District Heating Research Articles

Global sensitivity analysis of nuclear district heating reactor primary heat exchanger and pressure vessel optimization

Recently, small modular reactors (SMRs) have received greater interest as a source for clean and affordable district heating (DH). Compared to power plants, the low-pressure, low-temperature design and nearly 100 % efficiency reduce the cost of produced energy considerably. However, few practical implementations exist yet, and cost estimates and design principles are subject to uncertainties whose interactions remain largely unknown. In this work, we present a techno-economic optimization and sensitivity analysis of a natural circulation DH SMR primary heat exchanger. A Cuckoo Search variant augmented with a modified Hooke-Jeeves search was used as the optimizer, with SimDec (simulation decomposition) subsequently employed for global sensitivity analysis. The reactor pressure vessel and containment vessel specific costs exhibited the greatest impact on the cost of heat and the optimized configurations. While low-pressure, low-temperature design is central to heating reactor cost-effectiveness, optimized primary circuit temperatures clearly exceeded previous assumptions. In a 5260 full-load hours mid-load application, a 34–41 €/MWh cost range was found for produced heat at 8 % interest and 20-year lifetime. For heat exchanger optimization, the results indicate the potential for considerable performance improvement from using deterministic local search for terminal convergence and sensitivity analysis for dimensionality reduction.

Influence of supply temperature and booster technology on the energetic performance and levelized cost of heat of a district heating network with central heat pump

The development of new low-temperature district heating networks in existing urban areas can accelerate the replacement of fossil-fuel-based heating systems by collective heating using electricity or renewable heat sources. Buildings can either be connected to the network in their current, non-refurbished state for rapid deployment or be refurbished before connection to achieve maximum energy efficiency. To assess the difference between these two strategies, this paper examines eight low-temperature district heating concepts with central heat pump, comparing their energy use and levelized cost of heat. The study includes four scenarios for each strategy, considering supply temperatures ranging from 10 °C to 75 °C. Heat pumps or electric heaters are used as booster technology when the network supply temperature is too low for space heating or domestic hot water purposes. The use of booster heat pumps shows the highest seasonal coefficient of performance for both refurbished (4.61) and non-refurbished buildings (3.43). However, booster technologies result in the highest levelized cost of heat, driven mainly by the high investment cost of the network and booster units. Lowest levelized cost of heat of 213 €/MWhth is obtained for non-refurbished buildings at 75 °C and 297 €/MWhth for refurbished buildings at 55 °C without boosters.

Optimal operation of a residential energy hub participating in electricity and heat markets

The integration of electricity and heat networks provides significant benefits by enhancing system flexibility and improving overall energy efficiency. Energy hubs play an important role in these interconnected systems, facilitating the production, conversion, and storage of energy across different forms. Potential flexible loads that may exist in an energy hub can further optimize its resource utilization and operational stability. In this respect, this paper addresses the day-ahead energy management of a residential complex modeled as an energy hub, incorporating medium-scale generation and storage units, as well as must-run and flexible loads. We also consider energy hub operator’s energy transactions in power distribution system and district heating and aim to obtain the optimal bidding strategy of this profit-driven agent. The negotiations among the energy hub operator, distribution system operator, and district heat network operator are modeled as a single-leader multi-follower Stackelberg game. A Nash Equilibrium of this game can be obtained by modeling the interactions among players as a bi-level optimization problem. The lower-level problems account for multi-period optimal power flow, modeled as an exact AC optimal power flow, and multi-period optimal thermal flow. The upper-level problem models the energy management of the energy hub. Replacing the lower-level problems with their optimality conditions, the optimal bidding of the energy hub operator can be obtained by solving the resulted mixed-integer linear programming problem as a mathematical program with equilibrium constraints. Finally, we numerically evaluate the proposed framework in a case study for a large residential complex participating in a power distribution and a heat network.

