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09/10/2019

Meteo for Energy expands its presence in the solar thermal sector

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The software developed by Meteo for Energy helps track the solar radiation accurately and enables operators of solar thermal power plants to better plan and manage energy production. 

Results achieved in CSP plants:

This software has been in operation for over five years and has proven to be very successful in different thermal solar plants both in Spain and overseas.

The ability to anticipate what will happen in the upcoming minutes, hours and days help plants manage electricity production through storage and thus adapt it to the market's needs. In other words: it is now possible to inject electricity in the evening when there are no more hours of sunshine, but still, the demand for energy (and the price) increases due to domestic consumption.

Meteo for Energy has demonstrated its international focus, with a 20% market share of storage plants operating on the foreign market in countries such as Morocco, South Africa and Israel.

"We are currently the Spanish market leaders in solar thermal power plants with storage, with a 67% market share."

 

Software installed in CSP plants:

The latest installation of the METEOCAST software at the NOORoIII solar thermal power plants in Ouarzazate (Morocco) in July and Ashalim in the Negev desert (Israel) in April confirms the high accuracy of the solar predictions.

Our software combines historical weather data, artificial intelligence and weather forecasts for the next 48 hours.

Historical weather data is the basis for forecasting. This means that before predicting what will happen in the future, it is essential to know how the microclimate and localised events in the area behave and what has happened in recent years. For this reason, either direct historical measurements (obtained from meteorological stations) or indirect measurements (obtained from satellite images) are required to start from a good base.

On the other hand, Artificial Intelligence has enabled significant advances in terms of accuracy. The combination of neural networks, machine learning and statistical techniques allows us to identify patterns in data to make more accurate and reliable solar radiation predictions.

Finally, it is essential to incorporate into the forecast model how the different meteorological variables are expected to evolve in the next hours and days. In this last stage, it is vital to know which meteorological variables, from which sources of information and to what extent each one of them affects future forecasts to achieve the best results.

With the combined use of all this information, we can increase the accuracy of weather forecasts, achieving a MAE error of less than 110W/m² in daylight hours on an annual basis.

Advanced systems and technology

The Meteo for Energy software comprises the following systems and technologies: sky camera, satellite imagery and weather models.

Sky camera (MeteoCamera): this system helps operate efficiently during cloud transits at dawn and sunset.

Satellite imagery (MeteoSatellite): this system helps integrate production into the grid for the next 3 hours for the continuous market.

Meteorological models (MeteoModels): this system helps reduce deviation costs and integrate the energy produced into the day-ahead and intraday markets.

This way, Meteo for Energy wants to collaborate in integrating renewable energies into the electricity grid, such as PV, CSP, wind and hydroelectric plants that depend on natural resources (sun, wind and rain). The more renewables available, the more uncertainty will be created as to the amount of energy generated in each hour, causing instability in the distribution grid. This is why Meteo for Energy, through accurate energy production forecasts, aims to provide reliability to the market and stability to the grid so that renewable energies are manageable and predictable.

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