Effects of Flow-based Market Coupling for the CWE region
Hanneke DE JONG, Rudi HAKVOORT and Manoj SHARMA
Delft University of Technology
Faculty of Technology, Policy and Management, Department of Energy & Industry
Jaffalaan 5, NL−2628 BX Delft, The Netherlands
+31 15 278 2727 (phone); +31 15 278 3422 (fax)
[email protected]; [email protected]
Abstract
This paper examines the effects on price levels and welfare in the Central West European
countries (Germany, France, The Netherlands, Belgium and Luxemburg) of the transition from
present capacity allocation methods in the Central West European (CWE) region to a Flow-based
Market Coupling (FBMC) allocation method. Quantitative analyses are made by means of a
technical load-flow model combined with an economic model to solve the FBMC-optimization
problem. Amongst other things we have calculated the expected difference, in terms of regional
(and national) welfare, between applying the present allocation method and a flow-based market
coupling allocation method. Based on proxies for the demand and supply curves for each country
involved, price changes and welfare changes were calculated when applying the different allocation
models. The price and welfare changes were calculated against a base case with market clearing
without international exchanges.
Keywords: Europe, energy, electricity, market integration, congestion management, market
coupling, flow-based, PTDF, welfare maximization, FBMC, regional energy markets, CWE region.
1. Introduction
This paper examines the effects on price levels and welfare in the Central West
European countries (Germany, France, The Netherlands, Belgium and
Luxemburg) of the transition from present capacity allocation methods in the
Central West European (CWE) electricity market to a Flow-Based Market Coupling
(FBMC) allocation method. Flow-Based Market Coupling is a new (in Europe)
method for cross border congestion management which combines commercial
energy bids with physical reality to optimise network use with respect to market
value (ETSO-EuroPex, 2004). Commercial energy bids and available capacity are
evaluated simultaneously in an iterative process which should lead to a more
efficient use of transmission capacity with respect to commercial value.
Optimization is performed based upon commercial bids and the linear relation
between accepted bids and the physical flows on flow gates (defined in the PTDFmatrix). Since the functioning of Flow-Based Market Coupling is complex, the
sensitivities of the system are unknown and the effects, both on national and
regional level, are difficult to predict. As a result various important questions
remain unanswered at the moment (De Jong and Hakvoort, 2007).
2. Present congestion management practices in the CWE region
Recent regulation by the European Commission prescribes that congestion
management methods implemented by member states should be market based.
More precisely, capacity should be allocated through an explicit or an implicit
auction (European Commission, 2006). However, besides the choice for a certain
market clearing mechanism, congestion management comprises more
fundamental aspects. One may argue that the different congestion management
alternatives in Europe essentially stem from four basic choices (De Jong and
Hakvoort, 2007). These choices concern:
• The way in which the transmission capacity available for the market is
determined: individual (bilateral) or coordinated?,
• The way in which the transmission capacity available for the market is
distributed among borders, TSO-TSO interfaces or individual interconnections:
border-by-border or regional optimisation?,
• The way in which the transmission capacity available for the market is
assigned to market parties or energy transactions: contract based or flowbased?, and
• The way in which the market is cleared: explicit or implicit (integrated)?
Capacity determination
Presently, the transmission1 (inter-TSO) capacity available for the market is
determined on an individual (bilateral) basis. A Transmission System Operator
(hereafter: TSO) simulates the exchanges between two areas by increasing the
generation in one area and reducing correspondingly the power injection in the
other area. The available capacity is determined per border ― without taking into
account the interrelations with other borders ― while each TSO uses its own
assumptions. If values deviate, normally the lowest value is taken. An increased
level of coordination between TSOs during the capacity determination procedure
would most likely lead to more accurate outcomes. Furthermore, a high level of
inter-TSO coordination is an important prerequisite for the introduction of more
advanced congestion management methods such as flow-based market coupling.
