Techno-Economic Analysis of Operating Temperature Variations in a 4th Generation District Heating Grid—A German Case Study

Abstract

The decarbonization of the heat supply is crucial for the German energy transition. Integrating Power-to-Heat technologies like heat pumps (HPs) into district heating grids (DHGs) can support this process. The efficiency of HPs can be increased through temperature reduction in the DHG, though decentralized reheating may be required to supply sufficient heat for the end consumers. In order to investigate the associated trade-off, this study evaluates the economic, ecological, and technical effects of temperature reduction in DHGs using the software tool nPro. In a three-step process heat demand, the DHG design and operation are modeled. Three operating temperature scenarios are considered: 60 °C, 50 °C, and an ambient dependent flow temperature varying between 40 and 50 °C. As the temperatures decrease, the balance shifts between centrally produced HP heat and decentralized heat from instantaneous electric water heaters (IEWHs). The initial temperature reduction leads to reduced CO₂ emissions, primary energy demand, heat losses, and total annual cost (TAC). However, with a further reduction in the operating temperature, an increase in these parameters occurs. While the necessary cost and primary energy for central components decrease, an increase in the decentralized heat generation is necessary to properly supply the heat demand. This leads to higher TAC and CO₂ emissions overall.

1. Introduction

1.1. Motivation

Ambitious goals have been set by the Climate Change Act of Germany to reduce emissions over all economic sectors, including the building sector. However, recent years have shown that the building sector has not been able to meet the caps set for emissions [1]. In order to achieve the target emission reduction of 68% by 2030 compared to 1990, Germany as a whole and the building sector specifically must find ways to reduce emissions [2]. Many different possible approaches to reduce emissions exist [3]: One approach is the consideration of buildings not on the singular building scale but as part of a larger grouping of buildings to the level of neighborhoods or cities, herein generally considered districts [4]. Another approach is the reduction in final energy consumption, which can be achieved, for example, through an improvement of the heat transport efficiency by application of newer generations of district heating (DH) [5] or through renovation measures to improve the thermal insulation of the building envelopes [6]. Furthermore, increasing the share of renewable energy in the heating sector via the integration of renewable heat sources directly or the coupling with a more decarbonized electricity sector indirectly through Power-to-Heat (P2H) is a possible approach [7,8]. Some of the stated approaches will be elaborated in more detail in the following.

1.2. State of the Art

1.2.1. District

According to [9], the aim of zero energy buildings is the reduction in energy demand through efficient technologies with the simultaneous adoption of local energy inventory (renewable resources, etc.) for its production. However, the concept positive energy district (PED) aims to shift the focus of the buildings sector away from the consideration of single buildings and towards a larger scope of aggregated buildings, be that neighborhoods or cities. Considering buildings of a larger scope enables researchers and practitioners to see the full complexity of a sector [10]. Similarly, ref. [11] states that the aggregation of buildings shows advantages by the share of costs and resources. According to [11], this consideration of the ‘portion of a city’ coined with different terms like district, community, or neighborhood is still a relatively recent field of study. Existing definitions of PEDs and related concepts include the area, inhabitants and number of buildings, while considering clearly set lower and upper bounds [12]. Another important dimension which can be found in many analyzed publications from the scientific world as well as from technical pilot projects is the energy in the district. Herein, the focus lies on different aspects: an energy balance between production and demand is mentioned, typically with an annual time frame and aiming for an overall production surplus (hence the name PED). Further aspects include an integration of renewable energy sources, therewith aiming for climate neutrality, increased efficiency and flexibility (as a service for the superordinate energy grid), and a holistic approach to different forms of energy (consideration of all energetic aspects: lighting, heating, cooling, mobility, household appliances, etc.) [4,13,14,15,16,17]. Yet another relevant aspect is the relation of the utilized energy sources to the district at hand. There might be a direct geographic relation, where source and sink are in the same geographical vicinity. There could be a connection via the energy system, or a mere organizational relation [18].

1.2.2. District Heating Grid

District heating grids (DHGs) occur in many different forms and types and are divided into different categories. The boundaries are sometimes fluid and set differently depending on the definition [19]. There are multiple characteristics clearly defined by [20]: the direction of mass flow can be classified as directional or non-directional; the direction of energy flow can be uni- or bidirectional; and the grid’s topology is classically defined as a tree, ring or mesh structure. In addition to spatial and structural differentiation, DHGs can also be divided into high-temperature grids (operating temperature in the DHG above 70 °C) and low-temperature grids (operating temperature in the DHG below 70 °C) on the basis of their operating temperature [21].

The fourth generation of DHGs (4GDHGs) represents the transition to low-temperature grids and enables the integration of various centralized and decentralized heat sources. The temperature level in 4GDHGs is typically below 70 °C, mostly at 50–60 °C. Return temperatures are at or below 30 °C. In some grids, flow temperatures below 50 °C are also achieved. Due to the low operating temperature, a variety of different renewable heat sources, P2H technologies, combined heat and power (CHP) plants, waste heat and heat storage can be used particularly efficiently. Often, these heat sources are interconnected through sector coupling to ensure better supply security and to counteract fluctuations in the use of renewable energy sources (RESs) [8]. The flow of energy and media is controlled by central and decentralized circulation pumps in the heat network [5]. The cost of building and operating 4GDHG is also significantly lower than in previous generations [22]. By integrating renewable heat sources and using renewable electricity, the heat sector can be decarbonized. In addition, the combination and optimal use of CHP and P2H plants can stabilize the fluctuating power grid and support the security of the supply [23,24].

