District Heating Research Articles

Driving industrial symbiosis: Evaluating policy intervention effects on waste heat utilization in local district heating networks

This study aims to quantitatively assess the required policy support level to ensure economic viability for all stakeholders in the context of using industrial waste heat as source of local district heating (DH). Additionally, the research explores the potential of industrial symbiosis to support district-level energy transition in the context of Positive Energy Districts and Smart Cities. The proposed approach combines energy modelling tools, namely the Multi Energy System Simulator (MESS) model and financial concepts (i.e. Cash Flow Analysis, Net Present Value Evaluation) typical of industry’s project evaluation. It is then tested in the area of Bolzano, Italy. The study examines various policy scenarios, including financial incentives and the involvement of district heating operators (DHOs). Initial findings indicate that district heating offers significant cost advantages (up to –23%) over electrified systems. However, industries face challenges meeting standard payback periods without policy intervention in the context of projects using waste heat as a source of DH. A combined scenario involving DHOs handling connection costs with a 60% incentive and industries receiving a 15% Feed-In Premium shows promising results in achieving economic feasibility. The results underscore the critical role of policy support in overcoming barriers. Consequently, it is possible to strengthen cooperation between industries and districts fostering local energy transition. Nonetheless, the achievement of positive energy balance requires a stronger presence of RES particularly on the electricity side. The study highlights the broader implications for similar projects in other regions and the essential role of policy interventions in facilitating such industrial symbiosis.

Thermo-economic analysis of a solar district heating plant with an air-to-water heat pump

Solar district heating systems are essential in reducing carbon emissions and alleviating the energy crisis. By integrating with air-to-water heat pumps, it is possible to enhance the system’s efficiency and flexibility. However, the complementary mechanisms and coupling effects between the heat pump and the other components of the system are not clear. To address these issues, it is necessary to investigate the thermo-economic performance of the system, taking into account energy price fluctuations to achieve a more efficient and economically viable operational model. This study focuses on a solar district heating system in the Danish city of Ørum and investigates the impact of integrating a large-scale air-to-water heat pump. Based on energy and economic analyses, the impact of the heat pump on the system efficiency and the levelized cost of heat are investigated for 2020 and 2022. The results show that the heat pump can convert low-cost electricity into heat which will be stored in the tank, increasing the tank’s heat storage content by 1.2 times. The medium to low-temperature water in the tank can preheat the heat pump’s compressor, raising the COP of the heat pump from 3.3 to 3.5 and increasing the annual utilization cycle of the tank by 1.4 times by reducing the return water temperature to the bottom of the tank. Additionally, the system performance has been improved due to the installation of the heat pump, which increased the system COP from 1.22 in 2020 to 2.62 in 2022. Subsequently, the CO2 emission has been decreased from 192 kg/MWh to 74 kg/MWh with a larger share of sustainable energy. Due to the heat pump operation, the system-levelized cost of heat could be reduced to 68.6 EUR/MWh, compared to 94 EUR/MWh if the heat pump is removed. The investigation also revealed that the heat pump improved the flexibility of the heating system in response to energy prices, especially during the 2022 energy crisis in Europe. The findings of the paper provide a good reference for large solar heating applications.

Technical and economic analysis of digitally controlled substations in local district heating networks

Since the 1990s, there has been a noticeable increase in the establishment of local district heating networks in Germany, coinciding with the use of thermal energy from biogas and biomass facilities. Historically, the prioritization of economically efficient heat utilization was subdued due to favorable electricity feed-in tariffs. However, with the expiration of Renewable Energy Sources Act subsidies, operators shifted focus to the operational costs of district heating networks. This study aims to examine the effects of optimizing controllers and valves at district heating substations in single-family homes, utilizing two local district heating networks. The emphasis is on evaluating impacts on operating costs and determining whether the economic and energetic benefits justify implementation. Simulation studies in MATLAB/Simulink Simscape depict both consumers and district heating networks individually, without aggregation. In the most favorable scenario, investing in enhancing substation controllers in single-family homes is projected to yield returns after 13 years. Comprehensive optimization of all substation controls in single-family homes can potentially result in up to 9% savings in thermal energy demand due to reduced heat losses and a corresponding 9% reduction in electrical power consumption for the main pump.

