Digitization of the energy system

For centuries, we adapted to nature’s rhythms, grinding grain when the wind blew, or rivers flowed. But in our modern, demand-driven world, we expect energy to be available at our fingertips. Today's shift towards green energy must combine both: the rhythm of nature and the constant demand of the modern economy. Luckily, algorithms and IT infrastructure can assist us in creating an affordable, dispatchable, and reliable energy system based on green energy sources.

Digital management of energy flows

Imec is committed to contribute to the ‘smartness’ of the energy grid, balancing load, production, and consumption. For example, by developing demand-response algorithms to manage and adjust electricity demand in real time, in response to supply conditions. It is important to have all relevant data of current and historical consumption patterns to make predictions for future needs.

Machine-learning algorithms are key in this approach, to automatically detect and learn consumption behaviors, and to control them.

Imec studies:

• data-driven algorithms to control (groups of) flexible loads such as batteries, EV chargers and heat pumps, value stacking local objectives (e.g. PV self-consumption, capacity tariff) with energy market incentives.
• techniques to improve the scalability and explainability of such data-driven control algorithms
• decision support tools for power system operators, e.g. system imbalance forecasting
• data-driven fault classification techniques for solar panels
• secure and trusted data sharing for data access and data usage for AI algorithms. With this data-driven decision-making technology, imec aims at predicting and controlling the energy and storage assets of the smart grid.

Co-innovation hub & test environment for energy-management systems

IDLab, the associated lab of imec and Ghent University, has set up HomeLab, a unique residential test and co-innovation environment for smart home services. HomeLab offers a wide range of energy-related infrastructure to test and validate new energy applications such as energy monitoring services and the smart control of building systems (e.g. HVAC, blinds, windows).

The Python library HomeLabGYM simplifies the validation of new Home Energy Management Systems (HEMS) and demand-response services on the available infrastructure (home battery, heat pump, PV).

Scalable and trustworthy data sharing

Smart grids rely on real-time data from various sources to optimize energy distribution and balance supply and demand. While these technologies enable efficiency, they raise significant privacy concerns – think of citizen’s smart meters and behavioral insights being shared with external energy vendors. Imec develops technologies that allow for data sharing in energy applications in a way that privacy is preserved. This builds on imec’s expertise in:

• data spaces
• web-based data sharing
• privacy-preserving technologies (software & hardware)
• legal requirements and governance
• user centricity
• business models

Want to join our research? Click the contact button below to get in touch.