Capacity distribution
As yet, the amount of capacity available for the market (per border) is distributed
over the various interconnections according to a fixed distribution key (typically per
TSO-TSO interface). A more regional, market based distribution approach would
lead to more possibilities for economic optimisation. For example, one could
determine a single ‘capacity value’ for the entire Dutch-German border (including
both the TenneT-RWE and TenneT-E.ON interface2) and assign the capacity to
those market parties or commercial bids that value capacity from Germany to the
Netherlands or vice versa most. In such a system parties don’t have to bid for
each TSO-to-TSO interface separately (which inevitably leads to inefficient
outcomes).
Capacity assignment
Today, the assignment of available transmission capacity to the market is based
on the ‘contract path’ paradigm; as long as there is capacity available on the
contract path of the commercial transaction proposed, the bid is accepted
(evidently, only if the bid is sufficient high). The actual physical flows resulting from
the commercial transaction are not taken into account. In reality, however, each
transaction physically spreads over the entire network. A flow-based method
combines commercial transactions with physical reality in an iterative process. A
so-called PTDF (Power Transfer Distribution Function) matrix expresses the
1
In Europe, the TSO is responsible for solving any intra-TSO congestion and, if necessary, the corrective
measures are paid out of the regulated transmission tariffs.
2
Presently, the capacities on the TenneT-RWE and TenneT-E-ON interface are being auctioned separately.
relation between a commercial transaction and the resulting physical flows on the
(defined) network. The optimal network usage (e.g. in view of welfare) ― in terms
of transactions allowed ― may then be calculated while taken into account the
(jointly defined) technical constraints.
Market clearing
A last choice with respect to congestion management concerns the way in which
the market is cleared. An explicit market clearing approach separates the energy
market from the transport capacity market. Market parties purchase transport
capacity in advance in order to facilitate their energy transactions foreseen. In an
implicit market clearing system, market parties do not purchase transport capacity
in advance. The available capacity is automatically used (assigned implicitly) to
match the ‘best’ energy bid. Market coupling and market splitting are different
implementations of implicit market clearing. Although the operational processes
differ, the two concepts lead to the same market outcomes. Market coupling is a
mechanism in which market parties submit energy bids and offers on the
organized spot market within their own area. The bids and offers of the different
spot markets are being matched until the available interconnector capacity is fully
used or until all bids and offers are matched. In case of a transmission constraint,
a difference in market clearing prices (between the coupled areas) ensures that
the excess of accepted bids over offers in the high price area(s) and the excess of
accepted bids over offers in the lower price area(s) equal the available
transmission capacity on the congested link(s) (cf. Turvey, 2004). As apposed to
market coupling, market splitting uses only one centralized spot market. Without
congestion, the centralized spot market clears like a regular power exchange.
However, in case of congestion, the operator splits the region in different areas on
either side of the congestion and creates separate clearing prices for each area
created.
Today, both explicit and implicit market clearing mechanisms are being used in the
CWE region. On the NL-DE and the FR-DE border an explicit market clearing
mechanism with respect to long, medium and short term transmission capacity
allocation is operated. While the long and medium term transmission capacity on
the NL-BE and BE-FR borders is allocated by using a explicit market clearing
mechanism as well, the short term (day-ahead) capacity is being allocated by
means of a trilateral (NL-BE-FR) market coupling system since November 2006.
NL
BE
DE
BE
Long and medium term: explicit
Short term (day ahead): implicit (trilateral market coupling)
Long, medium and short term: explicit
Figure 1: Today’s market clearing practices in the CWE region
3. Towards flow-based congestion management in the CWE region
On 6 June 2007, the Ministers responsible for energy and the high level
representatives of the Regulatory Authorities, TSOs, power exchanges and the
Market Parties Platform of the CWE region signed a Memorandum of
Understanding (MoU) committing themselves to analyse, design and implement a
flow-based market coupling system between the five countries (including
Luxemburg) of the CWE region with January 2009 as a target date. Based on the
discussion above, Figure 2 visualizes the change from the present congestion
management system to the system foreseen to be implemented in 2009.
