Solar forecasting for grid management plays a vital role in the massive and secure injection of PV into the grid.

The use of solar energy, which relies on weather conditions, sets new challenges for grid operators. Indeed, the latter have to adapt the existing energy structure to integrate more renewable energy sources. This has to be done while maintaining the same level of quality (voltage and frequency) and of reliability (avoiding blackouts) in supplying electricity.

Plan solar energy production

Plan solar energy production

Transmission system operators (TSOs) coordinate electricity handover and are responsible for maintaining instantaneous grid stability. Consequently, solar forecasting for grid management is necessary to plan the coming day’s production units.

Take into account distributed energy production

Take into account distributed energy production

Distributed energy generation reconstruction helps distribution system operators (DSOs) avoid local voltage disruptions. Modelling and forecasting on medium and low voltage grids, hard to oversee, are a real challenge of grid management.

Calculate reserve requirements

Calculate reserve requirements

Solar generation can vary by nearly 80% within seconds. Therefore, significant primary reserve capacities can be summoned to overcome such variations. Each year, several million Euros are used to this purpose. Solar forecasting reduces grid management costs.

Products & Services

In order to guarantee optimal solar energy transmission and distribution, Reuniwatt has developed solar forecasting for grid management:

  • HourCast™: By having real time access to meteorological satellites’ images, Reuniwatt is able to offer solar forecasting for grid management activities. With HourCast™, the forecasts are available for the next hours, with frequent updates. This technology is used for country and portfolio forecasts. Moreover, it also refines day-ahead forecasts. Read more

  • DayCast™: Based on a hybridization of numerous weather data, DayCast™ is the most efficient forecasting method for 1 to 7-day ahead forecasts. Read more

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