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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.

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.

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.

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.

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.

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.

District heating (DH) is an alternative technology to Individual Heating (IH) for satisfying end-user’s needs. This paper assesses the competitiveness of a DH network in the center of Italy from energy, environmental, and economic points of view considering both thermal power plant and end-users’ sides. On the thermal power plant side, the energy analysis considers the Primary Energy Saving (PES) and the specific energy (Esp) of the fuel actually exploited in the thermal power plant compared to its Low Heating Value (LHV), while the environmental analysis considers the avoided CO2 and the economic analysis considers the Energy Efficiency Certificates (EECs). Results showed that the current thermal power plant configuration with two boilers and a Combined Heat and Power (CHP) unit reaches a yearly PES of 21.3% as well as 1099 tCO2 avoided. From the economic analysis of the thermal power plant side, 829 EECs with an economic return of 207,222€ are obtained, while from the end-users’ side the DH network is cheaper than IH in 84.7% of the cases. Further technologies are also studied to enhance the CHP unit flexibility.

We carried out several numerical experiments to analyze how different boundary conditions affect the ability to detect small pipeline leaks. Our method is based on determining the soil temperature gradient above a buried district heating channel. The equivalent thermal conductivity of a wet insulation (λeq) value of 0.5 W/(m·K) was used to mimic a small water leakage. To evaluate the heat loss through the channel cross section, the heat conduction model was used for the pipe insulation, the concrete, and the soil, while the convection model was considered within the channel. The following effects were used to simulate different operating conditions: heat convection at the soil surface, leakage only from the supply or return pipe, soil height above the channel, soil thermal conductivity, and pipe diameter. With the exception of leakage only from the return pipe and low soil thermal conductivity 0.4 W/(m·K), the results showed a doubling of the soil temperature gradient when compared with the no-leakage case. This fact undoubtedly confirms the potential of the method, which is particularly suitable for leak detection in old pipelines that have priority for renovation. A key added value of this research is that the soil temperature gradient-based leak detection technique was found useful in most foreseeable DH operating situations.

This study evaluates temperatures measured at district heating (DH) valves in manholes and their usability for non-destructively assessing the thermal performance of buried DH pipes. The study was conducted as a field test in which part of a DH network was shut down and the temperature decline in the valves was analysed in terms of absolute temperature and thermal response time from the DH pipe to the top of the valve. The calculated and measured supply pipe temperatures by the drainage valves were in good agreement, with 1% deviation. The valve measurement analysis from this study shows that the drainage valve has good potential to serve as a measurement point for assessing the thermal status of a DH network. However, the shutdown valve measurements were greatly affected by the manhole environment.

Buried and operating district heating (DH) pipes are exposed to thermal degradation of their polyurethane (PUR) insulation over time, and their status is hard to assess without excavation. By using DH pipe valves in manholes as measurement points during a shutdown with an ensuing cooling period, non-destructive assessments can be performed. This study compares new improved field measurements with numerical simulations of the temperature decline in drainage valves and shutdown valves. The drainage valve measurements were used to thermally assess part of a buried DH network. Results indicate that by using the drainage valves as measurement points in a cooling method, the thermal conductivity of the buried DH network could be predicted with an accuracy of >95%. In addition, a general diagnostic protocol has been established for assessing the thermal status of a DH network, ready for network owners to use.

DiGriPy is a newly developed Python tool for the simulation of district heating networks published as open-source software in GitHub and offered as a Python package on PyPI. It enables the user to easily build a network model, run large-scale demand time series, and automatically compare different temperature-control conditions. In this paper, implementation details and usage instructions are given. Tests showing the results of different scenarios are presented and interpreted.

The paper presents a developed methodology of short-term forecasting for heat production in combined heat and power (CHP) plants using a big data-driven model. An accurate prediction of an hourly heat load in the day-ahead horizon allows a better planning and optimization of energy and heat production by cogeneration units. The method of training and testing the predictive model with the use of generalized additive model (GAM) was developed and presented. The weather data as an input variables of the model were discussed to show the impact of weather conditions on the quality of predicted heat load. The new approach focuses on an application of the moving window with the learning data set from the last several days in order to adaptively train the model. The influence of the training window size on the accuracy of forecasts was evaluated. Different versions of the model, depending on the set of input variables and GAM parameters were compared. The results presented in the paper were obtained using a data coming from the real district heating system of a European city. The accuracy of the methods during the different periods of heating season was performed by comparing heat demand forecasts with actual values, coming from a measuring system located in the case study CHP plant. As a result, a model with an averaged percentage error for the analyzed period (November–March) of less than 7% was obtained.

