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Self-organising Thermal Operational Resource Management

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Smart controller boosts efficiency of district heating networks

An EU-funded project unveiled a smart network controller that optimises energy efficiency at the district level. Self-learning and artificial intelligence algorithms working between the network and the buildings help to maximise the use of waste heat and renewable energy sources.

Energy

District energy is a proven solution for delivering heating, hot water and cooling services. It is generated in a centralised location through a pipe network to residential, commercial or industrial consumers. Each network integrates flexible renewable and low-carbon energy sources. As the provision of central heating from solar energy is not sufficient to replace fossil fuels, other sources of sustainable heating are necessary – for example, residual heat from industrial processes or geothermal power. Balancing supply and demand are necessary to cope with the limited supply of sustainable heat. This is where the EU-funded STORM project comes in. Industrial and academic partners collaborated to develop the STORM controller. “The smart system shifts heat demand towards times when sustainable energy is at hand, thereby optimising the heating network efficiency,” notes project coordinator Johan Desmedt. Overall, the STORM controller is a framework for state-of-the-art smart heat grid technology management. It includes modules for forecasting, planning and dispatching demand-side management actions for the benefit of the whole value chain, spanning from heat production and distribution to consumption.

Multiple control strategies

STORM technology has several control strategies. “Through peak shaving, it reduces the heat demand in peak hours to avoid a spike in consumption. It also interacts with the electricity market: the system can move heat demand to off-peak electricity rates, ensuring comfort and lower prices. Finally, it does cell balancing and can solve network problems locally,” explains Desmedt. “What’s more, based on electricity price forecasting, the STORM controller can move heat demand to match higher spot prices, thereby increasing the financial gain of selling electricity,” adds Desmedt.

District heating embraces digital transformation

Digitalisation is driving district energy forward on multiple levels such as heat production, distribution and consumption levels. Based on self-learning algorithms, the STORM controller is one of the newest digital innovations that connects all the pieces of the puzzle. “The STORM controller enables buildings to communicate with each other in real time, and exchange information with the energy production and distribution systems about which energy sources are available at each time. The system can also learn by itself, making the whole energy system more efficient over time,” notes Desmedt. It thus leads to sustainable heating and cooling systems with low-emission sources and lower operational costs. The STORM controller was trialled and tested in two demonstration sites, showcasing the huge potential of the district heating sector for a digital transformation. In Heerlen in the Netherlands, Mijnwater is a geothermal low-temperature grid that acts as a renewable energy source and storage for heating and cooling. The mines are connected to a backbone energy network that connects a number of building clusters. STORM’s goal was to increase the backbone capacity to serve more clusters. Overall, the STORM controller enabled an increase in capacity from 37 % to 49 %, and an increase in the peak shaving efficiency (17.3 %). In Rottne in Sweden, a small district heating network of about 200 consumers is mainly based on two bio-fuel boilers, complemented by a traditional oil boiler for peak load usage as a backup. STORM’s goal was to minimise oil usage in peak hours and optimise bio-fuel boiler efficiency. Tests demonstrated a 12.75 % decrease in peak shaving compared to the reference scenario.

Keywords

STORM, STORM controller, district heating, peak shaving, self-learning, renewable energy, digital transformation, cell balancing

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