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To improve the energy, operational, and ecological efficiency of a district heating system (DHS) powered by a combined heat and power (CHP) plant or a heating plant, thermal energy storage (TES) should be used. The presented paper examines the impact of the use and operation of TES built in a CHP plant supplying a large DHS, based on the amount of particulates emitted into the atmosphere. Detailed research was carried out for the Siekierki–Warsaw and Białystok CHP plants in Poland. The analysis helped to determine the factors affecting the reduction in pollutant emissions and the volume of the energy effect of using TES in the CHP plant. In order to objectify the results of the comparative analysis of the impact of TES in the CHP plant on the emission of particulates, the so-called comparative index (CI) was introduced. The CI takes into account the volume of electricity and heat production and climatic conditions in the analyzed time periods. The CI for the analyzed years should have a similar value so that the results of the comparative analysis are fully representative. This condition is met for the CHP plant and DHS of Białystok, so the detailed results of the analysis are presented for this facility. As a result of the application of TES in the Białystok CHP plant, significant environmental effects related to the reduction in particulate emissions have been achieved; for example, the total amount of annual particulate matter (PM) emission (PM10 and PM2.5) has been reduced by 27% and the maximum emission by 29%. On the other hand, the average decrease in particulate emissions in the heating season varied in the range of 10–50%, while in the summer season, the values of particulate emissions were at a comparable level. A significant decrease in annual and one-hour average concentrations for PM10 and PM2.5 and particulate fallout for these two analyzed years was also found. The use of TES to reduce the occurrence and nuisance of the smog phenomenon, the main components of which are PM, is proposed, and selected models of forecasting concentrations of pollutants in the air, including particulate emissions, are presented in order to implement this type of activity. [ABSTRACT FROM AUTHOR]

Recently, wind energy has been developed as an important technology to address the energy crisis. However, due to an unreasonable energy structure, wind power curtailment is becoming increasingly severe. Combined heat and power dispatch (CHPD) provided a solution for wind accommodation by utilizing the flexibility resources of district heating systems. Because of the imperfect dispatch methods and CHPD platforms, many wind power heating projects have not effectively linked the use of abandoned wind and heating. The virtual power plant (VPP) achieves the reasonable combination of controllable power sources, distributed energy, controllable loads, and energy storage systems within a certain area. Thus, we propose a VPP model based on combined dispatch of wind power and heat energy, which integrates wind turbines, thermal turbines, CHP units, etc., into a whole to join in the grid operation. Besides, to preserve the privacy of energy agents, Benders decomposition algorithm is adopted to solve the proposed model in this paper. The validity and efficiency of the proposed VPP model and Benders decomposition algorithm are verified via numerical cases. [ABSTRACT FROM AUTHOR]

The FitFor55 legislative package sets ambitious targets to make Europe the first climate-neutral continent by 2050. Within it, the EU Emission Trading Scheme is mandatory for energy operators with an installed capacity higher than 20 MW, calculated by considering units with a rated thermal input under 3 MW. The paper highlights the impact of the Emission Trading Scheme on the investment choices made by district heating systems operators, considering both current regulations and the potential extension of the Emission Trading Scheme to encompass lower energy capacities. The analysis concerns the simulation of four revamping scenarios (two fueled by natural gas and two partially by renewables) within an existing district heating system located in Italy. The economic analysis shows that an extension of the Emission Trading Scheme alone is not effective in hindering scenarios based on natural gas production. A reform of the power exchange market is necessary to drive decarbonization and encourage investment in renewable generation plants. [ABSTRACT FROM AUTHOR]

District heating systems play a crucial role in the European Union for sustainable energy practices by contributing to reducing carbon emissions and thus counteracting climate change. In most European countries, the energy consumption for heating is a topic that needs to be considered. Germany has recognized the need for more awareness among its citizens about district heating systems. Therefore, an interactive website was developed in close cooperation with the city of Weilheim in Bavaria, Germany. The goal is to inform citizens about the district heating system under construction. The website aims to improve user engagement through interactive features, including a quiz, an interactive calculator, and various animations and images. The application focuses on the primary energy sources biomethane, solar energy, wood chips, and the use of environmental heat through heat pumps. The website was evaluated at a local secondary school, which yielded positive feedback. [ABSTRACT FROM AUTHOR]

