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It is widely understood that network capacity is a scarce resource for electricity trading as power flows on the electricity network are constrained by operational safety limits. Therefore, network capacity has to be auctioned to market participants, either via explicit or implicit auctions.
An explicit auction offers network capacity separately from energy trading. Hence, market participants must ensure have consistent positions in the network capacity and energy markets. An implicit auction, on the other hand, is a more advanced process that auctions network capacity and energy altogether.
Implicit auctions can be implemented via either market splitting or market coupling. Market coupling is deemed to be a better approach as the expected outcome of a market coupling process can be fully specified in terms of input-output only and does not depend on implementation choices, resulting in a more transparent allocation of network capacity. The market coupling also leads to higher welfare and improved consistency of price signals.
N-SIDE has acquired advanced knowledge in solving complex market coupling problems in Europe and beyond. In particular, N-SIDE develops EUPHEMIA, the algorithm used by the European Single Day-Ahead Coupling (SDAC) initiative to couple the electricity markets of 28 European countries.
In this article, we explain the two models used by the SDAC to conduct cross-zonal capacity allocation under a uniform pricing scheme and how its results should be understood. To that end, we will have to also provide high-level explanations of the capacity calculation process carried out by Transmission System Operators and Regional Coordination Centers. We will not discuss the rationale of the bidding zone definitions.
Available Transfer Capacity (ATC) is the historical way of modeling cross-zonal capacity. At the capacity calculation stage, the ATC is computed as the maximum incremental active power flow that can be handled by the border. Because complex and incomplete pieces of information (physical laws, historical and forecasted data, already allocated capacity, intra-zonal congestion, flows on other borders, …) have to be combined, the capacity calculation process has to be somewhat conservative and adopt significant security margins.
The ATC is eventually allocated to trades by the market coupling auction. The outcome of the ATC capacity allocation is easily understandable.
If the ATC is not constraining, the uniform market price is the same in the two adjacent bidding zones (assuming there are no other constraints or losses on the line). That is because the cross-zonal trades are not limited by the available capacity.
If the ATC is constraining, the uniform prices can differ between the two bidding zones, and the power flow (from the cheap bidding zone to the expensive bidding zone) is equal to the ATC. Cross-zonal trades are limited by the available capacity and an increment in social welfare would be feasible if the constraint was slightly relaxed. This increment of welfare (measured in €/MWh) is called the shadow price of the ATC constraint. Multiplied by the ATC, it is the congestion rent perceived by the transmission system operator. The congestion rents are available on ENTSO-E’s transparency platform.
A Flow-Based model provides a more accurate representation of the network constraints by approximating the underlying physical laws (i.e. Kirchhoff's voltage and current laws) around the grid’s forecasted functioning point. As a consequence, FB models better capture the interdependencies between power flows on different network elements and allow for less conservativeness at the capacity calculation stage.
As opposed to the ATC model, the flow-based model does not bind the active cross-border power flows specifically. Instead, it computes and bounds the active power flows on a set of so-called Critical Network Elements (CNE), which can be across or within the bidding zones’ borders.
The active power flow on a CNE is computed as a mathematical function of the net positions (import / export) of all the bidding zones of the Capacity Calculation Region (CCR). For example, the power flowing through a specific transformer located inside Belgium could be equal to 1E-2 times the net position of Belgium plus -3E-2 times the net position of France. The coefficients of this mathematical function are named the Power Transfer Distribution Factors (PTDF), as they model how power transfers are distributed over the CNE.
The active power flow on the CNE is bounded by a maximum acceptable power flow (called the Remaining Available Margin, or RAM, in the CORE FB methodology). For example, we could have 1E-2 * net_position_Belgium - 3E-2 * net_position_France <= 50.
This process is repeated for the base scenario (also called the N-0 scenario) as well as for so-called contingencies, i.e. scenarios simulating the failure of some network element (usually limited to the N-1 criterion).
The outcome of the FB capacity calculation are thus the PTDFs and the RAMs of all of these scenarios.
Eventually, the FB network capacity is allocated to trades by the market coupling auction. The market coupling algorithm searches the welfare optimal set of trades among all sets of trades of which bidding zones’ net positions satisfy the FB constraints. The outcome of the FB capacity allocation is slightly less straightforward to understand.
Indeed, uniform market prices should converge across all bidding zones of the CCR, unless at least one of the FB constraints is limiting the exchanges. If the market prices are not equal between the two bidding zones, there is (at least) one FB constraint that prevents shipping more electricity from the cheap bidding zone toward the expensive bidding zone. An increment in social welfare would be feasible if the constraint was slightly relaxed. As for ATC constraints, this increment of welfare (measured in €/MWh) is called the shadow price of the constraint. Multiplied by the RAM, it is the congestion rent. The congestion rents for CORE are available on JAO’s Publication Tool.
Moreover, FB market coupling can also produce results that are called non-intuitive. This means electricity is flowing from an expensive bidding zone toward a cheap bidding zone. Why? Because some non-intuitive exchanges can actually free up capacity, which then allows for larger exchanges on other borders. So, while a single border was non-intuitive, the model still increased social welfare in the big picture.
Still, non-intuitive results can be challenging to understand for many stakeholders. In fact, when EUPHEMIA was launched in 2014, it used a Flow-Based model of the network that automatically rejected non-intuitive power flows. In 2020, the algorithm was patched to allow for non-intuitive flows that result in greater social welfare, as per decision (123) of ACER’s Decision on Algorithm Methodology.
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While a FB model is more complex than an ATC model, it provides results that make the added complexity worthwhile. Indeed, a FB model requires less conservativeness at the capacity calculation stage, which releases more freedom available to market participants for trading. This larger capacity domain translates into more cross-border trading opportunities, increased price convergence, and increased social welfare.
The general expectation is thus for each region to move away from ATC models and towards FB models, especially since FB should be the default methodology for capacity calculation as per Article 20(1) of the Capacity Allocation and Congestion Management Guideline.
Source: ENTSO-E Market Report 2022
The Central Western European region (CWE), which includes Germany, Austria, France, the Netherlands, and Belgium, has operated on a Flow-Based model since 2015. In 2022, the entire CORE region, which includes the CWE countries plus Croatia, the Czech Republic, Hungary, Poland, Romania, Slovakia, and Slovenia moved to a Flow-Based model. We expect the Nordic Capacity Calculation Region to follow suit in the near future.
Learn more about the results of EUPHEMIA
Pierre-Paul holds an MSc in Applied Mathematics from UCLouvain (Belgium) with a specialization in Optimization. He has strong expertise in the design and implementation of electricity market auctions with more than 4 years of contribution to the successful deployment of N-SIDE's energy market solutions in Europe and India. Pierre-Paul is interested in mathematical programming, operational research, electricity markets and power systems.
Pierre-Paul Mouchet