DBDH publishes Hot Cool, but the main business is helping cities or regions in their green transition. We will help you find specific answers for a sustainable district heating solution or integrate green technology into an existing district heating system in your region – for free! Any city, or utility in the world, can call DBDH and find help for a green district heating solution suitable for their city. A similar system is often operating in Denmark, being the most advanced district heating country globally. DBDH then organizes visits to Danish reference utilities or expert delegations from Denmark to your city. For real or virtually in webinars or web meetings. DBDH is a non-profit organization - so guidance by DBDH is free of charge. Just call us. We'd love to help you district energize your city!
SPECIAL COLLECTION EDITION 1, 2023
INTERNATIONAL MAGAZINE ON DISTRICT HEATING AND COOLING
ARTIFICIAL, INTELLIGENCE & DIGITALIZATION
TECHNOLOGY & SUSTAINABILITY
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Contents Contents
FOCUS: ARTIFICIAL, INTELLIGENCE &
4
COLUMN FIRST THINGS FIRST By Steen Schelle Jensen
DIGITALIZATION TECHNOLOGY & SUSTAINABILITY
By Henrik Madsen 5
28
ARTIFICIAL INTELLIGENCE IN DISTRICT HEATING
TAKING THE INITIATIVE IN HEAT TRANSITION By Theo Venema and Marco Attema
32 33 38 43
9 12 18 22
CIRCULAR BIOMASS ENERGY WITH CITIZENS HEATED BY THEIR GARDEN-PARC WASTE By Ann Bouisset
COOPERATIVE DISTRICT HEATING IN THE MAKING By Rie Christiansen Krabsen and Gerwin Verschuur
EXPLOITING THE POWER OF DIGITALIZATION AND AI FOR END-TO-END OPTIMIZATION OF THE DISTRICT HEATING SYSTEM By By Jan Eric Thorsen, Emanuele Zilio, Tomaz Benedik and Oddgeir Gudmundsson
HOW TO GET LOW ENERGY COSTS By John Tang Jensen
RESILIENT AND SUSTAINABLE DISTRICT HEATING USING MULTIPLE
BOOSTING GREEN DISTRICT HEATING TRANSITION By Peter Lorenzen
HEAT SOURCES By Anders N. Andersen
DISTRICT HEATING IN GREATER COPENHAGEN –2050 By Lars Gullev
DISTRICT HEATING CAN BECOME EUROPE’S PATH TO AN EFFECTIVE GREEN TRANSITION By Jesper Koch
Cover photo showing Høje Taastrup Heat Storage during construction ©VEKS
DBDH Stæhr Johansens Vej 38 DK-2000 Frederiksberg Phone +45 8893 9150
Editor-in-Chief: Lars Gullev, VEKS
Total circulation: 5.000 copies in 74 countries 10 times per year
Grafisk layout Kåre Roager, kaare@68design.dk
Coordinating Editor: Linda Bertelsen, DBDH lb@dbdh.dk
info@dbdh.dk www.dbdh.dk
ISSN 0904 9681
https://www.districtheatingdivas.com/
What if digitalisation could make heating more sustainable?
The EU targets energy efficiency improvements of 36-39% by 2030 With frequent data readings, automated measurements and real-time decision-making, digitalisation is optimising the 4th generation of district heating. At Kamstrup, we have the know-how and digital solutions that can accelerate the green heating transformation and improve energy efficiency.
Read more at kamstrup.com/heat-solutions
FIRST THINGS FIRST Digitalization, including the use of AI solutions, is an undeniable step in utilizing the full potential of the increasingly complex district heating sector and ensuring an efficient and resilient system — now more critical than ever. But it doesn’t start with technology.
By Steen Schelle Jensen, Head of Business Development, Kamstrup
half of them provide hourly time series. Imagine what we can do when all these data points are turned into actionable in- sights! Many DH operators have already harvested significant results from the transparency smart meter data provides, including lower temperatures, more capacity, and reduced losses. This is great, and I’m happy to see that digitalization is now a top- ic in almost every DH event, just like national associations are expressing digital ambitions and sharing customer success stories. At the same time, international working groups are un- derway in Euroheat & Power, IEA DHC, etc. Keep up the good work! However, back to my point about digitalization often being the go-to answer — the fact remains that developing not just dig- ital solutions but the RIGHT digital solutions for DH operators starts with an in-depth understanding of the industry’s com- plexity, challenges, and processes that they must address. So, much like ChatGPT can provide an answer that isn’t wrong but lacks depth and tangibility, we, as suppliers, cannot sit in our ivory towers trying to guess what DH operators need and how to support them. The best digital solutions are those based on joint efforts of leading DH operators and innovative solution providers com- bining their hands-on experience with our deep technological expertise. We need to explore new opportunities together to make sure we are attacking and solving the right challenges for the benefit of our entire industry. Over the past 2-3 years, I’ve been involved in multiple dedicat- ed co-creation sessions doing exactly that: Having open dis- cussions, sharing strengths and weaknesses, data crunching, and prototyping together before ending a long workday with a pizza and doing it again the next day. A truly unique experi- ence that created the crucial openness and trust between all involved — and I look forward to many more. So, consider this my open invitation to combine our power of imagination — across DH operators and suppliers — to create the best possible solutions together. Because that is step one of driving the necessary digital transformation of district heating.
