How inaccurate weather forecasts are costing renewable energy operators millions in balancing fees
Explore how forecast errors from coarse weather models contribute to 10-30% of balancing costs, forcing operators into defensive strategies that erode profitability.
Renewable energy operators face a significant and growing operational expense: balancing costs. These are the fees incurred when the amount of electricity an operator promises to deliver to the grid doesn't match their actual production. Grid operators must step in to "balance" the system, often by purchasing expensive last-minute power, and they pass these costs on to the producer responsible for the discrepancy. On average, these costs range from 1 to 5€ per megawatt-hour (€/MWh).
While some deviation is unavoidable, a portion of these fees—between 10% and 30%—is the result of inaccurate weather forecasts. Operators rely on these forecasts to predict their solar and wind generation for day-ahead energy markets. When the forecast is wrong, their production estimates are wrong, and they are left with a financial penalty. This article examines how standard, coarse-resolution weather models contribute to this problem and why adopting high-resolution forecasting is a critical strategy for mitigating financial risk.
The direct line from forecast error to financial loss
The business model for a utility-scale wind or solar farm is straightforward: sell electricity. Most of this electricity is sold in day-ahead markets, where operators bid to supply a certain amount of power for each hour of the following day. Their bid is based almost entirely on a weather forecast that predicts sunshine (irradiance) and wind speed.
Here’s where the problem begins. If an operator uses a standard forecast from a model like the GFS (0.25° resolution) or ECMWF (0.1° resolution), they are working with data that approximate weather conditions over large areas. These models are not designed to capture localized phenomena.
For example:
- A forecast for a 9x9 km grid cell might miss a small, dense cloud bank that forms directly over a solar farm, causing a sudden 50% drop in production that wasn't in the bid.
- It might fail to predict how a coastal breeze will pick up in the late afternoon, causing a wind farm to overproduce compared to its committed amount.
In either scenario, the operator has created an imbalance.
- Under-production: The grid operator must find power from another source, usually a fast-reacting (and expensive) gas plant, to cover the shortfall. The cost of this emergency power is charged to the renewable operator.
- Over-production: The operator has injected excess power into the grid, which can cause instability. The grid operator may have to pay another plant to ramp down, and the renewable operator is often paid a very low, or even negative, price for the surplus energy.
These are not minor accounting adjustments. For a large-scale operator, balancing costs driven by forecast errors can amount to hundreds of thousands or even millions of euros per year, directly eroding profitability.
Why standard forecasts are a business risk
From a financial perspective, relying on coarse-resolution weather models is an unmanaged risk. These models are a poor tool for the job because their fundamental design ignores the very factors that create volatility in renewable generation. As detailed in our technical deep-dive on kilometer-scale models below, they smooth over complex terrain and use simplified estimates for processes like convection.
This creates a structural flaw in an operator's ability to plan. It forces them into a defensive posture in the market:
- Conservative bidding: To avoid the high penalties of under-delivery, many operators consistently bid below their forecast's median production estimate. This provides a buffer against forecast errors but means they are systematically leaving potential revenue on the table.
- Increased hedging costs: Operators may need to buy financial products or hold back capacity to hedge against the uncertainty created by poor forecasts, adding another layer of cost.
- Reactive operations: Instead of planning, teams are often left reacting to real-time deviations, which is inefficient and stressful.
The core issue is a lack of confidence. When you can't trust your primary input data—the weather forecast—every subsequent business decision is compromised.
High-resolution forecasting: a tool for financial control
This is where high-resolution numerical weather prediction (NWP) becomes a strategic asset. Weatherwise utilizes the Meso-NH model, running at resolutions down to 400 meters. This model directly simulates local weather phenomena instead of generalizing them.
What does this mean in practice?
- It can model the development of individual clouds, giving solar operators advanced warning of a drop in irradiance.
- It can simulate wind flow through specific valleys and around hills, providing a much more accurate prediction of turbine output in complex terrain.
- It resolves the sharp temperature and pressure gradients at coastlines that create sea breezes.
By providing a more accurate picture of future conditions, Weatherwise aims to reduce energy forecast errors by 10% to 30%. This translates directly to the bottom line by:
- Reducing imbalance penalties: A more accurate forecast means the operator's bids are closer to their actual production, minimizing the volume of energy subject to balancing costs.
- Enabling more profitable bidding: With higher confidence in the forecast, operators can bid more aggressively, capturing revenue they would have otherwise sacrificed as a safety margin.
- Improving operational efficiency: It allows for proactive decision-making around asset management and participation in ancillary services markets, opening up new revenue streams.
This shift is validated by research. A paper on high-altitude solar power noted that electricity generation is highly sensitive to localized meteorological conditions, including cloud cover and snow, which underscores the need for high-resolution data to create viable financial models for such projects.
Conclusion
Balancing costs are more than just a line item; they are a direct tax on forecast inaccuracy. As renewable energy becomes a larger share of the energy mix, grids will become less tolerant of imbalances, and these costs are likely to rise. Relying on outdated, coarse-resolution weather models is no longer a viable business strategy.
Operators who continue to use these generalized forecasts are accepting a level of financial risk that is largely avoidable. High-resolution models like Weatherwise provide the detail and accuracy needed to move from a reactive, defensive market position to a proactive, optimized one. By reducing forecast errors, operators can significantly cut their balancing costs, improve their bidding strategies, and ultimately increase their profitability.
Have a question about Weatherwise? Contact us at sales@weatherwise.fr