 2,91*** 0,39 -1,74*** -2,12

Helgoland 1,73 1,26*** 1,17 -0,35*** -0,41 -0,61*** -1,41 -0,04*** 0,30 1,76* 1,81 2,41***

Hof -1,07*** -0,69 -0,64 1,84 2,63*** 3,13 4,33*** 4,34*** 3,06*** 1,22 -1,47*** -1,54

Hohenpeissenberg -0,08 -0,42*** -0,49 -.98∗ ∗ ∗ -1,35 -1.52∗ ∗ ∗ -2,71 -1.55∗ ∗ ∗ 1,71 -0,38 0,09 -0,55***

Kahler Asten 0,33** -0,19 -0,02 -0,52 -0,67*** -1,58 -0,91*** -1,13 -0,87*** -0,40 -0,29*** 0,44

Kempten -2,69 -0,11*** 0,05 1,68*** 4,05 6,76*** 4,79 3,72*** 4,16 4,96*** -0,76 -1,71***

Konstanz 1,13*** 0,26 -0,08 1,68 3,30*** 5,66* 4,51*** 1,85 2,09*** 3,21*** 3,54*** -0,50

Leipzig/Halle -0,77 -0,06*** 0,28 1,26*** 1,66 2,80*** 1,57 2,32*** 2,52 0,19 -0,60 -2,00***

Lindenberg -1.64∗ ∗ ∗ -1,25 -0,89 1,35 1,25*** 2,16 2,42*** 2,46 1,36*** -1,22 -1,08*** -4,47

Magdeburg -2,80 -1,70*** -1,70* 2,27*** 3,57 3,65*** 2,60 4,68*** 3,49 -0,26 -3,42 -6,63***

Meiningen -1,86*** -0,67 0,19 1,62 2,52*** 3,40 4,13*** 3,59*** 2,59*** 1,01 -0,06*** -1,55

Neuruppin -1,67 -0,69*** -0,25 1,74*** 3,77 6,05*** 5,37 5,19*** 4,47 1,92* 0,44 -3,50***

Nuernberg 0,07*** 0,31 0,53 1,96 2,27*** 4,49* 5,60*** 4,12*** 4,74*** 3,38*** -0,30*** -0,37

Potsdam -2,35 -1,49*** -1.75∗ -0,34*** -0,31 -,52*** -1,51 0,01*** -1,13 -2,11** -2,08 -4,20***

Rostock-Warnemuende 0,79*** 0,56 1,41 1,92 ,86** 0,51 1,09*** 0,37 1,29*** 0,61 0,44*** -0,15

Saarbruecken-Ensheim 1,08 -0,10*** 0,20 2,32*** 2,27** 4,85*** 7,74 3,93*** 2,16 1,65* -0,51 -0,04***

Schleswig -0,42*** -0,25 -0,60 1,51 2,54*** 3,60 5,28*** 3,65*** 2,85*** -0,40 -0,09*** -2,40

Schwerin -1,20 -1,01*** -0,81 1,04*** 1,56 2,55*** 2,85 2,32*** 1,96 -1,02 -1,36 -4,83***

Straubing 0,18*** -0,07 0,96 2,09 1,64*** 5,42* 6,84*** 4,88*** 3,54*** 3,48*** 0,67*** -1,22

Stuttgart-Echterdingen 0,89 0,76*** 0,84 2,50*** 3,07 4,15*** 6,48 3,78*** 2,97 4,43*** 2,54 0,46***

Westermarkelsdorf 0,88*** 0,72 1,00 0,80 -0,09*** 0,07 1,33*** -0,08 0,40*** 1,19 1,32*** 1,14

Wuerzburg -0,95 -0,49*** 0,01 2,44*** 2,38** 4,99*** 5,09 4,22*** 2,99 3,61*** 0,45 -1,87***

Zugspitze 1,60*** 1,30 1,41 0,67 -0,34*** -1,15 -1,44*** -1,58 0,01*** 1,45 2,06*** 3,40

16 BTU Cottbus – Lehrstuhl für Energiewirtschaft – Prof. Dr. Felix Müsgens

Station Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Aachen -1,02 -1,22 -1,61 1,13 1,04 3,78 4,17*** 3,28 0,84 -2,37*** -1,51 -3,55

Augsburg -0,52 0,30 0,67 2,35* 2,73*** 4,60*** 5,41 3,99*** 3,40 3,87*** 1,66 -1,35

Bamberg 0,66 -0,49 0,43 2,29** 2,69*** 4,24*** 6,04*** 5,71*** 5,58*** 4,34*** -0,68 -1,32

Berlin-Tempelhof -1,22 0,23 0,51 1,85 1,82 2,82*** 2,77 3,36*** 3,02 1,73* 0,04 -2,38

Bremen -0,81 -0,31 0,27 1,03 2,22* 3,12 4,04*** 3,40*** 2,46** 0,51 -0,49 -2,62

Dresden-Klotzsche -0,56 -0,19 0,32 1,10 1,54 2,24 1,52 2,27 2,05 0,89 -0,80 -2,03

Duesseldorf -0,10 -0,66 0,57 1,49 1,97 3,24 4,70* 3,38 2,98 1,41 0,57 -1,40

Emden -0,19 0,36 0,51 1,39 2,52** 3,31*** 4,70 3,02** 2,81 0,48 -0,59 -1,41

Erfurt-Weimar -1,26 -0,68 -0,14 1,00 1,58 1,96 4,39* 2,22*** 2,11 -0,21 -1,01 -2,00

Fichtelberg 1,98** 0,94 1,26 -0,23 -0,96 -1,60 -2,08** -0,99 -0,34 0,92 1,47 4,17***

Frankfurt/Main 0,23 0,09 0,84 2,15 2,49*** 3,48 4,92 3,61** 3,10** 3,69*** 2,18 -1,63

