District Heating Grid Research Articles

Peak shaving at system level with a large district heating substation using deep learning forecasting models

The decarbonisation of urban district heating (DH) systems requires increased heating grid flexibility. Therefore, this article examines the optimised operation of a tank thermal energy storage (TTES) on the secondary side of a new DH substation for an industrial site in a German city, in order to shave the peaks of the whole DH system and thus reduce the need for heat-only boilers (HOB). The accuracy of heat load and return temperature forecasts for both the industrial consumer and the DH grid is critical to the performance of the optimisation-based operating strategy of the TTES. Therefore, long short-term memory neural networks are used in combination with continuous model updates through incremental learning to create two forecasting scenarios, one using only preceding data for the forecasts and the other including future weather data. The results show that high forecasting accuracy is most relevant for reducing the annual maximum peak, with a reduction of 2.8% in the preceding data scenario, 4% with future weather data and 7% in a benchmark with perfect forecasts. The economic viability of the storage through HOB heat savings is primarily affected by lower forecasting accuracy when the additional cost of HOB heat is less than 60 €/MWh.

Probabilistic state estimation in district heating grids using deep neural network

Probabilistic state estimation is critical for operating and controlling district heating grids efficiently. However, computational bottlenecks of traditional solvers limit the feasibility of uncertainty-aware Bayesian estimation. This paper proposes using deep neural networks (DNNs) to enable fast and accurate posterior estimation. Fully-connected neural networks (FCNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) are evaluated as candidate approximators of the physical model. Markov chain Monte Carlo sampling in the heat exchange space is leveraged to generate posterior samples. Experiments on a benchmark heating grid demonstrate FCNNs can efficiently learn the mapping from heat exchanges to network states. A FCNN trained on 20 training epochs after hyperparameter optimization provides the best approximation accuracy and uncertainty estimates, outperforming prior methods based on Deep Neural Networks. The results highlight the potential of data-driven deep learning models for probabilistic state estimation. The proposed framework could enable real-time uncertainty-aware control and decision-making for future intelligent district heating grids.

Long-term heat storage opportunities of renewable energy for district heating networks

This study compares different thermal heat storage solutions existing in the market, fuelled with energy from different renewable energy sources with a focus on integrating thermal heat storage into the district heating grid. The paper is based on a case in the municipality of Silkeborg, Denmark, which has the largest solar thermal panel plant in Northern Europe. A theoretical approach was used to compare with assumed excess power from wind and solar in the DK1 area, with Silkeborg's allocated excess power at 0.01%. This yielded overall efficiencies between η = 0.739–0.765 and η = 0.864–0.895 for the Silkeborg solar thermal plant. Four different thermal heat storage solutions were compared: tank thermal energy storage, pit energy storage, aquifer thermal energy storage and borehole energy storage (BTES). The analysis showed that, of the four solutions compared, BTES was the best for storing thermal energy for a longer period of time, with the lowest heat loss rate of 0.6% and the highest efficiency of up to 89.5%. However, some complications make it difficult to establish a BTES storage solution, since it is very much dependent on earth conditions and initial capital.

District heating potential in the EU-27: Evaluating the impacts of heat demand reduction and market share growth

This paper presents a novel approach to modeling the gradual reduction in heat demand and the evolving expansion of district heating (DH) grids for assessing the DH potential in EU member states (MS). It introduces new methodological elements for modeling the impact of connection rates below 100% on heat distribution costs in both dense and sparse areas. The projected heat demand in 2050 is derived from a decarbonization scenario published by the EU, which would lead to a reduction in demand from 3128 TWh in 2020 to 1709 TWh by 2050. The proposed approach yields information on economic DH areas, DH potential, and average heat distribution costs. The results confirm the need to expand DH grids to maintain supply levels in view of decreasing heat demand. The proportion of DH potential from the total demand in the EU-27 rises from 15% in 2020 to 31% in 2050. The analysis of DH areas shows that 39% of the DH potential is in areas with heat distribution costs above 35 EUR/MWh, but most MS have average heat distribution costs between 28 and 32 EUR/MWh. The study reveals that over 40% of the EU's heat demand is in regions with high potential for implementing DH.

