To know the weather and especially the wind in advance, allows us to carry out a better management and maintenance of our solar plants. Abengoa, therefore, is working on the creation and development of wind predictive models.
In solar energy plants, the area which is composed of the devices with the purpose of capturing or concentrating the sun’s energy, is the solar field (thermosolar concentration with
collectors,
heliostats for tower technology or
photovoltaic). It consists of a large number of structures, called reflectors or modules, which may occupy areas up to 1,000 hectares. The cost of these structures is approximately 10% of the total budget of the plant, so the
sizing of them and, especially, their
management and maintenance are essential.
The main agent which determines the structures design and management of the solar field is the wind, which has a
haphazard nature that is difficult to predict. In Abengoa, we design the structures in order to resist all kinds of adverse conditions when they are in their most favorable position. However, during plant operation, sometimes, these may be vulnerable which, in turn, may lead to damage taking place. It is therefore essential
to anticipate sudden weather changes and thus efficiently bring down all structures to a “defensive position” where they can remain during a storm.
Abengoa Research is working on the design and implementation of
predictive models to anticipate
real-time weather events to protect the solar fields. These mathematical models are able to
compile statistics from data recorded in anemometers and
estimate at a certain time horizon, the maximum wind speed reached. This estimate will let us know if there is a threat of structural risk, in which case a signal to all structures of the solar field is sent in order for them to be positioned safely.The predictive models use tested and powerful mathematical tools such as;
statistical characterization of the elements, the
setting of parameters using numerical techniques or prognosis using the
probability theory and the
Monte Carlo method.
The result enables a functional, comprehensive, efficient, dynamic and automated solar field management to take place, which ensures the structural integrity of the solar fields while optimizing production at the plant in operation.