Enhancing Heat Storage Capacity: Nanoparticle and Shape Optimization for PCM Systems

ABSTRACTPhase change material (PCM)‐based heat storage systems utilize the absorption or release of latent heat during a phase change of the storage material to store thermal energy. Nevertheless, the effectiveness of these systems is restricted by the shape and structure of their confinement, as well as the heat conductivity of the storage material. This work investigates a novel method to enhance the effectiveness of PCM systems by concurrently utilizing two techniques: the inclusion of nanoparticles and the alteration of the system's geometry. Introducing nanoparticles enhances the thermal conductivity of the storage medium while altering the shape, which improves heat transfer efficiency by adjusting the surface area available for heat exchange. RT‐35 was tested for use in latent heat thermal energy storage systems for space heating and cooling. With a melting point of 35°C, RT‐35 was chosen to moderate building temperatures by storing and releasing thermal energy for space heating and cooling. The results indicate that using nanoparticles and adjusting shape can greatly enhance the effectiveness of PCM systems. By incorporating Al2O3 nanoparticles, the melting time of the PCM was reduced by 20% compared to the pure PCM, and it is more efficient than the best case of shape modification. These findings indicate that including nanoparticles and modifying the shape are effective methods to improve the performance of heat storage devices. This technology's potential surpasses this study's limits and can be utilized in diverse applications, including solar thermal energy storage, district heating and cooling, and industrial process heat.

Combining energy generation and radiant systems: Challenges and possibilities for plus energy buildings

Radiant heating and cooling (RHC) systems are being more widely adopted considering the well-known technical advantages: increased thermal comfort, space saving, and reduced energy use. Since the building sector is currently one of the largest consumers of fossil fuels, many directives and regulations have been enacted to address the intense concern about energy use for space conditioning.Even though radiant systems are considered as an energy efficient technology for building heating and cooling, more effort is needed to fulfil the zero energy requirements outlined by recent standards and directives. Renewable Energy Sources (RES) are an effective solution to avoid using finite fossil fuels and related geopolitical issues enhanced by the recent world conflicts. Despite being primarily intermittent and subject to economic and regional constraints, RES offer suitable temperature levels to supply low temperature heating and high temperature cooling operation, a major advantage of RHC system.Although a limited number of studies directly report energy savings or CO2 emission reduction as the main outcomes of the research related to this combination, valuable insights have been obtained for the present review. Primary energy can decrease between 40% and 80% with different integration of RHC, photovoltaic, heat pumps and district heating. TABS can lead to load shifting up to 100%, allowing an increased self −consumption of renewable energy.This paper provides evidence on whether coupling radiant systems with renewables is a promising strategy for achieving nearly-zero annual energy balances in building stocks. It investigates recent trends, limitations and potential to support decarbonization goals.

Optimal energy management in smart energy systems: A deep reinforcement learning approach and a digital twin case-study

This research work introduces a novel approach to energy management in Smart Energy Systems (SES) using Deep Reinforcement Learning (DRL) to optimize the management of flexible energy systems in SES, including heating, cooling and electricity storage systems along with District Heating and Cooling Systems (DHCS). The proposed approach is applied on Meridia Smart Energy (MSE), a french demonstration project for SES. The proposed DRL framework, based on actor-critic architecture, is first applied on a Modelica digital twin that we developed for the MSE SES, and is benchmarked against a rule-based approach. The DRL agent learnt an effective strategy for managing thermal and electrical storage systems, resulting in optimized energy costs within the SES. Notably, the acquired strategy achieved annual cost reduction of at least 5% compared to the rule-based benchmark strategy. Moreover, the near-real time decision-making capabilities of the trained DRL agent provides a significant advantage over traditional optimization methods that require time-consuming re-computation at each decision point. By training the DRL agent on a digital twin of the real-world MSE project, rather than hypothetical simulation models, this study lays the foundation for a pioneering application of DRL in the real-world MSE SES, showcasing its potential for practical implementation.

