D3.2: Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version

HYPERGRYD. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101036656 WP 3 – ICT Modules and Simulation Tools Task 3.1 Exergoeconomic optimization tool for 4th-5th DHC design and operation D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version Ref. Ares(2024)6908971 - 30/09/2024 D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 2 DISCLAIMER The opinion stated in this report reflects the opinion of the authors and not the opinion of the European Commission. All intellectual property rights are owned by HYPERGRYD consortium members and are protected by the applicable laws. Reproduction is not authorised without prior written agreement. The commercial use of any information contained in this document may require a license from the owner of that information. ACKNOWLEDGEMENT This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nº 101036656. D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 3 Project Project Acronym HYPERGRYD Project Title Hybrid coupled networks for thermal-electric integrated Smart Energy Districts Grant Agreement number 101036656 Call identifier H2020-LC-GD-2020 Topic identifier LC-GD-2-1-2020 Innovative land-based and offshore renewable energy technologies and their integration into the energy system Funding Scheme Research and Innovation Action Project duration 42 months (From 1 October 2021) Coordinator ARCbcn Website http://hypergryd.eu Deliverable Deliverable No. 3.2 Deliverable title Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version Description The objective of this task is to develop tools to reach the highest cost-effectiveness and lowest exergy for the design and operation of future DHC networks. The developed exergoeconomic optimization tool will be based on CAPEX and OPEX as well as technical and environmental boundary conditions. WP No. WP3 Related task T3.1 – Exergoeconomic optimization tool for 4th-5th DHC design and operation Lead Beneficiary 8 – Güssing Energy Technologies GmbH (GET) Author(s) Christian Doczekal, GET Harald Trinkl, GET Contributor(s) Manuela Binder, SONNE Type R Dissemination PU Public Language English – GB Due 30/09/2024 Submission date 30/09/2024 Version Date Authors Description V.0.1 17/09/2024 Christian Doczekal (GET) First draft of the deliverable V.0.2 24/09/2024 Christian Doczekal (GET) Report for review V.0.3 30/09/2024 Christian Doczekal (GET) Report for submission D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 4 Table of Contents 1 Executive Summary ................................................................................................ 7 2 Introduction ........................................................................................................... 9 2.1 Scope ......................................................................................................................... 9 2.2 Audience ................................................................................................................... 9 2.3 Definitions / Glossary................................................................................................ 9 2.4 Abbreviations ............................................................................................................ 9 3 Scientific Foundations ............................................................................................ 11 3.1 4th – 5th generation DHC ......................................................................................... 11 3.2 Basics of the exergetic approach ............................................................................ 13 4 Description of the Exergoeconomic optimization tool ............................................ 16 4.1 General description of the Exergoeconomic Optimization Tool functions ............ 16 4.1.1 Input data from consumers and producers .................................................................. 16 4.1.2 Missing input data from consumers .............................................................................. 16 4.1.3 Functions of the tool ..................................................................................................... 17 4.1.4 Presentation of the results ............................................................................................ 19 4.1.5 Integration to the HYPERGRYD platform ....................................................................... 19 4.2 Detailed description of the functions of the Exergoeconomic Optimization Tool . 20 4.2.1 Structure ........................................................................................................................ 21 4.2.2 Additional mathematical functions ............................................................................... 31 4.2.3 Presentation of results .................................................................................................. 33 4.3 Stakeholder Feedback on the Development of the Exergoeconomic Optimization Tool 38 4.3.1 Relevance of Temperature Levels for DH Grid Operators ............................................. 38 4.3.2 Tool Applicability in Planning and Pump Power Optimization ...................................... 38 4.3.3 Customer Perspective on Heat Supply and Comfort ..................................................... 38 4.3.4 Researcher and Stakeholder Preferences for Tool Usage ............................................. 39 4.3.5 Importance of Flexibility in Electricity Pricing and the Integration of Renewable Energy Sources 39 4.3.6 Respond from a Questionnaire ..................................................................................... 39 4.3.7 Potential and Interest in Further Development ............................................................ 40 5 Conclusions ........................................................................................................... 41 6 References ............................................................................................................ 43 D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 5 D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 6 List of Figures Figure 1: Overview of the planned range of functions of the Exergoeconomic Optimization Tool ... 16 Figure 2. Integration of the Exergoeconomic Optimization Tool to the HYPERGRYD platform.......... 19 Figure 3. User interface and Python interpreter at QGIS .................................................................... 21 Figure 4. Description of the IDs and connection ................................................................................. 