Miskolc, which is the focus of our investigation, is the fourth most populous city in Hungary and the center of one of the most underdeveloped NUTS2 (basic territorial category for the regional policy of the European Union) regions in the European Union. The socialist heavy industry played a decisive role in the development of the city, the decline of which also left deep traces in the city. In its current position, the city tries to manage its available resources as efficiently as possible, and the city management is open to the use of modern urban development tools. This is supported by the fact that Miskolc was the first Hungarian city to join the Green Cities for Sustainable Europe movement in 2011, and then in 2015, it joined the Triangulum project of the EU Smart Cities and Communities program as a follower city. In the process of becoming a smart city, the dimensions of environmental sustainability and energy efficiency were given a prominent role, which should not be surprising considering the traditions of the city. Within this, we must first mention the construction of the geothermal central heating system, with which the city really took significant steps in this field. The main goal of the study is to develop a new smart local concept closely linked to regional development and the key energy sector, through which the local adaptation of the defining elements of the internationally defined smart city in several forms for the city of Miskolc will be presented. In our study, we review how the results achieved by Miskolc so far and the development plans for the future fit in with the smart energy developments of smart cities. Before exploring the processes in Miskolc, we will deal in more detail with the possibilities inherent in district heating and geothermal energy utilization and Hungary's capabilities. [ABSTRACT FROM AUTHOR]

In response to global warming and the critical need for sustainable energy solutions, district heating systems (DHS) have emerged as an area of significant focus due to their potential to enhance resource efficiency, reduce carbon emissions, and improve economic outputs. This research addresses the critical issue of establishing a methodological framework for evaluating the efficiency of DHS as market relations evolve within the heating sector. The study's aim is to develop and validate methodological principles that assess the operational, economic, and technological dimensions of DHS in a competitive market landscape. The framework encompasses the complex interplay of regulatory frameworks, market dynamics, production technologies, and consumer engagement strategies. Key objectives include analyzing current challenges in implementing competitive market structures, justifying the non-linear nature of cost functions for energy producers, and developing criteria for the equitable distribution of thermal energy losses. The overarching goal is to strategically propel DHS towards greater efficiency and sustainability by using renewable energy sources (RES). The research utilizes simulation modeling in a case study of Irpin city, Ukraine, employing machine learning and computer vision technologies through the "Solvergy: Buildings" mobile application to provide rapid, detailed energy performance assessments. Results from the simulation in "Solvergy: Systems" software indicate benefits from the market conditions functioning in DHS, quantified at $727,120 per year, and highlight the cost-saving potential of the RES-based supply diversification structure. The relative market efficiency at 28.05% suggests that the market delivers high potential economic value relative to a scenario without market conditions. The study concludes that transitioning to market relations and integrating independent energy producers can lead to substantial system efficiency improvements, lower thermal energy prices, and enhanced sustainability in DHS. Suggested policy measures advocate for the modernizing of infrastructure and the implementation of efficient technologies within a competitive, consumer-oriented market environment, thereby facilitating the decarbonization process and optimizing system performance. [ABSTRACT FROM AUTHOR]

Optimizing District Heating Systems (DHS) to achieve sustainability objectives and minimize costs requires access to comprehensive real-world datasets. This paper introduces a dataset comprising field data collected from a DHS system featuring five heating substations installed in residential buildings within the city of Niš, Serbia. Spanning a period of up to five years (2019–2024), the dataset originates from a SCADA system, capturing critical parameters such as heating fluid temperatures in the supply and return lines of both primary and secondary flows, energy transmission measurements, and outdoor temperatures from a local meteorological station. All measured data underwent comprehensive pre-processing using established methodologies, resulting in uniformly spaced hourly data free of errors or missing values. Furthermore, a preliminary exploratory data analysis was conducted to uncover insights into the underlying relationships and distributions within the data. We contend that this dataset is of considerable relevance to researchers and practitioners in the fields of smart cities, energy efficiency, and district heating.

