Overview of Demand Side Management in District Heating

Dear reader,

as we see increasing efforts to make district heating networks more efficient, demand side management and demand response are important topics with the potential to improve the system in various ways. And while in theory there are so many different ways to influence the network's demand side, it can be unclear how to actually achieve positive results in practice. In order to clarify the terminology and give a comprehensive overview of the current state of the art on these topics, we are happy that a recent review paper offers a great summary on demand response and demand side management in district heating networks. We hope that a better understanding of the current research in this area can support the transfer of these promising concepts from electric to thermal networks and thus help to put them into practice.

The short version

For this 10th issue of our newsletter we selected the paper Demand response and other demand side management techniques for district heating: A review by Elisa Guelpa and Vittorio Verda at Politecnico di Torino in Italy. By the paper's own account, this is the first comprehensive review of demand side applications in district heating networks. As such, it not only reviews the current state of the art and recent findings on this topic, but also sets out to clarify the necessary terminology: The authors identify demand side management (DSM) as the general term describing all measures that influence the heat demand in the network in order to achieve some objective(s) like reducing costs or emissions. Of these measures, demand response refers to all actions that are non-permanent (excluding e.g. building retrofits to permanently reduce the demand).

In addition, the paper defines 6 stages for implementing demand side management in practice, from defining the inputs and objectives, through considering the effects on the buildings' indoor climate and accounting for the network dynamics, on to designing the control system and implementing it. Furthermore, the paper gives an overview of both DSM experiments in actual networks and buildings, and of simulation studies investigating solutions and potentials of DSM in district heating. This review shows that so far, experiments have been mainly limited in scale to around 30 buildings. Nevertheless, the authors conclude that current research shows significant potential of DSM in district heating to reduce demand peaks by 30 % through peak-shaving, doubling the load factor, reducing primary energy consumption by 5 %, and cutting emissions and costs by 10 %.

Our full summary

We present the paper Demand response and other demand side management techniques for district heating: A review by Elisa Guelpa and Vittorio Verda in this newsletter because we think it is an important contribution to build a better foundation for future DSM applications in district heating networks.

The authors show that this foundation-building is necessary and that the application of DSM in thermal networks is less mature than in electric networks. As a clear indicator for this, the paper shows that current research on DSM is often published under several different terms without the general context of DSM, which slows joint progress in the field. This could be overcome by clearly relating future research to the terms "demand response" and "demand side management" together with clearly naming the district heating application.

The paper continues to serve as a good introduction to the topic by analyzing the motivation for DSM in district heating networks, which always comes down to bridging gaps between supply and demand efficiently. And every measure to decouple supply and demand requires to store heat in some way. To store this heat, the authors identify 3 different parts of the energy system:

  • Actual thermal energy storage system like hot water tanks
  • The water mass in the network
  • The building envelopes of the buildings connected to the network

While all these options for storing heat have advantages and disadvantages, the paper identifies general benefits that DSM can offer when the storage options can be utilized efficiently. On the one hand, DSM can help to reduce bottlenecks and congestion in the network and thus lower the necessary pump work and allow for easier fault management as well as open up potentials for expanding the network without the need to modify the piping network. On the other hand, DSM can unlock production flexibility and thus help to optimize the integration of renewables and optimize the coupling to electricity generation (CHP) and consumption (heat pump). Furthermore, DSM can allow for smarter design with reduced pipe diameters, less run-time for the peak load heat generators and less overall heat generator capacity needed.

Regarding the possible implementation of DSM, the paper goes on to distinguish between different types of measures and classifies them according to the time-scales of their responsiveness. The 3 resulting classes of DSM start with retrofitting the buildings, which of course acts on the largest time-scales for its effects from years to decades. The class of Indirect DSM consists of flexible heat tariffs which encourage the heat customers to shift their loads into the desired time slots and compensates them with lower prices for the heat. As customers cannot keep up with too frequent changes of tariffs, these measures have a relevant time-scale of months to years. And the class of Direct DSM consists of direct control of the heat loads which offer responsiveness between seconds and days. While Direct DSM has the largest potential for improving the network operation, it also involves high requirements for the control infrastructure. Yet, a general trend towards connectivity, digitalization and automation may support the feasibility of Direct DSM in the near future.

For the implementation process, the current research suggests 6 stages to arrive at the best possible DSM setup. In the first 2 stages, actors need to define the relevant input data available to the DSM system and define the one or more objectives which the DSM is intended to optimize (e.g. reduce peaks, reduce emissions and/or costs, maximize the share of renewables, etc.). In stages 3 and 4, the DSM setup needs to consider the possible effects on the indoor comfort of the connected buildings and also take into account the network dynamics, which according to the paper can be addressed by thermohydraulic models of the network. And stages 5 and 6 include the setup of what the paper calls the decision intelligence and finally implementing the DSM setup in the actual control system of the network. For the decision intelligence, the paper lists e.g. agent-based systems, heuristic optimization, and model-predictive control as promising options.

After these key points on DSM theory, the paper reviews the literature for actual implementation of DSM experiments in real networks and buildings as well as simulation studies reported in research papers. This overview shows that there is a certain amount of reported use cases that show a promising potential of DSM for district heating networks, even though the number of use cases is not yet very large and the experiments' scales only involve up to around 30 buildings. Nevertheless, the paper provides a good reference set of KPIs which show a potential of DSM measures to reduce the demand peaks in the network by 30 % through peak-shaving, to double the load factor, reduce the primary energy consumption by 5 %, and to cut emissions and costs by 10 %. These values can serve as a good reference set for future studies and as goals for future implementations.

At heatbeat, we frequently discuss the possible effects of DSM measures with our project partners both for existing networks as well as in early planning stages of new district energy systems. We think that the systematic overview and reference values from the presented paper can help to put own simulation results into perspective and will help us to model the effects of DSM in more detail.

Further Information

The original article is available as open access at and we not only recommend it for additional detail but also for the helpful visualizations of the main take-aways which helped us in writing this summary.

The next issue of our newsletter will be published September 1, 2021.

Best regards,
your heatbeat team