Allocation of scarce inter-nation transmission capacity
Market Based Methods
Capacity
Determination
Capacity
Distribution
Capacity
Assignment
Market
Clearing
Intensively coordinated
(between TSOs)
Individual (bilateral)
Border-by-border
TSO-by-TSO
Contract based
Explicit
Implicit
Today’s
CWE congestion
management
practices
Border-by-border
TSO-by-TSO
Contract based
Explicit
Implicit
Regionally optimised
Contract based
Explicit
Implicit
Flow based
Explicit
Implicit
FLOW
BASED
MARKET
COUPLING
Figure 2: Present and foreseen congestion management approach
Although in general all parties agree that the introduction of a flow-based market
coupling will lead to an increased level of regional welfare, the price- and welfare
effects for the individual countries remain unclear. A prerequisite for establishing
an efficient system from a regional perspective is that member states leave behind
national welfare interests in favour of regional welfare optimization. Furthermore,
one is dependent on the cooperation of individual parties such as power
exchanges and TSOs, all having their own specific interests. Considering the
current political debates on the development of the European Union and the
current discussions in the market integration arena, solving the political issues
may prove to be a larger challenge than the techno-economic implementation of
the flow-based market coupling approach.
In order to anticipate such political problems on time, it is necessary to gain a
more precise insight into the effects of introducing a flow-based market coupling
system on regional and national level. Therefore, we examined these effects by
using a technical-economic model of the CWE electricity market. The main
purpose of this analysis was to obtain some idea of the order of magnitude of the
various regional and national effects and of the sensitivities of the system. The
remainder of this paper discusses the analysis and its outcome.
4. Structure of the simulation model
The simulation model contains three important elements:
1) A transmission network module for the four countries3 (NL, BE, DE and FR).
The model uses the technical representation of the UCTE model made by the
University of Edinburgh (Zhou and Bialek, 2005) in Power World4 software. By
means of this technical model the Power Transfer Distribution Function
(‘PTDF’) matrix is derived5. This matrix expresses the relation between a
commercial transaction between two price areas and the resulting physical
flows on the flow gates defined (ETSO, 2007; Frontier Economics, 2006). The
transaction is modelled by scaling the output of all generators in the source and
sink area in proportion to their relative participation factors i.e. generators in the
source area increase their output, while generators in the sink area decrease
their output. The Power World simulator assumes that the buyer accounts for
the entire change in system losses. Figure 3 visualizes the sensitivity matrix
while taking the Netherlands as the sink node.
Figure 3: sensitivity and transaction specific PTDF matrix
From this sensitivity matrix one may, for example, conclude that (only) 80% of
a commercial electricity transaction from Belgium to the Netherlands physically
directly flows from Belgium to the Netherlands. About 20% of the transaction
value from Belgium to the Netherlands flows via France and Germany. From
the sensitivity matrix, the transaction specific PTDF values can be derived by
subtracting the relevant values (Sv) provided by the sensitivity matrix (Oren,
3
Luxembourg is also officially a part of the CWE region. However, Luxembourg has two electricity
transmission networks that are not interconnected, but are integrated with the networks of the neighbouring
countries, Germany and Belgium. Hence Luxembourg is not considered as a special price area and because
the network is not connected within Luxembourg it does not lead to any parallel flows.
4
See www.powerworld.com.
5
The calculated PTDF values have been validated by means of public publications on actual PTDF values
obtained from several public sources.
2006). For example, the influence of a transaction from Germany (A) to
Belgium (B) on the German→ Dutch border (Q) is Sv(AQ) (0,756) minus Sv(BQ) (0,196). This corresponds with the value of 0.56 given in the PTDF
matrix under a commercial exchange DE→BE for the German→Dutch border.
From the sensitivity and transaction specific PTDF matrix may be observed
that the sum of the numbers do not always add up to 1. This is because the
CWE region is not completely decoupled from the rest of Europe and some
flows also take place through other countries.