”Digitalization is an essential step in the evolution of district heating systems. It not only improves the reliability of the sys- tems but also makes them more sustainable and customer friendly”. This is the answer I got from ChatGPT when I recent- ly asked the AI service if digitalization was relevant in district heating. And it’s actually not a bad one. I’ll come back to that. Whenever things get complex, digitalization is considered the go-to answer. Few industries have seen an increase in com- plexity like the one in district heating — only intensified by the challenges the world has faced over the last few years, includ- ing the war in Ukraine and its impact on global energy supply. The necessary transition to renewables and waste heat, also known as 4th generation DH, represents a massive change. Multiple decentralized heat sources, a high degree of electri- fication, sector coupling, the need for lower temperatures, dif- ferent kinds of storage, active buildings, etc., are becoming the new reality. Efficiently balancing the fluctuating heat production and de- mand in an increasing number of connected buildings — with- out jeopardizing reliability — requires a fully connected value chain from production to end users. This truly calls for digital solutions for operational aspects as well as asset management, new attractive service offerings to end users, etc. Ultimately, digitalization has the potential to revolutionize the way we plan, maintain, and operate our district heating systems. And it’s already ongoing. Not least due to the mandatory roll- out of smart meters required in the Energy Efficiency Directive. However, a recent survey from the Euroheat & Power DHC+ Platform concludes that the demand side of the district heat- ing value chain (buildings/end users) is underserved, as many existing solutions mainly relate to production, forecasting, and energy trade. The smart meter roll-out creates a unique oppor- tunity to take a fully data-driven end-to-end approach to the DH system. The other day, I saw the latest numbers for Kamstrup’s hosting center and the smart metering solutions we manage on our customers’ behalf. We now collect data from more than 1.3 million heat meters across 400+ DH operators – and almost
Today there are many sensors in buildings and district heating/cooling systems with a high temporal resolution. Data from such sensors opens up for new AI and IoT-based solutions. Here we will describe the potential of some of such solutions for district heat- ing systems. However, we will also touch upon how such solutions potentially make the system more vulnerable and challenging with respect to privacy and GDPR. ARTIFICIAL INTELLIGENCE IN DISTRICT HEATING
By Henrik Madsen, Professor and Section Head on DTU Compute
Buildings and occupants The demand for smartness, trust, transparency, and versatility in managing heating, cooling, ventilation, lighting, and access control systems for a family home, public buildings like schools, and office buildings is growing. People want a comfortable, sustainable, cost-efficient, and safe place to live and work, and that's where sensors, AI, IoT, and automation jump in. GDPR and privacy Personal data are any information related to an identified or identifiable person. Only if the processing of data concerns personal data, the GDPR (General Data Protection Regulation) applies. This implies that the problem typically does not exist e.g., for a section of a district heating network, as well as for public buildings like schools and some office buildings. Today we are able to obtain electricity consumption data with a very high temporal resolution (e.g., every 15 seconds) also for single-family buildings. Given such data, it is often relative- ly easy to conclude which appliance has been used at which time. Likewise, most of us have hourly readings of water con- sumption online. We can see, for instance, that 2 liters have been used at 2 am, 3 am, and 5 am during a single night. This indicates frequent bathroom visits, which could lead to some privacy issues. Frequent and real-time data for district heating consumption can be used to better control heating and ven- tilation systems, but also to detect absence (e.g., holiday peri-
It will be argued that energy meter data and the use of AI in district heating provide the foundation for efficiency improve- ments in buildings and district heating systems. In addition, such data-driven methods give possibilities for CO2 and cost savings, better integration of wind and solar power, efficient in- tegration of the energy systems, and more satisfied end-users due to lower costs and a better indoor climate. One of the major problems today is that data and solutions often are linked to proprietary platforms. Consequently, it is challenging to implement cross-system solutions and harvest synergies from systems integration. Sadly, this often hinders the possibility of obtaining large savings and efficient imple- mentations. However, this cross-system functionality can be obtained using a non-profit data hub, like the national hub for smart energy and water systems at Center Denmark. For instance, Center Denmark is successfully used for cross-sys- tem optimization in the HEAT 4.0 project (see HOTCOOL no. 8, 2022). In the following we will start considering AI tools for individual buildings. Then we will consider the district heating networks, the plants, and conclude with remarks on district heating in re- lation to the energy system, the electricity/energy markets, and the society. The findings mentioned here are based on several district heating-related projects (CITIES, HEAT 4.0, FED, IDASC, ARV - please see the reference list).
Digital x-ray-based performance characterization Frequent energy meter data opens up for new inductive or data-driven tools. The tools act as a bit like a kind of x-ray vi- sion through the layers of the individual walls. Given this x-ray- based knowledge of the performance of the individual walls, the tool can provide evidence-based information about the performance of the separate buildings. This is useful, for in- stance, before deciding on a possible energy renovation. Similar AI tools and digital twin models can be used to obtain a better control of the indoor climate. This has been demon- strated, e.g., in the social housing Taastrupgaard in Høje-Taas- trup Municipality, where digital tools have been used to show that many radiators were misused. Sensors for the indoor CO2 levels, temperature, and humidity have also been installed in schools, e.g., in Hørsholm and Rudersdal, to monitor the indoor climate and obtain a better comfort and learning environment in the schools using the platform from Climify. Climify has also developed a FeedMe app, which can ensure individual and op- timized control of heating and ventilation of the classrooms, lowering the return temperature, minimizing mold risk, and obtaining energy savings. Forecasting Load forecasting obviously calls for data-driven tools. Lately, new methods for coherent forecasting of the heating load on all relevant time horizons from, say, 15 minutes to 96 hours ahead have proven to give considerable (15 - 30 pct) improve- ments in the accuracy of load forecasts at some of the largest DH operators in Denmark. These forecasting improvements lead to significant economic benefits in temperature control, production planning, and participation in the electricity mar- kets.
ods). Obviously, such data can be used to identify if the build- ing calls, e.g., for a renovation like replacing the windows.
