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District heating and cooling (DHC) systems are gaining importance due to their ability to integrate renewable and waste heat sources and connect with other infrastructures. However, the complexity of DHC systems rises as technical components and potential interactions grow, and energy demands increase. Heat transportation via water flow involves time- and temperature-dependent changes based on mass flow rates and heat losses, influenced by the dynamic properties of consumers and distribution pipes. To optimise DHC operations and simulate their complexities, dynamic modelling tools are necessary. However, dynamically simulating large-scale DHC networks are computationally demanding. One method to improve district heating networks (DHNs) is to replace dynamic pipe models with static models, simplifying the network's topological complexities. This paper proposes a framework for simulating DHNs using the NetworkX Python package to create and manipulate complex networks and the Modelica environment to simulate physical systems. The study highlights the constraints of the pipe replacement workflow due to its dependence on seasonal variation. The findings indicate that replacing pipe models can reduce CPU time by 30% to 65% in simulations, depending on the required accuracy during the summer season. [ABSTRACT FROM AUTHOR]

District energy networks (DENs) are recognized crucial to contribute to the carbon neutrality target by 2050, thanks to the possibility to integrate renewables at the local level in the heating and cooling sector beyond the electricity one. Thermal energy storage (TES) allows DENs to be more flexible by managing the temporal gap between supply and demand. The identification of the TES size in a DEN is challenging, depending on several variables as energy loads and prices, the coupling of energy processes within the DEN, and the different economic or sustainability priorities. The contribution of this paper is to present an optimization model for sizing TES under the eco-exergetic operation optimization of DENs. The model is based on mixed-integer linear programming and aims to determine the optimal operation strategies of the DEN and the optimal TES capacity with the goal to maximize the economic/exergetic performance of the network. As case study, a real DEN located in Torino is considered. From the comparison with the results obtained with the current case in the absence of the TES, it emerges that the optimization tool brings valuable economic and exergetic benefits to the DEN, thanks to the identification of the optimal TES capacity. [ABSTRACT FROM AUTHOR]

In response to the uncertainty and volatility arising from renewable sources, there is a growing need for enhanced flexibility within the energy system to maintain a continuous balance between power generation and demand. In this context, interest is growing around the so-called Multi-Energy Systems (MES) where different energy vectors coexist and optimally interact through conversion technologies and energy networks, creating additional flexibility opportunities. Nevertheless, there exists a gap in research regarding the impact of the network on flexibility availability. Typically, these complex systems are treated as power nodes or energy hubs without comprehensive network considerations. For this reason, the paper aims to propose a methodology and a tool to evaluate flexibility in a Multi-Energy System, considering not only the individual devices in place and the users' demands but also their interactions with the physical energy network. In detail, a simulation-based methodology is developed and described, and finally tested on a Case Study. As a result, both the physical and operational flexibilities (UP-flex and DOWN-flex) of the system regarding the electrical vector were obtained analytically and graphically. Particular attention was given to the evolution of key temperatures within the district heating network and the thermal power produced by the central unit in various flexibility scenarios. The outcomes demonstrate the utility of this tool for defining flexibility boundaries and profiles, as well as for assessing whether the flexibility demanded by grid operators aligns with the physical constraints of the network.

Advanced control strategies for delivering heat to users in a district heating network have the potential to improve performance and reduce wasted energy. To enable the design of such controllers, this paper proposes an automated plant modeling framework that captures the relevant system dynamics, while being adaptable to any network configuration. Starting from the network topology and system parameters, the developed algorithm generates a state-space model of the system, relying on a graph-based technique to facilitate the combination of component models into a full network model. The accuracy of the approach is validated against experimental data collected from a laboratory-scale district heating network. The verification shows an acceptable average normalized root-mean-square error of 0.39 in the mass flow rates delivered to the buildings, and 0.15 in the network return temperature. Furthermore, the ability of the proposed modeling technique to rapidly generate models characterizing different network configurations is demonstrated through its application to topology optimization. The optimal design, obtained via a branch and bound algorithm, reduces network heat losses by 15% as compared to the conventional length-minimized topology. [ABSTRACT FROM AUTHOR]

