Electricity Markets Working Papers WP-EM-08b Nodal Pricing in the German Electricity Sector – A Welfare Economics Analysis, with Particular Reference to Implementing Offshore Wind Capacities Florian Leuthold, Ina Rumiantseva, Hannes Weigt, Till Jeske, and Christian von Hirschhausen Reprint from Presentation at the ETE Modelling Workshop at K.U. Leuven (September 2005), the 3rd INFRATRAIN seminar in Berlin (October 2005), and the GRJM Workshop at DIW Berlin (October 2005) Dresden University of Technology Chair for Energy Economics and Public Sector Management
Transcript
Electricity Markets Working Papers
WP-EM-08b
Nodal Pricing in the German Electricity Sector –
A Welfare Economics Analysis, with Particular
Reference to Implementing Offshore Wind
Capacities
Florian Leuthold, Ina Rumiantseva, Hannes Weigt, Till Jeske, and Christian von Hirschhausen
Reprint from
Presentation at the ETE Modelling Workshop at K.U. Leuven (September 2005), the 3rd INFRATRAIN seminar in Berlin (October
2005), and the GRJM Workshop at DIW Berlin (October 2005)
Dresden University of Technology Chair for Energy Economics and Public Sector Management
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Nodal Pricing in the German Electricity Sector –A Welfare Economics Analysis, with Particular Reference to Implementing
Offshore Wind Capacities
Florian Leuthold, Ina Rumiantseva, Hannes Weigt,
Till Jeske, and Christian von Hirschhausen
Dresden University of TechnologyDREWAG - Chair of Energy Economics and Public Sector Management
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Agenda
1. Introduction and Background
2. Model and Data
3. Scenarios
4. Results
5. Conclusions
References
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Introduction
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• DENA study: high additional costs from integrating offshore wind energy in Germany mainly due to congestions
• Problem: uniform pricing congestions not properly determined?
NODAL PRICES
= locational value of energy:
node specific costs from energy generation and transmission (e.g. losses and congestion)
• Node: physical location on the transmission grid (incl. generators and loads)
• Calculation: market clearing prices for all nodes subject to physical and security constraints
reflect real conditions and costs in the grid for every nodeindicate and price congestions when overstepping transmission limits
Background
The purpose of this paper is to analyze the effect, for Germany, of offshore wind power in the North Sea under nodal pricing
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Studies & Nodal Pricing Applied
Studies:
• Social welfare rises: +1,5% compared to uniform pricing in England/Wales (Green, 2004)
• Analysis of congestion situation in Austria (Todem et al, 2005)
• Nodal Pricing provides market conform incentives to generators (Ding & Fuller, 2005)
• Stable, predictable and moderate prices with only a few spatial and time-related exceptions (California ISO, 2005)
Practice:
• New Zealand (1997)
• U.S. Markets (e.g. PJM 1998, New England 2003, CA from 2007)
• UK (2005 with introduction of British Electricity Trading and Transmission Arrangements/ BETTA)
• EU support
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Agenda
1. Introduction / Previous studies
2. Model and Data
3. Scenarios
4. Results
5. Conclusions
References
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DC Load Flow Model (DCLF)
• Advantages of the DCLF (Overbye et al, 2004) compared to an AC model:- problem becomes smaller (about half the size)- network topology does not depend on the power flowing and has to be factored once only
DCLF is adequate for modeling LMPs
• The DC load flow model
22ii
ii XR
RG+
=22ii
ii XR
XB+
=
B – susceptance, G – conductance, P – real power flow , R - line resistance,V – voltage magnitude, X – line reactance, δ – voltage angle
)sin()cos(2
kjkjikjkjijijk ·VVB·VV- GVGP δδδδ −+−=
= jk i iP BΘ ⋅Simplification yields:
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Optimization Problem
price
supply, demand(quantity of power)
pnref
pn
dnref
merit order (supply function)
cn(d)
marginal costs of production
social welfare
inverse demand function pn(d)
dn*
consumer surplus
producer surplus
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Optimization Problem
∑ ∫∫ ⎟⎟
⎠
⎞
⎜⎜
⎝
⎛⋅−⋅=
n
d
nnn
d
nnnn
dddcdddpdW**
0
***
0
*** )()()(max
maxii PP ≤
∑∑ +=n
nn
n Ldg
∑∑ ≤tn
tn
tn
tn gg
,
max,
,
s. t. line flow constraint
energy balance constraint
generation constraint (per type of plant)
⎟⎟⎠
⎞⎜⎜⎝
⎛−⋅⋅+= 11 *
refn
nrefn
refnn d
dppp
ε
• Objective function: Social welfare
• Inverse demand function for each node
• Assumption: Competition
• Optimization software: GAMS
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Data
• The German high-voltage grid (220 and 380 kV)• 291 regular and 19 auxiliary nodes
• 425 lines
• Supply function• Power plant mix to each node
• Merit order function according to the marginal cost for each node
• Linear demand function• Reference demand to each node according to the GDP weighted per capita
consumption for this node
• Reference price: 200-day-line of EEX price
• Demand elasticity of -0.25 at the reference point
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Data: Marginal Cost of Production
4.05Wind40.00Natural Gas
0.00Running water15.00Brown coal
13.33Pump water18.00Coal
50.00Fuel oil10.00Nuclear Power
Marginal costs [€/MWh]
FuelMarginal costs [€/MWh]
Fuel
Source: DENA (2005) and Schröter (2004).