The utilization of the district heating system (DHS) thermal inertia is greatly influenced by heating regulation modes and time steps, however, which has been rarely analysed in almost all integrated heat and power dispatch system (IHPD) models, for the purpose of wind power integration. In this study, an IHPD model considering the thermal inertia of the DHS was established, in which a data interaction problem amongst the power sources, heat sources and district heating networks was solved. Thereafter, four most commonly used heating operation modes and different heating regulation time steps were applied to the IHPD model, respectively, to analyse and compare their effects on the IHPD system under different operation conditions, mainly considering the operation cost and the wind power integration. Results of the case studies showed that the variable flow and variable supply temperature mode produced the lowest operation cost of the IHPD system during a whole heating season. Consequently, this mode is recommended to be applied in the optimization operation of the IHPD system with the wind power integration. Meanwhile, a heating regulation time step within 3 h could make full use of the DHS thermal inertia, thus to improve the operational flexibility of the IHPD system. [ABSTRACT FROM AUTHOR]

The article proposes a model for the functioning of the district heating industry under the conditions of the alternative boiler house mechanism. This model is used to build forecast scenarios for the development of the industry and its two segments: the alt boiler room and other district heating systems. In addition, options for reforming the industry are shown and mechanisms and tools are proposed for its transition to break-even operation. [ABSTRACT FROM AUTHOR]

Recently, wind energy has been developed as an important technology to address the energy crisis. However, due to an unreasonable energy structure, wind power curtailment is becoming increasingly severe. Combined heat and power dispatch (CHPD) provided a solution for wind accommodation by utilizing the flexibility resources of district heating systems. Because of the imperfect dispatch methods and CHPD platforms, many wind power heating projects have not effectively linked the use of abandoned wind and heating. The virtual power plant (VPP) achieves the reasonable combination of controllable power sources, distributed energy, controllable loads, and energy storage systems within a certain area. Thus, we propose a VPP model based on combined dispatch of wind power and heat energy, which integrates wind turbines, thermal turbines, CHP units, etc., into a whole to join in the grid operation. Besides, to preserve the privacy of energy agents, Benders decomposition algorithm is adopted to solve the proposed model in this paper. The validity and efficiency of the proposed VPP model and Benders decomposition algorithm are verified via numerical cases.

District heating system is the main way of heating in cities and towns in China. The development of district heating system still has the problems of low intelligence and low control accuracy, and there is the imbalance of heat supply and demand in heat distribution. Resulting in the energy consumed by the district heating system can account for more than half of the total energy consumption of the building. In order to alleviate this imbalance, this paper studies the control of heat distribution of each heat exchange station in the primary network. The heat model of primary network is established by recurrent neural network (RNN), and the data set used for modeling is the operation data of heat exchange station in reality. Combined with the heat load prediction model, a heat distribution strategy was proposed to optimize the primary flow of the heat exchange station. According to the predicted value, chaotic particle swarm optimization (CPSO) algorithm is used to optimize the primary flow sequence of each heat exchange station, and then the primary flow is adjusted to control the heat distribution of the secondary network. Finally, Simulink simulation model is used to simulate the water supply temperature of the secondary side of the heat exchange station. And analyze the operation status of the secondary side, the results verify the effectiveness of the strategy. The model simulation results show that the heat distribution scheme proposed in this paper can effectively distribute the heat of the heat exchange station according to the heat demand. [ABSTRACT FROM AUTHOR]