The fifth generation of DHGs (5GDHGs) share many characteristics with the 4GDHGs, but can be clearly distinguished from them in some aspects. Therefore, 5GDHGs should not be understood as a direct evolution but as a parallel emerging DHG type or a subclass of 4GDHGs [21,25]. Only a 4GDHG is analyzed in this work.

1.2.3. Domestic Hot Water Supply

To ensure domestic hot water (DHW) provision in district heating grids (DHGs), certain conditions must be met. In Germany, tap water must reach at least 45 °C, with some household applications requiring temperatures above 50 °C. Without a circulation system, DHW must be supplied directly at the point of use [8]. To prevent bacterial growth, such as legionella, systems without sufficient circulation require a minimum temperature of 60 °C [22]. Legionella formation at lower temperatures can be mitigated by limiting flow volume, but this approach faces installation challenges and high investment costs [26,27]. This work examines DHW provision in 4GDHG. If DHG operates at 60 °C, decentralized heat exchangers heat the domestic cold water (DCW). For lower operating temperatures, decentralized P2H systems can raise the DHW temperature, though technical conditions, heat generation, and costs vary depending on the system.

Refs. [27,28] analyze various P2H supply solutions. Each consumption point features a decentralized heat transfer station with heat exchangers and heat meters. This setup minimizes heat losses and utilizes the low operating temperatures of the DHG. In these stations, DHW is first heated to the DHG temperature via heat exchangers, then further warmed by decentralized P2H systems and, if needed, stored in a water tank. Heat meters regulate temperature at the transfer station and consumer level, also controlling the P2H systems [27].

1.2.4. Regulatory Framework

This work highlights the decarbonization of the heat supply of a district which is a part of the municipal heat transition. The regulatory framework of this is set out by the Buildings Energy Act (Gebäudeenergiegesetz, GEG) and the Heat Planning Act (WärmePlanungsGesetz, WPG). The GEG came into force in November 2020 and brought together the Energy Savings Act (Energieeinsparungsgesetz, EnEG), the Renewable Energy Heat Act (Erneuerbare-Energien-Wärmegesetz, EEWärmeG) and the Energy Saving Ordinance (Energieeinsparverordnung, EnEV). It was amended as of January 2024. At the same time, the WPG came into power. The two acts are adjusted and referenced to complement each other. While the GEG mostly regulates the heating technology and insulation standards of climatized buildings, the WPG was introduced to govern the heat planning process in Germany on a municipal level.

The WPG obligates the municipalities to perform a municipal heat planning process until mid 2026 for large communities (>100.000 inhabitants) and until mid 2028 for smaller communities (≤100.000 inhabitants). The process of the municipal heat planning consists of the following steps by law: Firstly, a suitability analysis is executed, where areas can be excluded from the duty of a heat planning process and no heat or hydrogen grids are defined. Thereafter, the current state of a municipal area is evaluated with an inventory analysis. Therein, the state of renovation, applied energy carrier and energy demand of all buildings (residential and industrial), as well as heat generation plants, distribution grids for heat, gas, electricity, and wastewater are considered. Based on these data, a potential analysis is performed. Herein, energy saving potential as well as potential sources for heat from renewable energies, unavoidable waste heat and central heat storage are taken into account. A categorization of the planned area into heat supply areas (heating or hydrogen grids) is performed and, lastly, a target scenario is developed and described based on this. Thereby, the main decision criteria are low heat production costs, low realization risks, high degree of supply security and low cumulative greenhouse gas emissions.

The Buildings Energy Act aims for a 65% share of RES for all new heating systems. To properly evaluate the share of RES, the primary energy factors of the energy supply are considered. The decision for a new technology is dependent on the result of the heat planning act in the area the building is located: if a building is located in a community where no heat plan will be elaborated, new heating systems have to meet the 65%-rule starting 2024. In large cities (>100.000 inhabitants) this rule has to be met from mid-2026, and in smaller cities/communities (≤100.000 inhabitants), from mid-2028. If a heating or hydrogen grid is part of the heat plan for the area in question, an energy transfer station or hydrogen heating system could be built. For new buildings in new development areas new heating systems must run on 65% RES starting in 2024. District heating and electricity sourced systems can meet different requirements, as the DH and electricity sectors themselves must meet emission reduction targets in the coming years.

1.3. Research Question and Structure

In the paper at hand, the aforementioned approaches to the decarbonization of the building sector—PED, DHG and P2H—as well as the regulatory framework of DHGs are examined in more detail. For this work, the nexus of the mentioned approaches is considered through its application in a specific use case. Therein, the aim is to answer the following research question: What are the techno-economic effects of the reduction and flexibilization of the DHG operating temperature with the integration of decentralized reheating?

To answer this question a data-based techno-economic model is applied, and the result is evaluated. The data regarding the considered case study and scenarios as well as the applied methodology are presented and explained in detail in Section 2. The modeling results are presented in Section 3. Furthermore, the results are assessed and discussed in Section 4. And lastly, a final conclusion is drawn in Section 5.

2. Materials and Methods

In this work, the heat supply of a district is investigated using a case study. For the techno-economic simulation and analysis, the web tool nPro is used [29]. The assumptions made and the nPro web tool used for the calculation are shown and explained in the following subsections.