Energy performance characterization of buildings Traditionally, energy performance characterization and labe- ling have mostly been based on deductive analysis, i.e., based on assumed theory for energy transfer and material proper- ties. Today the existence of frequent meter readings and, e.g., nearby meteorological observations data opens up for evi- dence-based inductive analysis, i.e., data-driven methods. The deductive approach used for buildings today Today the energy performance characterization and energy la- beling of buildings are based on rather simple calculations and a visit by an energy consultant. The cost of getting such a label is relatively high, around 700-1000 Euros. The methods used today are often criticized. The main problem is that two buildings, which in theory should be identical, might have a somewhat different energy performances in practice. This well-known performance gap between predicted and ac- tual building energy performance can be significant. Even after correcting for differences in user behavior and occupancy, the actual energy consumption can easily be 50-100 pct higher than the theoretical consumption. Generally, the technical sources for discrepancies between the theoretical performance and the measured performance can be broken into three baskets: The design and simulation phase (limitations, inaccuracies, and assumptions in the theory used to predict the performance); the construction and com- missioning phase (caused by the poor quality of workmanship and differences between assumed and actual materials, com- ponents and systems); and, the operation phase (poor-func- tioning of the systems and in particular the HVAC system).
Forecasting of PV and thermal solar energy production also calls for data-driven approaches for several reasons. By using
Simulation-Based vs Data-Driven Temperature Optimazation
.
.
Figure 1: Differences between simulation-based and data-driven temperature optimization
Access to energy meter readings from individual households has proven to give further advantages. A simple sketch of the change of the setting is shown in Figure 2. Obviously, this calls for using advanced aggregation techniques to ensure that the aggregated temperature is representative, and to respect pri- vacy and GDPR. The use of meter data implies that it is rather easy to operate with zonal temperatures. This new solution for using meter data and the methods for zonal temperature con- trol leads to further savings and better options for integrating local heat pumps. Today electricity prices are high from time to time, so the con- trollers have a built-in balance between reduction in heat loss- es and the pumping costs. Finally, it is crucial to notice that the use of data and AI methods implies that the tools are auto-cal- ibrated continuously. This means that the system is much eas- ier to operate and maintain.
data-driven methods, the forecasting tool can automatically consider complex shading and the time-varying dirtiness of the panels. Some of the new forecasting methods are imple- mented in, e.g., HeatFor and SolarFor. Temperature optimization (TO) Historically, methods for temperature optimization have been based on simulations using theoretical models and detailed knowledge about the network. A prerequisite for using such approaches is that the model is carefully updated with infor- mation about the physics (pipes, ground temperature, the hu- midity of the soil, properties of the insulation of the pipes, etc.). First of all, this is a very time-consuming procedure, and sec- ondly, such methods lead to suboptimal descriptions of the dy- namical characteristics needed for control of the temperatures. Exactly like for the buildings mentioned above, data-driven methods can provide significant improvements in tempera- ture optimization, such as in zonal control of the network tem- perature. Again the AI technologies implemented, for instance, in HeatTO, give a sort of x-ray vision of the thermal properties of the pipes and their surroundings. The resulting data-driven digital twin models describe the time delay, heat losses, and dynamics. According to the experiences with HeatTO, heat loss is reduced by 10 to 20 pct (see, e.g., https://enfor.dk/services/ heatto/).
Figure 2: Use of meter data in temperature optimization (HeatTO).
Production and bidding optimization for DH systems Data-driven forecasting and temperature control methods are now also used in new tools developed at DTU for production optimization in DH systems. The tools can be used for different planning problems, such as operational planning under uncer- tainty, optimization of bids to the day-ahead electricity market, and long-term evaluations of DH system operations. The tools are able to take advantage of the uncertainty, for instance, in the production of thermal solar heat as well as forecasts of the electricity prices on markets with varying horizons. The general applicability and performance of the approach are evaluated based on real data from the three Danish DH systems of Brønderslev, Hillerød, and Middelfart with different characteristics. When considering bidding, the new tool reduc- es cost in all cases and can save up to 42.1%. Conclusion Development in sensor technology and the rapid develop- ment in AI and IoT have provided district heating operators with new opportunities. Using AI or data-driven models to pro- vide information from sensors, the operations in the building, at the plants, the network, and market participation can be optimized. The key is data-driven and auto-calibrated tools for the modern operator. Tools for coherent load forecasting are central. Knowing the future demand with reliable uncertainty intervals allows for setting the water temperature and flow optimally rather than operating with a large-than-necessary safety margin. Such state-of-the-art forecasts are also the prerequisite for smooth solutions for bidding on the electricity markets.