District heating networks (DHNs) provide an efficient heat distribution solution in urban areas, accomplished through interconnected and insulated pipes linking local heat sources to local consumers. This efficiency is further enhanced by the capacity of these networks to integrate renewable heat sources and thermal storage systems. However, integration of these systems adds complexity to the physical dynamics of the network, necessitating complex dynamic simulation models. These dynamic physical simulations are computationally expensive, limiting their adoption, particularly in large-scale networks. To address this challenge, we propose a methodology utilizing Artificial Neural Networks (ANNs) to reduce the computational time associated with the DHNs dynamic simulations. Our approach consists in replacing predefined clusters of substations within the DHNs with trained surrogate ANNs models, effectively transforming these clusters into single nodes. This creates a hybrid simulation framework combining the predictions of the ANNs models with the accurate physical simulations of remaining substation nodes and pipes. We evaluate different architectures of Artificial Neural Network on diverse clusters from four synthetic DHNs with realistic heating demands. Results demonstrate that ANNs effectively learn cluster dynamics irrespective of topology or heating demand levels. Through our experiments, we achieved a 27% reduction in simulation time by replacing 39% of consumer nodes while maintaining acceptable accuracy in preserving the generated heat powers by sources.

Dynamic thermo-hydraulic simulations of district heating networks (DHN) are essential to investigate novel concepts for their sustainable design and operation. To develop solutions for a particular case study, numerous long-term simulations are required. Therefore, computational effort for simulation is critical. Heat consumers (HC) are numerous and determine the dynamics of mass flows and return temperatures in the DHN. Thus, the way in which HCs are modeled has significant impact on the computational effort and the results of the simulation. This article presents a novel Modelica-based model for HCs that builds on an existing simplified modeling approach (open-loop design). The calculation of mass flow and return temperature is improved in terms of robustness, plausible behavior and low computational effort. In particular, the model reacts to limited differential pressure and supply temperatures to ensure plausible behavior across all operating conditions, including undersupply situations. The model is successfully tested using an exemplary DHN. The analysis proves that the HC model itself requires little time to simulate. Nevertheless, it significantly influences the simulation time for the entire DHN, which varies by a factor of five for the investigated system depending on the HC model. Fast dynamics, including a bypass in the model and correction of deviations between set point and actual heat load increase the simulation time, so users should sensibly choose how to use these options. HC models triggering many state events result in high computational effort. Compared to other simple HC models, the proposed model produces more plausible results while maintaining at least equal simulation performance (for models without bypass) or even improving it (for models with bypass, CPU time is reduced by at least 35%). [ABSTRACT FROM AUTHOR]

This study focuses on the reduced-order modeling and the operational optimization of the heating network of Université Libre de Bruxelles (ULB) to decrease its energy consumption and CO2 emissions. A reduced-order model of the network was developed, and alternatives to the current operation were investigated to minimize its operational cost. As a very first improvement, replacing the current fixed-speed pumps with optimally operated variable-speed pumps, the pumping energy can be reduced by up to 96 %. Further optimizing the network operating temperatures can lead to a reduction of the pumping energy, the heat losses and the CO2 emissions by 99 %, 19 %, and 7 %, respectively, compared to the current operation.

Due to an urgent need to reduce the fast-progressing climate changes, a rapid, standardized and replicable solution for the infrastructure restructuring of the heat demand coverage of buildings at the district and city levels must be developed. As more and more communities in Europe, especially in Germany, are obligated to have plans for coverage of the heat demand, city planners and energy agencies seek a tool that will help them to design the first drafts of heating network routes, which can supply whole districts and cities with renewable energy. ArcGIS Pro Tools like Least-Cost-Path Analysis (LCPA) and Closest Facility (CF) allow us to find the shortest and "cheapest" way between the heat source and heat consumers in the analyzed areas. Starting from the community level, through the district, county, voivodeship and whole-country levels, the replication of the methods for generating district heating (DH) network infrastructure can be achieved. The proposed LCPA- and CF-based methods help to design the most suitable and efficient DH networks in the analyzed areas. As only a few open-source data inputs, like street networks and building footprints, are needed, the methods can be implemented in all communities in the country of Poland. In this paper, one example of one community in Wroclaw county, called Siechnice, and its surroundings is presented. [ABSTRACT FROM AUTHOR]

Load forecasting is an essential part of the operational management of combined heat and electrical power units, since a reliable hour- and day-ahead estimation of their thermal and electrical load can significantly improve their technical and economic performance, as well as their reliability. Among different types of prediction techniques, data-driven machine learning methods appear to be more suitable for load estimation in operational systems, compared to the classical forward approach. Research so far has been concentrated mainly on the magnitude of buildings with single load types. It has only been extended to a limited degree on the level of a district heating network where several end users with different characteristics merge into one bigger scale heat consumer (city or group of communities). In this study, artificial neural networks are utilized, to develop a load prediction model for district heating networks. A segmented analytical multi-phase approach is employed, to gradually optimize the predictor by varying the characteristics of the input variables and the structure of the neural network. The comparison against the load prediction time series generated by a local communal energy supplier using a commercial software reveals that, although the latter is enhanced by manual human corrections, the optimized fully automatic predictors developed in the present study generate a more reliable load forecast.