2.1. nPro

nPro is a webtool used to plan the energy supply of buildings and districts. It consists of three calculation models, which involve estimating the energy demand of the district, planning and designing the DHG and energy hub, and optimizing their operation. Explanations and validation examples of the calculation models are listed on nPro’s website [30,31]. Compared to the widely used software energyPRO, nPro simplifies the representation of operational behavior by modeling generation units as ideally modulating. Despite this simplification, nPro delivers results that closely align with those produced by energyPRO [32]. This aligns with the findings from [33], who demonstrate that incorporating detailed operational behavior has minimal impact on the design outcomes in mixed-integer optimization models. For this study, the version 2.0 of nPro is used. In addition to the website, refs. [34,35] provide a detailed overview of the functional scope and operation of nPro using case studies. In the following subsections, the three models are explained in detail.

2.1.1. First Model

In the first model, demand profiles are created based on the existing buildings in the district by specifying the energy efficiency of the buildings according to the energy standard for residential buildings of the Credit Institute for Reconstruction (Kreditanstalt für Wiederaufbau, KfW). The key figures and reference values of the KfW efficiency house standards are defined by the GEG and are stored accordingly in nPro [36]. nPro uses a simplified calculation method to create hourly resolved demand profiles based on the degree-day method [37,38]. The demand profile generation has been validated by open-source data from energy monitoring platforms for the city of Aachen [39] and the city of Frankfurt [40]. Furthermore, the heating system of the buildings and the supply and return temperatures for space heating and DHW are defined.

2.1.2. Second Model

The second model is used for the design of the DHG based on the heat consumption of all buildings, i.e., the sum of the heat energy required for the provision of space heating and DHW. The grid length is estimated according to the district buildings under the assumption that the heating grid has a star typology. To determine the nominal diameter, the buildings supplied by the respective route section are selected, and the optimal inner diameter of the pipes is pre-dimensioned [41]. The pipes are designed based on the maximum power required, derived from the hourly resolved building load profiles. Since the load profiles already consider hourly variations and account for the fact that peak loads do not occur simultaneously, no further diversity factor needs to be applied [42]. The maximum volume flow and the maximum mass flow are calculated in a detailed analysis for the pre-dimensioned internal diameters using the pipe roughness, the maximum pressure gradient, the maximum flow velocity, and the specific annual pumping work [41,43].

A central circulation pump is used to compensate for the pressure losses in the DHG. The maximum pump capacity depends on pressure loss along the DHG path, including the supply line, heat transfer station, and return line. Full load hours are calculated by dividing the total heat demand of all buildings by the maximum required output at the energy hub. The annual energy demand for pump work is then determined by multiplying the full load hours by the maximum pump capacity. The pump’s electrical demand is calculated as a relative share of the hourly heat output supplied by the energy hub [44]. The calculation method uses findings from fluid mechanics and has been validated with various hydraulic calculation tools, which can be found on the nPro website and are based on [45,46,47,48,49]. The heat losses of the DHG are calculated for the individual sections and the entire grid based on the grid temperature, grid length, pipe diameter, pipe installation depth, pipe insulation, soil type, soil temperature and thermal conductivity of the soil [50]. The calculation is based on the standard DIN EN 13941 [51] and has been validated with results from [52,53,54,55,56].

The determination of the optimal pipe diameters depends on many techno-economic parameters, such as the pipe and installation costs, the temperature difference between flow and return, the pressure and heat losses, and electricity prices for the pumping work. Therefore, a direct calculation is not possible, and nPro only gives a recommendation for the design of the nominal sizes.

2.1.3. Third Model

In the third model, the operation of the energy hub is simulated. Firstly, technologies for the energy supply of the district are selected, and cost and revenues for the technologies, energy purchase, energy generation, emissions, and energy inputs are defined. Secondly, the selected technologies of the energy hub are pre-dimensioned to continuously ensure the energy supply of the district while minimizing the total annualized cost (TAC), consisting of investment, maintenance, operating and energy costs [57]. Finally, the operation of the energy hub is optimized based on a linear model, widely used in research as described, for example, in [33]. The economic analysis is based on the German VDI guideline 2067 [58]. The net present value method is used to determine the annuities using the interest rate and technical lifespan of the technologies. The assumptions for the technologies are summarized on the nPro website [59] and based on [58,60,61,62,63]. The annuities are summarized along with all annual costs into the TAC. According to the VDI 2067, the residual values at the end of the lifespan are also considered, and if the technical lifespan is shorter then the project horizon, a replacement investment is considered. The value depreciation is carried out linearly over the lifespan [64].

2.2. Case Study

To determine the optimal supply solution for the district, three different scenarios are analyzed. Scenario I has a constant DHG operating temperature of 60 °C to supply the heat demand for space heating and DHW. In scenario II, the temperature is reduced to 50 °C, and in scenario III, the DHG temperature is dependent on the ambient air temperature and slides between 40 °C and 50 °C. To secure the DHW supply in scenarios II and III, decentralized heating with instantaneous electric water heaters (IEWHs) takes place in the buildings. The thermal energy is supplied via a DHG and provided by an energy hub. The electricity for the DHG and the energy hub is provided by local photovoltaic (PV) plants and electricity from the power grid purchased on the spot market, while the district buildings are supplied by the power grid with a fixed price. In the following subsections, further assumptions for the district, heat supply, the DHG, and the energy hub of the three scenarios are listed and explained.

2.2.1. District

The district of the case study consists of 40 newly built single-family residences (SFRs), 4 newly built multi-family residences (MFRs), and an energy hub. The data originate from a cooperation with a municipal utility.