A stackelberg game-based programming approach for industrial steam systems incorporating renewable energy considering demand response

Fossil fuels account for 37 % of industrial combustion, primarily used for steam production. Given the significant role of steam in industrial processes, industrial steam systems (ISS) offer a critical opportunity to accelerate the transition from fossil fuels to renewable energy by integrating renewable sources. Currently, ISS optimization focuses mainly on internal improvements, often neglecting stakeholder interactions and the inherent unpredictability of renewable energy sources. This paper introduces a novel bi-level programming approach based on Stackelberg game theory to address the balance between ISS and user interests. A new ISS model is proposed that integrates multiple renewable energy sources while accounting for their intermittency. Revenue models for energy servers (ES) and consumers (EC) are developed, and demand response mechanisms and real-time pricing are incorporated into the bi-level programming model. In the designed bi-level ISS (B-L-ISS) model, the upper level optimizes energy conversion equipment and pricing, while the lower level aims to minimize users' energy costs. The Karush-Kuhn-Tucker (KKT) conditions are used to reformulate the bi-level problem into a single-level programming problem with equilibrium constraints. Validation on a real coastal industrial steam system shows that the proposed method integrates renewable energy four times more effectively than traditional methods, reducing CO2 emissions by 15,790 tons annually, while balancing the interests of both the steam system and its users.

Evaluation of Electrochemical Properties and Life Prediction of Sensor Wire in Leak Detection Systems of Underground Heating Pipelines

In this study, we investigated the electrochemical properties and lifespan of the NiCr (NiCr 8020) sensor wire of a resistance leaking detection (LD) system to detect pipe corrosion and leakage in an actual district heating (DH) system. The temperature and applied stress of the sensor wire during the actual operation of the resistance LD system of the DH system were derived through simulations and calculations. The anodic dissolution of the sensor wire was accelerated with the increased temperature and the applied current. The corrosion type changed from localized corrosion, such as pitting, to uniform corrosion. The applied stress caused ductile fracture of the thinned sensor wire by anodic dissolution. In conclusion, we confirmed that in the resistance LD system of a DH system, where current and stress are applied at high temperatures, the sensor wire becomes thin due to the anodic dissolution and subsequent ductile fracture. In addition, the lifespan of the sensor wire was derived according to the resistance level measured in the resistance LD system of the DH system. Our findings contribute to preventing failure and improving the reliability of the resistance LD systems of DH systems.

Short-term heating load forecasting model based on SVMD and improved informer

Accurate heat load forecasting can not only effectively reduce energy consumption, but also improve the efficiency of the heating system and user comfort. In order to improve the accuracy of heat load forecasting, we proposed a forecasting method combining SVMD algorithm and improved Informer model and applied it to the heat load forecasting of district heating system. First, SVMD algorithm is used to decompose the heat load data, and then relative position coding is introduced into Informer model, and causal convolution and jump connection mechanisms are adopted to better capture the dependence of sequence data and enhance the ability of local information extraction. In order to verify the model performance, experiments were conducted on multiple data sets. The results show that the R2 evaluation index of the model reaches 98.7 %, which is 10.8 %, 17.1 %, 6.3 %, 4.9 %, 2.3 %, 1.5 % and 16.6 % higher than SVR, RNN, LSTM, Transformer, SH-Informer, iTransformer and DLinear respectively. The validity of the model is further verified by DM test, which provides a reference for further improving the timeliness of intelligent heating.

A novel heat load prediction model of district heating system based on hybrid whale optimization algorithm (WOA) and CNN-LSTM with attention mechanism