22 Figure 5. NodeIDs and connected lines ............................................................................................... 23 Figure 6. Geometry, with singleand polygon lines ............................................................................ 23 Figure 7. Definition of predecessor and successors ............................................................................ 24 Figure 8. Flow of the pipes are calculated .......................................................................................... 25 Figure 9. Pressure loss of the pipes ..................................................................................................... 26 Figure 10. Calculated dimensions of the grid ...................................................................................... 27 Figure 11. Different layers for visualisation ........................................................................................ 27 Figure 12. Defined values for data points ........................................................................................... 28 Figure 13. Demo structure of a customer with a time series .............................................................. 29 Figure 14. Example of a decentralized heat generator ....................................................................... 30 Figure 15. Visualisation of the flow rate in the grid ............................................................................ 33 Figure 16. Different layers and maps are available in QGIS for visualisation ..................................... 34 Figure 17. Temperature distribution of the district heating grid in summer operation on 23.07.2023 at 03:00 pm ......................................................................................................................................... 34 Figure 18. Flow rate in summer operation on 23.07.2023 03:00 pm ................................................. 35 Figure 19. Average pressure loss of individual pipe sections for the evaluation of bottlenecks ........ 35 Figure 20. Pressure drop per pipeline ................................................................................................. 36 Figure 21. Pressure drop summed up ................................................................................................. 37 Figure 22. Share of exergy and anergy for a consumer for one day ................................................... 37 D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 7 1 Executive Summary The HYPERGRYD project aims to develop scalable and replicable technical solutions that enable the integration of Renewable Energy Sources into thermal grids, while also creating synergies between district heating and cooling networks and electrical grids. The project focuses on addressing the challenges posed by the varying availability and dispatchability of renewable energy, providing key innovations in technology and ICT services to handle the increased complexity from the building level to Local Energy Communities. The ultimate goal is to accelerate the sustainable transformation and modernization of DHC systems towards 4th and 5th generation networks. HYPERGRYD is driven by three primary objectives:  Proving Smart Energy Networks as the future of efficient energy management in DHC, particularly in synergy with electrical grids within LECs and smart cities.  Defining the roadmap for the design, planning, and modernization of DHC networks, tailored to various climates and RES penetration levels, to support the transition to 4th and 5th generation systems.  Demonstrating RES-based enabling technologies and innovative Smart Energy Grid Solutions, empowered by integrated ICT tools and services, as key drivers of this evolution. As part of the HYPERGRYD project, this deliverable focuses on the development of the Exergoeconomic Optimization Tool. The tool is designed to maximize cost-effectiveness and minimize exergy losses in the design and operation of future DHC systems. By incorporating both capital (CAPEX) and operational (OPEX) expenses alongside technical and environmental boundary conditions, the tool provides a comprehensive framework for optimizing energy and economic performance in DHC networks. One of the key strengths of the Exergoeconomic Optimization Tool is its focus on improving the exergetic efficiency of DHC systems. By calculating and optimizing exergy efficiency at various temperature levels—such as supply and return temperatures on both the primary and secondary sides—the tool identifies opportunities to reduce heat losses, integrate renewable energy sources, and enhance overall system performance. This is especially critical as DHC systems transition to lower temperature levels characteristic of 4th and 5th generation networks, where the integration of renewable energy and decentralized heat sources like heat pumps is crucial. In addition to technical optimization, the tool provides robust economic analysis by considering variables such as fuel prices, electricity tariffs, and system heat losses. The ability to simulate flexible electricity tariffs, including hourly pricing and spot market rates, allows users to optimize the cost of heat generation in real time. The tool also supports the integration of renewable energy sources, making it easier to plan and manage decentralized heat inputs from sources like waste heat and photovoltaics. The tool integrates GIS data for visualizing critical operational parameters such as pressure losses, flow rates, and temperature distributions within the network. This visualization capability aids DHC D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 8 planners and operators in identifying network bottlenecks, optimizing pump placement, and planning future expansions. The target audience for this tool includes DHC planners, optimizers, system operators, and software developers. By supporting both the technical and economic optimization of DHC systems, the tool plays a vital role in the future of energy-efficient, sustainable, and cost-effective district heating and cooling operations. On behalf of Authors Christian Doczekal, Güssing Energy Technologies GmbH Harald Trinkl, Güssing Energy Technologies GmbH D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 9 2 Introduction 2.1 Scope The aim of this deliverable is to present the range of functions and the scientific basis of the exergoeconomic optimization tool. The development of the tool runs until month 36, so D3.1 is used to represent the preliminary version of the tool. 2.2 Audience The target group of this report are DHC planners, optimizers, system operators and software developers. The report provides the target group with information on how the exergoeconomic optimization tool is structured and which functions it already performs or will be able to do. The functions and scientific bases of the calculations are presented. One part deals with the programming of the models and is particularly interesting for developers. 