Estimating residential space heating and domestic hot water from truncated smart heat data

The EU aims to digitize the building stock across all member states to better understand energy use and achieve energy efficiency goals to address climate change. Smart heat meters are currently used for billing purposes in district heating (DH) grids. Their data is recorded as integer kWh values, which restricts usability for the modeling and analysis of DH networks. Previous research devised a methodology to estimate space heating (SH) and domestic hot water (DHW) energy from total heating data, but the data truncation process reduced accuracy. This study integrates the SPMS (Smooth–Pointwise Move–Scale) algorithm, which estimates decimal values from DH truncated measurements, to improve the accuracy of the DHW and SH disaggregation methods. The study applies these two methodologies to a dataset of 28 Danish apartments and compares the results against full-resolution and truncated data to evaluate performance. Another dataset, named “optimal dataset” is also assessed to determine overall estimation accuracy. Results show that SPMS reduces the disaggregation methodology error of SH and DHW compared to the truncated data. The optimal dataset outperforms the current methodology, indicating a potential for improving and scaling the methodology for larger datasets.

Deep learning-enabled MCMC for probabilistic state estimation in district heating grids

Flexible district heating grids form an important part of future, low-carbon energy systems. We examine probabilistic state estimation in such grids, i.e., we aim to estimate the posterior probability distribution over all grid state variables such as pressures, temperatures, and mass flows conditional on measurements of a subset of these states. Since the posterior state distribution does not belong to a standard class of probability distributions, we use Markov Chain Monte Carlo (MCMC) sampling in the space of network heat exchanges and evaluate the samples in the grid state space to estimate the posterior. Converting the heat exchange samples into grid states by solving the non-linear grid equations makes this approach computationally burdensome. However, we propose to speed it up by employing a deep neural network that is trained to approximate the solution of the exact but slow non-linear solver. This novel approach is shown to deliver highly accurate posterior distributions both for classic tree-shaped as well as meshed heating grids, at significantly reduced computational costs that are acceptable for online control. Our state estimation approach thus enables tightening the safety margins for temperature and pressure control and thereby a more efficient grid operation.

A prosumer-based sector-coupled district heating and cooling laboratory architecture

New control strategies for thermal systems and innovative district heating and cooling grids can help to decarbonize the thermal sector. Before implementing these new concepts, they should be validated, ideally with commercial hardware but without influencing user comfort. For this reason, the laboratory at the research center for Combined Smart Energy Systems (CoSES) at the Technical University of Munich (TUM) was designed. By combining commercial hardware with Power Hardware in the Loop simulations, the laboratory enables research in a controllable, but realistic setting without affecting real users. It consists of five prosumers equipped with heat generators and thermal storages. All prosumers are linked with an adjustable district heating and cooling grid. The modular hardware and control architecture presented in this paper covers management-, automation-, field-level control and offers interfaces to external control. A case study shows that prosumer integration into flexible district heating grids can reduce overall heating costs but requires intelligent control concepts for transfer stations and heat generators. The conducted experiments emphasize the importance of validating control strategies in laboratory environments. They allow the analysis of phenomena that are difficult and impractical to model accurately with existing simulation tools. The structure and capabilities of the laboratory are presented in order to foster collaboration with other researchers.