Table 1. Floor area and heat demand of the residential buildings in the district.

Buildings	Floor Area	Number	Total Floor Area	Heat Demand
Unit	m2		m2	MWhth
SFR (KfW 55)	150	40	6000	269
MFR (KfW 55)	1200	4	4800	372

gives an overview of the single and total floor area and heat demand of the buildings. For the residential buildings, the KfW55 standard is assumed. New buildings must meet this standard according to the GEG, which means they have to have 55% of the energy losses of a KfW reference building. The SFRs have an area-specific annual requirement of 41 kWh_th/m² for space heating and the MFRs of 35 kWh_th/m². The annual DHW requirement is identical for both building types and is 21 kWh_th/m².

2.2.2. Space Heating and Domestic Hot Water

The district’s heat supply is provided by a DHG, to which all buildings are connected. The DHG connections are installed together with an energy transfer station in the buildings and designed on the basis of the existing heat demands. The energy transfer station provides the required heat energy at the tapping points, where the heat energy of the DHG is transferred with the help of heat exchangers to the heating system of the buildings [65]. The space heating is supplied via underfloor heating. In scenario III, a linear relationship between the ambient air temperature and the heating demand is assumed, which is modeled in the form of a heating curve in nPro. The operating temperature in the heating system decreases linearly with increasing ambient air temperature [66].

Table 2. Heat demand of the district.

Heat Demand	Unit	Value
Space heating	MWhth	414
DHW	MWhth	227
Total heat demand	MWhth	641

shows the space heating, DHW, and the total heat demand of the district.

In all scenarios, the DHW is provided by increasing the flow temperature of the DCW with the help of heat exchangers in the energy transfer station. The required tap water temperature for DHW is assumed to be 55 °C according to the norm DIN 1988-200 [67]. The temperature of DCW is assumed to be 15 °C before entering the heat exchanger. For the heat transfer in the heat exchanger, a minimum temperature difference of 2 K is assumed. Therefore, the heat transfer gradient is 2 K. For scenarios II and III, the DCW temperature is increased to the required level for the DHW supply by decentralized IEWHs, which are installed in all buildings. Due to direct heating on demand, there are no heat losses, and no additional spatial capacities for DHW storage are required. Therefore, an efficiency of 100% is assumed, and the heat energy provided is identical to the electricity demand of the IEWHs. Figure 1 gives a schematical overview of the DHW supply in scenarios II and III. In scenario I, no extra heating for the DHW is necessary. The DHG and the IEWHs thus take over a different share of the DHW supply. Based on these shares, the electrical power of the decentralized IEWHs is determined, and the plants are sized according to the building specifications. To determine the heating shares, nPro uses a model based on [28]. The calculation method is available on the nPro website and described in Appendix A [68]. The electricity demand of the IEWH is referred to as the operating electricity. Henceforth, the DHG connection with the heat exchanger and the IEWH are summarized as a building energy system (BES).

Table A1. Cost parameters of the technologies adopted from [59,64] and based on [58,60,61,62,63].

Technology	Specific Investment	Fixed Investment	Lifetime	Maintenance
Units	EUR/kWhth, EUR/m3, EUR/kWp	EUR/Building	Years	% of Investment/Year
DHG connection	200	4000	40	0
IEWH	150	1000	25	1
DHG	see Table A2	-	40	1
Air-source HP	500	-	20	2.5
Electric boiler	80	-	20	3
Heat Storage (m3)	500	-	20	1
PV systems (kWp)	900	-	20	1

gives an overview of the defined cost parameters for the BES.

2.2.3. District Heating Grid

The DHG of the case study is designed based on the second step of the nPro model. It is identical for all scenarios and classified as a 4GDHG with a star topology. The DHG has a total length of 2280 m and consists of four different route sections: 1 main pipe (200 m), 4 secondary pipes (280 m each), 40 building connection pipes (BCPs) for the SFRs (20 m each), and 4 BCPs for the MFRs (40 m each). Figure 2 gives a schematical overview of the DHG.

The DHG has a unidirectional mass flow and energy flow and a two-pipe system. The pump work of the centralized circulation pump is assumed to be of 1.5% of the hourly heat output fed into the DHG according to [69]. The pipes are made of steel with a pipe roughness of 0.2 mm, and have a double reinforced isolation and an installation depth of 2 m. The thermal conductivity is assumed to be 1.5 W_th/m*K, a value typically associated with clay or silt soils, according to the German standard VDI 4640 [70]. To calculate the investment and maintenance costs for the DHG, a cost parameter table is used, in which the pipe and trench costs are predefined for certain inner diameters. The pipe costs are calculated twice per meter of grid (flow/return pipe), and the trench costs, only once. For non-given inner diameters, the costs are determined using interpolation. Table A1 shows the lifetime and maintenance cost paramter of the DHG and

Table A2. Cost parameters of the DHG adopted from [59,64] and based on [58,60,61,62,63].

Inner Diameter	Pipe Cost	Trench Cost
Unit	EUR/m	EUR/m
DN 25	260	240
DN 32	270	250
DN 50	290	270
DN 80	350	300

summarizes the cost parameter of pipes and trenches for selected inner diameters.