2.3 Definitions / Glossary Exergoeconomic Optimization Tool - This is a tool that performs thermal calculations of a district heating grid, taking into account the exergy. The exergy level should be optimized in order to avoid unnecessarily high temperatures in the network. In addition, the entire system can be economically optimized through the economic part of the tool. This serves as a decision basis for relevant stakeholders of a DHC system. QGIS - QGIS is a free and open-source cross-platform desktop geographic information system (GIS) application that supports viewing, editing, printing, and analysis of geospatial data. 2.4 Abbreviations 3GDH: third generation of district heating 4GDH: fourth-generation district heating 5GDHC: fifth generation district heating and cooling ATES: Aquifer Thermal Energy Storage BIM: Building Information Modeling CAPEX: Capital Expenses CHP: Combined Heat and Power COP: Coefficient of Performance DHC: District Heating and Cooling D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 16 4 Description of the Exergoeconomic optimization tool 4.1 General description of the Exergoeconomic Optimization Tool functions The Exergoeconomic Optimization Tool has a wide range of calculation and optimization functions. The main functions can be found in Task description 3.1. The project team has summarized the most important functions of the tool in Figure 1. Basically, the tool is based on QGIS, supplemented by Python scripts. Figure 1: Overview of the planned range of functions of the Exergoeconomic Optimization Tool 4.1.1 Input data from consumers and producers It can be seen that some input data from consumers and producers are necessary for the calculations, e.g. loads, energy, temperatures and prices. The tool is intended to plan and optimize the system and therefore only accesses historical data. Real-time data is therefore not required. The tool could perhaps also be available for real-time operation in the future in order to optimize real-time operation. However, this is not currently planned. Furthermore, the GIS data of the existing district heating grid must also be available or inserted as input for the tool. The DH grid should either be loaded into the program as an existing GIS file or drawn in the tool (QGIS user interface) itself, for example for new pipes. 4.1.2 Missing input data from consumers In many district heating systems, there are significant gaps in data availability and quality, particularly concerning customer-specific measurements. Not all systems have optimal data recording, which poses challenges for accurate simulation and optimization. In response to this, the Exergoeconomic Optimization Tool includes several strategies to address missing or incomplete data. ExergoeconomicTool consumer, producer: GIS data loads energy temperatures prices RES sources storages historicaldata / profiles variable tariffs of consumers and producers visualisation: pressure drops flow temperatures diagrams optimize operation, components and design exergetic life cycle assessment economic and thermal modeling for 4th and 5GDHC optimize GIS location of 5GDHC borehole storages heat losses pumping costs cost-effectiveuse, increase of decentralized RES and P2H import/export functions exergy-based analysis compare scenarios layer 1 technologies D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 17 The first step involves reading in the available customer data. However, for many customers, district heating measurements were either not recorded consistently, or the minute-by-minute recordings could not be clearly assigned to specific customers. To resolve this, daily average values from district heating meters can be used, which are typically available across all customers, as the starting point. While data could be available on a daily scale, the tool operates on a hourly basis, requiring transformation of the data for better temporal alignment. To generate the necessary hourly values, the tool approximated the daily values of each customer based on the daily patterns observed in the heating plant, which records data e.g. every 15 minutes. This method allowed for more granular estimates while ensuring coherence with the broader system's operation. Additionally, on the customer side (secondary side) of the district heating systems, there are usually very few available records. To generate the data for the secondary side, a standard control system of a typical district heating substation was recreated, providing a reliable basis for calculating missing customer-side data. This approach ensures that even in cases of limited data availability, the Exergoeconomic Optimization Tool can generate reliable input for simulations, helping to optimize the performance of district heating systems effectively. 4.1.3 Functions of the tool The main functions of the Exergoeconomic Optimization Tool include the optimization of the operation of the DH system, as well as the design of components, e.g. to find optimal pipe diameters in relation to the overall system. Other components are able to be optimized through parameter variations. This makes it possible to plan components in a way that makes economic and technical sense. This is to avoid oversizing. The optimization of the operation is particularly important, since on the one hand problems with existing DH systems can be identified (e.g. excessive flow rates from consumers, too high temperature levels, unused storage resources, ...) and on the other hand suggestions for improvement can be drawn up. The tool can thus be seen as decision support. In addition to the optimization of components, the cost-effective use of the overall system or individual subsystems is also important. Here, a cost reduction is achieved through a technical and economic comparison, for example through the use planning of heat generators. Parameter variations show the most sensitive points of the overall system and thus serve as a basis for decisionmaking. The calculations and comparisons make it possible to increase the use of renewable energy, especially in decentralized locations. This is possible, for example, through decentralized waste heat feeders or P2H systems (e.g. heat pumps). The tool from the partner GridSingularity