CO[formula omitted] handling in binary geothermal systems — A modelling approach for different CO[formula omitted] contents, salinity, pressure and temperature conditions

The decarbonisation of power and heat production is recently one of the major challenges in the Energy sector. Among other technologies, deep geothermal energy is an interesting option, because source temperature, plant capacity, low environmental footprint and high availability allow the application in district heating grids. The majority of geothermal reservoirs contains a certain amount of non-condensable gases dominated by CO2. With respect to corrosion and precipitation effects as well as environmental friendliness and economic optimisation, the handling of these gases is challenging.In this study three different technical options – a gas bypass system, a certain pressure regime and a secondary gas turbine – are investigated to overcome this challenge. The geothermal plant at Bruchsal/Germany was selected as case example for process simulation and further analyses.The major outcome shows that a gas bypass excluding CO2 from the power plant results to be the best option for the existing plant in terms of avoiding CO2 emissions and reaching highest possible power output. Furthermore, the study results recommend the best CO2 handling solution for different conditions within the simulated range (temperatures from 100 to 160 °C, salinity from 0–200g/L, pressures from 15–135bar, and flow rates until 100kg/s) as an essential contribution to the field.

Decentralised multi-grid coupling for energy supply of a hybrid bus depot using mixed-integer linear programming

The present work refers to two current problems in the context of achieving Greenhouse gas (GHG) neutrality: first the curtailment of renewable, volatile power generation units and secondly the high share of the mobility domain in total GHG-emissions. Both problems can be countered by a decentralised, smart energy system that supplies electricity, gas and heat to a hybrid public transport bus fleet and is simultaneously coupled to the public gas grid, public electricity grid and the district heating grid (Multi-Grid-Coupling). The enabling energy conversion unit is a reversible solid oxide cell (rSOC), which is operated in combined heat and power (CHP) mode or in power-to-gas (P2G) mode. P2G is primarily a solution approach for the first-mentioned problem and can thus successively lead to the replacement of fossil energy sources. Furthermore, by integrating industrial waste gases – as a necessary CO2 source for the P2G process – an additional benefit is gained from the CO2 that is emitted anyhow. The hybrid bus fleet constitutes an ecological alternative concept in public transport and therefore addresses the second-mentioned problem. The system, developed under the current state of the art technologies and the current ecological and economic conditions for Europe and Germany, can be operated profitably from the perspective of the system operator. This applies to the economically and ecologically optimised operating schedule of the controllable system elements such as the electrical, thermal and compressed gas storages, rSOC, compressor and the energy exchange with the public grids. To derive the optimal operating schedule of the cross-sectoral system, a mixed-integer linear programming (MILP) model is implemented and simulated under the current legal situation.

Demonstration of a concentrated solar power and biomass plant for combined heat and power

This study presents the design and performance of the Brønderslev hybrid plant – the world’s first concentrated solar power (CSP)-biomass plant to utilize waste heat. The combined heat and power plant consist of a 16.6 MW parabolic trough collector field, two 10 MW biomass boilers, and an organic Rankine cycle (ORC) system with an electrical output of 4 MW. The solar collector field and biomass boilers are hydraulically connected and supply heat in parallel to the ORC system. A multi-stage heat recovery process ensures that the waste heat is supplied to the local district heating grid. The solar field can also be operated independently of the biomass plant, supplying heat directly to the district heating grid. The plant operation was investigated by analyzing measurements for 2020, and the monthly heat and electricity generation was calculated for each component. Additionally, the performance of the solar field and ORC was elucidated, and the ORC was found to have a maximum electrical efficiency of 20.6 %. Detailed results are shown for one day with joint heat supply to the ORC from the solar field and biomass boilers, and the corresponding energy flows are visualized using a Sankey diagram. The total efficiency of the biomass plant for the investigated year was 93.6 % based on the higher heating value of the fuel. Overall, it was found that the hybrid plant performed reliably and successfully demonstrated the potential of CSP-biomass hybridization.