Heat load prediction is crucial to the heat regulation of district heating systems (DHS). In heat load forecasting tasks, deep learning can frequently achieve more accurate model building. A deep learning algorithm, the temporal convolutional network (TCN), has been used for DHS heat load prediction. However, there are many hyperparameters for TCN. Manually tuning the TCN parameters cannot make the model have good performance. This study presents a hybrid method based on sand cat swarm optimization (SCSO) and TCN. The SCSO is used to optimize the hyperparameters (number of filters, filter size, dropout rate, and batch size) of TCN. To verify the effectiveness of SCSO-TCN, another two hybrid models, particle swarm optimization with TCN and the sparrow search algorithm with TCN, are established for comparison. The historical heat load data of three heat exchange stations in Tianjin is utilized for the testing experiments. The findings demonstrate that SCSO-TCN has higher predictive accuracy and better generalization ability than the PSO-TCN and SSA-TCN models. [ABSTRACT FROM AUTHOR]

In China’s goal of reaching carbon neutrality by 2060, the blending-biofuel-based heating technique is being used to reduce CO2 and air-pollutant emissions in existing district heating systems in northern China. This brings a series of new system components, complex interactions, and multiple-polymorphic uncertainties to the heating systems, making it difficult for the heating-system manager to improve the traditional fuel management mode while considering the demands of society, economy, policy, environment, and system operation. To address this issue, this study proposes an inexact multi-recourse hybrid-fuel management model for a biofuel-penetrated district heating system (BDHS). The model minimizes the total heating cost by optimizing the biofuel blending ratio, coal and biofuel deficit-recourse pattern among different heating sources, and selecting the optimal CO2 reduction mode under uncertainties. An application of the model to a BDHS case in Dalian City shows that the 5% biofuel blending ratio is suitable for both main heating sources and that the 0 deficit of high-quality coal can be up to [2.48, 2.69] × 103 tonne with the “cold-degree” changing from “mild” to “cold”. The results also indicate that the proposed model can ensure biofuels and high-quality coal are not overused or misused, but instead consumed responsibly. Additionally, most of the CO2 produced in the pulverized coal boiler is traded, while most of the CO2 sourced from the circulating fluid bed boiler is treated by the chemical absorption equipment. Finally, the model reveals that a high system cost (up to [84.73, 96.83] × 106 CNY) and low CO2 emission (down to [66.13, 78.07] × 103 tonne) can be obtained at a high thermalization coefficient through the tradeoff analysis.

With the implementation of the carbon peaking and carbon neutrality strategy, China's heating system has witnessed various transformative trends, including source-network-load integration and the synergistic utilization of fossil and renewable energy sources. The district heating system, serving as a vital intermediary between heat sources and end-users, plays a crucial role in ensuring a stable energy supply. Particularly, as renewable energy integration increases and end-user requirements become more refined, the demand-supply matching aspect of district heating systems becomes increasingly critical. Therefore, it is essential to quantify and analyze user demand while establishing an adaptive pipe network capable of accommodating fluctuations in end-user load. This paper proposes an optimal topology structure method for district heating systems, taking into account the uncertainty of end-user load. Specifically, a quantitative model of end-user load uncertainty is developed, utilizing the Gram-Charlier A expansion method to capture the factors influencing end-user load uncertainty comprehensively. Furthermore, an optimization model for the pipe network topology is formulated, which considers the uncertainty of end-user load as a constraint and aims to minimize pipeline construction costs. This model optimizes the layout, size, and location of heating sources within the district heating system. The proposed method's practical applicability is validated through a case study of a district heating system in a northern Chinese city that involves 21 end-users. The results indicate that the end-user load uncertainty model outperforms the conventional normal distribution fitting model when dealing with bimodal load distributions. Additionally, the proposed method reduces construction costs by 6.24%, decreases heat loss by 5.30%, and eliminates redundant structures within the district heating system. This research significantly contributes to the planning and design of heating pipeline networks and the optimization and refurbishment of existing pipeline networks within district heating systems.