is to be used to use available excess electricity from a local energy community for P2H applications. For example, we can use the D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 18 electricity data (power, tariff level) from their tool to simulate decentralized P2H systems for heat generation. The calculations of the DH grid are based on thermal and economic functions, which are described in more detail in Chapter 4.2. In addition, it is possible to include the steam engine from Ranotor as a Layer 1 technology from Workpackage 2. Here, the amount of electricity and heat produced is taken into account in operational planning and economic efficiency. This enables operation planning as a power and heat generator (located decentralized) in order to enable economical operation for the entire system. In order to do justice to the market situation and to be able to present future developments better, it is possible to consider variable tariffs from heat consumers and heat generators. As a result, the planning of heat production can also be economically optimized and peak loads can be reduced. As an incentive system for consumers with higher return temperatures, a bonus can be calculated here in order to make a statement about what effect the reduction of the return temperature would have and what the economic effect would be. This is very much on the path towards 4th generation district heating and thus enables better integration of renewable energy into the grids. When optimizing operation, the focus is usually also on the electrical consumption of the pump. The pumping costs are able to be displayed and savings potentials are shown. It can compare overall economics, for example to show the effect of smaller pipe diameters (lower investment costs) with the higher pumping costs. This is particularly interesting when expanding the district heating grid. Decentralized heat generators should preferably be considered with a return-return feed-in or flowflow feed-in. This reduces strong fluctuations in the differential pressures in the grid. However, the tool also takes into account the mass flow that flows past this line, since a return-return feed, for example, is very dependent on the mass flow flowing past. This has an impact on the heat feeder and the heat output fed in. For the exergetic optimization of the DH system, the temperatures required by the consumers are analyzed, from which the flow temperature for the grid is specified. This provides information for the historical data as to what effect a lower flow temperature would have had over the course of the year. Exergetic indicators are defined for the evaluation, to analyze the exergetic operating modes and to optimize them based on this. This makes it possible, for example, to evaluate decentralized heat producers and also to provide appropriate tariffs depending on the temperature level provided. An exergetic (life cycle assessment) analysis is carried out for the calculated period under consideration in the form of these indicators, such as the exergetic efficiency. The calculations of profitability take into account the investment costs, consumption-related costs and operational costs. Here, the heat generation costs of the heat generator are used as input for the simulation. Through parameter variations and different use cases, an economically optimal operation and cost savings are calculated. Differences between the individual scenarios are calculated to show economical benefits. D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 19 4.1.4 Presentation of the results In order to visually direct the focus to parts of the grid, there is a colored representation in the visualization of the grid. Here, for example, the pressure losses of the pipes can be shown in color (see also chapter 4.2.3), or the flow, or the temperatures. It is possible to show heat losses from the pipes, e.g. with the unit W/m. Furthermore, optimizing the GIS locations of heat storage units (e.g., borehole storages for 5GDHC systems) is possible. However, the placement of these heat storages or the grid length would need to be manually adjusted within the program, since factors such as the availability of land plots or street layouts must be taken into account. Export functions are available to continue calculating with the results in other analysis tools, GIS (e.g. as .gpkg files) or BIM applications. Furthermore, graphics and time series, as well as KPIs, represent the technically and economically optimal case. 4.1.5 Integration to the HYPERGRYD platform The HYPERGRYD platform represents the common unit of the tools created in Workpackage 3. The platform is available as "Platform as a Service" (PaaS). The exergoeconomic optimization tool is integrated via an API interface (Figure 2). It is possible to visualize the results and diagrams on the platform. Different scenarios can be compared there. Access to the platform could be possible for different users, such as planners, DH grid utilities, decentralized heat generators, or the consumers themselves. The Exergoeconomic Optimization Tool itself is operated by the GET project team, as it requires the appropriate know-how and thus avoids user errors. The tool will therefore not be available as standalone software, but as a service that also includes the relevant know-how for the optimization of district heating systems. Figure 2. Integration of the Exergoeconomic Optimization Tool to the HYPERGRYD platform Exergoeconomic Tool Processing HYPERGRYD platformData input consumer, producer: GIS data loads energy temperatures prices RES sources storages historical data / profiles •visualizing results and graphs •based on scenarios •selectable level: heat generation, grid, consumers heat generation consumers grid API database database D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 20 The platform's district heating grid (GIS data, parameters, properties) is able to be taken over by the tool. Individual pipelines can be drawn into the platform, for example for customer extensions. The user can make some changes, e.g. of framework conditions, in the platform, which then serve as input for the tool. 4.2 Detailed description of the functions of the Exergoeconomic Optimization Tool There exist many tools for optimizing and simulating a grid. The problem with these tools are, that you need in a special format (grid as graph with edges and nodes), where almost no grid operator can provide, or you need to draw the whole grid in this simulating tool, which took a lot of time and can’t be used for other tools. The project’s approach is based on information which