The benefits of 4th generation district heating and energy efficient datacentres

This paper identifies the energy system benefits of transitioning district heating infrastructure from current 3rd generation district heating (3GDH) to 4th generation district heating (4GDH) with lower grid temperatures and more integration with other energy sectors. Previous papers have investigated the effect of going from 3GDH to 4GDH on a local perspective. In this paper it is investigated how this transition affects the energy system on a national level, where both the costs and energy effects are evaluated based on national hourly energy systems simulations including all energy sectors in a climate neutral energy system scenario. Moreover, the paper identifies the effects of different cooling solutions in datacentres. The analyses include effects on heat sources as well as the effect on the cost and losses in the district heating grid. This is done for the case of Denmark. In the case of Denmark, it is found that transitioning from 3GDH to 4GDH decreases the system cost by 220 M EUR/year, corresponding to 7 M EUR/TWh delivered heat at the end-user. Increasing the temperature outlet of datacentre waste heat has a value for the system as high as 52–59 M EUR/year, corresponding to 1.7–1.9 M EUR/TWh delivered heating at the end-user.

A Different Approach to Develop a District Heating Grid Based on the Optimization of Building Clusters

Currently, reducing energy consumption and fossil fuel emissions are key factors placed in the first position on the European agenda. District heating technology is an attractive solution, able to satisfy the energy and environmental goals of policymakers and designers. In line with this, a different approach to planning a district heating grid based on the optimization of building clusters is presented. The case study is Wilhelmsburg, a district of Hamburg city. This approach also investigates the usage of industrial waste heat as the grid’s heat source, which is CO2-neutral. First, the data acquisition regarding the buildings’ location and heat demand are described in detail. Based on the derived data and the source of the industrial waste heat, the district heating grid is created by clustering the buildings and connecting the obtained nodes. Furthermore, the grid’s efficiency is improved by eliminating nodes, which are too distant from the heat source, or have lower heat demand. Finally, a single building is simulated in Matlab/Simulink, showing the energy-savings and ecological results. The usage of the district heating grid saves 97.32 GWh annually, which results in financial savings of €5.83 million, and avoided CO2 emissions of 19,585 tCO2.

Planning to grow, planning to rock on – infrastructure management and development in shrinking municipalities

ABSTRACT Previous work on shrinking cities has mainly addressed shrinkage and its effects in large and former industrial cities and not as much in municipalities in rural areas. In this paper, we focus on infrastructure challenges, responsibilities, and growth strategies in Swedish municipalities. We argue that there is a tension between the responsibilities connected to the municipal operations and infrastructure challenges posed by being a shrinking municipality on the one hand, and the ways the municipalities are planning for growth on the other hand. The municipalities are all struggling with the effects of population decline, leading to economic strains in all areas, including infrastructure management and development, but investments in infrastructure are many times directed towards the establishment of specific industries such as the tourism or mining industry with the belief of attracting new inhabitants, visitors, firms and industry. In addition, many of the municipalities lack the capacity and jurisdiction needed to manage the infrastructure development in some areas such as fibre optics, district heating, and electricity grids. Also, in some municipalities, the populations are spread over large geographical areas but must still provide infrastructure services to all inhabitants.

Unlocking the Flexibility of District Heating Pipeline Energy Storage with Reinforcement Learning

The integration of pipeline energy storage in the control of a district heating system can lead to profit gain, for example by adjusting the electricity production of a combined heat and power (CHP) unit to the fluctuating electricity price. The uncertainty from the environment, the computational complexity of an accurate model, and the scarcity of placed sensors in a district heating system make the operational use of pipeline energy storage challenging. A vast majority of previous works determined a control strategy by a decomposition of a mixed-integer nonlinear model and significant simplifications. To mitigate consequential stability, feasibility, and computational complexity challenges, we model CHP economic dispatch as a Markov decision process. We use a reinforcement learning (RL) algorithm to estimate the system’s dynamics through interactions with the simulation environment. The RL approach is compared with a detailed nonlinear mathematical optimizer on day-ahead and real-time electricity markets and two district heating grid models. The proposed method achieves moderate profit impacted by environment stochasticity. The advantages of the RL approach are reflected in three aspects: stability, feasibility, and time scale flexibility. From this, it can be concluded that RL is a promising alternative for real-time control of complex, nonlinear industrial systems.