For the future, weather-driven society district heating is already recognized to play a central role since these systems can pro- vide much of the needed flexibility at a low cost. Digitalization of district heating systems based on sensor data will further strengthen the position of district heating as a sustainable and low-cost energy supply technology capable of reducing car- bon emissions and contributing to climate change mitigation. In addition, we have proven that using data-driven tools has a huge economic potential. According to the so-called Damvad Report from 2019, the potential in Denmark alone is 240 to 790 mill DKK annually with state-of-the-art data-driven meth- ods for temperature optimization. On top of that, most of the methods for digitalization mentioned in this article will lead to considerable extra economic and operational benefits for dis- trict heating systems and their users. Reference to projects: IDASC: https://issuu.com/dtudk/docs/district-heating- digitalized?fr=sM2FiMzQ4NjgwMg HEAT 4.0: https://dbdh.dk/wp-content/uploads/2021/04/HEAT40-AlfredHeller-Niras.pdf CITIES: https://smart-cities-centre.org/ Flexible Energy Denmark: https://www.flexibleenergydenmark.com/ ARV: https://greendeal-arv.eu/
For further information please contact: Henrik Madsen, hmad.dtu@gmail.com
COOPERATIVE DISTRICT HEATING IN THE MAKING Inspirations from Denmark and first achievements in the Netherlands
By Rie Christiansen Krabsen, Marketing Manager, EBO Consult and Gerwin Verschuur, Program Manager Buurtwarmte in Cooperation Energie Samen
In the Netherlands, district heating has a market share of only 5% because Dutch citizens and industries are heavily depend- ent on natural gas, which is abundantly available in the Dutch underground. The extraction of natural gas has led to continu- ous high CO2 emissions and severe problems with earthquakes, forcing the Dutch government to stop the extraction in 2030.
lands must become an importer of natural gas. The war in Ukraine has worsened the situation. Like in many other Eu- ropean countries, citizens and industries suffer from extreme price levels for natural gas, and the urgency to find sustainable solutions for heating is now broadly felt. Therefore, Dutch so- ciety is searching for more knowledge about district heating, specifically cooperative district heating, where consumers own the district heating grid. Today, only one district heating co-
From a position as a net exporter of natural gas, the Nether-
in Denmark. In the Netherlands, district heating cooperatives are only partially accepted and need to be manifested in pol- icy or implemented in the heat law. In addition, it is possible to make a profit by delivering heat. The Dutch tariff regula- tion is founded on the NMDA principle, which means that the prices should not surpass the costs a natural gas user would have for the same amount of heat – in other words, "a cap." Every year, at the end of December, the ACM (national regu- latory authority) publishes the maximum prices that district heating companies can ask their customers for the heat and cold supply. This is just one of the many examples in the study where there are differences between the conditions for devel- oping cooperative district heating in Denmark and the Neth- erlands. From the Dutch perspective, the analysis works as an inspiration on how to expand cooperative district heating in the Netherlands. From the Danish perspective, the study can be read in the start-up phase of expanding and exporting district heating in the Netherlands because it gives an over- all picture of which differences and similarities between the two countries one can be aware of when working with district heating. First steps toward a Dutch district heating support organization Another aim of the project was to develop a Dutch district heating support structure similar to EBO Consult A/S that ena- bles cooperative initiatives to develop and operate district heat- ing projects. On the 22nd of September 2022, we arranged a meeting with ten cooperatives from across the Netherlands. Every cooperative signed a declaration to develop the support organization jointly, and the first steps toward its development and implementation are already moving ahead. On the following day, the 23rd of September 2022, we held a Dutch conference about how municipalities, public organ- izations, and cooperatives can collaborate to develop pub- lic-civil district heating enterprises. At the conference, there were representatives from Dutch municipalities, Klimaatver- bond, ministries of the Dutch state, and several district heat- ing cooperatives. Representatives from the Danish Embassy in Haag, EBO Consult A/S, and Hvidovre municipality joined the conference. The conference's outcome was a list of build- ing blocks to foster the development of cooperative district heating in the Netherlands. As a direct spin-off from the con-
operative exists, but around 80 initiatives are in the process of establishing a district heating cooperative. In Denmark, there exist about 323 district heating cooperatives. A grant from the Danish Energy Agency For many years EBO Consult A/S has held a lot of presentations about district heating at international seminars and confer- ences. Every time the focus has been on cooperative district heating– how consumers can manage and operate district heating and how the Danish regulations of district heating en- able cooperatives to exist under a not-for-profit regime. After the presentations, there have been numerous requests and questions about district heating, which has often resulted in sparring processes about implementing district heating in various countries. One of the ongoing collaborations has been with Cooperatie Energie Samen, a Dutch membership organi- zation that wishes to support the development of cooperative district heating. In 2020, The Cooperatie Energie Samen and EBO Consult A/S developed an application for the Danish "En- ergy Export Initiatives Grants program." At the end of 2020, we got the happy news that we received a grant. EBO Consult A/S has been sharing know-how with Cooperatie Energie Samen since then. Together, we have written a comparative study of the Danish and Dutch district heating markets focusing on cooperatives. We have taken significant steps to develop the beginning structures of a cooperative support organization that helps local district heating initiatives to grow and be im- plemented. Danish and Dutch comparative study The study investigates cooperative district heating in Den- mark and the Netherlands, where we focus on political, legal, financial, and organizational themes. But also about the roles of municipalities in the heat planning process, the available district heating technologies, the district heating marketing, the tendering process, the construction process of district heating, and the maintenance of district heating. We discov- ered several remarkable differences in cooperative district heating in the two countries during the writing process. In Denmark, district heating cooperatives have existed for many years. They are legally positioned as any other district heating company and are tariff regulated by the non-profit principle, where costs and revenues balance. The principle follows that it is impossible to profit from producing and supplying heat
model, the non-profit principle established in the law for co- operative district heating.
ference in Groningen, the Dutch commission for the climate agreement invited Energie Samen and the Danish Embassy to organize a workshop of 75 minutes on the day of the Cli- mate Agreement in Utrecht on the 3rd of November 2022. This workshop centered around the cooperative model for the expansion of district heating in the Netherlands. Another spin-off from the conference in Groningen was a joint effort of municipalities and cooperatives to get the definition of en- ergy communities, as defined in European law, in the revised Dutch Heat Act that is under preparation. This definition is of great importance to get, similar to the Danish district heating
In other words, Dutch cooperative district heating is in the making with inspiration from Denmark.