Designing district-scale energy systems with renewable energy sources is still a challenge, as it involves modeling of multiple loads and many options to combine energy system components. In the current study, two different energy system scenarios for a district in Montreal/Canada are compared to choose the most cost-effective and energy-efficient energy system scenario for the studied area. In the first scenario, a decentral energy system comprised of ground-source heat pumps provides heating and cooling for each building, while, in the second scenario, a district heating and cooling system with a central heat pump is designed. Firstly, heating and cooling demand are calculated in a completely automated process using an Automatic Urban Building Energy Modeling System approach (AUBEM). Then, the Integrated Simulation Environment Language (INSEL) is used to prepare a model for the energy system. The proposed model provides heat pump capacity and the number of required heat pumps (HP), the number of photovoltaic (PV) panels, and AC electricity generation potential using PV. After designing the energy systems, the piping system, heat losses, and temperature distribution of the centralized scenario are calculated using a MATLAB code. Finally, two scenarios are assessed economically using the Levelized Cost of Energy (LCOE) method. The results show that the central scenario’s total HP electricity consumption is 17% lower than that of the decentral systems and requires less heat pump capacity than the decentral scenario. The LCOE of both scenarios varies from 0.04 to 0.07 CAD/kWh, which is cheaper than the electricity cost in Quebec (0.08 CAD/kWh). A comparison between both scenarios shows that the centralized energy system is cost-beneficial for all buildings and, after applying the discounts, the LCOE of this scenario decreases to 0.04 CAD/kWh.

In this study, a holistic energy, economic and environmental assessment was performed on a prosumer-based district heating system, including scenarios with varying district size, retrofitting stages and system configurations. A modeling framework was built which comprises a thermal network design and simulation model; a building energy demand model for districts; and supply and storage technology models that allow assessing system solar fraction, equivalent annual cost and greenhouse gas emissions of district heating systems (DHS). Furthermore, the approach allows comparing the performance of a DHS with individual heating systems (IHS) for the district with the same set of technology options (rooftop-mounted solar thermal collectors, gas boilers and thermal storage tanks). The framework was applied to a Swiss case study. The results of the case study show that DHS often outperform IHS; however, parameters such as the district size, the retrofitting stage of buildings and the system configuration have impacts on the performance of the DHS. The most important parameter lies in the adequate selection of the storage volume over solar collector area ratio, which indicates that DHS solutions are only advantageous if they are properly sized. Smaller districts and districts with retrofitted buildings especially benefit from DHS solutions in terms of energy, economic and environmental performance. Maximum solar fractions of 50% (non-retrofitted case) and 63% (retrofitted case) were reached with the DHS solutions.

As the need for clean energy increases, massive distributed energy resources are deployed, strengthening the interdependence of multi-carrier energy systems. This has raised concerns on the electricity-heat system’s co-operation for lower operation costs, higher energy efficiency, and higher flexibility. This paper discusses the co-operation of integrated electricity–heat system. In the proposed model, network constraints in both systems are considered to guarantee system operations’ security: the branch flow model is utilized to describe the electricity network, while a convexified model considering variable mass flow and temperature dynamics is adopted to describe the heat network. Additionally, novel models for heat pumps and the stratified water tank are proposed to represent the physical system more accurately. Finally, to preserve the information privacy of separate systems, a distributed algorithm is proposed based on the alternating direction method of multipliers (ADMM). Numerical studies show that the co-operation could provide a more economical and reliable solution than the decoupled operation of the heat network and electricity network. Moreover, the ADMM-based algorithm could derive solutions very close to the optimum provided by centralized optimization.