Thermal influences on groundwater in urban environments – A multivariate statistical analysis of the subsurface heat island effect in Munich

Shallow aquifers beneath cities are highly influenced by anthropogenic heat sources, resulting in the formation of extensive subsurface urban heat islands. In addition to anthropogenic factors, natural factors also influence the subsurface temperature. However, the effect of individual factors is difficult to capture due to high temporal dynamics in urban environments. Particularly in the case of shallow aquifers, seasonal temperature fluctuations often override the influence of existing heat sources or sinks. For the city of Munich, we identify the dominant anthropogenic and natural influences on groundwater temperature and analyse how the influences change with increasing depth in the subsurface. For this purpose, we use depth temperature profiles from 752 selected groundwater monitoring wells. Since the measurements were taken at different times, we developed a statistical approach to compensate for the different seasonal temperature influences using passive heat tracing. Further, we propose an indicator to spatially assess the thermal stress on the aquifer.A multiple regression analysis of four natural and nine anthropogenic factors identified surface sealing as the strongest and the district heating grid as a weak but significant warming influence. The natural factors, aquifer thickness, depth-to-water and Darcy velocity show a significant cooling influence on the groundwater temperature. In addition, we show that local drivers, like thermal groundwater uses, surface waters and underground structures do not significantly contribute to the city-wide temperature distribution. The subsequent depth-dependent analysis revealed that the influence of aquifer thickness and depth-to-water increases with depth, whereas the influence of Darcy velocity decreases, and surface sealing and the heating grid remain relatively constant. In conclusion, this study shows that the most critical factor for the mitigation of future groundwater warming in cities is to minimize further sealing of the ground, to restore the permeability of heavily sealed areas and to retain open landscapes.

Domestic hot water system in residential buildings: production, distribution and consumption energy loss. Monitoring campaign in two Danish detached houses

The share of the energy use for domestic hot water (DHW) in the total energy consumption of buildings is becoming more and more prominent. Depending on the building typology it varies between 20% to 50% of the total energy usage for old and new built single family house, respectively. The aim of this paper is to determine the energy losses in the DHW installation with division between: a) loss at the production point, b) loss in the distribution, and c) loss at the draw-off points using the results of the measurements of DHW consumption in two single family houses connected to district heating grid. The total Eloss for the two houses vary between 17% and 26%. For House 1, the production loss accounts for 8%, the pipe loss for 15% and loss at the draw off points for 3%. Moreover, the results shown that the layout of the house, in particular the placement of the bathrooms with showers or bath tubs has significant impact on the size of the distribution losses.

District heating network modelling for future integration of solar thermal energy

A key advantage of district heating networks is their ability to integrate different renewable energy sources, from geothermal to solar. However, the success of this integration depends on a variety of design and technical decisions, such as feed-in locations or operating temperatures, which need to be compared and analysed. For this purpose, dynamic models of district heating grids, which allow for an hourly representation of the thermodynamic conditions, are necessary. This type of models are nevertheless still uncommon, drastically limiting options to perform these comparisons accurately. To address this challenge, an open-source tool to model district heating networks is presented here and successfully applied to two case studies in western Switzerland. These simulations are then used in conjunction with simplified models of storage and solar thermal collectors to investigate, in a preliminary way, the impact of solar thermal integration on the mass flow and temperature of the network pipes, illustrating the interest of the proposed method to compare different configurations of renewable heat injections in district heating networks.