Identifying decarbonization strategies at the district level is increasingly necessary to align the development of urban projects with European climate neutrality objectives. It is well known that district heating and cooling networks are an attractive energy system solution because they permit the integration of renewable energies and local excess of hot or cold sources. The detailed design and optimization of network infrastructures are essential to achieve the full potential of this energy system. The authors conducted an attributional life cycle assessment to compare the environmental profile of five distribution network infrastructures (i.e., pipes, heat carrier fluid, trenches, heat exchangers, valves, and water pumps) based on a study case in Marseille, France. The work aims to put into perspective the environmental profile of subsystems comprising a district heating infrastructure, and compare pipe typologies that can be used to guide decision-making in eco-design processing. Rigid and flexible piping systems were compared separately. The results show that the main impact source is the pipe subsystem, followed by the trench works for most impact categories. The authors underlined the importance of pipe typology choice, which can reduce emissions by up to 80% and 77% for rigid and flexible systems, respectively. [ABSTRACT FROM AUTHOR]

The increasing share of renewable energy sources in energy systems will lead to unpredictable moments of surplus/deficit in energy production. To address this issue, users with heat pumps can provide support to power grid operators through flexible unit operation achieved via Demand Response programs. For buildings connected to low-temperature heating networks with ensured third-party access, further room for flexibility can be explored by investigating the production of surplus heat that can be sold to the network. A key aspect lies in the identification of the energy pricing options that could encourage such flexible operation of a heat pump by "thermal prosumers". To this aim, the present study investigates the impact of ad hoc variations in the electricity purchasing price through discounts or penalties included in the "network cost" component of the price on cost-effective operation of a heat pump connected to the thermal network. To discuss the effects of different pricing options in terms of increased flexibility, an office building located in Italy and equipped with a high-temperature heat pump is adopted as the case study. A heuristic profit-oriented management strategy of the heat pump is assumed, and dynamic simulations are performed. The results indicate that at current electricity prices, the heat pump operation is profitable both when supplying the heat to meet the building's requirements and when producing surplus heat for sale to the thermal network. In addition, it is revealed that the penalties applied to the electricity purchasing price are effective in encouraging changes in the heat pump operation strategy, reducing its average production (the building increasingly relying on buying heat from the network) and the associated electricity consumption by 46.0% and 79.7% in the "light" and "severe" local power deficit scenarios, respectively. [ABSTRACT FROM AUTHOR]

The potential of utilizing temperature‐variable district heating systems in rural areas was investigated. A local district heating system in Germany and the related project NATAR are briefly described; measurement data is evaluated. Operational experiences with a seasonal temperature reduction and consequent advantages for different heat generation systems and heat storages are discussed. The analysis of measurement data reveals a high optimization potential for solar thermal systems in case of an integration both into a low‐ and a high‐temperature storage. Furthermore, a combined heat‐and‐power plant in combination with a CO2 heat pump promises sector‐coupling potential and high electricity self‐consumption. [ABSTRACT FROM AUTHOR]

District heating networks are considered crucial for enabling emission-free heat supply, yet many existing networks still rely heavily on fossil fuels. With network pipes often lasting over 30 years, retrofitting heat producers in existing networks offers significant potential for decarbonization. This paper presents an automated design approach, to decarbonize existing heating networks through optimal producer retrofit and ultimately enabling 4th generation operation. Using multi-objective, mathematical optimization, it balances CO 2 emissions and costs by assessing different CO 2 prices. The optimization selects producer types, capacities, and for each period their heat supply and supply temperature. The considered heat producers are a natural gas boiler, an air-source heat pump, a solar thermal collector, and an electric boiler. A non-linear heat transport model ensures accurate accounting of heat and momentum losses throughout the network, and operational feasibility. The multi-period formulation incorporates temporal changes in heat demand and environmental conditions throughout the year. By formulating a continuous problem and using adjoint-based optimization, the automated approach remains scalable towards large scale applications. The design approach was assessed on a medium-sized 3rd generation district heating network case and was able to optimally retrofit the heat producers. The retrofit study highlights a strong influence of the CO 2 price on the optimal heat producer design and operation. Increasing CO 2 prices shift the design towards a heat supply dominated by an energy-efficient and low-emission heat pump. Furthermore, it was observed that even for the highest explored CO 2 price of 0.3 € kg − 1 , the low-emission heat pump, electric boiler and solar thermal collector cannot fully replace the natural gas boiler in an economic way. • Automated, physics-based heat producer retrofit for district heating networks. • Multi-objective optimization enables cost-efficient decarbonization. • The optimal heat producer design and operation depends strongly on the CO 2 price. • Choosing multiple heat producers allows low-carbon and cost-efficient heat supply. • The optimization exploits temperature-dependent heat producer efficiencies. [ABSTRACT FROM AUTHOR]

