As digitalisation in the utility industry continues to accelerate, there are already concrete steps utilities can take to change the future they are currently looking into. These are some of the things we at Sigholm are presently guiding them on – but first, let’s take a look at the obstacles.

#1 threat: Heat pumps

The biggest and most pressing challenge facing Swedish district heating utilities today is the increasing competition from heat pumps. Many consumers find the combination of heat pumps and district heating attractive because their base load is then covered by heat pumps running on low-priced electricity, while only the expensive peak loads must be covered by district heating. When you then add the mild winters we’ve seen in recent years, which also mean fewer earnings, it’s clear that a lot of utilities experience a strain on their finances.

This competition is intensified by the price models most utilities use today because models based e.g. on daily values, evens out the short peaks, which means utilities cannot bill accordingly. Additionally, when consumers only pay for the energy they use instead of available capacity, it makes utilities very vulnerable to those mild winters – especially because operational costs and long-term investments are necessary expenses regardless of their earnings. Instead, I think district heating should be billed in the same way as electricity: if you have a 20 amp fuse at home, then that is what you pay for.

Stronger price models with better data

We need new price models that remain strong regardless of peak load consumption, competition and weather conditions and considers the combination of district heating and heat pumps. That means price models where consumers pay according to capacity at an hourly level. This will also give consumers an incentive to update their heat installations to avoid or reduce peak loads, e.g. by optimising the hot water production. The basis for those new tariffs is high-quality meter data, so utilities must find suppliers who can ensure high performance (99,5%) – and maybe even take responsibility for collecting and delivering data to the utility.

Looking at the electricity grid where demands for meter reading performance and data quality are very high, it is clear that this is also achievable in district heating – it has simply never been a requirement. I predict that within a few years, we will be using 15-minute values as well.

According to a recent price model survey I did on 238 municipalities, only about a handful of utilities have implemented price models based on hourly values. Approx. 15% use a price model based on demand profiles, while another 15% use the highest daily average demand. The remaining utilities still mainly bill according to demand per category, where the peak demand is calculated theoretically based on the yearly consumption divided by a number of hours depending on your category type, or price models without any capacity or peak demand elements. It is especially critical for the utilities using models based on daily average demand to consider changing their price model. If they don’t, they will ultimately end up taking themselves out of the game e.g. regarding the competition from heat pumps, as they will lose their revenue on the base load.

Mälarenergi and Värmevärden are among the utilities that are already using price models based on hourly values.

Improved efficiency and new revenue

Hourly readings will, of course, require utilities to handle 24 times more data, but the technology to do so already exists with solutions like Kamstrups’ READy. In my experience, most utilities are aware of this issue, but only seven use price models based on hourly values today. Many collect hourly values to pass on to their customers for information but don’t use them for billing and accept that maybe 10% are estimations due to insufficient data quality. But considering today’s challenges and future business opportunities, I say it’s time to start using what is already available. And this is where I see digitalisation as the necessary tool to overcome today’s obstacles and drive future potential. Because inappropriate price models are only part of the problem.

For Swedish utilities to remain competitive, they must also become more efficient. This includes reducing costs while establishing new revenue streams at the same time – and digitalisation is a key enabler for both.

Data-based transparency in both the production and distribution of district heating will allow optimisation of just about everything from network temperatures to the identification of heat loss and faulty substations. But within the next few years, I also see a window of opportunity for utilities to expand the offerings and services they provide for their customers. Adopting new business models where they take more responsibility, e.g. for their customers’ heat installations, will result in closer and more long-term relations than what they have today. Or maybe even moving toward business models defined by whether a customer prefers heat that is green, convenient or low-priced.

Swedish district heating in a 2030 perspective

I absolutely still see a need for district heating in the future, not least because the electrification of all heating is neither profitable nor realistic as the electricity grid is already heavily loaded. On the topic of energy efficiency, I often encounter a general misperception that 1 kWh electricity and 1 kWh district heating are the same – but they’re not. In fact, to complete the electrification of the transport sector, we must use district heating for more simple things like heating of buildings because electrifying both transport and heating will require a factor four investment in the grid. Nevertheless, the district heating industry must first become an attractive co-player in a sector-coupled energy system. And that begins with data.

The increasing number of decisions utility professionals have to make every day and every hour require the right basis – from optimising production based on electricity prices and heat demand to making more accurate forecasts and  targeted maintenance efforts. Ultimately, the more data you have, the better your basis will be for making the right decisions.

And frequent meter data is not just part of the solution today. In the production optimisation system of the future, production, distribution and consumption will all be handled in the same system. Here, data will provide the basis for utilities to continuously balance production and consumption in the best and cheapest way possible. This includes forecasting when to produce, sell or use electricity based on electricity prices. Or deciding when to overproduce thermal energy to store in buildings and the distribution network. Or optimising temperatures based on demand and weather forecasts. Rather than relying on individual human factors, we need a much higher degree of automated decision making where the system makes suggestions based on historical data, and then you analyse and evaluate that suggestion. For example, if a main pipe needs replacing, the system might propose the optimal dates to do so based on activities in that city, allowing you to plan accordingly.

Already today, tools like Kamstrup’s Heat Intelligence help utilities make that kind of data-driven decisions. And I see those tools also enabling faster onboarding and making utilities less vulnerable to the upcoming generational change many of them face. This will only become more relevant as future generations might not remain in the same job for as many years as many district heating professionals do today.