2) An electricity demand and supply module to model the supply and demand
curves for the four countries.
The supply curves used in the model are based on real data on cost for
generation and installed generation in every country. As generation companies
often do no reveal any data about the cost of generation, data is collected from
various publicly available resources6 based however on real power plants.
Based on average data on variable generation cost (per technology the sum of
fuel, operation & maintaining and the CO2 emission costs) and on data on
actual installed capacities per technology (UCTE, 2007) a supply curve
approximation is established by finding a linear regression fit. The supply
curves are validated by means of data published by DG Competition (European
Commission, 2007; London Economics, 2007). The graphs were very similar
considering both the prices and the installed generation. Hence it is acceptable
to use the data form the public sources and still get realistic results.
As the model focuses on the wholesale market, the demand curves are not
assumed to be completely inelastic. However, an approximation of the demand
curves is established based on the average electricity demand per country and
the reliable capacity available (defined as national generation capacity minus
non-usable capacity, maintenance and overhauls, outages and system services
reserve) defined on a certain target date7. The demand and supply curve at
equilibrium would intersect to give the equilibrium price and quantity. Assuming
that the average demand would be the equilibrium demand and hence the
result of the intersection of demand and supply, this average demand value
was substituted into the supply curve. This then gives the equilibrium price and
forms one point of the demand curve, Furthermore, the reliable generation
capacity was assumed to be equal to demand when the price of electricity is
equal to zero. This gives the second point on the demand curve, which is
assumed to be linear in the simulation model. As the values used are based on
snapshots in time, the values were validated by using the Load Duration
Curves calculated for the last three years by the London Economics report (for
DG Competition’s sector enquiry). The values of the model are somewhat
higher than what is expected from past load duration curves. This could be
attributed to (i) the fact that the values in the LE study are older (if we consider
the annual increase this would lead to more convergence of the values) and/or
(2) to the snapshot considered by UCTE (January, Wednesday at 11:00).
6
Sources: (Tarjanne, 2007), (Vattenfall, 2006), (OECD, 2005), (Lise et al, 2006), (Hoogwijk et al, 2007) and
(UCTE, 2007).
7
Source (UCTE, 2007).
However, the values of UCTE are still are reflective of the trend and have been
achieved at the same instance of time in the past8.
3) A day-ahead electricity market optimization model. This is an optimization
model built in Microsoft excel) that simulates flow-based market coupling with
the data modules discussed above as an input. It provides the optimal dispatch
― in terms of the net imports (exports) ― on a day-ahead basis given the
objective function defined (see below).
Model constraints: the basic constraints of the model are (1) that net trade is
equal to zero and (2) that all electricity generated is also consumed. The other
constraints of the model are formed by the technical limitations of the network.
These latter constraints are the capacities available for commercial
transactions on the border-to-border interfaces between the countries as
determined by the TSOs (see section 2). The actual figures on these available
capacities are obtained from the various websites of the relevant TSOs and
shown in figure 4. In the model it is assumed that all available capacity is
allocated day-ahead.
Figure 4: available transmission capacities
Objective function: the model applies the optimization of total regional welfare
― the sum of consumer and producer surplus ― as the objective function. This
objective function is generally agreed upon in today’s discussions on the
introduction of FBMC. However, this regional optimization function also
requires that the individual member states waive their national (welfare)
interests. One may imagine that when the individual national consequences of
regional optimization become more tangible and concrete, this may lead to new
political discussions on the objective function, the design of the system and/or
international financial compensations.
Model outputs: with reference to a certain base case, the model calculates the
optimal system dispatch (level of import/export per country) as well as for each
individual country (i) the (equilibrium) electricity price, (ii) consumer surplus, (iii)
producer surplus, (iv) total welfare and (v) the utilization of the available
transmission capacity.
8
For more details on the construction of the supply and demand curves used in the model we refer to
(Sharma, 2007).