A realistic pursuit of decarbonization targets requires planning and designing new configurations of "multi-energy systems" to identify the optimal number, type, location and size of the energy conversion and storage units and their interconnections with the end users of different forms of energy. The common approach in the literature is to treat the optimization problem of energy conversion and storage separately from that of energy networks, and the few attempts to address the two problems simultaneously have led to oversimplifications due to the very large number of decision variables involved. To fill this gap, this study introduces " DOMES " (Design Of Multi-Energy Systems), a general optimization method for the integrated synthesis, design and operation of a multi-energy system in its entirety. With the goal of minimizing costs and reducing carbon emissions, DOMES can simultaneously find the location, type, size and operation of the energy conversion and storage units, as well as the topology and capacity of the energy networks, to meet the energy demand of the end users. To make the problem computationally solvable while ensuring sufficiently good accuracy of the solution, mathematical techniques such as linearization, problem decomposition and time series aggregation have been applied. DOMES is capable of finding the global optimum of the problem either while planning new systems from scratch or when starting from existing systems. Considering a densely populated urban district, the investment costs of renewable conversion plants outweigh those of the district heating network and electric microgrid, which together account for less than 10 % of the total. A much higher economic impact of energy networks is expected when considering larger, less densely populated areas. [Display omitted] • Optimal synthesis, design and operation of multi-energy systems including networks. • Development of a two-level evolutionary algorithm with a MILP sub-problem. • Decomposition of the topological optimization from the design and operation problem. • Design from scratch of new multi-energy systems or retrofit of existing ones. • Single or multi-objective optimization to minimize total costs and carbon emissions. [ABSTRACT FROM AUTHOR]

District heating networks offer promising solutions for remote communities, providing centralized heat supply, improved efficiency, and diverse energy sources, especially with existing diesel generation. Hence, this paper bridges gaps in the existing literature by developing comprehensive dynamic models of combined district heating networks within existing electric power networks in remote communities, which allows identifying challenges and benefits of district heating networks for these communities. It is shown that district heating networks allow utilizing waste energy to enable energy exchanges between the electricity and heating systems, enabling the provision of necessary ancillary services for remote microgrids with renewable energy sources. The presented dynamic district heating network model incorporates particular considerations in remote, northern communities such as soil limitations, extreme cold conditions, and piping insulation to minimize heat loss. It also addresses accurate sizing of heat pumps based on realistic thermal load requirements, weather conditions, and consumer profiles, proposing demand management controls to enhance frequency regulation for the integration of variable renewable energy sources. The main contributions of the paper include detailed dynamic modeling for district heating network operation, heat pump demand response control system design, and a comparative analysis between centralized district heating networks and decentralized electric thermal storage units that have been deployed for thermal supply in remote areas. The presented dynamic models are applied, tested, and validated in an existing electric microgrid at Kasabonika Lake First Nation in Northern Ontario, showcasing the role of a potential district heating network in facilitating renewable energy sources integration in isolated microgrids. • Use diesel generation exhaust for district heating in remote communities. • Use heat pump demand response for frequency control in remote microgrids. • Facilitate renewable energy integration using district heating networks in isolated microgrids. • Compare district heating networks with electric thermal storage units for thermal supply and frequency control in remote microgrids. [ABSTRACT FROM AUTHOR]

Due to an urgent need to reduce the fast-progressing climate changes, a rapid, standardized and replicable solution for the infrastructure restructuring of the heat demand coverage of buildings at the district and city levels must be developed. As more and more communities in Europe, especially in Germany, are obligated to have plans for coverage of the heat demand, city planners and energy agencies seek a tool that will help them to design the first drafts of heating network routes, which can supply whole districts and cities with renewable energy. ArcGIS Pro Tools like Least-Cost-Path Analysis (LCPA) and Closest Facility (CF) allow us to find the shortest and “cheapest” way between the heat source and heat consumers in the analyzed areas. Starting from the community level, through the district, county, voivodeship and whole-country levels, the replication of the methods for generating district heating (DH) network infrastructure can be achieved. The proposed LCPA- and CF-based methods help to design the most suitable and efficient DH networks in the analyzed areas. As only a few open-source data inputs, like street networks and building footprints, are needed, the methods can be implemented in all communities in the country of Poland. In this paper, one example of one community in Wroclaw county, called Siechnice, and its surroundings is presented.