For further information please contact: Rie Christiansen Krabsen: rik@ebo.dk
EXPLOITING THE POWER OF DIGITALIZATION AND AI FOR END-TO-END OPTIMIZATION OF THE DISTRICT HEATING SYSTEM
By Jan Eric Thorsen, Director, Global Application Expert, Danfoss
Emanuele Zilio, Solution Engineer, Danfoss
Tomaz Benedik, Solution Manager, Danfoss
Oddgeir Gudmundsson, Director, Projects, Danfoss
Recent history has shown the current energy system is vulnerable to disruptions in the fuel supply chains, leading to high and unpredicta- ble natural gas and electricity prices. The path towards a more resilient energy system is the green energy transition, which can only be ex- pected to accelerate from now on. The accelerated green transition is a new paradigm that the district energy sector needs to adapt to with an increasingly forward-looking response.
installations, the utilization of the building thermal mass and reliable demand forecasting of each building. With an end-to- end focus, the full potential for leveraging the flexibility of the district heating infrastructure can be unlocked. This will enable the efficient use of a wide variety of heat sources, such as data centers, supermarkets, Power-to-X, Carbon Capture, industry, wastewater treatment, and renewable sources, such as bio- mass, wind, solar thermal, or geothermal energy. This union of flexibility, efficiency, and adoption of intelligent digital tools significantly increases the resilience of the district heating infrastructure. The impact reaches far beyond the dis- trict heating systems, making the whole energy system smart- er, more efficient, and more reliable. For this reason, we are continuously digitalizing our hardware portfolio to create smart components upgraded with ad- vanced artificial intelligence (AI) based software solutions that maximize the value of information for helping utilities and building owners to make better and fact-based decisions.
Many district heating systems have taken important steps in the last decades, such as consolidating multiple local networks into one large system and operating multiple and varied heat sources instead of the traditional single-source approach. They are also transitioning from high-temperature to low-tempera- ture operation. Those steps enable efficient operation of, e.g., heat pumps for taking advantage of locally available waste energy and low-temperature renewable thermal sources and moving away from fossil fuels. While the systems are inevitably becoming vastly more complex, they are also opening an enor- mous opportunity for holistically optimizing the whole system from one end to the other. The question is how to deal with the increased complexity this transformation brings. In Danfoss, we see the solution in the extensive use of digitaization and AI throughout the entire dis- trict energy supply chain - in planning of new systems and in the extension or modernization and maintenance of existing systems. We also see the solution in the strategic location of new heat plants and in optimizing the heat generation and network operation in multi-source systems based on reliable demand forecasts. Last, but not least, we see the solution in the continuous operation parameter optimization of building
Danfoss solutions The outcome of our digitalization journey is a portfolio of soft-
ware and services under the umbrella of Leanheat for the con- trol and optimization of district energy systems, from the pro- ducer to the consumer. The Leanheat software suite includes four different solutions. • Leanheat Production: An advanced software for load fore- casting, planning, and optimizing district energy produc- tion and distribution. The cornerstone of Leanheat Produc- tion is a six-day AI-based demand forecast. The software calculates the cost-optimal production mix from available heat sources based on the estimates and energy spot prices. • Leanheat Network: A thermo-hydraulic modeling tool de- veloped specifically to support district energy system plan- ning, design, and operation. With the help of the Leanheat Network digital twin in the planning and design process, the cost of establishing new and modifying existing district energy systems can be minimized. Once in operation, the digital twin will support the district heating utilities by opti- mizing the operation, leading to lower operational expenses. • Leanheat Monitor: A dedicated software for efficient remote monitoring, optimizing, and managing substations and heating installations. Leanheat Monitor further simplifies collecting and visualizing data that the district heating util- ity can use to optimize its operation. By using the software, district heating utilities can remotely detect faults or wrong settings and perform tasks that before required on-site in- tervention – thereby resulting in time and cost savings. • Leanheat Building: A software solution for optimizing the operation of heating installations of buildings with a cen- tralized heating system. It utilizes the latest AI and machine learning developments to generate accurate thermody-
namic models of the buildings it controls. It combines in- door climate monitoring and weather forecast to achieve energy savings and decrease the volatility of indoor temper- ature associated with traditional heating control strategies, improving living conditions for occupants. Furthermore, the control algorithm can optimize consumption and shift load while maintaining indoor comfort. Case example: Flexumers project in Copenhagen with HOFOR and Copenhagen City Properties & Procurement. An example of a Danfoss software application is to be found in Copenhagen. Here HOFOR (district heating utility), Copen- hagen City Properties & Procurement (Municipality’s building department), and Danfoss are currently testing the potential of minimizing peak heating demand to increase CO2 neutral base-load heat production usage in Copenhagen by utilizing Leanheat Building AI-based heating control. The first part of the demonstration took place during the heat- ing season 2021/2022 and included 17 municipal buildings (mainly daycare centers). The buildings were already equipped with Danfoss communicative heating controllers, and they were connected to the Leanheat AI control via the Danfoss ECL portal. The main goal of the demonstration was to reduce the peak in heat demand that occurs in the mornings (6-10 am) by mak- ing the heat consumption more flexible. Thus, the project has been named district heating Flexumers since the buildings that previously were only seen as energy consumers have be- come an active part of the district heating system. Each build- ing acts as a virtual heat plant by increasing its consumption when heat production is cheap and ecological and decreasing during times of high demand by providing flexibility on the consumption side.
