model for area price determination-kjetil for area price determination and congestion management in...
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1SINTEF Energy Research
Model for Area Price Determination and Congestion Management in Joint Power
Markets
Market splitting Flow based market coupling
Gardermoen, 26-27 Oct. 2005
Kjetil Uhlen, Leif Warland, Ove S. GrandeSINTEF Energy Research
2SINTEF Energy Research
Outline of presentation
Background and aims of the modelHourly day-ahead markets, focus on the Nordic systemDifferent requirements and solutions for price calculations and congestion management
Alternative model for area price determination Implementation of the demo model
Case studies and sample resultsConcluding remarks
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Market splitting – Market coupling
Basically the same thingImplicit auction
Difference only in the way prices are calculatedMS: First compute system price. Then, split into separate price areas if exchange limits are violatedMC: First compute price vs. exchange in each area. Then, connectthe areas and compute actual prices and exchanges.
Best suited in systems where the areas are radiallyconnected” (refer to “EXCHANGES” not “FLOWS”)Well suited for hierarchical structures
MC can be used to harmonise two different markets (e.g. Nordel vs. Germany)
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Advantages of market splitting/coupling
The day-ahead prices in the total system are calculated simultaneously
The price differences between the predefined areas willadjust the power exchange to the available transfer capability (ATC) and
reflect the consequences of congested corridors
Separate auctions for the network are not necessary
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Limitations with market splitting
One major weakness / limitation of the Nordic Area Price model:
The model lacks representations of the physical lines / transmission corridors between the price areas
⇓
may lead to sub-optimal utilization of the transfer capacity (in meshed networks)
The real physical Flow as a consequence of different areaprices is not calculated
Major drawback in highly meshed networks
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E2
E1
E3
E4
1
2
3
4Ei = Net exchange
from area i
E2
E1
E3
E4
1
2
3
4Ei = Net exchange
from area i
Market splitting(zonal pricing)
LMP(nodal pricing)
Good solution from the market point of viewToo simple for congestionmanagement
Good solution for congestionmanagementComplex solution and less acceptable from the market pointof view
Compromise
?
”Flow of dollars vs. the flow of physics”
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Proposed model
Basic principle: Combination of market splitting and nodal pricing Each area is defined as one nodeUses a network equivalent representing the transmission system between the areasPrice calculation based on dc optimal power flowCriterion of optimization: Minimization of the socio-economic congestion cost
Improved utilisation of the available transfer capability and
reduced market player risk
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F12F13
F23
F24
F34
1
23
4(F = Flow)
E2
E1
E3
E4
1
23
4(E= Exchange)
Present model New model
Alternative model for Area Price determinationand congestion management
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Implementation
A demo model of the proposed method is implemented in MATLAB
Simple graphical interface to display results
Use of the model:Input data are:
aggregate supply curves (marginal cost of generation) for each area(Constant) load and exchange to external (not modelled) areas network impedanceslimits on transmission corridors
The model computes a solution by minimizing the cost functionwithout violating transfer limitsIn addition, the load flow equations must be fulfilled
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Case studies
Sample results to demonstrate the functionality of the model and the user interface.Examples are shown to highlight main features of the model:
How one congested corridor result in different area pricesHow marginal generation changes in one area can affect congested corridors and prices in other areas.How control of HVDC links is included to maximize utilization ofthe transmission system.
Disclaimer: The examples do not intend to represent a real system or a real operating situation.
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Concluding remarksAn alternative approach to area price determination in physical day-ahead (spot) markets has been described.
The method combines the advantages of market splitting (area pricing) with power flow calculations.
The criterion of optimization is minimization of the socio-economic congestion cost.
The proposed method is implemented and demonstrated in a demo model
The proposed method/model corresponds to the FMC model proposed by EU
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POWERNEXT
Transmissionconstraint
Market regionin which pricescorrelate
Nordic/Baltic
Mittel
Iberia
FRANCE
NorthwestEurope
RUSSIA
FINLAND
AUSTRIA
ITALYSPAIN
SWEDEN
NORWAY
GERMANY
HUNGARYROMANIA
BULGARIA
TURKEY
DENMARK
POLAND
BELARUS
UKRAINE
UK
CZECH REP.
SLOVAKIA
GREECE
BELGIUM
IRELAND
SERBIA
ALBANIA
MOLDOVA
LITHUANIA
LATVIA
ESTONIA
LUX.
MONTENEGRO
BOSNIA
CROATIA
SLOVENIASwitz
MACEDONIA
Britannica
EPX/POWERNEXT(2001 ?)
UKEKUKPX
USAPX
APX
SPXIPX
(2001 ?)
PPX
NORDPOOL
EEXLPX
APX
UKEKUKPX
USAPX
POWERNEXT
SPX
PPX
PHELIX
NORDPOOL
IPX
European Power Exchanges
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Congestion Management in Continental Europe
ETSO paper and EU statements conclude that:Market splitting (Area pricing) is a very interesting principle for congestion management, but it has severe requirements that have to be addressed before considering implementation outside Nordel:
* In highly meshed networks will congested lines change withdemand and generation
* Neighbouring constraints are strongly interdependent
(* Impact on bilateral trade)
Optimal utilisation of a highly meshed network will imply co-ordinated load-flow calculations as an integral part of the final allocation procedure.