Machine learning models, particularly long short-term memory (LSTM) networks, have been extensively employed for heat load prediction in district heating systems (DHS). Nevertheless, the over-reliance on default hyperparameter settings in most methods hinders further enhancement of prediction accuracy. A novel load prediction model is presented, which integrates the whale optimization algorithm (WOA) to refine the hyperparameters of an LSTM model bolstered by an attention mechanism (ATT) and convolutional neural network (CNN). Three hybrid models (WOA-CNN-ATT-LSTM, PSO-CNN-ATT-LSTM and GA-CNN-ATT-LSTM) are constructed by comparing WOA with particle swarm optimization (PSO) and genetic algorithm (GA). The proposed hybrid models are evaluated against traditional LSTM models using an 1100-hour dataset from a real DHS. The outcomes reveal that the WOA-CNN-ATT-LSTM model surpasses both the PSO-CNN-ATT-LSTM and GA-CNN-ATT-LSTM models in heat load prediction accuracy, achieving improvements of 1.9% and 3.2% respectively, and attaining the highest prediction accuracy (R2=0.9962, MSE=0.0001, MAE=0.0082). Moreover, the WOA-CNN-ATT-LSTM model demonstrates superior performance across various time scales (half-day, one-day, three-days, and one-week), highlighting its robustness in heat load prediction. This novel model adaptively adjusts its hyperparameters to identify the optimal configuration, thereby significantly augmenting the overall predictive capabilities of the model.

Validation of an Enhanced Drinking Water Temperature Model during Distribution

Drinking water temperatures are expected to increase in the Netherlands due to climate change and the installation of district heating networks as part of the energy transition. To determine effective measures to prevent undesirable temperature increases in drinking water, a model was developed. This model describes the temperature in the drinking water distribution network as a result of the transfer of heat from the climate and above and underground heat sources through the soil. The model consists of two coupled applications. The extended soil temperature model (STM+) describes the soil temperatures using a two-dimensional finite element method that includes a drinking water pipe and two hot water pipes coupled with a micrometeorology model. The extended water temperature model (WTM+) describes the drinking water temperature as a function of the surrounding soil temperature (the boundary temperature resulting from the STM+), the thermal sphere of influence where the drinking water temperature influences the soil temperature, and the hydraulics in the drinking water network. Both models are validated with field measurements. This study describes the WTM+. Previous models did not consider the cooling effect of the drinking water on the surrounding soil, which led to an overestimation of the boundary temperature and how quickly the drinking water temperature reaches this boundary temperature. The field measurements show the improved accuracy of the WTM+ when considering one to two times the radius of the drinking water pipe as the thermal sphere of influence around the pipe.

Fouling fault detection and diagnosis in district heating substations: Validation of a hybrid CNN-based PCA model with uncertainty quantification on virtual replica synthesis and real data

This research presents an advanced fouling fault detection (FFD) model tailored to district heating substations, integrating convolutional neural networks, principal component analysis, and uncertainty quantification (UQ). Through careful hyperparameter tuning, the model attained optimal configurations, showcasing accuracy metrics ranging from 96.58 % to 98.19 %. Key findings include the influence of input vectors, particularly the incorporation of overall heat transfer coefficient (UA), which contributed to heightened accuracy and precision. Visualizing predictive uncertainty emphasized the model's adaptability to dynamic conditions, crucial in scenarios involving progressive fault evolution. Generalizability analysis across diverse substations affirmed the model's adaptability, surpassing existing algorithms. Comparative evaluation against recent models underscored the proposed model's superiority, with an accuracy range exceeding 96.58 %. This research introduces UQ as a unique advantage, offering a state-of-the-art solution for FFD in dynamic systems. The model's validation on real data further solidifies its efficacy.

Optimization of the pipe diameters and the dynamic operation of a district heating network using a robust initialization strategy

District heating networks (DHN) are systems offering a substantial solution for the decarbonization of heat production. As these systems involve significant costs, the development of digital tools for their optimization becomes a necessity. In the literature, there is a lack of studies optimizing the sizing of the DHN while considering a dynamic operation of the system with a detailed physics. In this work, we developed a Non-Linear Programming (NLP) optimization used in a case study of a DHN where the pipe sizing and the operation are optimized with a precise modeling of the distribution network. The method of Orthogonal Collocation on Finite Elements (OCFE) was employed to discretize the partial differential equation (PDE) governing the temperature evolution in the pipes. In addition, the pressure drops were modeled using the Darcy–Weisbach equation. Three optimization criteria were used: investment cost of the pipes, operational cost, and the global cost. The optimization provided the pipe diameters that best fit each optimization criterion. Furthermore, several initializations were tested to verify if the obtained optimum was a confidence optimum and if the resolution was robust. Finally, the optimization provided a coherent response to the variation in the cost of heat production.

