Modelling cross-border power flows and market coupling

Why cross-border flows matter for price formation

Cross-border power modelling has become vital in European market analysis as nations increasingly integrate their systems. Market coupling significantly enhances efficiency by matching supply and demand across borders, prioritising the most cost-effective generation. Consequently, coupled markets often show more aligned prices, particularly when there are no transmission constraints.

Imports and exports also mitigate volatility by balancing supply and demand. Surplus areas export to deficit regions, decreasing reliance on costly peaking plants. When flow directions change, factors like high renewable generation or low demand can lower prices in other areas. As a result, analysts need to consider not only local fundamentals but also the influences from neighbouring zones.

How market coupling works in practice

The core of European integration is the flow-based market coupling algorithm, commonly used in Central Western Europe. This algorithm considers available generation, demand, bids, offers, and the physical limits of the transmission network. Instead of setting fixed border capacities, it evaluates how each extra unit of trade impacts the broader grid.

Capacity allocation and congestion management happen simultaneously. To aid understanding for modellers, key elements often include:

• how the algorithm selects the set of trades that maximises welfare across all participating zones

• how network constraints limit the ability of price differences to converge fully

• how congestion rents emerge when prices diverge across constrained borders

For analysts, grasping these interactions is crucial for any cross-border modelling strategy.

Modelling interconnectors and cross-zonal capacity

Effective interconnector modelling starts with dependable data sources. Essential inputs include ENTSO-E data from the European Network of Transmission System Operators for Electricity, offering transparency on flows and capacities; transmission system operator (TSO) reports on planned and unplanned outages; and REMIT, which discloses insider trading, market manipulation, and unforeseen events in the wholesale energy market. These datasets enable analysts to develop a precise understanding of available capacity and its fluctuations over time, serving as a solid basis for reliable cross-border power forecasts.

Building network constraints into models involves more than inserting a single capacity value. Analysts often account for:

• directional limits and seasonality

• ramp rates and loss assumptions

• flow-based constraint relationships (where applicable)

After constraints are established, models can simulate congestion rents and net positions. This enables analysts to understand how imbalances spread throughout the region and identify the likely formation of structural bottlenecks.

Forecasting congestion and price spreads

Predicting congestion involves combining structural insights with scenario analysis. Historical constraint data, observed price spreads, and network studies identify common bottleneck locations. Additionally, sudden events like outages or severe weather can significantly escalate congestion risks.

Weather sensitivity is especially crucial. Wind patterns in Germany influence flows into the Netherlands, Belgium, and France. Nordic hydro levels affect whether the region exports to or imports from continental Europe during critical times. Hot summers and cold spells can reverse typical flow patterns across the Alps, leading to atypical price spreads.

Interconnector expansion or prolonged outages introduce further uncertainty. New cables can reduce congestion and tighten spreads, while outages may increase them. Analysts who explicitly model these scenarios are better positioned to forecast which borders are likely to become constrained and when.

Regional insights: UK, DACH and Nordic systems

The UK system offers a distinct example. After Brexit, Britain no longer partakes in the single European day-ahead auction and instead employs explicit capacity allocation. This often leads to wider and more volatile price spreads. However, the UK remains closely connected to the continent due to strong wind capacity, gas generation exposure, and several new interconnector projects.

The DACH region operates within the flow-based framework, so cross-zonal interactions depend heavily on internal German constraints. Northern wind output may drive exports, but internal bottlenecks frequently restrict flows. Swiss and Austrian capacity also affects whether Germany imports during scarcity periods.

The Nordic region behaves distinctly again. Hydro resource availability is the main factor driving cross-zonal spreads. Low reservoir levels increase prices and attract imports, while high inflows lead to extended export periods. These changes significantly impact flows between the Nordics, Germany, the Netherlands, and the UK.

Conclusion

Cross-border power modelling is now the backbone of pan-European market foresight. Market coupling has created an extensively interconnected system where domestic fundamentals are only part of the overall picture. Analysts who comprehend transmission constraints, interconnector behaviour, and the impacts of weather and outages will be best positioned to forecast credible price spreads and spot profitable opportunities. As Europe's grids continue to expand and integrate, the importance of robust cross-border analysis will only increase. Those who master these methods will shape the next wave of trading strategies and policy choices.