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Proposed concept to integrate market information in Power system simulators
Load data:-Load forecast-outage schedules
Market inputs:Aggregate bid curves
MARKETmodel
DISPATCHmodel
SIMULATORS-Power flow-Dynamic simulator- …
User control
Prices
Exchanges
Pgen, Pload
Topologydata
Technical data:-Network data-generator data
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Application examples(Potential benefits of a market model)
It can be used simply as an aid to establish study cases of interest.
It becomes possible tostudy the impact of changing transfer limits on system security, stability, etc. and on market prices study the impact of the (supply and demand) bid curves on systemoperation (sensitivity analysis) e.g. study the impact of having more price-responsive demand.
Educational purposes – useful enhancement of operator training simulators
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Further work and possibilities
The Nordic Area price model is considered as a basis for the future model of the European spot market
Advantages seen from ETSO and EU- The day ahead prices in the system are calculated
simultaneously- Price differences between areas adjust the transfer to
the transfer capacity- Separate auctions of transfer capacity will not be
needed
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A|Fmax|
(deficit)B
(surplus)
ps
pA
Fre
d
pB
Fred
ConsumersurplusProducersurplus
a
b c de
f
g
hc’
e’d’
i
j
Flow
Price
Flow Flow
PricePrice
Area A Area B
System
i’
b’
Socio-economic congestion costs
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Price
MW
Supply ADemand A
FmaxpA-B
System Price
MW
Σ Supply
Σ Demand
Price
MW
Supply B
Demand B
A B|Fmax|
(surplus) (deficit)
F1
Fmax
pSpA
pB
CongestionCongestion managementmanagementPrinciplePrinciple ofof Market SplittingMarket Splitting
p’A
p’B
F1
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Price
Price vs. exchange A
Price
MW
Price vs, exchange B
A B|Fmax|
(surplus) (deficit)
-F1
pS
CongestionCongestion managementmanagementPrinciplePrinciple ofof Market Market CouplingCoupling
p’A
p’BF1
MW
pA-B
Area Prices at Fmax
MW
FmaxpA
pB
pS
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Methodology and approach
The proposed model meets the following requirements1) Each node represents a predefined price area
(reflecting network constraints)2) The network equivalent represent the real power lines
between the price areas3) The Bid Curves are estimated based on real curves
from Nord Pool4) Optimisation criterion: Minimization of the Socio-
economic congestion cost
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Optimisation criterion
Reduce the total congestion costs in the Nordic systemAn optimal power flow problem
pi Ci
Ei
psi
Esi
pi0
Ei0
bEEEpp
Ep iisi
isii +
−−
≈0
0)(
22
0
0 )(21)(
21
siii
isii
isi
isi EE
Ep
EEEEpp
C −∆∆
=−−−
=
2
1
1 ( )2
Ni
total i sii i
pCost E EE=
∆= −
∆∑
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Supply and demand curves
Data from Nordpool
C:\Flex\SAPRI\20011015\ak20011015t20.txt
0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 2000 4000 6000 8000 10000
MWh/h
NOK/M
Wh
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Optimisation problem..
An optimal power flow problem with a quadratic objectfunction and linear constraints:
2
1
1min ( )2i
Ni
i siE i i
p E EE=
∆−
∆∑maxmin
gigigi PPP ≤≤
maxijij SS ≤
maxjiji SS ≤
1min min ( )2
T Ttotalx x
Cost G x x Hx F x= = +
bAx ≤ eq eqA x b= bb UxL ≤≤
The HVDC links are represented in the optimization as controlvariables (load or generation) with zero cost
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Methodology … cont.
Solving the load flow- The method used is Direct Current load flow- Relevant in cases where the only interest is in the active
power flow and where a rough approximation is accepted
The power Ei injected into the bus for each area isE=P+Yδ,
P is the flow in the HVDC links and represented as load/generation depending on the direction of the flow
The flow Fij between area i and j is Fij=Yij(δi-δj)
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Methodology … cont.
Estimating the impedances in the area model
12
1312 12
1 2313*
223 12*
*12 12 113*
13 *2
23 23^^
112 1212^
13 ^2
1323^
23
0 0 0 00 0 0 0 0
0 0 0 0 00 0 0 00 0 0 0 00 0 0 0 00 0 0 00 0 0 0 00 0 0 0 0
FFY YFY
Y FY Y
FYY F
Y Y FY
FY
F
δδ
δ
δ
δ
δ
⎡⎢
−⎡ ⎤ ⎢⎢ ⎥⎡ ⎤ ⎢⎢ ⎥⎢ ⎥ ⎢⎢ ⎥⎢ ⎥ ⎢⎢ ⎥⎢ ⎥ ⎢−⎢ ⎥⎢ ⎥ ⎢⎢ ⎥⎢ ⎥ ⎢=⎢ ⎥⎢ ⎥ ⎢⎢ ⎥⎢ ⎥ ⎢⎢ ⎥⎢ ⎥ ⎢−⎢ ⎥⎢ ⎥ ⎢⎢ ⎥⎢ ⎥ ⎢⎣ ⎦⎢ ⎥⎢ ⎥⎣ ⎦
⎣
⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥
⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎦
12 12 1 12
13 13 2 13
23 23 3 23
00
0
Y Y FY Y F
Y Y F
δδδ
−⎡ ⎤⎡ ⎤ ⎡ ⎤⎢ ⎥⎢ ⎥ ⎢ ⎥− =⎢ ⎥⎢ ⎥ ⎢ ⎥⎢ ⎥⎢ ⎥ ⎢ ⎥−⎣ ⎦⎣ ⎦ ⎣ ⎦
⇒