In a major research project, scientists from the Centre for Solar Energy and Hydrogen Research in Baden-Württemberg (ZSW) are looking to use improved, self-learning algorithms to get a more detailed picture of energy flows in the electrical grid.
These algorithms will serve to forecast more accurately consumers' needs and the amount of electricity generated from renewables. Satellite data will also be used to improve feed-in forecasts, and the results of the researchers' efforts are to be tested and refined in power companies' grids. As renewables feature prominently in Germany's power grid, a comprehensive view of energy flows is deemed necessary to ensure power is delivered cost-effectively and as reliably as ever — energy service providers' new business models require precise forecasts of energy flows through to the distribution grids, as do the operators of smart grids.
As part of a four-year project that goes by the name of C/sells, ZSW researchers are striving to chart current and future energy flows with unprecedented precision. The objective is to optimise the technical and business operations of power grids with very high solar penetration in 46 sample regions and neighborhoods (cells) in southern Germany.
Transmission grid operators TransnetBW and TenneT, distribution grid operators, municipal utilities, energy and software service providers, and research institutes are all on board. The Federal Ministry for Economic Affairs and Energy is funding C/sells with some €50m as part of an initiative called Smart Energy Showcase – A Digital Agenda for the Energy Transition.
Balancing supply and demand
Supply and demand — that is, power in-feed and consumption — must always be balanced in the power grid. This is easy to do given a central feed from a power plant park, where the power supply is simply adjusted on the fly to match consumption. However, says ZSW, striking the right balance is far more difficult with increasingly decentralised feeds from fluctuating solar and wind power sources. This requires a high-resolution picture that captures all the details of local energy flows.
Today's forecasts for power feeds into the grid are not mapped in sufficient detail and are too inaccurate under certain weather conditions — and on top of that, households and smaller businesses' consumption is merely estimated using standard load profiles. Consumer behavior has changed in recent years, so these estimates no longer accurately reflect the reality of the situation.
Now Big Data is making inroads into the power supply chain with researchers applying smart, new methods to map the electrical grid's energy flows in greater detail. ZSW is 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 analyse 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 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. Says Binder: "The goal is to deliver data in a form and level of quality beyond that of commercially available products."