To analyze the cost benefits of reducing the operating temperature of the DHG, the cost reduction gradient (CRG) by [71] is applied. The CRG indicates how much cost can be saved per delivered MWh and per reduced °C. It is calculated as follows for two scenarios with different operating temperatures:

$${CRG}_{Sc1-Sc2}={\displaystyle \frac{\Delta {LCOH}_{Sc1-Sc2}}{\Delta T_{Sc1-Sc2}}}={\displaystyle \frac{{LCOH}_{Sc1}-{LCOH}_{Sc2}}{T_{Sc1}-T_{Sc2}}}$$

(1)

with the following:

• LCOH_scX is the levelized cost of heating of scenario X [EUR/MWh_th];
• T_scX is the DH temperature of scenario X [°C].

2.2.4. Energy Hub

The energy hub consists of a heat pump (HP), an electric boiler, and a heat storage. To lower the supply cost and increase the share of renewable energy in the heat supply, PV systems are installed in the district. The missing power is provided by the electricity spot market. The PV system feeds electricity that cannot be used into the electricity grid. Figure 3 gives a schematic overview of the energy hub.

To determine the generation profiles of the PV systems, nPro uses a detailed calculation model, described by [72,73]. The radiation on the inclined module surface is calculated from weather profiles with the global horizontal irradiation and the direct normal irradiation, and is based on the calculation method of [74]. The calculation model used in nPro assumes that the direct current of the PV system is converted into an alternating current. The inverter model uses a calculation approach from the well-known and validated calculation tool PVWatts [75]. The results of the PV electricity generation by nPro are validated using the widely used tool PVGIS [76]. The PV systems are located on the roofs of the SFRs, MFRs, and the energy hub: 400 m² in the south direction, 100 m² in the west direction, and 100 m² in the east direction. The modules are installed at an angle of inclination of 30° and have an efficiency of 17%. The total collector area is 600 m² with a total module output of 102 kW_p. The cost parameters of the PV systems are summarized in Table A1.

The HP considered in this case study is an air-source HP. The values of the ambient air temperature are based on datasets from [77]. The restriction is that the HP is only operated at temperatures above −20 °C. To determine the efficiency of the HP, the calculation of the coefficient of performance (COP) is performed in nPro using interpolation. The COPs are defined for the specified temperature levels of the heat source and heat sink, which are based on manufacturer data validated by series of Keymark certification bodies according to the EN 14825 standard [78]. The heat source is the ambient air temperature, and the heat sink is the DHG. In the calculation, there may be deviations between the measured data and calculated COPs due to the interpolation procedure.

Table A3. Calculation table of the COPs.

Temperature of the Heat Source	Temperature of the Heat Sink
	35 °C	45 °C	55 °C
−7 °C	3.13	2.42	1.70
2 °C	4.04	3.33	2.61
7 °C	4.95	4.24	3.52
12 °C	5.86	5.15	4.43

shows the temperature levels of the heat source and heat sink selected for the calculation of the COPs. The cost parameters of the HP are summarized in Table A1.

The electric boiler has a thermal efficiency of 98%. The cost parameters of the electric boiler are summarized in Table A1. The heat losses of the heat storage depend on the ratio of storage surface to storage volume, the temperature of the medium in the storage, the thickness of the thermal insulation, the ambient temperature, and the temperature stratification within the storage [79]. For simplification, nPro calculates the heat losses based on the size of the heat storage, using data from sources [80,81]. In the case study, it is assumed that the temperature difference between a fully charged and a fully discharged storage tank is 20 K, and standby losses of 20% occur every 5 days [79]. The cost parameters of the heat storage systems are summarized in Table A1.

2.3. Further Assumptions

The assumptions for the techno-economic simulation of the district solutions are described below and are summarized in

Table A4. Assumptions for the energy supply of the techno-economic simulation.

Entry	Unit	Value
Time horizon	years	20
Interest rate	%	5
Heat transfer gradient	K	2
Day-Ahead Spotmarket	EUR/MWh	Time series
Operating power costs	EUR/MWh	300
Electricity emission factor	t/MWh	0.387
Electricity primary energy factor		1.5
CO2 price	EUR/t	45
PV feed-in compensation	EUR/MWh	74.3