Figure 1. Average daily heating profile with and without Leanheat control
Figure 2. Hourly peak power in buildings, with and without Leanheat control
HOFOR sees district heating Flexumers as an important measure to minimize fossil-based peak load production and incentivize renewable-based heat-producing units. The same principle of peak power optimization can be utilized in de- mand-response solutions for district heating concerning charging and discharging the thermal energy in the building mass. Leanheat can offer flexibility from an aggregated build- ing stock, making it possible for the district heating company to produce heat and use it in the buildings when it is most beneficial economically or ecologically. The DH company will thus reduce the use of fossil-based heat sources and prior- itize renewable-based ones. For example, with the advanced knowledge of the building thermal mass, Leanheat Building can enable price signals to adjust the building heat supply to take advantage of low-cost periods, for example, when there is a large share of fluctuating renewable energy in the system. Case example: optimization of domestic hot water storage tank control. Having data available from communicative components or controllers gives a high degree of freedom for testing and val- idating new functionalities. Besides the heating supply to the building, the domestic hot water (DHW) system is also relevant for improving performance based on digitalization and AI. As part of the HEAT 4.0 project (Digitally supported Smart Dis- trict Heating, IFD ref. no.: 8090-00046B), the operation of DHW storage tanks was analyzed to develop a control method for reducing the district heating return temperature and the peak power. One of the test site installations is shown in figure 3.
Leanheat’s AI learns how the building’s thermal mass reacts to ambient conditions and evaluates the flexibility potential based on the forecasted weather and set comfort require- ments. The district heating utility can then use the estimat- ed flexibility to minimize the load during peak-load periods. For example, during the morning peak, the control allows the discharging of the heat previously stored in the buildings by reducing the supply temperature for space heating by up to 30%. After this, it is recharged as soon as possible before the following day. The variation of supply temperature during this period still ensures that the thermal comfort in the building stays within the recommended limits. As presented in Figure 1, the initial results of the demonstra- tions for the cluster of 17 buildings show that the average morning peak decreased by 14%, compared with the average peak consumption before the implementation of the smart control. The connected buildings also reduced their heat- ing energy consumption, predictively factoring in upcoming changes in weather, such as solar radiation and wind. Based on the demonstration results, the overall economic and envi- ronmental benefits of Flexumers in Copenhagen will be quan- tified by mid-2023. The peak demand data measured at the buildings in Figure 2. Hourly peak power in buildings, with and without Leanheat control show that the maximum peak power has decreased from 27.5 kW/building to 21.5 kW/building (-22%). The calcu- lation compares the highest peak during load shifting to the highest measured peak in the previous heating season at the same outdoor conditions.
Figure 3. DHW storage tank with an internal heating coil (background) and heating mixing shunt (foreground)
a pre-set value is exceeded. While the functionality sounds straightforward, it has certain complexities in determining an appropriate setting. A too high setpoint results in no or limit- ed engagement of the function, and a too low setpoint com- promises the DHW temperature. Further, the optimal setpoint varies over the year due to seasonal variations in the cold-water temperature and the district heating supply temperature. The ratio of the DHW consumption and DHW circulation will also influence the optimal settings. Examples of seasonal variations are given in the figures below: As Figure 4 shows, the cold water varies by 12°C during the year, and the daily DHW demand varies by ~+/-35% of the annual average DHW demand. Based on these variations, it’s under- standable that the setting is not straightforward. An adaptive method was developed to address this challenge. The adaptive return temperature principle continuously adjusts the control- ler settings and adapts to the actual boundary conditions. An example where the adaptive control is compared to the refer- ence control can be seen in Figure 5. In the shown example, a reduction of the district heating re- turn temperature of 3.7°C and 4.6°C was realized, compared to pre- and post-reference periods, respectively. The temperature spikes are related to the disinfection of the DHW system, which has a similar impact on the reference and adaptive control pe- riods. Because of the control principle, the DHW temperature is slightly reduced in short periods during the adaptive control period, engaging the limiter function in an optimal way. In addition to the adaptive district heating return temperature limiting function, a power limiting function was developed. This control principle requires an energy meter to measure the power supplied to the DHW tank, which is becoming more ap- plied due to general energy awareness and heat cost alloca- tion. An example is given in Figure 6, where a reduced district
Often DHW tank applications suffer from high district heat- ing return temperatures and high-power peaks. This typically relates to poor controller settings, wrong control valve sizing, poor heat transfer via the tank coil, poor DHW stratification, and a high share of DHW circulation loss compared to DHW tapping. To improve the performance of DHW tank applica- tions, a new functionality was developed for the Danfoss ECL controllers. The new functionality is an intelligent district heat- ing return temperature limiting function. It works so that when a pre-set return temperature is exceeded, the charging flow to the tank coil is reduced, and thus the district heating return temperature will be kept below the pre-set value.