Heat metering is an important measure of China's heating marketization reform as well as an energy-saving policy implemented in buildings nearly 20 years ago. However, this policy has not achieved the expected results due to various reasons. It is important to note that although northern China is dominated by large apartment buildings, resulting in unique thermal characteristics and user behavior, the measured results of these characteristics are not common. In this study, data from cities in northern China were obtained using household heat meters and field testing. Based on the three levels of station, building, and household, the effects of location, heat outages, and users' regulation behavior on energy consumption were analyzed. The results show that different locations lead to a considerable difference in heat consumption among users, and an outage leads to a significant increase in consumption by surrounding users. For typical buildings in northern China, it is challenging to realize energy savings with district heating systems based on user behavior, and household metering and charging are not fair and reliable. Adjusting the heat meter unit from the household to the building was suggested. [ABSTRACT FROM AUTHOR]

The technical and economic indicators of the district heating system of Chita are analyzed, as well as the efficiency of the modeling results of the impact of thermal power plants and heat networks on the financial result on the heat market. Accounting of variable and fixed costs in the context of each facility of the district heating system provides for an accurate overview of the situation on the heat market for heat supplying companies. [ABSTRACT FROM AUTHOR]

As the improvement of district heating systems, the fine-tuning of operational equipment has become a promising direction. However, in the practical district heating systems, relying solely on manual control and the existing basic models, the dynamic hydraulic-thermal process is hard to cover. Consequently, the primary side control in the current district heating system mainly results in the over or under-supply of heating substations. This article constructs a dynamic hydraulic-thermal model and performs a balanced operation strategies optimization based on the hydraulic-thermal model. The linearization and iterative optimization process are adopted to simplify the hydraulic and thermal modeling for the single pipe, and the hydraulic and thermal calculation are conducted sequentially. For the primary side network in the various operation conditions, the dynamic hydraulic-thermal model is established. The heating supply imbalance of the primary side return temperatures among different heating substations is defined and optimized based on the dynamic hydraulic-thermal model and the particle swarm optimization algorithm. In the case analysis, the results of heating supply imbalance before and after optimization are 0.81 K and 0.47 K, respectively, with a 42.0 % reduction in heating imbalances after optimization. The proposed method could contribute to the fine-tuning of heating substations and the reduction of energy waste due to the imbalanced heating supply, which is significant for the operation strategies of district heating systems. [ABSTRACT FROM AUTHOR]

The need to modernize existing district heating systems is due to increased requirements for their flexibility, energy efficiency, and environmental friendliness. The technical policy on district heating pursued in different countries centers on the listed goals and takes account of historical, climatic, and regional features of the resource, technology, and economic availability of various thermal energy sources. This study aims to analyze methods designed to improve the flexibility, energy efficiency, and environmental friendliness of district heating systems. The focus of the study is district heating system, which provides heating and hot water supply to consumers and consists of various types of thermal energy sources. The work shows the possibility for the heating system to transition from the third generation to the fourth one, which differ in their level of intellectualization. The establishment of an intelligent control system will ensure the interaction of various heat sources, but this is a separate strand of research. In this study, a model and a methodology were developed to optimize the structure of thermal energy sources and their operating conditions when covering the heat load curve of a territory with a predominance of household consumers. Gas-reciprocating and gas-turbine cogeneration plants are considered as the main thermal energy sources, whose efficiency is boosted through their joint operation with electric boilers, thermal energy storage systems, low-grade heat sources, and absorption chillers. The primary emphasis of the study is on the assessment of the environmental benefit to be gained by using cogeneration plants as a factor of enhancing the investment appeal of the district heating systems. The findings suggest that the transition of district heating systems to the next generation is impossible without changing the institutional environment, strengthening the role of active consumers, and introducing intelligent control for district heating systems. [ABSTRACT FROM AUTHOR]