already exists. The Exergoeconomic Optimization Tool is realized as plugin in the open source tool QGIS (Figure 3). QGIS is a tool which can handle a huge variety of input formats including GeoJSON, GeoPackage and GML. The most interesting function is the possibility of importing AutoCAD plans. Most of the district heating operators have their grid plans available as AutoCAD plans, which are hardly used due to the high license fees and cannot be used by external programs. The plugin DXF Import / Convert reads all Layers in the AutoCAD-plan and imports all objects with all accessible information to QGIS. Equally common is the use of csv / xlsx lists to store additional information on pipelines and consumers. QGIS can also read these lists and save them as layers without geometry. The assignment of the list entries to the customers is always problematic. IDs are not updated without automatic management, which is not possible in Excel, and the IDs are written in different ways or are not set everywhere when changes are made, which means that IDs set manually become unusable sooner or later. The customer name is for many operators the most important feature to identify the customers. But even here, people change, spellings are not unique and errors occur in the designation. With the build in function “Nominatim-Sammelgeokodierung” it is possible to generate a point layer where the information from the csv is stored in a georeferenced point. All the Objects in QGIS can be accessed and modified by its build in Python interpreter (PyGIS). This Python interpreter is a unmodified Python installation connected to QGIS and can use all packages available for Python. The intepereter can be accessed from QGIS directly and code can be written to the intepretor or a file can be readed. A second way is to develop a plugin for QGIS, so the code can be run by clicking on an icon and a GUI can be used. D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 21 Figure 3. User interface and Python interpreter at QGIS 4.2.1 Structure The programme code is separated in different classes in one package. The most important classes are:  pipeset consisting of pipe where the information about the installed pipelines are installed. Especially the information about dimension, diameter and thermal conductivity is stored in this class.  the calculate class where physical calculations can be called collectively  the class singleline where all information concerning the individual pipe including the type of pipe used (class pipe) is stored.  the Customer class, where information about the individual customer, such as contractual connected load, current output, temperatures and installed transfer stations is stored.  the classes edges and nodes, where the actual plan is being translated for external tools.  the class FWnetwork, which consists of the lines, the customers and some functions.  the classes Simulation and Calculation where Calculation stores the results of the calculation for a given timestamp and Simulation stores a list of calculations for the given time period. The first step for the calculation is to create a new object from the FWnetwork class and pass the name of the layer for the grid. The given layer is searched for items with valis geometry and all founded items are added to the FWnetwork object as a singleline object. Also the IDs of the items are stored in an index for faster searching and a spartial index is build up with these items for getting the D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 22 distances between two objects very fast. After this a standard pipeset is generated if no existing pipeset is given. Then the function “set_all_connected” gets for all generated singlelines the first and the last point of the line and gets with the spartial index the next 10 neigbours. The amount of neighbours is a variable of the FWnetwork-class and can be modified. The same for the maximum distance between the neighbours. This function unfortunately does not work reliably, so the distance between the endpoints and the neighbours are calculated again and if the distance if higher than the given accuracy, the neighbor is discarded. The results of the neighbour detection are stored in the singleline-objects (Figure 4). Figure 4. Description of the IDs and connection A lot of tools need nodes and edges to work. Edges are basically the singleline objects, but the nodes need to be generated. For all singlelines in the network the before detected neigbors are read and is stored in an index. If there is no entry in the index a new node is generated with the coordinates from the according endpoint and the neighbours are set. If there is an entry already in the index, what arises when the same point is tested from one of the neighbors, nothing is done. After this the nodeIDs are set in the singlelines (Figure 5). D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 23 Figure 5. NodeIDs and connected lines The next step is to add the customer layer to the FWnetwork. Analogous to the grid layer there is a check if a geometry exists and is valid and if so the geometry and the connection power are stored as object customer in the FWnetwork. The spatial index is used to get the nearest line, which ID is stored in the customer too. The geometry can be a single point or a polygon (Figure 6). Figure 6. Geometry, with singleand polygon lines D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 24 Now all necessary data is prepared. The next step is to set for all lines a predecessor and successors (Figure 7). Starting from a given line a minimum spanning tree is created where each line has exactly one predecessor. All the other connected lines are successors. Predecessor -1 means that no predecessor was found. On the one hand, this can be due to the fact that there is no predecessor, since it is the first line after the boiler, or that this line cannot be reached from the starting point. Figure 7. Definition of predecessor and successors Next, the flow is set. For this purpose, the required flow for every customer is calculated with the required load and the flow and return temperatures, which are assumed to be e.g. 80/50 if no other values are specified. The calculated flow of each customer is added to the singleline connected to the customer and all predecessors. So the flow in the whole grid is calculated (Figure 8). D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 25 Figure 8. Flow of the pipes are calculated The pressure loss is then calculated using the flow rate and the properties of the installed pipe. With this information, the specific pressure loss for