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Agenda
1. Introduction / Previous studies
2. Model and Data
3. Scenarios
4. Results
5. Conclusions
References
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Scenario Overview
grid extension13 GWnodalaverage low high
(4) Nodal prices plus 13 GW
existing lines8 GWnodalaverage low high
(3) Nodal prices plus 8 GW
existing lines0 GWnodalaverage low high
(2) Nodal prices without offshore wind
existing lines0 GWfixaverage low high
(1) Status quo
Grid capacityCapacity of offshore wind
Pricing model
Demand casesScenario
All results refer to hourly values
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Agenda
1. Introduction / Previous studies
2. Model and Data
3. Scenarios
4. Results
5. Conclusions
References
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Results (1): Uniform vs. Nodal Pricing
+1.3%44.43.2339.43.19Low
+0.6%79.25.6773.15.67High
+0.9%62.04.4856.34.44Average
Welfarechange
Demand[GWh]
Welfare[Mio. Eur]
Demand[GWh]
Welfare[Mio. Eur]
Nodal pricingCost min. underuniform pricing
Demand
case
Welfare and demand increase under nodal pricing scheme
Annual increase in welfare through nodal pricing ~€350 Mio. (average scenario)
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Results (1): Uniform vs. Nodal Pricing
Average demand: price level under nodal pricing about 60% of the reference price
Results (2): Nodal Prices plus 8 GW OffshoreNodal price difference (“without offshore” minus “plus 8 GW offshore”)
Results:- Additional welfare gain of 1%- Average nodal price drops about 10% to 15.4 €/MWh- Nodes in Northern Germany profit. Southern part of Germany is nearly unaffected by
offshore wind generation.
0
2
4
6
8
10
12
14
16
18
1 15 29 43 57 71 86 100
114
128
142
156
170
184
198
212
226
240
254
268
283
298
node
€/M
Wh
North South North
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Results (3): Nodal Prices plus 13 GW Offshore and Grid ExtensionNodal price difference (“plus 8 GW offshore” minus “plus 13 GW offshore”)
Results :- Maximum installed offshore capacity with grid extension: 13.3 GW- Additional welfare gain ~0.8%- Average price decreases about 2.5% to 15.06 €/MWh- Nodes in Northern Germany benefit
Results (4) Summary: Relative Increase in Welfare Through Nodal Pricing
0,00%
0,50%
1,00%
1,50%
2,00%
2,50%
3,00%
3,50%
low average high
Wel
fare
Gai
n
nodal nodal+8GW nodal+13GW
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Agenda
1. Introduction / Previous studies
2. Model and Data
3. Scenarios
4. Results
5. Conclusions
References
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Conclusions
• Welfare gain by changing the pricing scheme
• Construction of offshore wind capacities causes welfare increase
up to 8 GW without investments in the grid
mainly Northern German nodes benefit
• Congestion occur in all nodal price scenarios but increase largely with the feed in of offshore wind
Congested lines correspond only partially to the DENA findings
Introduction of nodal pricing is economically favorable (independent from offshore wind facilities)
Investment issues (such as for response power) are not considered
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Bibliography
Borenstein, S., Bushnell, J., and F. Wolak (2000): Diagnosing Market Power in California’s Deregulated Wholesale Electricity Market. PWP-064, University of California Energy Institute. Retrieved September 01, 2005, from http://www.ucei.berkeley.edu/ucei/PDF/pwp064.pdf
DENA (2005): Summary of the Essential Results, Planning of the Grid Integration of Wind Energy in Germany Onshore and Offshore up to the Year 2020 (dena Grid study). Deutsche Energie-Agentur.
