For the future, weather-driven society district heating is already recognized to play a central role since these systems can pro- vide much of the needed flexibility at a low cost. Digitalization of district heating systems based on sensor data will further strengthen the position of district heating as a sustainable and low-cost energy supply technology capable of reducing car- bon emissions and contributing to climate change mitigation. In addition, we have proven that using data-driven tools has a huge economic potential. According to the so-called Damvad Report from 2019, the potential in Denmark alone is 240 to 790 mill DKK annually with state-of-the-art data-driven meth- ods for temperature optimization. On top of that, most of the methods for digitalization mentioned in this article will lead to considerable extra economic and operational benefits for dis- trict heating systems and their users. Reference to projects: IDASC: https://issuu.com/dtudk/docs/district-heating- digitalized?fr=sM2FiMzQ4NjgwMg HEAT 4.0: https://dbdh.dk/wp-content/uploads/2021/04/HEAT40-AlfredHeller-Niras.pdf CITIES: https://smart-cities-centre.org/ Flexible Energy Denmark: https://www.flexibleenergydenmark.com/ ARV: https://greendeal-arv.eu/
optimization in DH systems. The tools can be used for different planning problems, such as operational planning under uncer- tainty, optimization of bids to the day-ahead electricity market, and long-term evaluations of DH system operations. The tools are able to take advantage of the uncertainty, for instance, in the production of thermal solar heat as well as forecasts of the electricity prices on markets with varying horizons. The general applicability and performance of the approach are evaluated based on real data from the three Danish DH systems of Brønderslev, Hillerød, and Middelfart with different characteristics. When considering bidding, the new tool reduc- es cost in all cases and can save up to 42.1%. Conclusion Development in sensor technology and the rapid develop- ment in AI and IoT have provided district heating operators with new opportunities. Using AI or data-driven models to pro- vide information from sensors, the operations in the building, at the plants, the network, and market participation can be optimized. The key is data-driven and auto-calibrated tools for the modern operator. Tools for coherent load forecasting are central. Knowing the future demand with reliable uncertainty intervals allows for setting the water temperature and flow optimally rather than operating with a large-than-necessary safety margin. Such state-of-the-art forecasts are also the prerequisite for smooth solutions for bidding on the electricity markets.
For further information please contact: Henrik Madsen, hmad.dtu@gmail.com
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