District heating networks (DHNs) are essential municipal infrastructure in the north of China. Obtaining accurate resistance characteristics is a critical step to improve the operating regulation level of DHNs. In this paper, pipe hydraulic resistances (PHRs) are introduced to express the resistance characteristics. A hydraulic model of a DHN can be established by using observed data of pressures and discharges. The boundary nodes are taken as observed sites. After establishing a matrix equation and analyzing the rank of its coefficient matrix, the authors propose a method to determine all the PHRs uniquely, by using a small number of observed sites and operating conditions. Furthermore, when observed errors are introduced, the adverse impact can be weakened by increasing the number of operating conditions and the accuracy of observed devices. When the observed error ranges are 1% and 0.5%, the results show that the average relative errors of identified PHRs are 2.4% and 1.1% respectively, which can be acceptable in engineering. Then, a loop DHN can be transformed into several branch DHNs, which are identified individually.

This work aims at the development and the experimental characterization of new applications for adsorption heat pumps and chillers driven by industrial waste heat or renewable sources that can provide heating and/or cooling. Adsorption technologies offer the advantage of providing heating and cooling from low temperature sources below 100 °C without using refrigerant with high Global Warming Potential and with very low electricity consumption. Therefore, the technology enables the use of large untapped heat sources, increasing the energy efficiency of the heating and cooling sector with very limited impact on the environment. Several applications were investigated numerically for Switzerland using a simplified model of an adsorption heat pump. Four scenarios were identified as interesting: (1) the valorization of low-grade industrial waste heat in district heating networks, (2) energy efficiency improvement of district heating substations, (3) an autonomous adsorption heat pump with a wood pellets burner and (4) cooling applications. These scenarios were experimentally validated with a laboratory test of a commercial silica gel/water machine. Results show that there is a gap of up to 40% between the prediction of the simplified model and the experimental results. Therefore, there is huge potential to improve the performances of this commercial unit for these applications.

Multi-district integrated energy system (IES) can make full use of the complementary characteristics of district power and thermal system, and loads in different districts. It can improve the flexibility and economy of system operation, which has a good development prospect. Firstly, based on the general energy transfer model of the district heating network (DHN), the DHN system is described by the basic equations of the heating network and nodes considering the characteristics of the transmission time delay and heat loss in pipelines. A coupling model of DHN and multi-district IES is established. Secondly, the flexible demand response (FDR) model of electric and thermal loads is established. The load characteristics of each district in IES are studied. A shiftable load model based on the electric quantity balance is constructed. Considering the flexibility of the heat demand, a thermal load adjustment model based on the comfort constraint is constructed to make the thermal load elastic and controllable in time and space. Finally, a mixed integer linear programming (MILP) model for operation optimization of multi-district IES with the DHN considering the FDR of electric and thermal loads is established based on the supply and demand sides. The result shows that the proposed model makes full use of the complementary characteristics of electric and thermal loads in different districts. It realizes the coordinated distribution of thermal energy among different districts and improves the efficiency of thermal energy utilization through the DHN. FDR effectively reduces the peak-valley difference of loads. It further reduces the total operating cost by the coordinated operation of the DHN and multi-district IES.

Heat demand of a district heating network needs to be accurately predicted and managed to reduce consumption and emissions. Detailed thermal parameters are essential for predictions using physics-based energy models, but they are not always available or sufficiently accurate. To reduce the simulation time in calibration and the dependency of accurate data of buildings, this paper develops a prediction approach using a building energy model whose parameters are calibrated by Bayesian-based calibration method to match the recorded data of monthly heat demand. In the proposed calibration approach, an emulator is established to evaluate the untested values of thermal parameters using Bayesian method, and then use the evaluation results to search for the most suitable parameters value. The designed approach greatly accelerates the calibration speed. The method is used to calibrate a single parameter and multiple parameters of the building thermal energy models for a district heating network. After it has been verified with measured data, the developed calibration method is used to calibrate parameters of building energy models. The output of the calibrated model can predict the hourly building heat demand in district heating networks.

