Electricity Storage Management

Problem addressed

To increase the self consumption of energy from photovoltaic (PV)-systems, electrochemical energy storages are used. For the operation of such systems energy management is necessary which is not a trivial task. Different strategies can be applied as goals of the energy management but practical controllers usually use only one optimisation goal or are not optimised at all. As a result the maximum efficiency cannot be reached.

Energy and economic efficiency is usually calculated for the design point, which is problematic because in operation the system reaches this point rarely. Malfunction and variable operation parameters can be additional problems.


In this project an adaptive scheduling controller will be developed which determines an optimal policy for the use of renewable energy (such as PV), storage devices and certain electricity loads (such as heat pumps). Key element is a dynamic and robust optimisation of the scheduling controller. The following features will be incorporated:

  • User input of desired comfort/energy saving level as well as user information about the control algorithm.
  • Integration of weather- and load-forecasts based on meteorological models and self learning algorithms.
  • Robust to uncertainties concerning forecasts and the optimisation-algorithm.

Additionally topics of energy meteorology will be discussed with a focus on prognosis of energy production of renewables which includes the enhance of models and the integration of measured values in models for learning purposes.

Simulations and tests will be carried out to analyse the functionality of the control algorithm. Furthermore the data will be used to evaluate the efficiency of the whole system over the lifetime of the components. Through data analysis methods for failure detection will be developed.


  • Development of an adaptive scheduling controller for the use of renewable energy, storage devices and certain electricity loads which incorporates weather- and load-forecasts and robust optimisation.
  • Development of specific meteorological energy forecast methods.
  • Analysis of energy efficiency of energy systems over the whole lifetime and development of failure detection algorithms.

Project data

Duration: 01.03.2015 - 31.12.2017
Budget: ca. 1.016.000 € total costs and ca. 700.000 € funding contribution

  • ASIC - Austrian Solar Innovation Center (coordinator)
  • Software Competence Center Hagenberg
  • Blue Sky Wetteranalysen Traunmüller u. Reingruber OG
  • Heliotherm Wärmepumpentechnik Ges.m.b.H.
  • Fronius International GmbH

Funding partner: Klima- und Energiefonds, Energieforschung - 1st Call (handeled by FFG)


Bernhard Freudenthaler

Freudenthaler Bernhard

Area Manager Data Science
Phone: +43 50 343 850