The district heating system (DHS) consumes a lot of energy in winter, and its control accuracy needs to be improved urgently. To apply advanced process control (APC) in DHS, the thermal dynamic model of the existing buildings is essential. This paper uses the subspace method which is a data-driven approach for modelling the thermal dynamics of the building. The model's performance is analyzed using the collected data, and the differences compared to the classical methods are also analyzed. The method reduces the RMSE by about 20% compared with the ARX model for the same complexity. Subsequently, the analysis of the training residuals indicates that the estimate of periodic intra-building disturbance can be obtained by minimizing the training residuals. By introducing the estimated disturbance function, the RMSE on the test set is further reduced by 26%. At the end of the article, a simple parameter extrapolation experiment is conducted, and the result shows that the parameters can be extrapolated to other buildings without large errors. [ABSTRACT FROM AUTHOR]

This work is focused on presenting the main results and discussions concerning the environmental benefits of reducing the non-condensable gases emitted from the Nesjavellir geothermal power plant. The primary objective of this study is to conduct a life cycle evaluation to analyse the overall environmental benefit effects of producing 1 kWh of electricity and 1 kWh of thermal energy in the geothermal power plant at Nesjavellir, which is located in Iceland. The assessment is performed both before and after implementing an abatement system designed to reduce CO2 and H2S gases. The production of geothermal energy is increasing every year and, therefore, it is crucial to identify and quantify the key environmental factors of producing this type of energy and improvements for the future energy transition of the energy generation sector. Firstly, the results show that the environmental impact of electricity production is higher compared to heat production. More in detail, the emissions due to the nature of the geothermal fluid and the construction phase represent the most relevant environmental load for both electricity and heat production for nearly all the 18 environmental impact indicators studied. Furthermore, considering the abatement system for the non-condensable gas emissions, reductions of 78% and 60% in global warming potential is achieved for a production of 1 kWh of electricity and 1 kWh of thermal energy. In terms of external environmental costs, the implementation of an abatement system results in a reduction exceeding 95% for both electricity and thermal energy production per kilowatt-hour. The outcomes obtained from both the baseline scenario and the application of the abatement system undeniably prove that the latter results in a substantial decrease in the overall environmental impacts linked to the generation of 1 kWh of electricity and 1 kWh of heat, encompassing a notable reduction in external environmental costs (externalities). [ABSTRACT FROM AUTHOR]

Accurate heat load prediction is essential for heat production and refined management of district heating systems (DHSs). More advanced technology can often achieve more accurate forecasts. This paper suggests using temporal convolutional network (TCN) and categorical boosting (CatBoost) for heat load prediction. To test the performance of TCN and CatBoost in heat load prediction missions, two additional benchmark models, the decision tree model (DT) and the statistically based multiple linear regression (MLR), are built for comparison. A DHS in Tianjin, China, is used as the study case. Two historical operational characters (day-ahead heat load and hour-ahead heat load) and four meteorological characters (outdoor temperature, relative humidity, wind scale, and air quality index) are selected as input features for the models. The prediction results of every model on the test set are displayed and discussed. The experimental findings indicate that the prediction results of TCN and CatBoost are more accurate than the traditional prediction models, while the modeling process of CatBoost is simpler. Overall, TCN and CatBoost are potential heat load prediction methods. [ABSTRACT FROM AUTHOR]

Advance in renewable penetration has promoted the interaction between the AC and DC power system and multi-energy sources. However, the disparate nature of different energy utilization technologies presents strong challenges for the economic dispatch of such a complex system. This paper proposes a comprehensive characterization of the AC/DC power system considering multi-energy and renewable integration. Detailed models are elaborated for the AC/DC power grid, the district heating system (DHS), coupling units, and renewables to describe their inner interactions accurately. On this basis, an economic dispatch method is developed to minimize the operation cost and renewable's abandonment. Simulations indicate that the interaction between the AC/DC power systems and multi-energy sources can enhance voltage levels and improve operational economy. [ABSTRACT FROM AUTHOR]