Fault detection for district heating substations: Beyond three-sigma approaches

The topic of this paper is fault detection for district heating substations, which is an important enabler for the transition towards fourth-generation district heating systems. Classical fault detection approaches are often based on anomaly detection, commonly making the implicit assumption that the errors between the measurements and the predictions made by the baseline model are i.i.d. and following an underlying Gaussian distribution. Our analysis shows that this does not hold up in the field, showing clear seasonality in the error over time. We propose to replace the Gaussian error model by a quantile regression model in order to provide a more nuanced fault threshold, conditioned on time and other input variables. Additionally, we observed that properly training the baseline model comes with its own challenges due to this time dependency, which we propose to resolve by employing an ensemble of models, trained on different periods of time. We demonstrate our method on unlabelled operational data obtained from a Swedish district heating operator to illustrate its use in the field. In addition, we validate it on labelled data from our residential lab setup, testing a variety of common faults.

Return-Temperature Reduction at District Heating Systems: Focus on End-User Sites

This review presents a comprehensive examination of recent advancements and findings related to return-temperature reduction in District Heating (DH) systems, with a focus on enhancing overall system efficiency at end-user sites. The review categorizes and clarifies various return-temperature reduction techniques, emphasizing aspects such as building energy performance, heat emitters, thermostatic radiator valves, and substation units. One shall note that return temperature is not a parameter that can be directly controlled within a DH system; instead, it is influenced indirectly by adjusting various system parameters throughout the design, commissioning, operation, and control phases. Key insights include the direct impact of heat demand on return temperatures; the pivotal role of indoor heating systems in optimizing thermal energy use in relation to heat demand; the significance of thermostatic radiator valves in regulating heat output and maintaining low return temperatures; the advantages of ventilation radiators and add-on fans in enhancing radiator efficiency; the necessity for effective substation operation to improve system cooling capacity; and the critical role of operational control strategies in achieving optimal system performance. These findings underscore the need for integrated approaches in DH system design and operation to achieve lower return temperatures and improve overall system efficiency.

Oceanographic preconditions for planning seawater heat pumps in the Baltic Sea – an example from the Tallinn Bay, Gulf of Finland

Abstract. The use of low-temperature seawater heat for renewable energy installations is demonstrated with an example from the Tallinn Bay, Baltic Sea, based on Copernicus Marine Service reanalysis data. Tallinn and its surrounding seaside counties are home to about half a million people and produce about half of Estonia's gross domestic product (GDP). The Tallinn Bay with an area of 223 km2 extends to the north and has an open connection to the Gulf of Finland. Depths more than 50 m that cover the halocline already appear at a distance of 3–4 km from the coast. Surface layers get too cold during winter to be used in heat pumps for district heating; therefore, a feasible option is to pump slightly warmer seawater from the deeper halocline layers. The lowest monthly mean halocline temperature – down to 2.6 °C at 50 m depth and 3.3 °C at 70 m – is found in March and April based on reanalysis data from 1993–2019. The seawater seasonally cools below 3 °C on average on 1 January at 20 m depth and on 12 February at 50 m depth. At the 70 m depth, the average start of T<3 °C was calculated on 28 February, although only 14 winters out of 26 had such water present; in 12 winters the condition T>3 °C was always fulfilled. The median number of cold days is 11, with a maximum of 128 d in the winter 1993/1994 when stratification became rather weak due to the prolonged absence of Major Baltic Inflows of saltier and warmer North Sea waters. During the recent warmer period of 2009–2019, the start of the cold seawater period was delayed on average by 5–10 d. Tallinn has, among other Baltic Sea cities and industrial sites, a favorable location for seawater heat extraction because of the short distance to the unfreezing sub-halocline layers. Still, episodically there are colder-water events with T<3 °C, when seawater heat extraction has to be complemented by other sources of heating energy.