Despite their obvious benefit in terms of energy efficiency and their potential benefit on pollutant emissions, Flue Gas Condensers (FGCs) are still not widely spread in biomass combustion plants. Although their costs have significantly decreased during the last decade, the economic viability of FGC retrofits is not straightforward and their return on investments is mainly dependent on the temperature of the available heat sink and the moisture content of the fuel. Based on a new techno-economic model of a FGC validated with recent industrial data, this paper presents a methodology to assess the economic viability of an FGC retrofitting in a medium-scale biomass combustion plant. The proposed methodology is applied to the case of a typical District Heating plant for which real data was collected. For the first time, the usual assumptions of constant process data generally used are challenged by considering the variability of the return temperature and heat demand over the year. Furthermore, a new concept of optimal configurations in terms of energy savings is introduced in this paper and compared to a strictly economic optimum. The economic feasibility is mainly evaluated by means of the Net Present Value (NPV), Discounted Payback Period (DPP), and the Modified Internal Rate of Return (MIRR). As expected, results show that the higher the humidity level and the lower the return temperature, the higher the economic profitability of a project. The NPV is, however, increased when considering variable inputs: Even with an average return temperature of 60 °C, a mixed operation of the FGC as a condenser and an economizer along the year is predicted, which results in an increased profitability assessment. Considering a constant return temperature over the year can lead to a 20% underestimation of the project NPV. An alternative averaging method is proposed, where two distinct temperature zones are considered: above and below the flue gas dew point. The discrepancy with a detailed temperature variation is reduced to a few percents. Our results also show that increasing the FGC surface beyond the highest NPV can lead to substantial energy savings at a reasonable cost, up to a certain level. The energetic optimum we defined can lead to an increase in energy savings by 17% for the same relative decrease of the NPV.

This paper develops a nonlinear analytical algorithm for predicting the probabilistic mass flow of radial district heating networks based on the principle of heat transfer and basic pipe network theory. The use of a nonlinear mass flow model provides more accurate probabilistic operation information for district heating networks with stochastic heat demands than existing probabilistic power flow analytical algorithms based on a linear mass flow model. Moreover, the computation is efficient because our approach does not require repeated nonlinear mass flow calculations. Test results on a 23-node district heating network case indicate that the proposed approach provides an accurate and efficient estimation of probabilistic operation conditions.

A better integration across sectors is an essential element of 4th generation district heating and smart energy systems allowing to react to volatile renewable energy generation. This sector coupling enables to use more cost-efficient storage as storage prices differ for different forms of energy. Thermal energy for example can be stored in comparably cheap storage tanks. Besides such dedicated storage, the thermal inertia of a heating grid can be used as thermal storage as well. In this paper, a classic unit commitment optimization for scheduling of combined heat and power units not considering grid dynamics is extended to cover thermal dynamics of heating grids. First an outer approximation of the grid storage capabilities is developed. Second, a very efficient formulation for the storage dynamics of a heating grid is introduced and its capabilities are shown in a motivating case study. In this study additional savings of several thousand Euros per day are achieved using the thermal inertia of a heating grid as storage.

District heating networks are considered as convenient systems to supply heat to consumers while reducing CO 2 emissions and increasing renewable energies use. However, to make them as profitable as possible, they have to be developed, operated and sized carefully. In order to cope with these objectives, simulation tools are required to analyze several configuration schemes and control methods. Indeed, the most common problems are heat losses, the electric pump consumption and the peak heat demand while ensuring the comfort of the users. In this contribution, a dynamic simulation model of all the components of the network is described. It is dedicated to assess some energetic, environmental and economic indicators. Finally, the methodology is used on an existing application test case namely the district heating network of the University of Liège to study the pump control and minimize the district heating network heat losses.

A district heating (DH) system is one of the most important components of infrastructures in cold areas. Proper DH network design should balance the initial investment and the heat distribution cost of the DH network. Currently, this design is often based on a recommended value for specific pressure loss (R = ∆P/L) in the main lines. This will result in a feasible network design, but probably not be optimal in most cases. The paper develops a novel optimization model to facilitate the design by considering the initial investment in the pipes and the heat distribution costs. The model will generate all possible network scenarios consisting of different series of diameters for each pipe in the flow direction of the network. Then, the annuity on the initial investment, the heat distribution cost, and the total annual cost will be calculated for each network scenario, taking into account the uncertainties of the material prices and the yearly operating time levels. The model is applied to a sample DH network and the results indicate that the model works quite well, clearly identifying the optimal network design and demonstrating that the heat distribution cost is more important than the initial investment in DH network design.