Cross-border electricity trading: towards flow-based market coupling

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1 1 Cross-border electricity trading: towards The transmission network constitutes the limitation for international trade. Consequently, the way in which the available capacity for trading is calculated has a substantial impact on the market. Today, the mechanism is utilized. However, this methodology is planned to be replaced by a flow-based (FB) approach for Central Western Europe in Without actually influencing power flows in the system, leads to a more efficient use of transmission capacity by better taking into account the effect of trade on the network. This fact sheet explains the main features of the flow-based method, in comparison to the current ATC mechanism. Cross-border electricity trading in Europe Electricity trading is conducted through different consecutive electricity markets in order to guarantee the instantaneous balance between electricity generation and offtake. Towards this end, forward and future markets are successively followed by a day-ahead and intra-day market, concluded by a final real-time settlement of imbalances 2. This fact sheet focusses on the dayahead market, which represents an important time frame in the context of electricity trading. A different classification can be made based on the trading platform. Over-the-counter (OTC) trading implies bilateral trading at a mutually agreed price. Power exchanges on the other hand offer standardized trading products at a transparent market price. European Target Model Historically, electricity markets were organized nationally, each country focusing on self-sufficiency in terms of electric power supply. Based on these foundations, the target model for electricity trading proposed by ENTSO-E makes use of a zonal approach, building on a number of interconnected markets 3,4. These markets are called bidding zones and typically correspond with Member States, except for Scandinavia and Italy (Fig. 1 5 ). On the other hand, Germany, Luxemburg and Austria constitute a single bidding zone. Within each zone electricity can be traded freely, without taking into account network constraints. In contrast, for crossborder trade, the interconnection capacity with other bidding zones is considered in the trading process. 1 Central Western European (CWE) region: Belgium, France, Germany, Luxemburg and the Netherlands 2 See KU Leuven EI fact sheet on The current electricity market design in Europe 3 ENTSO-E = European Network of Transmission System Operators for Electricity 4 As opposed to the idealized nodal approach, i.e. optimizing energy exchanges under full network constraints 5 Ofgem, FTA Team, Bidding Zones Literature Review, July 2014 Figure 1: Current bidding zone configuration in Western Europe (Source: Ofgem, 2014) Coordinated capacity calculation & allocation method Cross-border electricity trading requires a coordinated capacity calculation and allocation mechanism. Coordination across bidding zones is essential since electricity flows cannot be restricted by commercial agreements but follow the laws of physics. For example, when Germany exports electricity to France, part of the electric power will flow through the Netherlands and Belgium, instead of following the direct path between the two countries. As such, this transaction also has an impact on the remaining interconnection capacity at the Dutch and Belgian borders. Flow-based versus Available Transfer Capacity The goal of a coordinated capacity calculation mechanism in the context of cross-border trading is to guarantee an efficient allocation of the available transmission capacity. A trade-off has to be made between offering cross-border transmission capacity to the market and ensuring a reliable operation of the power system. In this respect, a replacement of the current ATC method by a new flow-based approach is considered across Europe. Both mechanisms are compared below for the four steps that can be distinguished in the daily coordination process.

2 2 1. Calculation of available capacity Based on historical data for a reference day, taking into account potential loop flows, seasonal impact and a justified security margin, each Transmission System Operator (TSO) determines a Net Transfer Capacity (NTC) value for each direction on each border of its control area. The NTC values can be interpreted as the maximum allowable commercial exchanges that push a critical network element to its maximum physical flow. At this stage, TSOs of neighboring countries coordinate bilaterally to align the NTC values on their common border, generally selecting the lowest NTC. From the NTC figures, the Available Transfer Capacity (ATC) value can be derived after subtracting long-term nominations. See further, Fig. 5. Instead of supplying fixed commercial capacities, the FB methodology formulates the constraints which reflect the physical limits of the grid. To this end, a simplified network model is constructed, represented by a combination of nodes and lines (Fig. 2). In line with the European zonal approach, different nodes are aggregated into zones, indicated by the colored areas. Each TSO provides input data, which is combined at the regional level. Two elements are essential to the flow-based mechanism: Power Transfer Distribution Factors (PTDFs): Power Transfer Distribution Factors (PTDFs) are introduced, denoting the physical flow induced on a transmission line, as the result of power injected at a specific zone. This way, it can be monitored which combinations of cross-zonal exchanges threaten to overload a specific line. Critical branches (CBs): To arrive at a simplified network model without having to consider each individual line, critical branches are introduced. They consist of all crossborder lines, as well as internal lines that are significantly impacted by cross-border exchange. An example of a set of CBs is given by the lines highlighted in red in Fig. 2. Only these branches are considered in the model. The available physical capacity for each CB is determined, based on the physical limit of the line and taking into account necessary security margins Verification of capacity domain The meshed nature of the European power system calls for verification to validate the supplied input data. This verification step is performed two days before delivery and consists of a combination of tests, such as load flow analyses, checking voltage limits of components and assessing voltage stability. 2 Days before delivery, a Congestion Forecast (D2CF) file is created by each TSO, providing a view on the expected power flows. Under the ATC regime, this file is constructed for only two timestamps, namely 3h30 and 10h30, and contains minimally the following information: Available grid topology (+ anticipated outages of components) Generating units and their estimated output levels (+ anticipated outages) Load forecast Exchange programs Next, all individual D2CF files are merged by Coreso, leading to the so called base case 7. This forecast is used to verify the proposed NTC values. In the ATC regime, two base cases are created given the two considered timestamps. The consecutive verification step assesses local grid security and leads to adaptations in case of security breaches. The ATC methodology checks all the NTC corners, illustrated by the following example. Figure 2: Graphical representation of a transmission network (4 zones) 6 Physical capacity is the amount of power that can flow over a line without saturating it. Commercial capacity is the amount of trade that pushes a critical network element to its maximum physical power flow. Figure 3: ATC domain 7 Coreso (COoRdination of Electricity System Operators)

