Boosting energy efficiency at district level through the use of waste heat, renewable energy sources and storage systems.


Building on state of the art technical developments and advanced business models
  • Starting from control algorithms suited for both existing and new 4th generation DHC networks
  • Using market-based multi-agent systems combined with reinforcement learning
  • Applying self-learning and self-adaptive control, combining recent developments in model-based multi-agent systems and model-free control
  • Creating an add-on to many existing DHC network controllers and SCADA systems
Developing an innovative controller for district heating & cooling (DHC) networks
  • Balancing supply and demand in a cluster of heat/cold producers and consumers
  • Integrating multiple efficient generation sources (renewable energy sources, waste heat and storage systems)
  • Including three control strategies in the controller (peak shaving, market interaction, and cell balancing). Depending on the network, one or more of these strategies can be activated.


  • Demonstrating the benefits of smart control systems;
  • Quantifying the energetic, economic and environmental benefits of the controller.


Developing innovative business models needed for the large-scale roll-out of the controller at reduced costs
  • Investigating exploitation possibilities to facilitate the platform market uptake
  • Distributing the value amongst the different market players (producers, transporters, consumers of energy) by applying the control strategies in the controller
  • Taking into account different market set-ups to replicate in other countries than the ones of the demonstrators
Designing a scalable and performing self-learning control approach requiring limited external experts
Increasing awareness on the need to control DHC networks in a smart way