Geothermal district heating is a technology that has been established for over 50 years and offers a number of significant advantages. It allows multiple homes to be converted to renewable energy sources simultaneously, allows a stable heat supply with long-term fixed prices, and generally presents lower risks. The utilization of geothermal energy as a heat source for the network can be considered cost-free during operation, but has a critical economic aspect related to the initial investment. Geothermal district heating networks are in fact capital-intensive systems (CapEx), requiring substantial investments for the installation of the geothermal infrastructure. However, operating costs (OpEx) are significantly lower compared to conventional systems. This study examined the implementation of a district heating network in a medium-sized city in northern Italy. An energy and environmental impact assessment was conducted to determine the optimal plant configuration that maximises the use of the geothermal resource and minimises greenhouse gas emissions. Additionally, a sensitivity analysis was carried out to assess the impact of market variables on the overall cost of implementing the district heating network. This included an evaluation of changes in investment costs in response to variations in the value of electricity taxes -oneri di sistema-, of the revenues from the sale of thermal and electrical energy, as well as of the fuel costs. This study aims to provide a complete and detailed overview of the energy, environmental and economic implications associated with the implementation of a geothermal district heating network

Integration of prosumers in district heating networks brings new challenges to the market and the network operation since they can change the thermal flow and increase competition. Thus, it is mandatory to develop new market structures and network management mechanisms. In this scope, this work proposes the implementation of a coordination methodology based on a peer-to-peer market without a supervising entity. The goal is to achieve higher revenue by coping with the requirements of each agent. Furthermore, the model is validated through network nodal analysis inspired by the power sector. The results in a Nordic network point out that the coordination methodology can provide compromise solutions between market negotiation and network operation. This methodology succeeded in providing reliable network solutions, fixing 99.88% of network burdens just after one iteration, and encouraging prosumers' integration. This increases market competition which lowers the energy costs for consumers while avoiding the network's operating burdens. [ABSTRACT FROM AUTHOR]

The energy efficiency of a system and the performance level of its equipment and installations are the two key elements based on which the investment decision in its modernization is made. They are also very important for setting up optimal operation strategies. The energy audit is a well-known and worldwide recognized tool for calculating energy performance indicators and developing improvement measures. This paper is a synthesis of the energy audit results performed for a district heating network that uses geothermal energy as its primary source of energy. The location of the heating system is inside a university campus. The first part explains the necessity of a comprehensive study on district heating networks and introduces the defining elements that characterize the analyzed equipment and installations. The complex energy balance methodology that has been developed and applied to this district heating system is presented in the second part of the paper. Next, the methodology for collecting the input data for the energy and mass balance is explained. In the final part, the numerical values of the performance indicators and the technical measures that must be applied to improve energy efficiency are shown, and conclusions are drawn. [ABSTRACT FROM AUTHOR]

The development over the last decade of the district heating (DH) network of Gleisdorf (Austria) represents an innovative example for a transition to a 4th generation DH network (4GDH). The local DH operator has strong ambitions towards implementing a highly efficient, nearly 100% renewable DH system using local resources. These goals will be achieved by the development and implementation of a novel control strategy, namely a “Virtual Heating Plant” (VHP) control concept. The VHP concept is an intelligent high level control system using operating data for real time calculations and a 24h prediction to optimise the overall DH network. The VHP control is fully implemented into the DH system and led to an improved and more flexible unit commitment, higher solar yields and more stable operating conditions. This paves the way to integrate multiple decentralised local heat sources and existing boiler units as well as other systemic approaches (e.g. sector coupling with waste water treatment plant, cascading heat supply out of the return pipe, stepwise system temperature reduction, implementation of an intelligent, decentral storage concept) and non-technical measures (e.g. user and stakeholder integration).

A district heating network has generally more than one production unit, and then there are different ways to fulfill the energy demand of the network. This paper deals with the planning problem which is a problem of optimization under constraints. The production units are modeled using MINLP (mixed integer non-linear programming), in particular the following technical aspects are considered: storage capacity (for municipal wastes and biomass), allowable operating range, allowable variation of power over time, partial load performances, minimum working time when a unit runs, and minimum waiting time when it is in stand-by mode. The environmental constraint is the annual share of renewable energies in the global energy mix (for example, at least 50% over a year). The model aims to minimize the operating cost of the network. The problem cannot be solved by a one-shot optimization, so the paper proposes a sequential approach: first, a target of renewable share is calculated for each month of the year; secondly, the planning production is determined each day according to the calculated target. The results of a case study show that the planning strategy that only aims to minimize the operating cost violates the environmental constraint. Using the developed planning model raises a little bit the cost (4.5%) but fulfills the heat demand and environmental constraint at the same time. Considering a reduced VAT eligible in France when the constraint on renewable share is met, the proposed planning is even better in term of the total cost (10% lower).

