Effects of Flow-based Market Coupling for the CWE region

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) r.a.hakvoort@tudelft.nl; h.m.dejong@tbm.tudelft.nl 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 transmission 1 (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 interface 2 ) 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 Individual (bilateral) Intensively coordinated (between TSOs) Capacity Distribution Border-by-border TSO-by-TSO Border-by-border TSO-by-TSO Regionally optimised Capacity Assignment Market Clearing Contract based Contract based Contract based Flow based Explicit Implicit Explicit Implicit Explicit Implicit Explicit Implicit Today s CWE congestion management practices 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 countries 3 (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 World 4 software. By means of this technical model the Power Transfer Distribution Function ( PTDF ) matrix is derived 5. 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 (S v ) 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 S v (AQ) (0,756) minus S - v(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 resources 6 based however on real power plants. Based on average data on variable generation cost (per technology the sum of fuel, operation & maintaining and the CO 2 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 date 7. 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 past 8. 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 FR 10. 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. 60,00 50,00 Prices (Euro/MWh) 90,00 80,00 Electricity Demand (GW) 10,00% 5,00% % demand imported (negative means import) 40,00 30,00 70,00 60,00 50,00 0,00% -5,00% 20,00 10,00 40,00 30,00 20,00-10,00% -15,00% 10,00-20,00% - No coupling CS FBMC 0,00 No coupling CS FMBC -25,00% CS 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) Producer Surplus (Euro/hr) Welfare (Euro/hr) $10,00 1,40 12,00 Millions $9,00 $8,00 Millions 1,20 Millions 10,00 $7,00 $6,00 1,00 0,80 8,00 $5,00 $4,00 $3,00 0,60 0,40 6,00 4,00 $2,00 $1,00 0,20 2,00 $0,00 - - No coupling CS FBMC No coupling CS FMBC No coupling CS 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. Change in welfare (Euro/h) % Utilization of available capacity 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 0,00 100,00% 80,00% 60,00% 40,00% 20,00% 0,00% -20,00% -40,00% -60,00% -80,00% -100,00% BE-FR BE-NL DE-FR DE-NL 1 2 3 4 CS FBMC CS FBMC 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 MW 11 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 50,00 40,00 30,00 20,00 10,00 - No coupling CS FBMC +1000MW +2000MW 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 1 2 3 4 CS FBMC +1000MW +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).

7. Literature De Jong, H. M. and R. A. Hakvoort (2007). Congestion Management in Europe: Taking the Next Step. 9th IAEE European Energy Conference Energy Markets and Sustainability in a Larger Europe, June 10-13, Florence, Italy. Economics, L. (2007). Structure and Performance of Six European Wholesale Electricity Markets in 2003, 2004 and 2005; presented to DG Comp 26th February 2007. ETSO (2007). Regional Flow-based allocations: State-of-play. ETSO - EuroPEX (2004). Flow-based Market Coupling: A Joint ETSO-EuroPEX Proposal for Cross-Border Congestion Management and Integration of Electricity Markets in Europe. European Commission (2006). Commission Decision (2006/770/EC) of 9 November 2006 amending the Annex to Regulation (EC) No 1228/2003 on conditions for access to the network for cross-border exchange in electricity European Commission (2007). DG Competition Report on Energy Sector Inquiry, SEC(2006) 1724. Brussels. Frontier Economics, CONSENTEC, et al. (2006). Economic Assessment of Different Congestion Management Methods, Report for Federal Network Agency. Hoogdijk, M., D. P. van Vuuren, et al. (2007). "Exploring the impact on cost and electricity production of high penetration levels of intermittent electricity in OECD Europe and the USA, results for wind energy." Energy 32(8): 1381-1402. Lise, W., V. Linderhof, et al. (2006). "A game theoretic model of the Northwestern European electricity market - market power and the environment." Energy Policy 34(15): 2123-2136. OECD (2005). "Projected Costs of Generating Electricity 2005 Update." Oren, S. (2006). Nodal Pricing and Transmission Rights; IEOR 190d: Market Engineering and Applications Sharma, M. (2007). Flow-Based Market Coupling: What we know, What we don't know and What we need to know; Master Thesis. Delft, Delft University of Technology; Faculty of Technology, Policy and Management. Tarjanne (2007). EU Policy and Carbon Emission Trading: Implications For The Energy Market. The Adam Smith Institute's Conference, European Nuclear Forum "Realising the potential of the nuclear renaissance", March 13-14, Paris, Lappeenranta University of Technology Turvey, R. (2004). "Interconnector economics." Energy Policy 34(13): 1457-1472. UCTE (2007). UCTE System Adequacy Forecast 2007-2020. Brussels. Vattenfall (2006). Vattenfall Annual Report. Zhou, Q. and J. W. Bialek (2005). "Approximate Model of European Interconnected System as a Benchmark System to Study Effects of Cross-Border Trades." IEEE Transactions on Power Systems 20(2): 782-788.