5. Results
By using the model, three cases are compared. The base case is the
(hypothetical) case in which the countries are not coupled at all (no coupling). This
situation is compared with a representation of the current situation (CS) and a
situation of full flow-based market coupling (FBMC)9. An approximation of the
current situation (CS) of trilateral market coupling between NL, BE and FR and
explicit auctions between NL, DE and DE, FR (see figure 1) is modelled as follows:
first the explicit trades are executed (from DE to NL and from FR to DE) assuming
that all capacity is being used. Subsequently, a new optimization problem is set for
the trilateral market coupling countries NL, BE and FR10. In the flow-based market
coupling case (FBMC), the whole region applies an implicit market clearing
mechanism (market coupling) combined with a flow-based capacity assignment
approach (see section 2). Thus the FBMC case uses the sensitivity matrix (PTDF
matrix) as illustrated in figure 3.
Prices (Euro/MWh)
€ 60,00
Electricity Demand
% demand imported
(GW)
(negative means import)
10,00%
90,00
€ 50,00
80,00
5,00%
70,00
€ 40,00
0,00%
60,00
€ 30,00
NL
40,00
€ 20,00
BE
FR
DE
-5,00%
50,00
-10,00%
30,00
-15,00%
20,00
€ 10,00
-20,00%
10,00
€-
0,00
NL
BE
FR
No coupling
CS
DE
NL
BE
No coupling
FBMC
FR
CS
DE
-25,00%
CS
FMBC
FBMC
Figure 5: Prices and demand
From figure 5 it follows that in the current situation (CS), Dutch and Belgian
consumers seem to benefit most of the possibility to import lower-priced electricity
from other countries. In the situation of FBMC, Dutch consumers would benefit
most and import approximately 23% of its total national demand for electricity.
Consumer Surplus (Euro/hr)
Welfare (Euro/hr)
€ 1,40
$9,00
$8,00
€ 12,00
Millions
Millions
$10,00
Millions
Producer Surplus (Euro/hr)
€ 1,20
$7,00
€ 1,00
$6,00
€ 0,80
€ 10,00
€ 8,00
€ 6,00
$5,00
€ 0,60
$4,00
€ 4,00
€ 0,40
$3,00
$2,00
€ 2,00
€ 0,20
$1,00
€-
$0,00
NL
No coupling
BE
FR
CS
DE
FBMC
€NL
No coupling
BE
FR
CS
DE
NL
FMBC
No coupling
BE
FR
CS
DE
FBMC
Figure 6: Surplus and welfare
9
The case FBMC has also been compared with the situation of contract path (NTC) based market coupling.
However, there was an issue of defining the contact path with addition of Germany. Germany offers a parallel
which can not be defined using a contact path approach. The makes the definition of contract path complex.
10
It is assumed that all capacity is allocated through day-ahead market coupling (no long term auctions exist).
From the surplus and welfare figures (figure 6), one may observe what may be
expected: importing countries experience an increase in consumer surplus and a
decrease in producer surplus (and exporting countries vice versa). At first glance,
the total welfare change, both nationally and regionally, does not seem to be
significant.
% U tiliz a tion of a va ila b le
Change in welfare (Euro/h)
€ 100.000,00
ca pa city
€ 90.000,00
€ 80.000,00
1 0 0 ,0 0 %
8 0 ,0 0 %
6 0 ,0 0 %
€ 70.000,00
€ 60.000,00
4 0 ,0 0 %
2 0 ,0 0 %
0 ,0 0 %
-2 0 ,0 0 %
-4 0 ,0 0 %
€ 50.000,00
€ 40.000,00
€ 30.000,00
€ 20.000,00
BE -FR
BE -N L
D E -FR
D E -N L
-6 0 ,0 0 %
-8 0 ,0 0 %
-1 0 0 ,0 0 %
€ 10.000,00
€ 0,00
1
2
CS
3
4
FBMC
CS
FBM C
Figure 7: absolute change in welfare and utilization of transmission capacity
However, on a closer look (see figure 7), one may observe that the Netherlands
would actually experience a major increase in total welfare in case of FBMC. An
increase of approximately 22.000 Euro/h implies an increase of about 190 million a
year (although it should be noted that the demand and supply curves applied in
the model are based on a snapshot during a peak hour in January).