The majority of small district heating systems (DHS) in Poland are supplied with coal and do not have a status of efficient ones according to Energy Efficiency Directive. They should transform into efficient ones by taking an appropriate measures by the end of 2035. At the same time, a significant reduction of CO 2 emissions is expected in this sector. One of the possible solution for such systems is to apply unites fueled with fuels alternative to coal such as biomass. Cost benefits analysis of implementation a biomass ORC (Organic Rankine Cycle) cogeneration unit in municipal district heating system of 22 MW thermal capacity is the subject of the paper. The research included building configuration scenarios of a heat source regarding ORC size and operation modes. Different economic inputs such as electricity, fuel and European Union Allowance (EUA) for CO 2 prices as well as financial support mechanisms were considered. Economic indexes (Net Present Value, Internal Rate of Return and payback times) have been derived for both Polish and German conditions. [ABSTRACT FROM AUTHOR]

The paper addresses the joint determination of nodal thermal energy prices and reliability indices in district heating systems for various emergency conditions (failures of components). The main goal is to establish the relationship between the obtained indices and determine the “narrowest” areas (bottlenecks) corresponding to the maximum economic losses due to a disruption in the heat supply to consumers. Calculation of the nodal thermal energy prices relies on the Lagrange multiplier method and factors in different production costs at heating sources and flow distribution in the heating network. The parameters used in the problem solved include heat undersupply to consumers in emergency states of the system and related economic damages. The general methodological framework for modeling thermal-hydraulic conditions in the system (including emergency ones) rests on the methods of the theory of hydraulic circuits. The developed methodological and computational tools are used to conduct a computational experiment for a test diagram of a district heating system. The obtained results are analyzed, and their graphical interpretations are presented.

The Hellisheidi geothermal power plant, located in Iceland, is a combined heat and power double-flash geothermal plant with an installed capacity of 303.3 MW of electricity and 133 MW of hot water. This study aimed to elucidate the environmental impacts of the electricity and heat production from this double-flash geothermal power plant. In this vein, firstly, the most updated inventory of the plant was generated, and secondly, a life-cycle assessment approach based on the exergy allocation factor was carried out instead of applying the traditionally used allocations in terms of mass and energy. The functional unit was defined as the production of 1 kWh of electricity and 1 kWh of hot water for district heating. The life-cycle stages included the (i) construction, (ii) operation (including abatement operations and maintenance), and (iii) well closure of the geothermal plant. All of the life-cycle stages from construction to dismantling were considered. Finally, the results on the partitioning of the environmental impact to electricity and heat with exergy allocations showed that most of the impact should be charged to electricity, as expected. Furthermore, the distribution of the environmental impacts among the life-cycle stages determined that the construction stage was the most impactful for the electricity and heat production. This result was attributable to the large consumption of steel that was demanded during the construction of the geothermal power plant (geothermal wells, equipment, and buildings). Impacts due to the abatement stage demonstrated that this stage satisfactorily reduced the total impact attributed to the three life-cycle stages of the geothermal power plant. [ABSTRACT FROM AUTHOR]

Investments in the development of the district heating system require a thorough analysis of the technical, economic, and legal aspects. Regarding the technical and economic context, a mathematical model of the district heating system has been proposed. It optimizes both the technical and economic aspects of the function and development of a district heating system. To deal with non-linearities, the developed linear programming model is divided into three phases: flow, thermal, and pressure. Therein, non-linear dependencies are calculated between the linear optimization phases. This paper presents the main assumptions and equations that were used to calculate the parameters of the heating system, along with the optimal level of heat production, the flow rate of the heating medium in the heat nodes and edges of the network graph, the heat, power, and temperature losses at each edge, and the purchase costs of heat and its transmission. The operation of the model was tested on a real-world district heating system. The case study results confirm that the model is effective and can be used in decision support. The economic results of the model, before the calibration process, were 3.6% different from historical values. After the calibration process, they were very similar to the real data—all percentage deviations were within 1%. [ABSTRACT FROM AUTHOR]