Goerlitz 0,20 -0,26 0,41 2,18 3,12 4,11* 2,95 4,66** 4,16 1,10 -0,29 -1,37

Greifswald -0,84∗ ∗ ∗ 1,02 0,34 2,06 2,80*** 5,23 7,85*** 3,86* 3,42*** 2,99*** 0,24*** -3,62

Hamburg-

Fuhlsbuettel 0,17 -0,65*** 0,50 1,63*** 2,78 3,61*** 5,02 3,29*** 3,04 0,55 -0,21 -2,87***

Hannover -1,07*** -0,37 0,26 1,47** 2,68*** 3,90 4,03*** 3,86*** 2,91*** 0,39 -1,74*** -2,12

Helgoland 1,73 1,26*** 1,17 -0,35*** -0,41 -0,61*** -1,41 -0,04*** 0,30 1,76* 1,81 2,41***

Hof -1,07*** -0,69 -0,64 1,84 2,63*** 3,13 4,33*** 4,34*** 3,06*** 1,22 -1,47*** -1,54

Hohenpeissenberg -0,08 -0,42*** -0,49 -.98∗ ∗ ∗ -1,35 -1.52∗ ∗ ∗ -2,71 -1.55∗ ∗ ∗ 1,71 -0,38 0,09 -0,55***

Kahler Asten 0,33** -0,19 -0,02 -0,52 -0,67*** -1,58 -0,91*** -1,13 -0,87*** -0,40 -0,29*** 0,44

Kempten -2,69 -0,11*** 0,05 1,68*** 4,05 6,76*** 4,79 3,72*** 4,16 4,96*** -0,76 -1,71***

Konstanz 1,13*** 0,26 -0,08 1,68 3,30*** 5,66* 4,51*** 1,85 2,09*** 3,21*** 3,54*** -0,50

Leipzig/Halle -0,77 -0,06*** 0,28 1,26*** 1,66 2,80*** 1,57 2,32*** 2,52 0,19 -0,60 -2,00***

Lindenberg -1.64∗ ∗ ∗ -1,25 -0,89 1,35 1,25*** 2,16 2,42*** 2,46 1,36*** -1,22 -1,08*** -4,47

Magdeburg -2,80 -1,70*** -1,70* 2,27*** 3,57 3,65*** 2,60 4,68*** 3,49 -0,26 -3,42 -6,63***

Meiningen -1,86*** -0,67 0,19 1,62 2,52*** 3,40 4,13*** 3,59*** 2,59*** 1,01 -0,06*** -1,55

Neuruppin -1,67 -0,69*** -0,25 1,74*** 3,77 6,05*** 5,37 5,19*** 4,47 1,92* 0,44 -3,50***

Nuernberg 0,07*** 0,31 0,53 1,96 2,27*** 4,49* 5,60*** 4,12*** 4,74*** 3,38*** -0,30*** -0,37

Potsdam -2,35 -1,49*** -1.75∗ -0,34*** -0,31 -,52*** -1,51 0,01*** -1,13 -2,11** -2,08 -4,20***

Rostock-

Warnemuende 0,79*** 0,56 1,41 1,92 ,86** 0,51 1,09*** 0,37 1,29*** 0,61 0,44*** -0,15

Saarbruecken-

Ensheim 1,08 -0,10*** 0,20 2,32*** 2,27** 4,85*** 7,74 3,93*** 2,16 1,65* -0,51 -0,04***

Schleswig -0,42*** -0,25 -0,60 1,51 2,54*** 3,60 5,28*** 3,65*** 2,85*** -0,40 -0,09*** -2,40

Schwerin -1,20 -1,01*** -0,81 1,04*** 1,56 2,55*** 2,85 2,32*** 1,96 -1,02 -1,36 -4,83***

Straubing 0,18*** -0,07 0,96 2,09 1,64*** 5,42* 6,84*** 4,88*** 3,54*** 3,48*** 0,67*** -1,22

Stuttgart-

Echterdingen 0,89 0,76*** 0,84 2,50*** 3,07 4,15*** 6,48 3,78*** 2,97 4,43*** 2,54 0,46***

Westermarkelsdorf 0,88*** 0,72 1,00 0,80 -0,09*** 0,07 1,33*** -0,08 0,40*** 1,19 1,32*** 1,14

Wuerzburg -0,95 -0,49*** 0,01 2,44*** 2,38** 4,99*** 5,09 4,22*** 2,99 3,61*** 0,45 -1,87***

Zugspitze 1,60*** 1,30 1,41 0,67 -0,34*** -1,15 -1,44*** -1,58 0,01*** 1,45 2,06*** 3,40

17

Participation in Direct Marketing


28,210 MW of wind power installed in Germany

(source: Fraunhofer IWES 2011)

More than 50% of wind turbines opted for direct marketing for February 2012

Source: www.eeg-kwk.net

18

Conclusion


Location of wind turbine may effect revenues in the range of +/- 4

€/MWh up or down – as there are no variable costs associated with

this effect, it translates directly into profits

Question of high empirical relevance as more than 50% of installed

wind capacity used direct marketing in the beginning of 2012

Methodology could easily be applied to any specific wind plant

19

Wind Speed to Energy - Data Preparation

Scaling of historical wind speeds to 80m hub height

vh1 = v h0

log ℎ1

− log(𝑧

0)

log ℎ0

−log(𝑧0)

Transformation of wind speed to energy using data of GE 1.5 MW

benchmark turbine.


0

200

400

600

800

1000

1200

1400

1600

0 2 6 5 10 11 12

Po

wer

Ou

tpu

t [k

W]

Wind speed [m/s]

the influence of spatial effects on wind power …...compared to average monthly spot prices from...

Documents