3 Example: Consider three bidding zones A, B and C. Zone A is connected to both B and C. The ATC trading domain can be illustrated as in Fig Each combination of commercial exchanges falling inside the rectangle is allowed for trading purposes. The four indicated corners are checked. Corner 1 represents a situation in which zone A exports the maximally available commercial flows or NTC values to both zones B and C. Similarly as for the ATC approach, each TSO composes a D2CF file with the same minimum information requirements. In this case however, the file has to be constructed for 24 timestamps, as opposed to only 2 timestamps for ATC. 3. Long-Term adjustments The Net Transfer Capacity (NTC) is derived from the Total Transfer Capacity (TTC), after deducting a Transfer Reliability Margin (TRM). While the TTC is the maximum commercial exchange possible between two zones in one direction, the TRM is reserved to be able to cope with emergency situations or unexpected deviations in neighboring countries. Finally, to arrive at the value, the already known long-term nominated power flows are subtracted from the NTC value, indicated by the red area in Fig. 5 on the left. The ATC is subsequently available for trade on the day-ahead market. 3 Consequently, 24 base cases can now be composed from the individual D2CF files. Merging all constraints on critical branches leads to a global Security of Supply domain. Each combination of values inside the trading domain is allowed. The verification step now consists of checking the vertices, instead of the corners under ATC 9. Example: Using the same simple three zone example as above, the hourly trading domain for the FB mechanism is illustrated in Fig. 4. Each combination inside this domain is allowed for trading purposes. The eight vertices that have to be verified are also indicated. The FB domain corresponds with the global Security of Supply domain. Instead of assuming one NTC capacity value per direction on each border, all constraints imposed by the critical branches are considered. Each constraint corresponds with a dotted line in Fig. 4. Figure 5: Derivation of ATC (ATC) and RAM (FB) A similar approach is implemented to derive the margin available to the market under a FB approach. However, while for ATC an aggregate value per border is taken into account, the FB mechanism considers each line individually. Fig. 5 on the right illustrates the process. Initially, the maximal flow (F_max) is available. A security margin or Flow Reliability Margin (FRM) reflects the uncertainty inherent to the process of determining the remaining capacity, while F_ref represents the physical flow that will be present due to the already known long-term nominations. What will eventually be offered to the day-ahead market is the Remaining Available Margin (RAM). 4. Allocation of available capacity (market coupling) Figure 4: FB domain 7 Trading domain can be represented on a two-dimensional scale since we only consider 3 interconnected zones in this simple example 8 Vertices is a broader term used to denote the points that describe the corners or intersections of geometric shapes. Terminologically, it is convenient to distinguish between corners for the ATC domain and vertices for the FB domain For both methodologies, the result of the three preceding pre-coupling steps is a potential trading area. These areas actually denote a combination of constraints, which serve as input to the market coupling algorithm. Comparing both domains in Fig. 6, it is clear that the larger FB domain surrounds the ATC domain. As a consequence, the FB mechanism offers more trading opportunities to the market. Therefore, FB market coupling leads to a solution equal or better in terms of social welfare compared to the ATC market

