The STORM project
– Demonstrating the benefits of district-level smart control systems.

The project tackles energy efficiency at district level by developing an innovative district heating & cooling (DHC) network controller. It aims to demonstrate that thanks to a smart DHC network controller energy savings can reach up to 30%. In that perspective, the project partners will develop a controller based on self-learning algorithms.

The developed controller will enable to maximize the use of waste heat and renewable energy sources in DHC networks.

The STORM project tackles energy efficiency at district level. It aims to demonstrate that, thanks to a smart District Heating & Cooling (DHC) networks’ controller, energy savings can reach up to 30%. In that perspective, the project partners will develop a controller based on self-learning algorithms. The developed controller will enable to maximize the use of waste heat and renewable energy sources in DHC networks. It will be implemented in two pilot sites, at Mijnwater BV in Heerlen (NL) and Växjö Energi in Rottne (SE), where the resulting energetic, economic and environmental benefits will be assessed. Through replication, dissemination and education efforts, the project outcomes will be transferred to several stakeholders across the EU, and will thus contribute to a wider deployment of intelligent DHC networks at the EU level.

Storm Objectives

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.

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.