Innovative Technology for District Heating and Cooling

Reaching climate targets, while maintaining sustainable economic growth and social cohesion, needs a revolution in how we operate across multiple sectors. One project offers a step-change for increased energy efficiency.

If the European Union is to meet its Energy Roadmap objective of reducing greenhouse gas emissions by 80-95 % by 2050 (compared to 1990 levels), it must accelerate plans for increased energy efficiency. A sustainable and long-term approach to district heating and cooling (DHC) is part of accomplishing such a transformation.

The EU-supported InDeal project offers a solution that distributes DHC among the network’s buildings (localised mix of residential and business). The online platform-based system enables 24/7 production plant and substation monitoring and, by tapping into artificial intelligence (AI) smart meters, improves efficiency by reducing over-production by 30-50 %.

Accompanying this was the development of short-term weather forecasting for demand prediction and storage modelling based on deep learning algorithms. Additionally, two energy harvesting solutions were created to power sensors.

The project also created an insulation material with 11 % reduced thermal conductivity compared to the industrial standard polyurethane (PUR), alongside a new pipe joint, making installations faster and easier.

Intelligent management

While the future development of DHC systems will have to include energy savings and conservation technologies, a key challenge will be integrating those within a larger smart energy system covering renewables, waste and storage.

Additionally, the vast majority of current DHC systems are controlled manually, reducing efficiency due to management oversights and imprecise operations. For example, inaccurate ambient and water temperature control result in under- or over-heated buildings, wasting energy.

InDeal adopted AI to model uncertainties for better understanding and knowledge of the physical systems, leading to energy efficient control strategies. “We were surprised by the effectiveness of deep learning algorithms to model the energy components of DHC networks. They enabled us to replace time-consuming simulation tools that are based on analytical equations,” says project researcher Dr Serafeim Moustakidis.

The system has been designed to be easily integrated into the majority of current DHC systems. For example, the proposed advanced metering infrastructure can be installed in most production plants and their substations, and is able to communicate with the majority of meters already there (in effect rendering them all ‘smart’).

The InDeal system and its components have been validated in both laboratory conditions, as well as in real conditions at two pilot sites (the district heating network in Vransko, Slovenia and the DHC networks in Montpellier, France).

Towards 2050’s carbon energy solutions

Today, the DHC sector is considered a green industry, as on average 86 % of heat for district heating in Europe is sourced from a combination of recycled and renewable sources. Yet, this does not tap the sector’s full potential, accounting for only a 10 % market share of renewable energies. InDeal helps lay the groundwork for fulfilling this potential.

“We are proud of the AI-enhanced decision support system based on an advanced hierarchical control and modelling framework. Also of providing a holistic solution which tackles challenges including: control strategies, energy demand prediction tools, novel insulation materials, piping systems, energy harvesting solutions and smart meters,” says project coordinator, Prof. Nicos Karcanias.

The next steps for the project include demonstration of the systems’ efficiency for more prolonged periods of time (1-2 years) and data-driven continual improvement of the technology.

Potential customers include project partners, the network operators SERM and ENERGETIKA. The team are already in contact with interested operators in Poland and France.

Keywords
InDeal, district, cool, heating, energy, renewables, waste, storage, smart meters, artificial intelligence, algorithms