DEWI (2005): Deutschlands Windindustrie bleibt Weltmeister. Deutsches Windenergie-Institut. Retrieved September 06, 2005, fromwww.dewi.de/dewi_neu/deutsch/themen/statistic/pdf/pm28072005.pdf.
Ding, F., and J. D. Fuller (2005): Nodal, Uniform, or Zonal Pricing: Distribution of Economic Surplus. In: IEEE Transactions on Power Systems, 20, 875-882. Retrieved September 01, 2005, from http://ieeexplore.ieee.org/iel5/59/30784/01425584.pdf?arnumber=1425584
Green, R. (2004): Electricity Transmission Pricing: How much does it cost to get it wrong? University of Hull Business School.Hogan, W. (1999): Transmission Congestion: The Nodal-Zonal Debate Revisited. Harvard University, John F. Kennedy School of Government,
Center for Business and Government. Retrieved August 29, 2005, from http://ksghome.harvard.edu/~whogan/nezn0227.pdfHogan, W. (2004): Successful Market Design (“SMD”) and Electricity Markets. International Energy Agency, Workshop on Transmission
Network Reliability in Competitive Electricity Markets, Paris. Retrieved August 29, 2005, from http://www.iea.org/textbase/work/2004/transmission/hogan.pdf
Hsu, M. (1997): An introduction to the pricing of electric power transmission. In: Utilities Policy, 6, 257-270.Lunze, K. (1987): Berechnung elektrischer Stromkreise. Berlin, VEB Verlag Technik.Overbye, T. J., Cheng, X., and Y. Sun (2004): A Comparison of the AC and DC Power Flow Models for LMP Calculations. In: Proceedings of
the 37th Hawaii International Conference on System Sciences 2004.Pundt, H., and P. Schegner (1997): Wissensspeicherheft: Elektroenergiesysteme. Dresden University of Technology, Institute for Electrical
Power Supply.Schröter, J. (2004): Auswirkungen des europäischen Emissionshandelssystems auf den Kraftwerkseinsatz in Deutschland. Diploma thesis,
Berlin University of Technology, Institute of Power Engineering. Retrieved September 01, 2005, from http://basis.gruene.de/bag.energie/papiere/eeg_diplarbeit_schroeter_lang.pdf.
Schweppe, F. C., Caramanis, M. C., Tabors, R. D., and R. E. Bohn (1988): Spot Pricing Of Electricity. Boston, Kluwer.Stigler, H., and C. Todem (2005): Optimization of the Austrian Electricity Sector (Control Zone of VERBUND APG) under the Constraints of
Network Capacities by Nodal Pricing. In: Central European Journal of Operations Research, 13, 105-125.Stoft, S (2002): Power System Economics: Designing Markets for Electricity. Piscataway, NJ, IEEE Press, Wiley-Interscience.Todem, C., Stigler, H., Huber, C., Wulz, C., and H. Wornig (2004): Nodal Pricing als Analyseinstrumentarium zur Untersuchung der
volkswirtschaftlichen Auswirkungen eines marktbasierten Engpassmanagements bei Engpässen im Verbundsystem. Graz University of Technology, Department of Electricity Economics and Energy Innovation.
UCTE (2004): Interconnected network of UCTE. Dortmund, Abel Druck.VGE (2004): Jahrbuch der europäischen Energie- und Rohstoffwirtschaft 2005. Essen, Verlag Glückauf.
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Thank you very much for your attention!
Contact details:
Dresden University of TechnologyDpt. of Business Management and EconomicsChair of Energy EconomicsMr. Florian LeutholdD-01062 DresdenTel: ++49-(0)351-463-339766Fax: ++49-(0)[email protected]
