This technology tackles the energy efficiency of district heating networks at district level. The controller is based on self-learning algorithms and will enable to maximize the use of waste heat and renewable energy sources in DH networks. The controller optimizes the consumption of the buildings and districts and the demand of the network and optimally uses the potential of activating the building thermal mass as thermal energy storage system. The technology controls the supply and consumption side (‘demand side management’) of district heating networks and the different components of the energy system (such as storage units, heat pumps, etc.).
Three business strategies are included in the controller (peak shaving, interaction with the electricity market, cell balancing or balancing the energy between buildings in districts). Depending on your DH network and operation, one or more of these business strategies can be activated. The controller is developed as an add-on to many existing DH network controllers and SCADA systems and can easily be implemented.
- Easy to implement - add on to existing SCADA systems
- Using multi-agent technology
- Integration of self-learning algorithms
- Applicable in new and existing DH networks
- Reduce your peak loads in DH networks
- Easy extendable with other energy systems or components
- Different business models available
- Interaction with electricity market
- Balancing between surrounding buildings and districts
- Peak load reduction
- With the simulation software the potential for your DH can be evaluated
- Automatic control of your DH networks depending on the selected business strategy
- Optimal operation of your DH networks
- Project developers
- Heat distribution companies
- HVAC control companies
- Energy production companies
- Utility (energy) companies
This technology is a controller for state of the art control algorithms suited for both existing and new, 4th generation, DHC networks. By harvesting the flexibility in this wide range of networks, the controller contributes to a more sustainable energy mix of renewable energy and waste heat utilisation.
The technology was tested and implemented in real life DHC networks in Mijnwater BV in Heerlen (NL) and Rottne in Växjö (SE), demo cases in the H2020 STORM project.
Test results of the demo sites
The technology consists of different control strategies: peak shaving, market interaction and cell balancing. Peak heat reduction tests in the Swedish demonstration site led to a long-term peak heat reduction of 12.75% on average compared to the reference scenario without the STORM controller active. It should be noted that even during months with low heat demand reductions of up to 57% were achieved.
The Market Interaction strategy is a strategy that uses both charging and discharging capabilities to adapt to a set of electricity spot prices. Based on these prices, the STORM controller moves heat demand to match spot prices, thereby ensuring heat delivery and comfort. This strategy resulted in a 15% reduction on the electricity purchase price and an overall electricity procurement costs reduction by 6%. This option of the controller is beneficial for electric systems such as heat pumps and cogeneration units, especially when sufficient thermal buffering is provided in the system, making it possible to charge energy independently of the energy demand at times when the electricity price is most favourable.
For the cell balancing strategy in the Dutch Mijnwater system the controller was able to reduce the flow over the entire test period without jeopardising the energy delivery to customers. A peak shaving potential of 17.3% could be determined here. Furthermore, an improved capacity could be derived ranging from 37% up to 49% (median value 42.1%) which corresponds to a total of 48,200 normative Home Equivalents (nHE) that can be additionally connected to the existing system.
In each of the demo sites a CO2 emission reduction of around 11,000 ton/year was achieved, which is equivalent to the CO2 emissions of 600 flights from Barcelona to Madrid.