Economic Attractiveness of the Flexible Combined Biofuel Technology in the District Heating System

Economic Attractiveness of the Flexible Combined Biofuel Technology in the District Heating System

Aggregate power flexibility of multi-energy systems supported by dynamic networks

The multi-energy system, encompassing electricity networks, district heating networks (DHNs), and hydrogen-enriched compressed natural gas (HCNG) networks, provides an alternative for promoting intermittent renewable energy accommodation and enhancing operational flexibility. This paper investigates the aggregate flexibility of the multi-energy system based on dynamic network models and admissible power fluctuation regions of decomposed subsystems. The dynamic processes within HCNG networks/DHNs are analyzed, and the inner-box method is employed to approximately quantify the individual flexibility of the dynamic networks. Furthermore, the optimal dispatchable regions of decomposed subsystems, accounting for internal gas/heating load uncertainties, are evaluated based on distributionally robust optimization. Through distribution-level power aggregation, the flexibility of the multi-energy system is quantified utilizing geometric methods. Numerical results on two test systems verify the effectiveness of the proposed methodology.

Electrolysis and waste heat utilisation in the sustainable transition of Germany's energy system

The article examines the use of the by-product waste heat in hydrogen projects in Germany. It identifies several conditions for the use of waste heat. These relate to infrastructure, electrolysis operating modes and sector coupling. It uses a set-theoretic method to attribute causality between these conditions and the outcome, in order to arrive at a robust comparison of the heterogeneous cases. Counter-intuitively, our analysis suggests that the absence of gas infrastructure, along with existing district heating systems, is the strongest explanatory condition for waste heat recovery, with industrial participation supporting this.

The technological transformation of the Russian energy sector forced by carbon regulation

RELEVANCE of the study lies in the assessment of the impact that various carbon regulation instruments stimulating the achievement of national climate goals have on the development scale of different electricity and heat production technologies in Russia.THE PURPOSE. To consider the change in the optimal technological structure of the electric power industry and district heating in Russia by 2050 assuming the introduction of various carbon regulation instruments in 2030.METHODS. We used the developed at ERI RAS system technological model EPOS for the optimization of the energy technology structure in the Russian energy sector according to the criterion of the minimum total discounted costs for energy supply to the economy until 2050.RESULTS. The article provides an analysis of the scale of changes in installed capacity and electricity production of various types of power plants in the UES of Russia, as well as changes in heat production of different heat supply sources by 2050 for 16 carbon regulation options and business-as-usual scenario. Also it describes the optimal technological structure in the electric power industry and district heating under the conditions of certain administrative, fiscal and economic instruments of climate policy. Carbon regulation options based on the corresponding increases in the installed capacity of power plants in the UES of Russia by 2050 are compared.CONCLUSION. Decarbonization of the electric power industry in Russia will mainly occur by expansion of nuclear energy, heat production – by deployment of electric boilers, taking into account current forecasts of scientific and technological progress. At the same time, for carbon regulation options leading to a greenhouse gas emissions reduction by 30% relative to 2019 level, nuclear power plants could become the new dominant technology in the structure of electricity production instead of gas thermal power plants. The total increase in installed capacity of power plants in the UES of Russia by 2050 may differ by almost seven times for various carbon regulation options. Among the climate policy options considered, emission quotation and carbon taxes have the strongest impact on the technological structure of the electric power industry and district heating in Russia.