To ensure the correct functioning of district heating networks and minimize critical failures, utilities allocate every year a significant part of their budget to maintenance operations. In the present work we describe a risk-based approach implemented to tackle the problem of designing optimal multi-year maintenance campaigns, applied to the Italian city of Brescia, showing how data-driven techniques can help decision makers assess the long terms impacts of budget allocations.

Urban city districts nowadays contain heterogenous mixes of buildings with different age classes, usage profiles, heating demands and temperature requirements. Furthermore, increasing numbers of new and refurbished buildings are equipped with renewable energy sources that partially or entirely cover the building’s heat demand. These sources often have the potential to generate heat surpluses, turning the building into a ‘heat prosumer’. The successful integration of prosumers into district heating networks (DHN) is paramount to their decarbonization but is often limited by the high supply temperatures required by unrefurbished buildings.To respond to this challenge, this paper presents a concept for a cascading three-level DHN with multiple prosumers and thermal storage which aims to increase the efficiency and the share of renewable energy integration in the heat supply. The concept has been developed and is currently being implemented for a complex of five university buildings at the Technische Universität Berlin which exhibit a variety of heat consumers and decentralized sources such as waste heat from data centers and geothermal energy.

The occurrence of a large spontaneous leakage in a district heating network leads to a pressure wave through the entire network. For safe operation, it is necessary to determine the leakage location and to exclude the affected network part. For optimal evaluation of the pressure wave, pressure sensors must be placed in the network to provide high-quality data. Already installed sensors can be used, but improved transmission technology is needed. For practical application, as few sensors as possible should have to be equipped accordingly. This paper addresses the questions of how many sensors are needed and where they need to be placed for a given network. In a joint research project, a method was developed that allows the evaluation of the pressure wave even with noisy input data. The method used for pressure wave evaluation works in two stages. In the first stage, the pressure drop times are determined from the measurement data. In the second stage, these times are compared with test leakages, for which corresponding pressure drop times are calculated. The second stage provides the test leakage locations at which the leakage can probably be localized. For the placement evaluation, all possible leakages should be included and compared with all test leakages. This paper shows how the resulting large amount of data is consolidated to achieve a suitable metric for the quality of the placement. This procedure is used for the optimal positioning of the sensors and the network is currently equipped accordingly. From the calculations, results for different positioning and with different numbers of sensors are shown. Additionally, it is addressed that some positions in the network might be unsuitable due to unfavorable superposition of traversing waves and resulting difficulties to detect the pressure drop time points. Exemplarily simulation results and measurement data are shown.

Sizing distribution equipment of a district heating (DH) network is a complex yet decisive process to target techno-economic optimality for these systems. This paper describes and validates a framework that uses multi-objective optimization to support decisions regarding the sizing of a DH network. A genetic algorithm (NSGA2) is employed to generate optimal pipe diameters with respect to the operational and investment cost. This framework is then validated using an elementary toy problem consisting of a straight horizontal two-tube network, with a set of identical consumers equally distributed in the network and supplied by a unique heat source. We show that the framework, on the aforementioned problem, reaches very good convergence and diversity for the optimal solutions.This sizing method is then compared to typical local sizing method. We demonstrate a significant decrease of global cost by 40% by using DH network system simulation to take into account local interactions inside the DH system.

Fast localization of leakages in district heating networks is a demanding yet important task for network operation. Every leakage results in a loss of medium thus of energy. In case of small leakages, the medium lost can be replenished to maintain network operation. However, if the loss of medium is too large, it may be inevitable to shut down network operation. This can be prevented by separating the damaged network part using exclusion areas. For this, the leakage must be assigned to the correct exclusion area as fast and accurately as possible. This is a major challenge for current district heating networks without existing or with leakage localization systems delivering results too slow, so that other methods of leakage detection and localization are needed here. In a joint research project, three model- and data-based approaches are developed to localize leakages in real time. These are based on operating data which are typically measured for operational needs such as pressure or flow rate. The first approach evaluates the pressure wave which traverses the network at speed of sound when a leakage occurs. The second, purely data-driven approach uses machine learning models to localize leakages. The training phase of this approach is based on a large number of simulated leakages using a network model. The last approach uses the same network model to numerically compute the network state and localize leakages depending on available measurement data. In previous publications these approaches are evaluated using simulated measurement data with encouraging results. In this paper the work is continued and carried out to measurement data of a real district heating network. It is shown that each method is individually applicable to localize leakages under real conditions and provides plausible results. This encourages further development for using synergies which may deliver even better results.

