Customer application 27 August 2020

Storage Systems Balance out Solar Power

As part of the Smart Grid Solar project, the Bavarian Center for Applied Energy Research is testing how storage devices can help equip local networks for the increased supply of solar electricity. Controllers from WAGO ensure secure communication between individual systems and the control center.

The integration of current from fluctuating sources into the low-voltage networks is one of the greatest challenges of the energy transition. The Bavarian Centre for Applied Energy Research (ZAE Bayern), together with partners from science and industry, has initiated a field test in the small city of Arzberg in Upper Franconia that should provide some solutions. “How is it possible to link a large number of PV arrays into the local network without costly expansion of the latter? How will it be possible to operate the local networks securely and efficiently in the future, when even more renewable energy systems are supplying the power?” These are two of the questions behind the project, according to Project Leader Philipp Luchscheider from ZAE Bayern.

Integrating Storage Systems Reliably – Here’s How WAGO Supports You:

  • WAGO’s controllers offer a high degree of flexibility for integrating various systems.
  • Efficient data transfer between the controllers and control center via MQTT protocol
  • Reliability – even in adverse weather conditions

Solar Power for Electrolysis

To get answers, the researchers established a smart grid test center in the Schlottenhof district of Arzberg. In addition to PV modules with a total peak output of approximately 50 kilowatts, the facility includes three redox-flow batteries as short-term buffers, as well as an electrolysis system with a hydrogen tank and fuel cell for long-term storage. A drop cable connects the test field to the local network in Schlottenhof. The location offers the best conditions for the project, because many households operate solar arrays, and some are equipped with residential storage units. The test will conclude at the end of the year.

Controlling Storage Devices Intelligently

“To balance fluctuating generation and smooth out supply peaks, short- and long-term storage devices must cooperate intelligently. This includes taking weather predictions and expected consumption into account,” explains Luchscheider. These tasks are performed at the control level using a powerful server; the control center itself is located in a prefabricated office at the edge of the test center. The computer generates instructions, with the aid of simulations and mathematical models, which it then transmits to storage devices as signals.

A Large Amount of Important Data

Controlling such a complex system requires reliable information about the state of the storage devices and the PV array, as well as the network load and capacity. ZAE Bayern collects detailed data from all of the system components every second of every minute. Production and consumption from twenty Schlottenhof households, four with battery storage units, are also recorded every second, in addition to the voltage, current and phase angle at the local transformer station. The researchers also measure weather data, like local solar radiation or wind speeds. The data is transmitted to the central server via VPN tunnel, linked to predictions about solar production and network load and processed into control signals.

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Philipp Luchscheider, Manager of the Smart Grid Solar Project (left), checks the assembly of the power-to-gas system for producing hydrogen.

Getting a Handle on Midday Peaks

What does such control actually look like? For example, on a sunny day, the residential battery storage units begin charging somewhat later in the day instead of the early morning. This ensures that they still have free capacity when the solar arrays reach maximum production. The owners of the storage devices don’t see any change – the batteries are still charged when they need electricity in the evening. The server calculates the optimum time for energy intake based on the network and system data, as well as the weather and load predictions.