Integrating bio-oil production from wood fuels to an existing heat and power plant - evaluation of energy and greenhouse gas performance in a Swedish case study

Combined heat and power (CHP) production in combination with a district heating (DH) grid gives an energy-efficient use of wood fuels. The heat demand in the DH grid will, however, decline in the coming decades, and operators are seeking additional heat sinks. In this case study, the integration of a pyrolysis unit into an existing CHP plant was investigated as a possible solution. The retrofitted pyrolysis unit makes use of excess heat and yields a liquid bio-oil. Pyrolysis integrated with CHP production was shown to give a net energy yield of at least 80%, and to decrease the net heat output to the DH grid. The carbon footprint of the delivered heat was very low at maximum 1.6 g CO2eq/MJ. Prolonging the operation of the pyrolysis unit to periods without heat delivery to the DH grid would increase the use of existing installations, but at the cost of energy yields decreasing to 63–70%. Up to 2.8 PJLHV/yr crude bio-oil could be produced at the investigated CHP plant. The bio-oil was shown to have a low carbon footprint, 1.7–4.0 g CO2eq/MJLHV, which makes it attractive for the rapidly expanding transport biofuel market.

Drivers and barriers for prosumer integration in the Swedish district heating sector

Despite the apparent usefulness of prosumers in the DH grid, there has not been a systematic investigation into why Sweden has not seen a general uptake in prosumer integration in the district heating (DH) sector. The transition to 4th generation district heating (4GDH) and smart energy system concepts are conducive to prosumer integration. Nonetheless, the integration of heat prosumers has been slow in Sweden. Our current study investigates the drivers and barriers for prosumer integration in the Swedish DH sector and looks at if and how rules, regulations and policies may affect the enabling of prosumer integration. The research questions posed in this study are: what are the drivers and barriers for prosumer integration in the DH sector in Sweden, and, what are the rules, regulations and policies that can affect the enabling of prosumer integration in the DH sector in Sweden. A directed Content Analysis of systematically selected scientific and industry-related literature is analyzed to investigate the drivers and barriers for prosumer integration in the Swedish DH sector. The drivers for prosumer integration can be broadly categorized as DH-side drivers, prosumer side drivers and macro-trend drivers. In terms of the DH-side, costs savings, more flexibility, increasing the environmental and commercial profile of the company and increasing the effective use of energy are the main drivers. Similarly, for the prosumers, it is additional income, increased self-sufficiency and raising the environmental profile which are the main drivers. In the barriers side, not up-to-date policies and regulations about the energy use and required energy efficiency for buildings, and conflicting regulations about accounting of energy use disincentivize prosumer integration in the Swedish DH sector. There are barriers on both the DH-side and prosumers’ side hindering the integration of prosumers in the DH sector. These barriers are also enhanced by ambiguous policies, which hinder the prosumer integration in the Swedish DH sector.

New experimental facility for innovative district heating systems—District LAB

Low temperature district heating (LTDH) is a key technology for the efficient integration of renewable energies and waste heat in our energy systems. Energy system studies indicate that a further development of LTDH systems is needed for a decarbonization of the heating sector and to reach the climate goals, but is also put in question. To face these challenges, research on innovative district heating concepts is required like the feed-in of decentralized renewable energy or the design of new modes of operation.Against this background, the new experimental facility District LAB is set up serving as a test and development platform for innovative district heating systems in close cooperation with collaborates from industry and research. By using the facility, it is targeted to investigate the system behaviour of flexible heating networks, to conduct component tests and to develop innovative control concepts for new management strategies. To reach these goals the District LAB consists of a flexible heating grid, a pipe test bench for mechanical tests and management respectively control units. This way the possibilities of the already existing facilities are expanded. Compared to tests in real operating district heating grid (demonstration) the experimental environment is more flexible since no public utility mandate is connected to the facility.A first started project comprises three different fields of research as investigations of new operational strategies or digitalization, tests of components and the development or validation of simulation tools. The results of the project and the knowledge generated at the District LAB will make a significant contribution to the decarbonization,​ transformation and digitization of district heating.The paper presents a more detailed description of the District LAB and the started research activities to foster the discussion about needed research and to invite possible partners from academia and industry for future cooperation.