and What to Do about Them A Study of the German Power Market Maria Woodman Student Economics Department New York University Research Motivation The German Power Market and Negative Wholesale Power Prices ID: 567142
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Slide1
Negative Wholesale Power Prices: Why They Occur and What to Do about Them
A Study of the German Power Market
Maria Woodman, Student
Economics Department, New York UniversitySlide2
Research Motivation:The German Power Market
and Negative Wholesale Power Prices
Market Anomaly
Electricity doesn’t obey traditional commodity price behaviors
Negative wholesale prices can result
Three Causes
Increases in wind
infeed
when demand is low
Government
incentives to favor wind producers
Flat rate prices cause customers to consume electricity regardless of the value of each MWSlide3
Wind BehaviorSlide4
[
ALZ1]
[ALZ1]
FIX THESE!!!!
Power Prices and Wind GenerationSlide5
Power Generation Supply (without Wind)
Hydro
Nuclear
Lignite Coal
Bituminous
Coal
Gas Turbine
Wholesale clearing price
MW of Capacity
Marginal Cost
Demand
Supply
Merit Order Curve
Combined Cycle Gas TurbineSlide6
Nuclear
Lignite Coal
Bituminous
Coal
Wholesale clearing price
MW of Capacity
Marginal Cost
Demand
Supply – with Wind
Shifted Merit Order Curve
Combined Cycle Gas Turbine
WIND
0
Power Generation Supply (with Wind)
Supply – No WindSlide7
Increased Wind Infeed
An influx of wind power shifts that merit order curve rightward, which drives prices downSlide8
Flat Rate Retail Prices
Retail prices don’t represent a consumers true willingness to pay
In the case of negative wholesale prices, they grossly overpay in the retail market.
D
Retail Market
S
L
P
Wholesale Market
0
P
L
S
D
DSlide9
Is Dynamic Pricing the Answer?
What is dynamic (“time-of-day”) pricing?
Allows retail prices to match wholesale prices in real time
Stimulates a demand side price response
How can it impact negative prices
?
The solution isn’t simple
An estimated retail demand curve isn’t defined when prices are negativeSlide10
Existing Studies of Dynamic Pricing
Studies evaluating
the results of implemented programs have returned varied ranges of
end-user
price response
Long Run Elasticity – 0.3-0.5 (
Borenstein
, 2005)
Short Run
Industrial End-User Elasticity – 0.01-0.27 (
Boisvert, 2007; Neenan, 2004; Braithwait and Sheasy, 2002; Patrick and Wolak, 1997)
Using these ranges, the studies focused on the use of dynamic pricing to reduce peak price and loadSlide11
Method for Analyzing RTP
Construct
wholesale supply
and demand curves for a set of hours representing different combinations of demand and wind infeed
Construct demand curves representing different levels of price response using differing price
elasticities
Induce an increase in wind power by shifting the supply curve
Solve for the new equilibrium points given the new supply and demand curvesSlide12
Allocation of Hours
It was found that a necessary condition for negative prices appeared to be either high wind in-feed ( >12 GW) coupled with moderate system demand (40-50 GW) or low system demand ( <40 GW) coupled with moderate wind in-feed (5-10 GW) (Genoese, 2010).
Using these
metrics,
I was able to disaggregate the hours into their respective buckets for
analysis
High Wind Infeed
Low Wind Infeed
High
Demand
Jan 12 - 7 PM - Weekday
Oct 7 - 8 PM - Weekday
Wind MW: 12594.75
Wind MW: 2361.5
Total Demand: 72826
Total Demand: 65847
Low
Demand
Mar 8 – 9 AM - Weekend
May 17 – 5 AM - Weekend
Wind MW: 9914.25
Wind MW: 2276
Total Demand 43358
Total Demand: 33394
Hours of focusSlide13
Constructing the ModelSlide14
Preliminary ResultsFor the hour type
of low demand and high wind infeed,
on average, a
price elasticity of at least
-
0.14
was needed to have a market clearing price of €
0.00
For the case of increased wind generation
Following
the same logic, a price elasticity of approximately -0.44 was necessary to raise the price to equal the existing retail flat rate price. The elasticity value is unrealistic given previous estimates of consumer price responseSlide15
Occurrence of Negative Prices
Weekday
Weekend
Grand Total
Early Morning
14.08%
36.62%
50.70%
Mid Day
2.82%
1.41%
4.23%
Night
21.13%
23.94%
45.07%
Grand Total
38.03%
61.97%
100.00%
Negative Prices: Hours of Occurrence
Of the 1% of hours that were affected in 2009
The vast majority fell during early morning and weekend hoursSlide16
Conclusions
RTP
may not have a significant effect and in some cases might even be a hindrance to the market.
Othe
r demand side management techniques may be more effective in mitigating the market inefficiency of negative prices
Additional R&D in electric vehicles, smart grid
technology
and implementation, and smart appliances could aid in making demand side management viableSlide17
Thank You!
Questions?
Maria Woodman, New York University
Email: mjw399@nyu.edu