Two ways to manage complexity in a transforming heating system: Part 2 

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In this article, we explore best practices employed by Nordic district heating leaders to address the increasing complexity of district heating systems. Dive in to check out these two best practices: working towards full automation and preparing the data for predictive maintenance. 

District heating companies face several challenges today, from fuel price volatility to accelerating the transition to renewable energy sources distributed across the network.  

The previous article on tackling the growing complexity of heating systems showed how leaders can prepare for the new reality. They can improve their system knowledge and use digital technologies to unlock value from all acquired data. 

Here are two more best practices companies can take to address system complexity, in line with industry best practices shared by the leaders of district heating companies in the Nordics. 

Best practice 3: Work towards full automation and autopilot 

Heating systems are growing in complexity, and connecting the dots across systems and roles in real-time is increasingly difficult. That’s why district heating leaders are looking into opportunities for addressing this complexity with automation. Some even share a vision of future energy systems running in “autopilot mode,” with human operators being able to oversee how the system behaves with intelligent digital control. 

Companies need novel optimisation systems that analyse heat sources and the network in a single integrated way. This implies a departure from traditional data analytics tools that may oversimplify the network and model what is occurring inside it. Such tooling doesn’t consider aspects such as heat propagation, hydraulics, energy sources, storage, and consumers. When faced with limited data, making an optimal decision is next to impossible. 

Heating companies that aim to embrace automation need a digital solution that can calculate optimal operating setpoints in real time. Today, production is frequently optimised for the day ahead. Also, system parameters, such as forward temperatures, are managed by static temperature curves.

However, to maximise the benefit of the system's flexibility, a solution would need to forecast on a granular network level and optimise the overall system against it, offering dynamic setpoints rather than static and respecting system restrictions for temperatures and the production schedule.  

Juha Räsänen, Business Director at Savon Voima: “[…] We collected 6.3 billion data points last year, and we have the capability to control all networks from one control room. To create more value out of the data, we need more automation, even autopilot. To take into account fuel prices, weather forecasts, forward temperature, return temperature, control of accumulators, control of electricity production, etc., we can't do it all alone, so we need long-term partners for this.”  

Best practice 4: Prepare your data for predictive maintenance 

Making use of data for operations is already a significant challenge for district heating companies. Still, the vision of leveraging data for predictive maintenance is becoming increasingly realistic with each passing year.  

That’s primarily because many district heating grids have been in operation for decades, and the piping in the ground and equipment at customers have deteriorated with time. Upgrading existing networks is too time-consuming and often cannot be funded all at once.  

On the one hand, maintenance tasks are costly, and heating companies cannot fix everything. On the other hand, if they do nothing, they face the risk of unanticipated leaks and outages. 

Data opens the door to driving maintenance and repair initiatives proactively, where data guides you to the right starting point for maintenance and where your network is most vulnerable. With this idea in mind, companies may add more sensors to the network to bring all the data together in one platform for predictive maintenance.  

Modern analytics tools use Al to give insights for both short-term and long-term maintenance planning to help companies save resources, reduce downtime, and realise capex savings by precise replacement suggestions and eliminating wasteful replacements. 

Öresundskraft - Why data should be used to replace old infrastructure 

Öresundskraft is a municipal energy company that supplies district heating and cooling to customers in Helsingborg, Sweden. One of the major challenges for Öresundskraft is reinvesting into modernising its grid. Replacing it would cost an enormous amount of money and would theoretically take 500 years to complete.  

Since the condition per pipe varies, the company should first understand where to start revamping its network. To learn which parts need replacing, Öresundskraft invested in gathering more data by installing sensors throughout the network.  

Johan Klinga, Head of District Heating at Öresundskraft: Predictive maintenance of the district heating infrastructure has not received the attention it should get in the sector... So far, maintenance has been only reactive, which will not hold. Smart use of data and Al is necessary to be future proof and to provide security of supply.”  

Wrap up

As heating systems become more complex, companies can prepare for the new reality by: 

  • Building a greater understanding of their existing systems even as they become more integrated and volatile.

  • Using digital solutions to draw conclusions and extract value from all the collected data.

  • Working towards automation or even a full autopilot mode.

  • Using data to enable predictive maintenance to understand where to start with maintenance and keep the grid in shape.

Do you want to know how industry leaders are preparing for other known certainties, such as rising fuel prices or sector coupling? 

Read our Market Research Report for practical tips backed up by real-life case studies based on interviews with 17 Nordic district heating leaders.
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