Harnessing geothermal energy in Hungary

This paper provides a summary of the current status of geothermal energy utilisation in Hungary, with a brief reference to geothermal potential. Approximately 1,000 thermal wells are in operation, producing from two principal aquifers: carbonate aquifers (predominantly Mesozoic) and Upper Miocene siliciclastic aquifers. The utilisation of geothermal energy in Hungary commenced with balneological exploitation, with documented evidence dating back to the Roman era. Of particular note is the city of Budapest, which has the distinction of being home to 16 thermal spas, a fact that has led to its designation as the spa capital of Europe. In the Hungarian portion of the Pannonian Superbasin, 26 municipalities have implemented geothermal district heating systems. Furthermore, geothermal energy plays a significant role in the agricultural sector, with approximately 300 thermal wells currently in operation. Nevertheless, the generation of geothermal electricity is still in its infancy, with only one operational geothermal power plant. However, based on the exploration permits submitted, there are significant prospects for further development. The same is true for ground source heat pumps, where there is considerable potential for growth, particularly in comparison to our neighbouring country's utilisation. Recent research directions are promising, ranging from the use of geothermal energy from abandoned hydrocarbon wells as deep borehole heat exchangers, to the potential for extracting critical materials from thermal water, to exploring the possibility of underground heat storage. The favourable geothermal conditions in Hungary offer the potential to achieve up to 15-20 times the current production of 6.5 PJ, assuming the application of existing technologies. This presents a significant opportunity for the transition to a more sustainable energy source, particularly in the context of heating.

Environmental and Social Life Cycle Assessment of Data Centre Heat Recovery Technologies Combined with Fuel Cells for Energy Generation

The energy sector is essential in the transition to a more sustainable future, and renewable energies will play a key role in achieving this. It is also a sector in which the circular economy presents an opportunity for the utilisation of other resources and residual energy flows. This study examines the environmental and social performance of innovative energy technologies (which contribute to the circularity of resources) implemented in a demonstrator site in Luleå (Sweden). The demo-site collected excess heat from a data centre to cogenerate energy, combining the waste heat with fuel cells that use biogas derived from waste, meeting part of its electrical demand and supplying thermal energy to an existing district heating network. Following a cradle-to-gate approach, an environmental and a social life cycle assessment were developed to compare two scenarios: a baseline scenario reflecting current energy supply methods and the WEDISTRICT scenario, which considers the application of different renewable and circular technologies. The findings indicate that transitioning to renewable energy sources significantly reduces environmental impacts in seven of the eight assessed impact categories. Specifically, the study showed a 48% reduction in climate change impact per kWh generated. Additionally, the WEDISTRICT scenario, accounting for avoided burdens, prevented 0.21 kg CO2 eq per kWh auto-consumed. From the social perspective, the WEDISTRICT scenario demonstrated improvement in employment conditions within the worker and local community categories, product satisfaction within the society category, and fair competition within the value chain category. Projects like WEDISTRICT demonstrate the circularity options of the energy sector, the utilisation of resources and residual energy flows, and that these lead to environmental and social improvements throughout the entire life cycle, not just during the operation phase.

Shallow geothermal energy systems for district heating and cooling networks: Review and technological progression through case studies

Heating and Cooling constitute a major part of society's final energy use and a significant contributor to greenhouse gas emissions. The world society ought to mitigate climate change through decarbonisation, which must include the transition to low-temperature, sustainable and renewable heating and cooling technologies. Shallow Geothermal Energy is one of the most energy efficient and least greenhouse gas emitting available alternatives to provide space heating and cooling. The decarbonisation of the heating and cooling sector may have to comprise both individual systems and shared electrified heating and cooling systems from renewable sources of energy, where economies of scale and synergies between different types of consumers can be exploited. To this end, the focus of this paper is on the integration of shallow geothermal energy technologies into district heating and cooling systems. A key contribution of this work is the illustration of a number of practical case studies, highlighting the potential of existing shallow geothermal systems for DHC networks, which, as front runners in adopting such technologies, serve as paradigms for future development. Follows a discussion providing an outlook over the next 25 years. All in all, the future of utilizing shallow geothermal energy for district heating and cooling seems to be promising to play a pivotal role in sustainable urban development and decarbonizing the heating and cooling sector.