In the same way, there is the option for a power limiting function, limiting the district heating charging power when
Figure 4. Seasonal variation of cold-water temperature (left) and energy used for preparing DHW (right)
heating return temperature of 6.2°C and 6.7°C was realized. Further, the power (magenta line) generally shows fewer and lower peaks during the adaptive control periods. Based on tests in six buildings, the realized flow-weighted re- turn temperature reduction is, on average, around 2°C for the adaptive return temperature limiter and around 4°C for the adaptive power limiter annually. This is a significant decrease in the district heating return temperature for the service of DHW. Regarding peak power reduction, a 25-30% reduction is real- ized based on 30 min average values. Conclusion The energy transition is high on the agenda, and it’s great to see how district energy is a key enabler and a fundamental part of the future smart and sector-coupled energy system. Digitalization and AI-based tools are a precondition for district energy systems’ efficient, cost-optimal, and resilient operation.
utilization of green resources, avoidance of fossil-based peak load boilers, and better utilization of the existing distribution infrastructure. The example of optimizing the DHW tank charging resulted in a reduced return temperature of 2-4°C and 25-30% peak power reduction, leading to distribution heat loss savings, bet- ter utilization of the energy source and is often a precondition for reducing the supply temperature or increase of the distri- bution capacity. The presented cases are two of many underlining that we are continuously developing new and improving existing prod- ucts and offerings at Danfoss. We believe digitalization and AI- based end-to-end control solutions are the keys to unlocking the grid’s full potential and realizing District Energy 4.0..
For further information please contact: Jan Eric Thorsen, jet@Danfoss.com
The Copenhagen Flexumers case showed an average capacity reduction of 14% during peak load hours, leading to better-
Figure 5. Comparing district heating return-temperature during adaptive control and reference control periods
Figure 6 Comparing district heating return temperature and power peaks during adaptive and reference control periods
RESILIENT AND SUSTAINABLE DISTRICT HEATING USING MULTIPLE HEAT SOURCES
This article gives a description of the Danish district heating company, Hvide Sande Dis- trict Heating, which has become independent of fossil fuels by using wind and solar energy. This has resulted in lower consumer heating prices in a time when other fossil fuelled district heating plants are raising their heating prices due to higher fossil fuel pric- es. The article describes the flexibility that daily optimization tools must have to be used to handle the multiple heat sources and the participation in the multiple electricity mar- kets, and the need of digital twins for the medium- and long-term planning of the plant.
By Anders N. Andersen, PhD, Ext. Ass. Professor at Aalborg University, R&D projects responsible at EMD International
Hvide Sande, at the West coast of Jutland in Denmark, is a small fishing town. The district heating plant provides heat to 1,637 consumers. From being a natural gas fired Combined Heat and Power (CHP) plant, it has in recent years become more resilient by investing in a solar collector, wind turbines, a heat pump, an electrical boiler as well as more thermal stor- age capacity. Today, it is independent of natural gas. Fact-box 1 shows the present production units and storages at Hvide Sande District Heating. The two thermal storages of 2,000 m 3 and 1,200 m 3 , respective- ly, are able to store around 200 MWh-heat, which allows a very flexible market-based production on the different production units. The heat delivered to consumers can thus be produced many hours or days before delivery.
However, to take advantage of this flexibility, a vast digitaliza- tion of the plant together with advanced bidding methods have been required. Figure 1 shows a picture of the Hvide Sande fishing town. The solar collector is shown in front, the three wind turbines are placed close to the North Sea and the two red arrows points at the two thermal storages, the one placed at the solar collector site and the other placed at the site with the CHPs, heat pump and electric boiler. The production units are shown in details in this YouTube film Planning of day-ahead bids in Hvide Sande Even in the day-ahead market, the daily market-based produc- tions are a challenge to plan. The manager has before 12 o´-
Figure 1: The small fishing town Hvide Sande at the West coast of Jutland in Denmark. The red arrows show the location of the two thermals storages.
Factbox 1:
exported, which shall be offered to the variable operation and maintenance costs of the wind turbines.