In addition, a large part of the actual regional welfare increase should result from
the fact that TSOs may be less conservative in view of the determination of the
transmission capacity available for market activities. As a FBMC mechanism takes
into account the actual physical effect of commercial transaction on the network,
TSOs may in principle lower their transmission safety margins. In terms of the
model this means that the constraints of the optimization problem could be
stretched. Furthermore, implicit market clearing (like market coupling) involves the
efficient use of available transmission capacity. In practice, the capacity available
for the market is often not fully used on borders (TSO-TSO interfaces) where one
applies explicit market clearing.
Regarding the utilization of available capacity, one may notice that in case of
contract path-based market coupling (like the coupling between NL, BE and FR in
the CS case), the theoretical optimal dispatch is the situation in which either the
prices in the connected countries are equal or the connecting transmission
capacity is fully used. Contrary, in a system of FBMC, the optimal dispatch could
mean that a price difference between two connected countries continues to exist
even if the connecting transmission capacity is not fully used (see the DE-FR
connection in figure 7). This observation results from the fact that in case of a flowbased approach, the transaction of one additional MW from A to B could lead to an
indirect flow from A to B through C. Here the connection between A and C or
between C and B could already be fully used by other (economically more
efficient) transactions and therefore restrict further trade between A and B.
To obtain some idea of the order of magnitude of welfare increase that could result
from the situation in which more transmission capacity is available for the
markets, all transmission constraints in the model (see figure 4) have been
stretched by 1000 MW11 respectively 2000 MW. Figure 8 shows the corresponding
results with respect to price and absolute welfare. From these results, one may
conclude that an increase of the capacity available for the market could have a
significant impact on prices and welfare.
Prices (Euro/MWh)
Change in welfare (Euro/h)
€ 60,00
€ 100.000,00
€ 90.000,00
€ 80.000,00
€ 70.000,00
€ 60.000,00
€ 50.000,00
€ 40.000,00
€ 30.000,00
€ 20.000,00
€ 10.000,00
€ 0,00
€ 50,00
€ 40,00
€ 30,00
€ 20,00
€ 10,00
1
€NL
No coupling
+1000MW
BE
CS
+2000MW
FR
DE
FBMC
CS
FBMC
2
+1000MW
3
4
+2000MW
Figure 8: effect of an additional 1000/2000 MW available capacity
6. Conclusion
Compared to the (hypothetical) base case in which the countries are not coupled
at all (no coupling), the model outcomes of the current situation (CS) ― trilateral
(contract path-based) market coupling between NL, BE, and FR and explicit
auctions between NL, DE and DE, FR ― differ from those of a system of full flowbased market coupling (FBMC), which is foreseen to be implemented in 2009.
Compared to the current situation, Dutch consumers and German producers seem
to benefit most from the introduction of FBMC. Although the impact of FBMC on
both national and regional welfare seems limited at first glance, a closer look
shows that effects on welfare could be quite significant for individual countries on
an annual basis.
Additionally, due to the more precise assignment of network flows to market
transactions, the present available capacity values ― which by definition must be
rather conservative in order to be able to cope with the inaccuracies of unexpected
flows related to the contract-path paradigm ― may be re-assessed. Such reassessment will probably yield a lower reserve margin and consequently a higher
amount of capacity which may be offered to the market. This would then most
likely lead to a higher level of regional welfare. Moreover, FBMC seems to offer a
better use of the available interconnector capacity both technically (as one assures
that the available capacity is fully used) and with respect to the economic value of
the transactions allowed.
11
First, a sensitivity analysis with regard to the PTDFs was performed. Installation of new transmission
capacity between two countries appeared to have only a small impact on the PTDF values – typically only at
the third decimal place (Sharma, 2007).
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