each individual pipe section can be calculated and with the length of the section, the pressure loss of the entire section is calculated. To calculate the pressure loss of the entire grid, the losses of the individual parts are summed up, starting from the heating plant (Figure 9). D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 32 The pressure drop is then calculated by: pipe friction coefficient× 1 𝑝𝑖𝑝𝑒 𝑖𝑛𝑛𝑒𝑟 𝑑𝑖𝑎𝑚𝑒𝑡𝑒𝑟×𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑤𝑎𝑡𝑒𝑟 2× 𝑣𝑒𝑙𝑜𝑐𝑖𝑡𝑦 𝑖𝑛 𝑝𝑖𝑝𝑒 2 Equation 6. Equation of the pressure drop of the DH pipes This value is valid for a Reynolds number > 2320. For smaller values the Reynolds number is divided by 64. For the pipe sections there is also a 10 attribute POWER_LOSS, which calculates the heat loss as follows: 𝑃𝑜𝑤𝑒𝑟_𝐿𝑜𝑠𝑠=𝑢𝑣𝑎𝑙𝑢𝑒_𝑝𝑖𝑝𝑒∙𝑝𝑖𝑝𝑒𝑙𝑒𝑛𝑔𝑡ℎ∙𝐹𝑙𝑜𝑤𝑡𝑒𝑚𝑝+𝑅𝑒𝑡𝑢𝑟𝑛𝑡𝑒𝑚𝑝 2−𝑠𝑜𝑖𝑙𝑡𝑒𝑚𝑝 1000 Equation 7. Equation of the power loss of the DH pipes Uvalue_pipe is the heat transfer coefficient, the soiltemp is the ground temperature. It is possible to calculate exergy efficiency without resorting to entropy and enthalpy values because we are dealing with liquid water without any phase change or pressure differences. In such cases, a simplified formula for exergy for liquids can be used, where the temperature difference between the liquid and the environment is the most important factor. Exergy efficiency is based on the ratio between the usable exergy and the maximum possible exergy that can be extracted from the system. For liquids, this can be described by the following simplified equations: 𝜂𝑒𝑥 =1−𝑇0 𝑇1 Equation 8. Simplified equation of the exergy efficiency T0: Ambient temperature in K T1: Temperature of the medium in K The exergy efficiency is calculated for each relevant temperature in the system, such as the supply and return temperatures on both the primary and secondary sides. By applying this method, the exergy efficiency can be determined for various components, such as district heating pipelines or consumers within the network. This provides a detailed understanding of how effectively the system utilizes its thermal energy at each stage of operation. By calculating exergy efficiency for different temperatures, key areas for optimization can be determined. For instance, reducing heat losses in the district heating network becomes possible by adjusting temperature levels to maximize exergy efficiency. A higher exergy efficiency indicates that less energy is being wasted as unusable heat, and more energy is being effectively utilized. This leads to improved overall system performance, with lower thermal losses contributing to better energy D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 33 conservation and cost savings. Moreover, calculating exergy efficiency enables enhanced integration of renewable energy sources into the district heating network. Renewable sources such as heat pumps or solar thermal systems often operate at lower temperature levels. By optimizing the exergy efficiency at these lower temperatures, the network can more effectively incorporate renewable energy inputs, reducing the reliance on fossil fuels and increasing the system’s sustainability. This exergy-based analysis provides a robust framework for improving system performance across various operational scenarios. An exergetic life cycle assessment is done for the period of calculation. By focusing on the exergy efficiency of both supply and return temperatures, system planners and operators can make data-driven decisions to reduce heat losses, increase the potential for renewable energy integration, and ultimately enhance the overall efficiency of the district heating system. 4.2.3 Presentation of results One of the best features of QGIS is the ability to flexibly display georeferenced information. The information stored in the individual objects can be used for visualization. As an example, the flow rate of the network was used for the line thickness of the pipes (Figure 15). Figure 15. Visualisation of the flow rate in the grid Another advantage of using QGIS is that information from different sources can be used and blended together. Here, the representation of the pressure drop was blended with a satellite image and a semi-transparent 3D road map (Figure 16). In addition, the property boundaries were drawn from the digital cadastral map provided by the Austrian authorities. The temperature distribution of the district heating grid in summer operation for one hour in the afternoon was shown in Figure 17. The flow rate in summer operation for one hour in the afternoon can be seen in Figure 18, where it can D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 34 be seen that mainly only one large customer requires heat. The average pressure loss of individual pipe sections is shown in Figure 19 and is used to evaluate bottlenecks, for example to see where problems could arise when the grid is expanded. Figure 16. Different layers and maps are available in QGIS for visualisation Figure 17. Temperature distribution of the district heating grid in summer operation on 23.07.2023 at 03:00 pm D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 35 Figure 18. Flow rate in summer operation on 23.07.2023 03:00 pm Figure 19. Average pressure loss of individual pipe sections for the evaluation of bottlenecks D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 36 Figure 20 illustrates the pressure loss in the district heating pipeline, either for the design case or for an actual operating scenario. This analysis is crucial for identifying bottlenecks within the network, particularly in the context of planned network expansions. By visualizing pressure losses, the tool enables the identification of optimal locations for booster pumps, ensuring that the system can be effectively reinforced where needed to maintain operational efficiency and meet future demand. Figure 20. Pressure drop per pipeline Figure 21 shows the cumulative pressure losses for each pipeline from the heating plant to the endpoints of the network. This representation highlights the weakest points within the network, providing valuable insights into where pressure sensors should be placed to optimize pump control. Additionally, the figure indicates which areas of the network still offer potential for easy expansions, as the lower pressure losses in these regions suggest minimal resistance to additional load or infrastructure development. This dual-purpose analysis supports both operational improvements and strategic planning for future network growth. Figure 22 illustrates the distribution of exergy and anergy for a district heating customer