. The assumptions are made on the basis of the legal framework in force during the preparation of the work and on the basis of representative data. For time-varying assumptions, such as costs, prices, emissions, and primary energy requirements of the electricity mix, average values for the years 2019–2021 are selected in order to exclude the effects and price shocks of the energy crisis caused by the Russian invasion of Ukraine for the simulation. Other flat-rate costs for planning the district solution, delivery, installation, commissioning, measurement, and control technology and other unforeseeable costs are also not considered. The observation horizon is 20 years, and the imputed interest rate is set at 5%. Electricity procurement for the energy hub is mapped using a day-ahead spot market time series from European Energy Exchange AG (EEX) with 8760 hourly values [82]. The hourly values are calculated as an average for the years 2019–2021. In addition to the costs for procurement and sales, grid usage fees, taxes, levies, and charges are incurred for electricity purchased from the day-ahead spot market. The costs of the grid usage fees are regulated by Section 20 Energy Industry Act (Energiewirtschaftsgesetz, EnWG) and Section 3 of Regulation Governing Energy Grid Charges (Stromnetzentgeltverordnung, StromNEV). To simplify matters, the grid usage fees are determined using the average values for the years 2019–2021 for non-households with an annual consumption of 20 MWh to 500 MWh [83]. The electricity tax is determined by Section 3 of the Electricity Duty Act (Stromsteuergesetz, StromStG) and the concession levy by Section 2 of the Concession Levy Ordinance (Konzessionsabgabenverordnung, KAV). The offshore grid levy according to Section 17 f of the EnWG and the CHP levy according to the Combined Heat and Power Act (Kraft-Wärme-Kopplungsgesetz, KWKG) are not applicable for the operation of electric HP due to Section 22 of the Energy Financing Act (Energiefinanzierungsgesetz, EnFG). In this paper, it is assumed that these levies are waived for all P2H plants of the energy hub that are supplied with electricity from the day-ahead spot market, i.e., the electric boiler. Added to this is the VAT of 19%, which is due for all listed cost items. The operating electricity demand is covered by the electricity grid. A fixed price is assumed for this, which is an average value for the years 2019–2021 for households with a consumption of 2500–5000 kWh per year [84]. Procurement and distribution costs, grid usage costs and taxes, levies and charges are taken into account for electricity. Fixed feed-in tariffs are assumed, which are determined in accordance with the Renewable Energy Sources Act (Erneuerbare-Energien-Gesetz, EEG) and the KWKG. For partial feed-in PV systems that are installed exclusively on buildings and have an installed capacity of up to and including 1 MW_p, the fixed feed-in tariff applies in accordance with Section 48 of the EEG. The primary energy factor is defined for the energy procurement types by the GEG and includes, in addition to the actual energy demand of a system, the amount of energy required by upstream process chains outside the system boundary for the extraction, conversion, and distribution of the energy source. However, the primary energy factor for electricity procurement has already been below the level specified by the GEG for several years and is therefore determined from the non-renewable cumulative energy consumption for the years 2019–2021 in the study by [85]. The CO₂ emissions from electricity procurement are also derived from the results of the study and are assumed to be identical for the electricity grid and the day-ahead spot market. The price for the resulting CO₂ emissions is based on Section 10 of the Fuel Emissions Trading Act (Brennstoffemissionshandelsgesetz, BEHG) in the year 2024 set at 45 EUR/t.

3. Results

3.1. Techno-Economic Optimization

In the following subsections, the design and simulation of the heat demand (see Section 3.1.1), DHG (see Section 3.1.2), and energy hub (see Section 3.1.3) of the district are presented and analyzed based on the assumptions made in Section 2. The three-step approach of nPro on which these results are based is explained in Section 2.1 in detail. Concretely, an economic dispatch of the energy systems and energy hub is computed, minimizing the TAC.

3.1.1. Heat Demand of the District

The total floor area of the buildings is taken into account to create the load profiles and calculate the heat demand of the district. The demand for space heating and DHW and the resulting total heat demand of the district are the same for all three scenarios. However, due to the difference in the temperature of the DHG, the centralized and decentralized heat provision is distributed differently between the scenarios. Figure 4 shows the annual heat supply from the DHG and the decentralized IEWH. Further, it shows the annual demand for space heating, DHW, and total heat. In scenario I, the DHG supplies the total heat demand, while in scenarios II and III, part of the heat demand is supplied by decentralized IEWHs. In scenario II, 94% of the heat provision comes from the DHG, while the DHG in scenario III only supplies 70% due to the sliding and averagely lower DHG temperature. In the following, the central heat supply via the DHG is taken into account.

According to the power values derived from nPro for central and decentralized heating, grid connections and IEWH are designed and installed in the district. The electricity to operate the decentralized IEWH is supplied by the power grid at a fixed price of 0.30 EUR/kWh.

Table A5. Design and costs of the BESs for scenario I.

Scenario I	Unit	DHG Connection	IEWH
Number		44	-
Capacity	kWth	435	-
Useful energy	MWhth	641	-
Investment	EUR	262,932	-
Annuity	EUR/a	17,122	-

,

Table A6. Design and costs of the BESs for scenario II.

Scenario I	Unit	DHG Connection	IEWH
Number		44	44
Capacity	kWth	357	11
Useful energy	MWhth	601	40
Electricity demand	MWhel	-	40
Investment	EUR	247,352	45,662
Annuity	EUR/a	16,108	3388
Maintenance	EUR/a	-	457

and

Table A7. Design and costs of the BESs for scenario III.

Scenario I	Unit	DHG Connection	IEWH
Number		44	44
Capacity	kWth	357	61
Useful energy	MWhth	451	190
Electricity demand	MWhel	-	190
Investment	EUR	247,307	53,083
Annuity	EUR/a	16,105	3938
Maintenance	EUR/a	-	531

give an overview of the installed BESs and the costs for the three scenarios. The TAC, including the annual investment and maintenance costs, the operating power supply costs, and the emission costs are shown in Figure 5. The annual investment costs are slightly lower for scenario I in comparison to the other two scenarios (17,000 EUR/a vs. approx. 20,000 EUR/a). This is due to the IEWHs that are installed only in the latter scenarios and not in the first one. The electricity procurement costs for the operation of the decentralized heating make up a very large portion in the BES annual cost of scenario III, being more than five times as much as for scenario II.

3.1.2. Dimensioning and Operation of the District Heating Grid

Based on the assumptions made of the district, the length, diameter and heat losses of the pipe sections are estimated.

Table A8. Detailed pipe dimensioning analysis for the diameters of scenario I.

Parameter	Unit	DN 25	DN 32	DN 40	DN 80
Max. pressure gradient	Pa/m	80,088	21,599	6633	175
Max. flow velocity	m/s	10.7	6.5	4.2	1
Specific annual pump work	kWh/m/a	92	24.8	7.7	0.21
Max. volume flow rate	m3/h (L/s)	18.9 (5.3)
Max. mass flow rate	kg/s (t/h)	5.2 (18.7)

,

Table A9. Detailed pipe dimensioning analysis for the diameters of scenario II.