Thermal request optimization of a smart district heating system

This paper proposes a solution approach to manage the heating plans of tenants served by a district heating plant located in Sweden. To do that, the daily temperature request of each household in the associated pilot region is obtained, and the daily temperature profile of each household is optimized with the help of the proposed decision support system and smart valves. The hot water inflow rates of radiators are remotely controlled via smart valves at each flat to minimize the total energy consumption, carbon emission and cost associated with the energy consumption of the district heating plant. We aim to shave the peak demands while fully satisfying the temperature requests of households without violating their thermal comfort. Peak demand shaving is achieved by generating preheating schedules via mathematical optimization and using the thermal storage potential of the insulated flats. The resulting mathematical optimization model presents significant computational challenges that cannot be efficiently solved using optimization solvers within a reasonable time limit. To this end, we develop three genetic algorithm approaches that are computationally scalable for realistically-sized instances and verified to yield near-optimal solutions for the test instances. Extensive numerical analyses show the effectiveness of the proposed approach and the genetic algorithm since we yield significant carbon emission reduction and cost savings compared with the method that the experts of the utility company propose.

Local multi-modal energy market for thermal-electric energy systems with consideration of temperature flexibility in heating subnetworks

District heating systems are critical in this transition towards climate-neutral energy systems as they enable a close coupling of thermal and electric energy systems. However, the operation of these multi-modal energy systems and adequate representation of conversion dependencies, such as from heat pumps, remain challenging.To address this challenge, a novel method is presented that incorporates the temperature of subnetworks of district heating systems into the market matching of a local energy market for thermal-electric energy systems. This market-based coordination approach includes a linear formulation of market orders tailored to the needs of consumers, producers, and prosumers in local multi-modal energy systems. Emphasis is put on the temperature flexibility of heat pumps that concurrently participate in both heat and electricity markets. Complementary market commodities are introduced to account for temperature constraints set by the market participants. The physical models of the district heating network and electric distribution grid are formulated linearly and incorporated into the market-matching.The proposed method is applied in a case study representing a German district heating system. We conclude that the temperature flexibility of heating subnetworks, and dependencies involved in energy conversion can be incorporated within a linear market-matching problem.

Towards maximum cost-effectiveness: multi-objective design optimisation of insulating glass flat-plate collectors

A significant challenge in the advancement of solar thermal heating systems lies in the unexplored techno-economic potential of insulating glass flat-plate collectors. These collectors are constructed in accordance with the specifications of standard insulating glass units and have emerged over the past decade as a promising design concept for enhancing the cost-effectiveness of solar thermal systems. However, substantial findings regarding the techno-economic viability of their production are still pending. The aim of this paper is to optimise insulating glass collector designs for solar district heating applications by identifying key design parameters that maximise cost-effectiveness. This study employed a five-stage methodology. It included thermo-hydraulic collector modelling using MATLAB/Simscape and the CARNOT Toolbox. The model was validated against experimental performance tests. A Latin hypercube computational design with 250,000 samples was set up to train supervised machine learning metamodels and perform a multi-objective optimisation using an elitist genetic algorithm. The study identified the argon concentration, collector length, and width as critical parameters influencing efficiency. Larger, thinner collectors demonstrated superior performance due to reduced convective losses and increased aperture-to-surface ratios. The optimisation revealed that the insulating glass collectors could achieve a 7.7 percentage point increase in efficiency, a 19.4 % reduction in material cost, and a 14.5 % decrease in weight compared to market-available flat-plate collectors. However, the direct economic comparison was not considered strong in evidence due to a lack of economic data from technology providers. The most cost-effective designs featured an argon concentration of 99 %, sealing thickness of 31.2 mm, and a glazing thickness of 4.1 mm, and 4.5 mm, while collector length and width varied more significantly. The research findings indicate the techno-economic potential of insulating glass collectors, demonstrating their ability to outperform conventional flat-plate collectors in terms of cost-effectiveness and efficiency. Future studies should focus on producing and testing larger modules and incorporating production costs to fully realise their potential for solar district heating applications. This study provides valuable guidelines for IGU designers and producers aiming to develop cost-effective and efficient solar thermal collectors for district heating systems.

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.