Waste heat plays a sign Picard: role in obtaining the 4th and Sth generation of District Heating (DH) System in cities. This article presents the possibilities of integrating selected waste heat emitters into DH, with the objective of meeting the demand for heat for the selected residential area (approx. 4000 inhabitants) in the city of Gliwice (180 000 inhabitants). The total heating demand of the studied area was estimated at 19 800 GJ including both space heating and domestic hot water. The maximum thermal power was estimated at approx. 2.45 MW. The demand was calculated on the basis of registered metering values for individual buildings which were processed and summarized due to the lack of collective meters for the district. A detailed data classfication, correction and completion procedure was elaborated to deal with non-uniform and low-quality data registration. Two industrial objects with waste heat generation were examined to be integrated with the local DH network. The waste heat generation potential equals 9.0 MW for plant #1 and 0.9 MW for plant #2. Apart from the constant generation declared by the industrial entities, realistic profiles including possible shaft-work and maintenance periods were created. It has been shown that the total heat demand for selected residential areas can be covered by integrating waste heat into the current DH network. Depending on the waste heat generation profile, the local area heat demand can be covered entirely or to a large degree (coverage factor ranges from 72 to 100%). The waste heat utilization factor ranges from 6.3 to 8.3%. To manage the remaining waste heat potential, it is required to build additional district heating pipelines and nodes connecting to the existing network to receive an additional 7.45 MW thermal power. The potential of waste heat recovery is sigrdficant at the scale of a medium sized city: integrating two large industrial emitters allows up to 13% decarbonization of heats production in the local district heating plant. [ABSTRACT FROM AUTHOR]

This research investigates the integration of model predictive control (MPC) with seasonal thermochemical energy storage systems (STES) within district heating networks, focusing on the Nottingham district heating as a case study. The primary aim is to develop an MPC strategy that utilizes machine learning models for accurate heat load forecasting. This strategy optimizes the charging and discharging cycles of thermochemical energy storage systems to mitigate the mismatch between heating energy supply and demand by storing surplus heat during summer and utilizing it during winter. We employed and validated machine learning models, including support vector regression (SVR), regression trees, and long short-term memory (LSTM) networks, using historical heat load and meteorological data. A validated numerical model of the thermochemical energy storage system (TCES) was integrated into the MPC framework, formulated as a mixed-integer linear program to optimize the STES's operations. The performance of the MPC strategy was benchmarked against a rule-based control approach under varying supply capacities to evaluate scalability and robustness. Our findings reveal that each machine learning model achieved comparable performance, with CVRMSE values within the 9–11% range. The LSTM model, in particular, provided accurate multi-step forecasts essential for the MPC framework. Incorporating these models into the MPC strategy allowed for precise heat demand predictions, enhancing the management of energy storage and distribution. Results confirmed that MPC effectively shifts energy seasonally, reduces reliance on auxiliary heating during winter, and minimizes waste heat. The MPC strategy outperformed the rule-based control by storing a significantly higher percentage of waste heat and meeting a greater portion of the additional heat demand that was not covered by the auxiliary heat supply. The system demonstrated effective performance under varying supply capacities, with the MPC strategy efficiently utilizing stored heat to meet demand at 80% supply capacity, achieving a waste heat reduction to 4% and meeting most of the heat demand. However, performance declined at 60% capacity, indicating the need for careful consideration of supply capacities in system design. This study highlights the potential of integrating machine learning models with MPC to enhance the performance and adaptability of district heating systems with STES, minimizing waste heat and efficiently meeting energy demands. • Implemented model predictive control (MPC) strategy for seasonal thermal energy storage (STES). • MPC strategy simulated in a real-world district heating (DH) system in the UK. • Used SVR, regression tree, and LSTM models for heating demand forecasting in MPC. • Achieved high predictive accuracy with LSTM for 12-h ahead forecasting. • MPC controlled thermochemical storage system stored all the waste heat to and reduced unmet demand to 15% at 80% supply. [ABSTRACT FROM AUTHOR]