Using scenario modelling to better understand system changes   

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Heating systems are growing, and district heating leaders strive to decarbonise the heat supply at the same time. However, given that fuel prices are rising and volatile, this is a complex mission. Coupled with public perception often focusing on individual heating solutions, these developments illustrate how the energy crisis offered a new urgency to accelerate the sector’s transformation. 

While Europe may be out of the woods from the worst phase of the energy crisis, volatility has remained, and global factors may still shock the market.
Natural Gas Dutch Prices, June-October 2023

Fuel prices are currently relatively stable on a per-month basis; however, they remain volatile on a per-day level. It’s expected that gas and electricity prices will increase in the future.  

Given that your network is undergoing deep changes, how can you still be sure not to risk your supply security or encounter new unforeseen problems? 

District heating leaders need tools that are up to the task 

Companies need tooling capable of running simulations that cover the entire year and include validation with real-world data to gain insight into what a change really means for their day-to-day operation.  

Traditional tools can only model scenarios for a hot or cold day, taking 30-60 minutes to deliver an outcome that is static. This means that every network change calls for model recalibration and running the scenario again, which is time-consuming. This makes it difficult to carry out a sensitivity analysis of your network change. 

Traditional offline design tools aren’t up to the task. Since models aren’t validated with real-world and real-time data, they may turn out to be inaccurate. Translating such results into business objectives is challenging.  

How can district heating providers deal with the market volatility and constantly changing nature of their networks, given their tools are not up for the task?  

Here’s one best practice that emerged from our interviews with leaders in the Nordics. 

Best practice: Use scenarios to model high-impact situations 

An emerging trend among district heating companies is using full-year scenarios to deal with market volatility and predict future events

Since uncertainties affect all heating system components - from production to network and consumption - scenarios should consider the entire district heating system rather than just one of its aspects, such as suppliers or consumers.  

That’s because focusing on a single portion of the system increases the risk of generating suboptimal or malfunctioning results. In a well-functioning energy system, all aspects are taken into account when making calculations and predictions. This is especially important in the context of the increasing diversification and decentralisation of heat sources. 

Consider the following example: 

When choosing a heat source to adopt, you usually consider the temperatures your consumers require as well as the flow limits in your network, depending on your source location. 

If you carry out separate simulations for each aspect, such as source, network, and consumer (demand), you may find out that a geothermal heat source has a poor business case due to its low operating temperature since you’ve used high temperatures in your network (at least, last winter). 

However, if you look at the network and client-side options, you may discover that it’s still possible to decrease temperatures outside of peak hours, making the geothermal source still feasible. Furthermore, sophisticated solutions such as heat accumulators, network buffering, and active demand steering can further boost the business case. 

Case study: Helen – Using scenarios to model phasing out coal and gas plants 

An example of a scenario with an end-to-end impact is Helen. Driven by its plan for achieving carbon neutrality by 2030, Helen used Gradyent’s Digital Twin solution to simulate the phase-out of coal and gas-powered facilities.  

The company evaluated network behaviour for each scenario by balancing CO2 emissions and production costs. Scenario feasibility was assessed by checking the hydraulic stability of the network (pressures and flows) to ensure operation within system boundaries. 

Moreover, Helen monitored the estimated heat reaching customer homes. This was done to make sure that all customers always receive the required energy and the minimum contractual temperature in all scenarios.  


The Digital Twin’s research found that shutting down two coal facilities is possible, and that the energy supply can be replaced with renewable sources such as biomass and both existing and planned heat pumps.  

Helen closed the Hanasaari power plant in the spring of 2023. By retiring all coal facilities and increasing the use of heat pumps and biomass plants, the company could reduce CO2 emissions by up to 80% compared to 1990 levels. 

How to get started 

Modelling scenarios is a necessity for all heating companies undergoing changes. Given that price trends and public perceptions are in constant flux, it’s essential to know the real-world impact of a change before applying it. 

Using digital tools based on real-world data for this purpose is a novel strategy. However, many existing tools lack this capability and are too rigid and siloed to allow for running such end-to-end scenarios. 

To set up resilient scenario modelling to future proof your network, consider using the following framework:

Continue learning about other district heating challenges 

Do you want to know how industry leaders are preparing for other known certainties, such as growing system complexity, 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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