Funded by the European Union's H2020 Programme under grant agreement n°649743.
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