Renewable energy communities are catalysts of social innovation, the citizens' engagement in energy actions, and the exploitation of local resources. Thus, this paper defines a model for analyzing and optimally sizing energy systems serving renewable energy communities. Then, the proposed and replicable model was tailored to the economic feasibility analysis of a renewable energy community in the municipality of Tirano (Northern Italy). An energy audit was carried out to identify the electricity production and consumption within the perimeter of the primary substation and the thermal energy demand of the existing district heating network. The technical features of the energy conversion systems serving the renewable energy community were determined: an organic Rankine cycle biomass-based cogeneration plant, a mini-hydro plant, and a distributed photovoltaic system. Moreover, several different scenarios have been identified, in terms of cogeneration operating mode, photovoltaic penetration, and thermal energy economic value. The results show that, moving from 4.22 MW to 5.22 MW of photovoltaic peak power, the annual renewable electricity production increases by 10.1%. In particular, the simple pay back ranges between 4.90 and 4.98 years and the net present value between EUR 12.4 and 13.3 M for CHP operating at full power mode, considering that thermal energy available from the cogeneration unit is sold at EUR 49.2/MWh. These outcomes demonstrate the economic feasibility of wood-biomass-based renewable energy communities, which may help to enlarge the contribution of renewable technologies other than photovoltaic. [ABSTRACT FROM AUTHOR]

Large spontaneous leakages in district heating networks (DHNs) require a separation of the affected network part, as interruption of the heat supply is imminent. Measurement data of 22 real events was analyzed for localization, but suitable results were not always achieved. In this paper, the reasons are investigated and a model for data evaluation (MoFoDatEv) is developed for further insights. This contains prior knowledge and a simplified physical model for the reaction of the DHN in the case of a large spontaneous leakage. A model like this does not exist so far. It determines the time point and the duration of the pressure drop of the pressure wave which is caused by such leakages. Both parameters and the evaluation time frame are optimized for each event separately. The quality assessment leads to a categorization of the events based on several parameters, and correlations between the pressure and the refill mass flow are found. A minimum leakage size is deduced for successful evaluation. Furthermore, MoFoDatEv can also be used for leakage localization directly, combining two steps from previous publications. Therefore, more data contribute to the result. The application is conducted with artificial data to prove the model concept, and also with real measurement data. [ABSTRACT FROM AUTHOR]

District heating systems are provided solutions for the increasing energy problems in high-population cities. Energy costs go up depending on increasing heat loss in DHS's distribution network. Heat loss from the network consists of 5-20% of transferred energy, and this loss is higher than the other losses in the heating system. In the study, heat losses from the pipes such as supply-return pipes, hot water and circulation pipes into heat canals are investigated based on energy, exergy, economic and environmental. Optimum insulation thicknesses, energy savings, reduction of CO2 emissions, the first investment costs and payback periods of the pipes in the network of all-generation district heating systems are investigated by using Life Cycle Cost Analysis (LCCA) method for fuel types like natural gas, fuel oil and coal. Optimum insulation thicknesses are calculated for different nominal sizes of pipes and various insulation materials such as glass wool, and rock wool for the different climatic zones. According to the results of the study, the heat losses from pipes in the 4th generation DHS network are decreased between 38.19% and 33.33% from the warmest climate zone to the coldest climate zone according to the 3rd generation. Energy savings, reduction of CO2 emissions, payback periods and optimum insulation thickness values of supply and return pipes in the network are respectively changed between 7.80-98.86 $/m, 39.61-322.32 kg CO2/year, 0.028-0.38 years and 0.025-0.0105 m depending on various fuel types, insulation materials, nominal size pipes, climatic zones and all generation types. [ABSTRACT FROM AUTHOR]

District heating networks (DHNs) can provide electrical power systems (EPSs) flexibility by utilizing heat storage abilities. There are three challenges to the real-time quantification of the flexibility offered by the DHN. First, the balance between the heat supply and consumption in the long term must be considered. Second, the robustness and economy of the quantified result must be guaranteed. Third, the reliable dynamic temperature distribution in the DHN must be obtained. Thus, this paper designs a flexibility-providing mechanism for the DHN to interact with the EPS, consisting of the service period and the recovery period. The real-time quantification of the flexibility of the DHN is formulated as an optimization problem, where both flexibility revenue and the operation cost increase in the CHP units are considered. A novel thermal dynamic state estimation (TDSE) model with a bad data filtering process is proposed, which is used to provide the dynamic temperature distribution to support the real-time quantification of the flexibility. Numerical results verified that the proposed real-time quantification model has advantages in ensuring the benefits of the DHN. The performance of the TDSE in providing data support for flexibility quantification is also verified. Finally, key factors impacting the quantified flexibility are exploited by simulations. [ABSTRACT FROM AUTHOR]