Parameter	Unit	DN 25	DN 32	DN 40	DN 80
Max. pressure gradient	Pa/m	53,811	14,529	4469	119
Max. flow velocity	m/s	8.8	5.3	3.4	0.86
Specific annual pump work	kWh/m/a	63	17.2	5.3	0.15
Max. volume flow rate	m3/h (L/s)	15.5 (4.3)
Max. mass flow rate	kg/s (t/h)	4.3 (15.4)

and

Table A10. Detailed pipe dimensioning analysis for the diameters of scenario III.

Parameter	Unit	DN 25	DN 32	DN 40	DN 80
Max. pressure gradient	Pa/m	53,610	14,484	4459	120
Max. flow velocity	m/s	8.7	5.3	3.4	0.85
Specific annual pump work	kWh/m/a	40	10.9	3.4	0.09
Max. volume flow rate	m3/h (L/s)	15.4 (4.3)
Max. mass flow rate	kg/s (t/h)	4.3 (15.3)

show the results of the detailed pipe dimensioning analysis, which are used to calculate the required pump work of the pipe sections.

Table 3. Pipe section and heat losses per pipe section length of the DHG.

Pipe Section	Length	Diameter	Heat Losses of Scenario
			I	II	III
Unit	m		kWhth/m
Main pipe	200	DN 80	125	92	67
Secondary pipes	1120	DN 40	97	71	52
BCPs SFRs	800	DN 25	80	59	43
BCPs MFRs	160	DN 32	87	64	47

summarizes the characteristics of the pipe sections, and Figure 6 depicts the electricity demand of the pump work for the three scenarios. The length and diameter are identical for all scenarios, but the heat losses differ due to the different operation, namely the different DHG flow and return temperatures (scenario I: 60 °C/40 °C, scenario II: 50 °C/30 °C, scenario III: 50–40 °C/30–20 °C), and therefore varying heat transmission across the scenarios.

The total heat losses for the three scenarios are calculated based on the heat losses of each pipe sections multiplied with the length of the pipe section. In Figure 4, the centralized (energy hub) and decentralized (IEWH) heat generation, heat losses, and demands are displayed for all three scenarios. The heat feed-in and heat losses are the highest in scenario I due to the higher DHG temperature and the higher amount of heat transmission (no decentralized heating). With a temperature reduction in scenarios II and III, the central supply from the DHG decreases. Simultaneously, the decentralized DHW supply increases to meet the DHW temperature of 55 °C. Therefore, the pumping work and heat losses decrease as well. The electricity consumption of the central energy hub, the DHG pumps, and the decentralized IEWH is displayed in Figure 6. From this, we can derive the total electricity consumption of the three scenarios and how it is distributed. From the first to the third scenarios, a shift of the electricity supply from the central towards decentralized heat generation plants can be observed. Additionally, the pumping work is reduced, as the utilization of the DHG decreases in favor of the decentralized IEWH.

Since the structure of the DHG is equal for all three scenarios, the costs are also the same.

Table A11. Cost for the DHG; separated into the different pipe sections and the Building Connection Points (BCPs) for SFRs and MFRs.

DHG Section	Investment	Annuity	Maintenance
Unit	EUR	EUR/a	EUR/a
Main pipe	200,000	13,024	2000
Secondary pipe	914,667	59,564	9147
BCPs SFRs	608,000	39,594	6080
BCPs MFRs	126,400	8231	1264
Sum	1,849,067	120,414	18,491

summarizes the investment, annuity, and maintenance costs for the different DHG sections.

3.1.3. Design of the Energy Hub

The systems of the energy hub are designed on the basis of the previously simulated energy demands, the assumed economic parameters, and operating restrictions.

Table A12. Design and costs of the air-source heat pump.

Air-Source HP	Unit	Scenario I	Scenario II	Scenario III
Nominal heating power	kWth	380	324	260
Generated heat	MWhth	788	751	551
Eletricity demand	MWhel	303	212	143
Full load hours	h/year	3190	2617	2201
COP		2.60	3.54	3.85
Investment	EUR	202,000	162,000	130,000
Annuity	EUR/a	16,209	12,999	10,432
Maintenance	EUR/a	5050	4050	3250

,

Table A13. Design and costs of the electric boiler.

Electric Boiler	Unit	Scenario I	Scenario II	Scenario III
Nominal heating power	kWth	365	346	365
Generated heat	MWhth	73	14	18
Eletricity demand	MWhel	74	14	19
Full load hours	h/year	199	40	115
Investment	EUR	29,840	27,680	29,200
Annuity	EUR/a	2394	2221	2343
Maintenance	EUR/a	895	830	876

,

Table A14. Design and costs of the photovoltaics system.

Photovoltaics	Unit	Scenario I	Scenario II	Scenario III
Installed capacity	kWp	126
Generated electricity	MWhel	116
Full load hours	h/Jahr	922
Investment	EUR	113,400
Annuity	EUR/a	9100
Maintenance	EUREUR/a	1134

and

Table A15. Design and costs of the heat storage.

Heat Storage	Unit	Scenario I	Scenario II	Scenario III
Storage capacity	kWhth	1274	1088	755
Storage volumen	m3	54.8	46.9	32.5
Full charging cycles		281	321	347
Investment	EUR	31,069	23,426	16,256
Annuity	EUR/a	2493	1880	1304
Maintenance	EUR/a	311	234	163

in the Appendix B show the exact dimensioning and costs of the HP, electric boiler, and heat storage for the three scenarios. Figure 7 summarizes the TAC of the energy hub for the three scenarios, including the different cost types and revenue. The exact numbers are itemized in

Table A16. Energy balances, emissions and total annual costs of the energy hub.

