We develop innovations for the future of energy
in Berlin
Our mission
We support our customers comprehensively in the technical and economic modelling of energy infrastructures as well as in the development of data-driven solutions in the context of the energy transition and climate protection.
Our core competencies
We have many years of experience in developing IT solutions for the energy sector. Our expertise in energy engineering, energy economics, machine learning and software development enables us to create highly practical and robust solutions tailored to our customers’ needs.
The background of Qantic
Under the Qantic brand, we have been developing IT solutions and web-based services for modelling complex energy systems since 2013. Our team combines expertise in energy economics, engineering, software development and data science.
References
Below you will find a selection of projects in which we supported our clients through consulting services, software solutions or the development of data-driven applications.
First real-time monitoring on federal state level
Client: State Ministry for Nature, Environment and Consumer Protection North-Rhine-Westfalia
Conceptual design, implementation and maintenance of the Strommarktmonitoring NRW application. As the first system of its kind, it provided real-time monitoring of the energy transition at the level of a German federal state, including detailed generation data, renewable energy availability, emissions and electricity consumption.
Emission reduction considering grid constraints
Client: German Energy Agency (dena)
Design and implementation of a calculation logic for zonal emission factors (ECO zone) . Analysis of incentive schemes for energy control to avoid curtailment of renewable generation, as well as assessment of grid bottlenecks and redispatch measures.
Customisation of a microgrid planning tool
Client: MAN Energy Solutions SE
Extension and adaptation of the Q-System planning software with customer-specific functionalities. Focus on modelling the dynamic behaviour of gas engines and their interaction with battery storage systems within complex microgrid setups.
Digital twin of a district energy system
Client: naturstrom AG
Development of a digital twin for a district energy system consisting of a central heat pump, photovoltaic generation and various thermal and electrical storage options. The geothermal borehole field was modelled using machine learning methods, in particular an LSTM model.
Asset valuation and revenue forecasting
Client: Various clients
Support in the development, analysis and evaluation of planning variants for different energy systems, including microgrids, behind-the-meter battery systems and grid-connected large-scale storage systems in stand-alone and hybrid configurations.
Modelling of redox flow batteries
Client: Enerox GmbH
Adaptation of the Q-System software solution to accurately represent the dynamic operating behaviour of redox flow batteries, including the consideration of dynamic efficiencies and technology-specific characteristics.
Consulting on energy data collection
Client: Regulatory authority of an EU member state (confidential)
Consulting and technical support in the design and implementation of energy market data collection schemes, with a focus on data structures, collection methodologies and content requirements.
Analysis of publications on data transparency
Client: German transmission system operator
Support in researching and analysing publications by European TSOs and other relevant stakeholders on data transparency. Structured evaluation and comparative analysis of the findings.
Simulation of multi-market optimisation for battery storage
Client: International project developer
Development of a simulation framework for multi-market optimisation of battery storage systems. Calibration based on measured portfolio performance and application to real-world projects.
Reinforcement-learning-based energy control
Client: International plant manufacturer
Development of a real-time control strategy based on deep reinforcement learning. Setup of a simulation environment for agent training as well as parameterisation and validation under realistic boundary conditions.