The operating flexibility of the power units is getting increasing attention from power systems especially those with large-scale fluctuating renewable energies. However, the combined heat and power (CHP) units are getting a bottleneck because their electricity productions are restricted by heat productions. This study aims to develop an electric-heat coordinated control strategy to make the CHP units more flexible. First of all, the dynamic model for a 300 MW CHP unit is set up, and its linear state-space description is obtained. A control strategy based on linear quadratic regulator (LQR) is then developed to satisfy different heat-power demands in various operating conditions. The control weights Q and R are optimized by particle swarm optimization. Moreover, the improved coordinated control strategy based on precise energy balance is put forward to increase the CHP power ramp rate considering electricity priority strategy and recovery control of the heat source. Finally, the simulation results show that the improved strategy is suitable for various CHP operating scenarios, and the case for electricity priority and heat recovery control significantly improves the unit power rate on the premise of stable heat supply. This work provides a reliable and flexible control mode for CHP units, which can support the power system stability and renewable energy integration. [ABSTRACT FROM AUTHOR]

Using a variety of renewable energy sources can significantly improve energy system flexibility and efficiency. Energy hubs, which have the function of generating, converting, and storing energy in various forms, are vital facilities in micro-energy networks (MENs). In this paper, we present a Solar-Assisted Compressed Air Energy Storage (SA-CAES) hub which can accommodate and flexibly supply multi-energy by being connected to a power distribution network (PDN) and a district heating network (DHN). We formulate economic dispatch models of the SA-CAES hub, the PDN, and the DHN, respectively. The economic dispatch model is formulated as a mixed-integer linear programming problem (MILP) that can be solved by commercial solvers. Further, the operationally feasible region of the SA-CAES hub is explored by thermodynamic analysis. The results indicate that the operation costs have been reduced by 4.5% in comparison with conventional MENs. [ABSTRACT FROM AUTHOR]

Featured Application: This work can be applied to find the pressure drop time points (PDTPs) for leakage localization in district heating networks (DHNs). It can be used to evaluate the pressure data of each sensor in the case of a large, spontaneous leakage. The PDTPs can be used within a framework to localize the leakage. It highlights the usage of different algorithms to find the PDTPs and presents the most suitable algorithm for several real events. It is strictly applied in DHNs where leakages are a demanding task to ensure an uninterrupted supply of energy. Leakages can occur in a district heating network, resulting in high economical damage. The propagating pressure wave resulting from large, spontaneous leakages reaches sensors at different locations in the network. This leads to pressure drops registered at each sensor at a different point in time. The time differences help to localize the leakage. Different algorithms are presented and applied in this paper to estimate the pressure drop time points based on non-uniform, time-discrete sensor signals. Five of the nine algorithms are self-developed with, e.g., parts of linear regression, whereas the other four algorithms have already been described in the literature, such as change-point detection. In this paper, several recorded events were investigated, and the algorithms were applied to real measurement data. After detection, leakage localization was performed to determine the affected exclusion area. A performance criterion was used as a measure to compare the algorithms. For practical application, the best-performing algorithm was identified. Furthermore, the events were classified according to how well they could be evaluated. [ABSTRACT FROM AUTHOR]

Factors affecting both the Estonian and Serbian district heating improvement are analysed, including geographical and climate factors, as well as economic and legal factors. This analysis is added by evaluation of main technical and economic parameters related to the district heating networks from the case studies (Estonian Narva city and Serbian Kragujevac district heating). This analysis uncovered the weakest points of Kragujevac district heating and explain why district heating is not considered as sustainable and environmentally friendly heating option.

This work compares the economic viability of active and passive condensation in a medium-scale biomass combustion plant considering the variability of the return temperature and heat demand over the year. A typical District Heating plant with a total installed power of 9.5 MWth is considered. The economic feasibility is evaluated by means of the Net Present Value (NPV), Discounted Payback Period (DPP), and the Modified Internal Rate of Return (MIRR). Compared to passive condensation, the NPV of an active condensation plant is 66% higher and reduces the primary energy consumption by more than 50%. However, a higher initial investment and a higher DPP are calculated. Assuming constant return temperature and average heat demand over the year lead to an overestimation of the NPV by more than 110% for an active condensation plant and by more than 160% for a passive condensation plant. The NPV, DPP and MIRR are strongly impacted by a variation of the return temperature of the network.