Using digitalisation for unlocking the flexibility needed for an efficient transformation of our energy system, by Henrik Madsen, professor DTU 

Today energy systems are often operated in silos using a centralized setup and rather simple digital solutions.  Similarly, electricity markets are typically based on static and deterministic economic principles like bidding and clearing. The future weather-driven energy systems call for a fundamental shift towards new market principles which respect to the dynamics and stochasticity arising e.g., from physics and human interactions. 
In addition, we are facing a decentralized setup with energy systems integration at all levels. These fundamental shifts call for a massive digitalisation of the energy system. In this talk we will describe digitalisation for unlocking the needed flexibility at all levels of the energy systems for an efficient large-scale integration of fluctuating renewable generation. This includes methods for hierarchical forecasting, flexibility functions for characterizing the flexibility, and MIMs (Minimum Operability Mechanisms); e.g., for defining an operational link between the markets and the physics. These tools are integrated in the Smart-Energy Operating-Systems (SE-OS) which aims at providing a hierarchy of optimization and control-based methods for providing both balancing and smart grid services.

Energy sector coupling and how to obtain a market equilibrium, by Associate Professor Trine Boomsma, KU
With many interlinkages between different energy subsystems, e.g., electric, heat and gas systems, sector coupling can contribute significantly to the green energy transition beyond what is realized by separate planning for each sector, notably in the presence of appropriate policy measures. However, the assessment of the sector-coupling potential requires a consolidation of technical and economic modeling. We develop optimization and equilibrium models that account for the technical characteristics of operation and investment within each sector, use these to establish a market equilibrium model for coordinated planning among sectors, and aim to devise government policy to stimulate coordination. Modelling will rely on linear, convex and mixed-integer programming, mixed complementarity modelling and hierarchical optimization. Given the complexity of modelling, it is necessary to develop solution methods based on reformulations and approximations.