over the course of a day. A lower proportion of exergy indicates reduced heat losses in the network and suggests that the district heating supply temperature closely matches the required temperature on the consumer side. This comparison allows for an evaluation of how well the customer or the district heating network is suited for fourth-generation district heating systems. Additionally, it provides D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 37 insight into the efficiency of decentralized heat sources in contributing to the network, helping to assess their effectiveness in future heat supply strategies. Figure 21. Pressure drop summed up Figure 22. Share of exergy and anergy for a consumer for one day D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 38 4.3 Stakeholder Feedback on the Development of the Exergoeconomic Optimization Tool The development of the Exergoeconomic Optimization Tool has garnered valuable feedback from various stakeholders, including DH grid operators, district heating customers, planners and researchers. This feedback is essential to refine the tool’s functionality, applicability, and potential for broader adoption. By analyzing the responses, we can identify critical areas for improvement and integration of features that align with stakeholder expectations and operational requirements. 4.3.1 Relevance of Temperature Levels for DH Grid Operators One of the primary concerns raised by stakeholders is the significance of temperature levels in district heating grids. Network operators emphasized that controlling temperature levels is crucial for optimizing network efficiency and reducing operational costs. The feedback indicated that network operators face challenges in managing temperature gradients, particularly in cases where the diameter of pipelines is unknown. For example, one stakeholder highlighted a network with 150 customers connected via a 3.5 km pipeline, where incomplete data regarding pipe dimensions posed a difficulty in the optimization process. This highlights the need for more detailed data collection, particularly for older networks where infrastructure details may be missing. Additionally, many network operators are in the process of upgrading their infrastructure with new technology, such as new controlling devices for the substations. These upgrades aim to enhance data collection on customer heat consumption, which will, in turn, improve the overall effectiveness of the optimization tool. The feedback suggests that the tool can support these operators by integrating data from these emerging technologies to optimize both planning and operational phases. 4.3.2 Tool Applicability in Planning and Pump Power Optimization Several stakeholders found the tool to be highly beneficial during the planning stages of district heating networks. One recurring theme was the tool’s ability to optimize network design by aiding in the dimensioning of pipes, which has direct implications for the network's energy efficiency and costeffectiveness. Once the planning phase is complete, stakeholders noted that optimizing pump power becomes more critical, as it directly influences operational costs. Moreover, the integration of pressure sensors in networks has been cited as a proactive step toward reducing pump power. Some networks have already adopted such measures, achieving improved regulation of pressure points and reducing unnecessary energy consumption. The tool’s potential to align with these existing measures and further enhance pump efficiency is seen as a strong point by operators. 4.3.3 Customer Perspective on Heat Supply and Comfort From the customer standpoint, maintaining comfort while keeping energy prices reasonable is a central concern. Feedback from a district heating customer emphasized the importance of ensuring D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 39 a stable supply of 60°C water for domestic hot water use. In addition to comfort, customers are willing to provide the necessary data for system optimization, suggesting that they see value in the tool’s ability to enhance system performance without compromising service quality. This input highlights the dual focus of the tool: ensuring technical optimization while maintaining user satisfaction. By striking this balance, the tool can ensure broader acceptance among end-users, particularly in customer-facing aspects like heat supply and pricing stability. 4.3.4 Researcher and Stakeholder Preferences for Tool Usage Feedback from researchers and some stakeholders indicated a preference for using the tool independently rather than relying on a service to optimize the network. This highlights the tool’s potential for integration into research and academic environments, where it can be used as a part of broader studies on energy efficiency and district heating networks. Researchers are particularly interested in the flexibility the tool provides, allowing them to test different scenarios and assumptions autonomously. Conversely, network operators expressed that offering the tool as a service would be more suitable for their needs. Many operators prefer to outsource such expertise rather than develop in-house capabilities. This distinction between user groups suggests that the tool can be positioned both as a self-service platform for those with technical expertise and as a managed service for operators seeking professional support. 4.3.5 Importance of Flexibility in Electricity Pricing and the Integration of Renewable Energy Sources The tool’s capacity to accommodate flexible electricity tariffs, including spot market pricing, was identified as a critical feature. Stakeholders emphasized that this flexibility is particularly relevant when incorporating renewable energy sources into the network, such as waste heat and heat pumps. These decentralized heat sources are becoming increasingly important in district heating systems, and the tool’s exergy-based approach is well-suited to handle the integration of these variable sources. Moreover, stakeholders noted the growing importance of reducing supply and return temperature levels in district heating networks. This trend aligns with the broader push toward energy efficiency and sustainability, and the feedback indicates that the tool’s exergy-based optimization approach is particularly valuable in meeting these future challenges. By facilitating the integration of decentralized heat sources and supporting temperature reduction efforts, the tool can help network operators transition toward more sustainable operations. 