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Internet of Things  
16 March 2017

ZSW scientists use algothrims to manage electric supply and demand

Scientists from the ZSW foundation are looking to implement self-learning algorithms to get a more detailed picture of energy flows in the electrical grid.

ZSW, short for the Centre for Solar Energy and Hydrogen Research in Baden-Württemberg has launched a major research project, which will seek to use algorithms to more accurately forecast consumers’ needs and the amount of electricity generated from renewables.

According to PV Magazine, satellite data will also be used to improve feed-in forecasts. The results of the researchers’ efforts are to be tested and refined in power companies’ grids.

The fact that renewables feature prominently in Germany’s power grid, necessitates a comprehensive view of energy to ensure power is delivered cost-effectively and as reliably as ever.

PV Magazine’s report adds that energy service providers’ business models need precise forecasts of energy flows through to the distribution grids, as do the operators of smart grids.

Named “C/sells”, the four-year project will see researchers chart current and future energy flows, with the aim of optimising the technical and business operations of power grids with very high solar penetration in 46 sample regions and neighbourhoods (cells) in southern Germany.

Project stakeholders include the transmission grid operators TransnetBW and TenneT, distribution grid operators, municipal utilities, energy and software service provid-ers, and research institutes.

The Federal Ministry for Economic Affairs and Energy is funding C/sells with some €50 million as part of an initiative called Smart Energy Showcase – A Digital Agenda for the Energy Transition (SINTEG). The project’s overall budget runs to around €100 million and involves 42 partners.

Smart technology

ZSW is reported to be using high-performance computer platforms based on graphics card clusters to develop state-of-the-art methods aimed to gain deeper insight into regional sections and local cells of the grid and to better forecast future conditions and energy flows.

“These new methods analyze vast amounts of complex information and are designed to process a variety of data sourced from power plants, environmental monitors, measurements and satellites,” says Dr. Jann Binder, who heads up the Photovoltaics: Modules Systems Applications department at ZSW.

“They sift through this mountain of data to independently filter out crucial properties for forecasting. These are key factors that influence green power plants’ expected electricity yields and consumers’ demand for electricity. These methods are also called self-learning algorithms for their ability to act autonomously.

Binder adds, “The goal is to deliver data in a form and level of quality beyond that of commercially available products.”

 

Image credit: Shutterstock

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