Advanced Weather Intelligence for Grid Stability & Energy Planning

Presented by: Meteomatics, UK Power Networks and Swissgrid

Date: March 25, 2025

Duration: 1 hour 10 minutes

Participants:

• Meteomatics experts: Rob Hutchinson (Energy & Utilities Lead), Markus Schwab (Weather Expert and Climatologist), Stefanie Kieferle (Product Owner, Web)
• UK Power Networks: Jamie Bright (DSO Data Science and Development Manager)
• Swissgrid: Christoph Glanzer (Principle Specialist: Research and Digitalization)
• Moderation: Alexander Stauch (Head of Marketing)

The webinar "Advanced Weather Intelligence for Grid Stability and Energy Planning", hosted by Meteomatics, explored how high-resolution weather data and forecasting can enhance grid resilience and renewable energy integration. Moderated by Alexander Stauch, the session featured Meteomatics experts Markus Schwab, Rob Hutchinson, and Stefanie Kieferle, along with guest speakers Jamie Bright from UK Power Networks and Christoph Glanzer from Swissgrid.

Markus Schwab opened by examining the growing complexity of grid operations as renewable energy expands and climate change drives more frequent extreme weather. He highlighted key risks, such as convective thunderstorms, fog, snow, and icing, and presented Meteomatics’ vision for “Weather Experience 2.0,” built on high-performance computing, instant data access, and flexible visualization.

Rob Hutchinson demonstrated how Meteomatics implements this vision through its Weather API and the high-resolution EURO1k model. He detailed how EURO1k delivers 1 km forecasts (down to 90 m), integrates multiple data sources (including Meteodrones and third-party models), and improves wind forecast accuracy by 10–15% over ECMWF. A case study on Storm Eowyn showed how Meteomatics provided five-day advance warnings of damaging wind gusts, enabling operators to prepare and reduce economic and environmental impacts.

Stefanie Kieferle previewed upcoming MetX enhancements, including a time-navigation slider, self-service GeoJSON uploads, natural-event layers, and expanded animation and parameter controls, further strengthening Meteomatics’ decision-support capabilities.

Customer perspectives rounded out the session. Jamie Bright described how UK Power Networks uses Meteomatics’ ensemble forecasts for dynamic outage management, cutting losses and emissions while increasing flexibility and customer satisfaction. Christoph Glanzer outlined Swissgrid’s nationwide solar forecasting project, which combines Meteomatics data with the Pronovo PV database to produce 280,000 hourly forecasts and backcasts, helping balance the grid and inform future policy.

Key Takeaways

• Weather Resilience 2.0: Meteomatics’ strategy combines high-performance computing, real-time data access, and flexible visualization to manage increasingly volatile weather conditions affecting energy grids.

• EURO1k Model Advantage: The proprietary 1 km-resolution EURO1k model, downscalable to 90 m, improves wind forecast accuracy by 10–15% over ECMWF and supports detailed, site-specific applications.

• Comprehensive Data Integration: Meteomatics aggregates inputs from Meteodrones, satellites, radar, lightning, oceanic data, and about 30 external weather models to provide over 1,800 parameters via a single Weather API.

• Operational Impact Proven: During Storm Eowyn, Meteomatics forecasts warned of 140 km/h gusts five days in advance, enabling utilities to plan and minimize outage-related costs and emissions.

• Dynamic Outage Management: UK Power Networks uses Meteomatics’ ensemble forecasts to re-evaluate outages closer to real time, avoiding millions of pounds in losses and preventing thousands of tons of CO₂ emissions.

• Nationwide PV Forecasting: Swissgrid combines Meteomatics’ forecasts with the Pronovo database to generate hourly predictions for roughly 280,000 solar installations, ensuring more accurate grid balancing and supporting future control strategies.

• Flexible Visualization with MetX: Upcoming MetX features (time slider, GeoJSON upload, natural-event layers, and advanced animation) will empower users to customize and act on weather intelligence faster.

• Model Selection Flexibility: Users can choose Meteomatics’ Mix model mode for automated best-model selection or request outputs from multiple models for detailed comparisons.

Q&As

Q: Can we retrieve past forecasts (not hindcasts) as they were originally issued for a given date and time?
A:Yes. We archive forecast runs from key models. Due to data volume we cannot store every run of every model globally, but important runs are kept and can be accessed on request.

Q: In a polyline query, is it possible to specify different heights for different points?
A: Yes. All models are interpolated in 1 m increments from ground level up to 20 km, so different points can have different heights.

Q: Which models does Swissgrid use for solar forecasting?
A: We rely on Meteomatics’ Mix model approach. At present this primarily uses the 1 km × 1 km EURO1k model, automatically switching to the best available model for longer-term forecasts. It’s a “fire and forget” process.

Q: What are the optimal latency and lead-time requirements for load balancing and outage planning?
A: It depends on the use case. For half-hourly updates, latency must be under 30 minutes. UK Power Networks reduced weather-data latency from six hours to one hour with Meteomatics’ help, improving short-term accuracy. For day-ahead market scheduling or outage planning, forecasts can take longer to prepare as long as they meet daily deadlines. Control methods differ, some generators can be switched automatically, others require manual action, so the ideal latency varies.

Q: How does space weather (e.g., geomagnetic storms) affect power grids, and are current forecasts sufficient?
A: Severe geomagnetic storms can affect power grids, but modern networks are better protected. Space-weather indices are available via the Meteomatics API and can be included in forecasting models if needed.

Q: How does Meteomatics manage and verify real-time forecasts in highly variable regions like India, and how do you measure accuracy?
A: We continuously verify model performance with in-house tools. Meteomatics ingests global data and the API automatically delivers the best model mix for any location and parameter. While India’s monsoon season is challenging, the mixed-model approach compensates for regional variability and maintains accuracy.

Q: When requesting a forecast, can Meteomatics customers access outputs from multiple models for comparison, or only the best model?
A: Both are possible. Users can rely on the Mix model mode for automated best-model selection or request forecasts from individual models to compare results themselves.