clock the day before to decide for each of the hours tomorrow how much electricity he wants to sell and how much electrici- ty he wants to buy in each hour, and at which prices. Because of the large thermal storages, the manager must look more days ahead, when deciding the bids for tomorrow as well as considering the heat amount in the thermal storage right now. His decisions are based on forecasts more days ahead on wind velocity, solar radiation, ambient temperatures, and fore- casts more days ahead for Day-ahead prices. Furthermore, what complicates the Day-ahead bidding in Hvide Sande, is that the wind turbines are behind own me- ter (is private wire operated). This means that the electricity delivered by the wind turbines and used by the heat pump avoids grid and tax payment. Therefore, the sale price bids for the wind turbine production shall typically be split into two parts. The amount of the wind turbine production matching the consumption of the heat pump shall be offered at a lower price compared to the wind turbine production that will be Present production units and storages at Hvide Sande District Heating: • 2 natural gas fired gas engine Combined Heat and Power units each 3.7 MW-elec and 4.9 MW-heat • 3 wind turbines each 3 MW-elec • Heat pump of 5 MW-heat • Electrical boiler of 10 MW-heat • Solar collector of 9,500 m2 • Hot water storages at plant of 2,000 m3 • Hot water storages at solar collector site of 1,200 m3 • Gas peak boilers
Hvide Sande participates in three out of four balancing markets Factbox 2 gives an overview of the balancing markets in West Denmark. Hvide Sande District Heating participates regularly in three out of these four balancing markets. It participates in the FCR, mFRR and mFRR EAM markets. Participating in FCR To participate in the FCR market, bids must be made symmet- ric in 4-hour blocks and shall be able to be activated in 30 sec- onds. The electrical boiler can easily fulfil an activation in 30 seconds. Note that to make a symmetric bid on the electrical boiler, the offered capacity has at least to be traded in Day- ahead market in the same 4-hour block. As an example, if a 2 MW symmetric bid in FCR is given for the electrical boiler from 00:00 to 04:00 tomorrow, at least 2 MW must be purchased in the Day-ahead market in the same hours, which will allow both positive and negative frequency regulation of 2 MW to be made on the electrical boiler. Also, FCR-bids, are regularly being made on the CHPs. A gas engine CHP cannot regulate in 30 seconds if it is not running. So, what Hvide Sande District Heating has done is only to sell 80% of the CHP capacity in a certain 4-hour block in the Day- ahead market. Thus, being able to offer the remaining 20% of the capacity in the FCR market. Participating in mFRR The mFRR market is an hourly reserve market. Winning a mFRR capacity bid in a certain hour tomorrow, gives an obligation for the plant to make an offer of this capacity into the mFRR EAM market. However, the plant decides itself at which prices the upward regulation is offered.
When it is windy or sunny it will often be cheaper to produce the heat on the heat pump, the electrical boiler, or the solar
Factbox 2:
The balancing markets in West Denmark FCR, Frequency Containment Reserves The FCR market is a rather small market, and as the name of the market indicate, this market shall not bring the frequency back to 50 Hz, but only contain a frequency problem, e.g., stop a reduction in frequency. A production unit shall be able to be activated in maxi- mum 30 seconds, and the activation shall be able to be maintained 20 minutes. The bids are split into 4-hour blocks and is symmetric, that is the won FCR shall be able to deliver both positive and negative frequency regulation. Gate closure is 8 a.m. the day before, and there is Marginal pricing. That is all won bids gets the same price. There is no payment for energy activation.
FRR, automatic Frequency Restoration Reserves Month ahead market. The reaction time is maximum 15 minutes. The bids are symmetric. The prices are settled as Pay-as-bid. mFRR, manual Frequency Restoration Reserves The reaction time is maximum 15 minutes. Asymmetric bids are allowed, either for upwards regula- tion or for downward regulation. Hourly bids and there are Marginal pricing. Gate closure 9.30 the day before. mFRR EAM, Energy Activation Market in mFRR Gate closure is one hour before the operating hour. For won mFRR bids in a certain hour it is obligatory to make an offer of this capacity into the mFRR EAM in that hour. Marginal pricing. The mFRR EAM market is often called the regulating power market.
Figure 2: The red arrow points at a won activation of the two CHPs in mFRR EAM. The green prices show the prices in the Day-ahead market. The blue prices show the upward regulation prices and the yellow prices show the downward regulation prices in mFRR EAM. The lower graph shows the content in the two thermal storages. As is seen, the heat produced in the won activation of the two CHPs is partly stored in the thermal storages.
collector, rather than producing the heat on the CHPs. In such an hour tomorrow, it is obvious to offer the CHPs in the mFRR market, and when coming to the hour and if there is not suffi- cient content in the thermal storages the obligatory activation bid can be made sky high to avoid winning the activation. The heat pump is operated in many hours. In these hours it is again possible to offer mFRR, because closing a heat pump reduces the electricity consumption and thus offers an upward regulation. Participating in mFRR EAM As mentioned, after winning an mFRR bid in a certain hour it is obligatory for the plant to make an offer of this capacity into the mFRR EAM. However, even if it has not won an mFRR bid in a certain hour, it may still offer activation in mFRR EAM. The simple starting point for making bids in mFRR EAM is to make it as the opposite bid as won in Day-ahead. As an example, if 1 MWh purchase bid has been won on the heat pump in Day-ahead in a certain hour, the opposite bid of 1 MW can be offered as upward regulation in mFRR EAM. Note that winning an upward regulation on the heat pump has the consequence that less heat is produced, which may have the consequence that the thermal storages will be emptied, and the gas boilers must be started. But that is in fact the way bidding prices are calculated – as the economic consequences of winning a bid. At www.emd-international.com/livedata we show online the operation of Hvide Sande District Heating. Figure 2 shows an example of a won activation of the two CHPs in mFRR EAM.
used for the daily planning of bidding amounts and bidding prices in the different electricity markets. However, it is also im- portant that the manager maintains a digital twin of the plant. The daily optimization will often give inspiration to new invest- ments to be made. It is also about finding the right balance be- tween investments in production units, storages, and grid infra- structures and regularly the manager has to make budgets for the coming periods. That is what the digital twin shall be used for. In Figure 3 is shown the digital twin that Hvide Sande District Heating is using. An overview of different digital twin tools is shown in this article
Figure 3: Hvide Sande District Heating is using the energyPRO energy system analysis tool for making the digital twin of the grid and plant, that it uses for budgetting and investment analysis.
Digital twin of Hvide Sande District Heating This article illustrates that daily optimization tools must be
For further information please contact: Anders N. Andersen: ana@emd.dk
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