4 4 coupling algorithm. Furthermore, when a TSO provides ATC constraints, he needs to make a choice in advance on how to split the capacity among its borders (A to B and A to C), even before the market participants bids are known. In contrast, under the FB approach, the entire Security of Supply domain is offered to the market. Driven by bids and offers, the market itself decides on the repartition of commercial capacity among market players. offline simulated FB market coupling. Positive values for all countries are observed. Congestion Revenue (CR) is the product of the price difference between two zones and the constrained energy exchanged between them. As the network is less constrained under FB market coupling, there is less CR. Overall, welfare (consumer + producer surplus + CR) in the CWE region is higher for the simulated FB than for ATC market coupling. Figure 7: Average weekly change in surplus (consumer + producer surplus) per country and average weekly change in CR for CWE region in 2014 (Source: Parallel run CASC) Increased price convergence Figure 6: Comparison of ATC versus FB trading domain Flow-based market coupling in practice While the flow-based approach is clearly beneficial compared to ATC from a theoretical point of view, practical considerations are also taken into account. The ENTSO-E network codes recommend the use of the FB approach for meshed grids, while ATC should be maintained in areas where the distribution of power flows is only slightly influenced by electricity trade in non-adjacent bidding zones. For the Central Western European (CWE) region, clearly the FB methodology is preferable. To test the FB procedure, a parallel run has been organized by CASC to simulate the outcome of FB day-ahead market coupling, while still operating under the ATC regime 10. This way, gradual improvements to the FB market coupling mechanism can be made, before the final go-live. Effect on market functioning Overall welfare gain The objective of market clearing is to optimize welfare, defined as the sum of the consumer and producer surplus and congestion revenue (CR). The producer surplus equals the benefit to producers derived from selling at a market price that is higher than the least that they would be willing to sell for. The consumer surplus is the gain obtained by consumers as they can purchase electricity at a price that is less than the highest price that they would be willing to pay. Fig. 7 displays the average weekly welfare gain per country, comparing the actual ATC to the 10 CASC is the central auction office for cross-border transmission capacity for CWE, the borders of Italy, Northern Switzerland and parts of Scandinavia, providing a single auction platform and single point of contact FB market coupling leads to increased price convergence, which can be interpreted as the percentage of time that prices are equal across the entire CWE region. Fig. 8 pictures the price convergence rate for each available week of A higher convergence rate can be identified for FB than for ATC for each week of Figure 8: Price convergence ATC versus FB in 2014 (Source: Parallel run CASC) Main challenges for implementation Influential design parameters The input parameters provided by individual TSOs influence the FB market outcome. Consequently, transparency about the design of all the parameters is necessary in order to allow a nondiscriminatory, efficient and market-based use of cross-zonal capacity. Specifically, the freedom of the TSOs in this field should be limited to prevent them from making choices that could affect the market outcome in a negative way A. Marien, P. Luickx, A. Tirez and D. Woitrin, Importance of Design Parameters on Flowbased Market Coupling

5 Unpredictable auction outcomes The outcome of the FB market coupling algorithm is not always intuitive. Although it is reasonable to assume that a low price country exports electricity to a high price country, with FB market coupling this is not always the case. As the FB algorithm optimizes welfare for an entire region, it does not take into account the prices or volumes of each zone separately. This way, it can happen that some electricity flows occur from high to low price zones. Since this raised concern, an adapted algorithm FB Intuitive (FBI) market coupling has been tested during the parallel run program. Although the FBI solution leads to less overall welfare gain than the plain FB solution (but still higher welfare than ATC), it is more acceptable to market parties. Finally, it has been decided to go-live with the intuitive version of the FB market coupling algorithm 12. Conclusion Flow-based market coupling leads to a more efficient use of generation and transmission resources. While under ATC, TSOs themselves determine capacity values based on forecasts and historical data, the FB mechanism allows TSOs only to derive the impact that trade will have in terms of physical flows on the network. Subsequently, it is the market who decides how transmission capacity is allocated over market parties. More capacity is offered to the market under FB market coupling, resulting in an overall welfare gain and increased price convergence. However, the flow-based solution is less transparent than the ATC mechanism. The necessary input data TSOs have to provide is complex and influences the market outcome. Also, the capacity calculation process is less straight-forward and flow-based market coupling occasionally leads to unpredictable auctions outcomes. This may be confusing for market parties. Finally, does not solve the problem of congestion management inside bidding zones. In this respect, it might be necessary to review the current bidding zone configuration in Europe, reflecting also on the idealized nodal pricing solution. 5 1) Calculation of available capacity TSO coordination Result of capacity calculation Available Transfer Capacity Border-by-border, bilateral coordination between TSOs Available commercial capacity (NTC) values per direction on each border Flow-based Coordination at regional level with interaction among all TSOs A set of critical branches and their corresponding available physical capacity 2) Verification Grain of verification Two timestamps verified daily Twenty-four timestamps verified daily 3) Long-term adjustments 4) Allocation of available capacity Grain of adjustments Constraints to MC algorithm Capacity allocation Adjustment on value per direction on each border Constraint for each direction on each border Capacity is already allocated over borders by TSOs in Step 1) Adjustment applied on each considered critical branch Constraint for each considered critical branch Market-oriented capacity allocation, based on market bids and offers Table 1: Summary of the key differences between the ATC and FB methodology. The highlighted boxes indicate at which step capacity is allocated for both methodologies 12 KU LEUVEN ENERGY INSTITUTE Celestijnenlaan 300 box 2421 B3001 Heverlee

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