4.3.6 Respond from a Questionnaire The questionnaire, which was completed within the HYPERGRYD project, provides an overview of stakeholder feedback on the Exergoeconomic Optimization Tool. The responses reveal key D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 40 preferences regarding how stakeholders would like to use the tool and their thoughts on its effectiveness in optimizing district heating networks. A significant 80% of respondents expressed a preference for using the optimization tool as a service rather than managing it themselves. This suggests that most stakeholders see value in outsourcing the expertise needed for optimization. In terms of proposing changes, opinions were split. While 40% found it sufficient to suggest optimization changes through the HYPERGRYD platform with the addition of personal contact, another 40% preferred that the service provider handle all adjustments directly. This underscores the importance of close collaboration between the tool’s users and service providers during the optimization process. Furthermore, all respondents highlighted the importance of maintaining personal contact with the service provider to discuss and plan optimizations. This reflects a clear need for communication in ensuring the tool’s successful application. Additionally, 80% of participants indicated that flexible tariffs for electricity and heat, such as those based on spot markets, are either currently or potentially relevant for their operations in the future, indicating the growing importance of economic flexibility in energy management. In terms of sector coupling and the integration of decentralized renewable energy sources into DH grids, 60% of stakeholders rated this as "very important," while the remaining 40% deemed it "important." These results point to a strong interest in integrating sustainable energy solutions and leveraging the tool’s capabilities to support such initiatives. With regards to the tool’s optimization approach, 60% of respondents found exergy-based optimization to be highly beneficial in practice. This positive reception highlights the tool’s potential to improve energy efficiency in real-world applications. Lastly, a strong majority of 80% expressed high interest in further learning about the tool once its development is complete, showing enthusiasm for future use and potential collaboration. 4.3.7 Potential and Interest in Further Development Overall, there is significant interest in the tool's further development. Many stakeholders expressed enthusiasm about learning more once the tool is fully developed, particularly in relation to its practical applications. However, it is also clear from the feedback that stakeholders would like to see ongoing engagement with the tool’s developers, including opportunities for direct communication to discuss optimization suggestions. This interest reflects the importance of continued stakeholder engagement and the potential for further customization of the tool to meet diverse operational needs. Regular feedback loops and opportunities for collaboration between developers and users will be crucial in ensuring the tool's success in a wide range of district heating applications. The stakeholder feedback on the Exergoeconomic Optimization Tool underscores its potential as a critical asset for both planning and operational optimization of district heating networks. The D3.2 Description and report of exergoeconomic optimization tool for 4th-5th DHC. Final version 41 feedback highlights the need for flexibility, data integration, and ease of use, particularly in the context of evolving technologies and decentralized heat sources. By incorporating these insights into future development, the tool can serve a wide range of users, from researchers to network operators, while also supporting the broader goals of energy efficiency and sustainability. 5 Conclusions The Exergoeconomic Optimization Tool developed for district heating and cooling systems in the HYPERGRYD project offers significant advantages in optimizing both the design and operation of modern DHC networks, particularly 4th and 5th generation systems. Through the combination of exergy analysis and economic evaluation, the tool addresses critical challenges in energy efficiency, cost management, and renewable energy integration. One of the key strengths of the tool lies in its ability to calculate and optimize based on exergy, a measure of the useful work potential of energy. This allows the system to reduce unnecessary high temperatures, minimizing energy losses and improving overall system efficiency. The tool’s capacity to calculate the exergy efficiency for various temperatures—such as supply and return temperatures on both the primary and secondary sides—provides users with critical insights into where heat losses can be reduced and how temperature levels can be adjusted to maximize efficiency. This approach also supports the effective integration of renewable energy sources, such as heat pumps and solar thermal systems, which often operate at lower temperature levels. The tool further enhances the planning and operational phases of DHC systems through its detailed simulation capabilities. It assists in optimizing pipeline diameters, minimizing pump costs, and reducing heat losses throughout the network. By simulating various scenarios, including the integration of decentralized heat producers like heat pumps or CHP systems, it helps identify the most cost-effective heat generation strategies. This feature is especially useful in modernizing existing networks or planning expansions, as it enables the identification of bottlenecks and optimal locations for booster pumps. Another advantage of the Exergoeconomic Optimization Tool is its consideration of flexible electricity tariffs, such as hourly rates or spot market pricing. This allows operators to optimize the economic operation of heat producers by selecting the most cost-effective energy source based on real-time electricity prices. Additionally, the tool incorporates comprehensive cost calculations, including capital (CAPEX) and operational (OPEX) expenses, fuel prices, and heat losses, providing a detailed economic analysis for each scenario. From a practical perspective, the tool is integrated with GIS data, enabling users to visualize pressure losses, flow rates, and temperature distributions within the network. This visual representation aids in identifying areas for improvement and planning future expansions. The flexibility of the tool’s interface, which is built on the open-source QGIS platform, further enhances its usability, as it can import a wide range of data formats and integrate with external tools.