Energy Hub	Unit	Scenario I	Scenario II	Scenario III
Electricity procurement day-ahead spot market	MWhel	309	171	120
Heat generation	MWhth	852	757	564
Electricity generation	MWhel	116	116	116
Electricity feed-in	MWhel	35	50	66
CO2 Emissions	t/a	120	102	120
Investment Annuities	EUR/a	30,196	26,200	23,179
Maintenance expenditures	EUR/a	7390	6248	5423
Electricity procurement costs	EUR/a	53,624	29,661	20,847
Emission costs	EUR/a	5385	3671	5409
Revenues	EUR/a	−2615	−3700	−4620
TAC	EUR/a	93,980	62,080	50,238

in the Appendix B.

The P2H system in scenario I has the highest nominal heating power, full load hours and electricity demand since it generates the most heat. The storage in scenario I is also the largest, while in scenario III, it has more charging cycles due to the varying DHG operating temperature over the year. An identically sized PV systems is installed in all three scenarios. Therefore, the electricity production is the same; however, due to the different electricity demands, the electricity feed-in differs over the three scenarios. The energy hub in scenario I has substantially higher heat production cost. However, the total heat production cost for scenarios II and III consist of the heat production cost of the IEWH and the energy hub. Therefore, the total heat production costs, electricity demand, and CO₂ emissions of scenarios II and III have to be considered separately by accounting for import from the operating power grid.

The energy hub-related TAC and its components for all scenarios can be found in Figure 7. While the investment annuity, maintenance and emission cost are slightly decreasing from scenario I to III, a steep drop can be witnessed for the electricity procurement cost. The electricity sales, on the other hand, are increasing from scenario I to III. In the context of the (central) energy hub, it can be explained with the shift from the central to decentralized generation of heat. While in scenario I all heat is generated in the energy hub, for the other two scenarios, a portion of the heat is generated decentrally by the IEWHs. Therefore, there is an increase in decentralized cost components of the system, while we see a decrease in the central cost components when moving from scenario I to III.

3.2. Summary of the Energy System Sections

This section summarizes the perspectives of the three previous sections to provide an overview and a holistic comparison of the economic and technical parameters over the three scenarios. Figure 8 summarizes the main results of the techno-economic optimization. Scenario II has the lowest primary energy demand and emissions, TAC, and levelized cost of heating (LCOH). Therefore, scenario II represents the best supply solution for this specific case study.

3.2.1. Primary Energy Demand and Energy Flows

Figure 9 shows the energy flows from its generation by PV plants or procurement from the spot market over the P2H generation plants to the final energy demand in the building. This graphic representation clarifies the shift from the centralized heat supply (scenario I) to a more decentralized supply (scenarios II and III): the amount of electricity procured for and self-consumed by the energy hub decreases, as well as the thermal energy from the energy hub via the DHG to the consumer, while the energy related to the decentralized IEWH units increases. The seasonal performance factor (annual COP) of the energy hub’s heat pump increases, as decentralized heating units are utilized in time steps where the central heating unit would perform suboptimally. At the same time, the losses in the DHG decrease with a smaller amount of transported thermal energy.

The total amount of imported electricity over the district borders is 309, 211, and 310 MWh_el for scenarios I, II, and III, respectively. Considering the assumed primary energy factor of 1.5 (see Table A4), we see primary energy demands of 464, 316 and 465 MWh, respectively. As a result, the primary energy demand of scenario II is the lowest over all scenarios.

3.2.2. Emissions

All emissions in the considered scenarios stem from electricity imported from the overlaying grid, either to the central energy hub or the decentralized IEWHs. The assumed emission factor connected to the imported electricity is 0.387 t/MWh as can be seen in Table A4. As the imported energy of scenario II is the lowest, the resulting emission of scenario II is the lowest of all scenarios.

3.2.3. Costs

Figure 10 shows the TAC of the three scenarios divided by different categories: in Figure 10a, the division into system sections (energy hub, DHG, heat demand) from the sections before is continued. The cost for the DHG over all scenarios is identical. However, while the cost for the central energy hub is decreasing somewhat linearly from scenario I to III, the cost for the decentralized BESs is increasing in an exponential manner. This is based on the fact that the energy provision is shifted from predominantly centralized to decentralized generation. Figure 10b, on the other hand, provides a cost division by different cost types like investment annuity, maintenance, electricity procurement, etc. From this, a deeper insight into the cost distribution over scenarios and cost types is possible. Most cost types decline from scenario I to III. The increase in electricity procurement costs, on the other hand, can be explained by the much larger fraction of energy that is used for decentralized heating. The decentralized heating is less efficient than central heating due to the very efficient central heat pump and rather small DHG losses. Additionally, the decentrally procured electricity has to be paid by a higher fixed consumer price in contrast to the, on average, lower spot market price paid at the energy hub. Similarly, the slight emission cost increase is due to a less efficient decentralized energy generation and therewith higher primary energy demand and emission (see previous Section 3.2.2).

From scenario I to scenario III, the cost increase for the BES overshoots the smaller cost decrease for the energy hub leading to scenario III, being the least economically viable. The next most cost-effective scenario is scenario I, and the most cost-effective is scenario II. Here, the balance of centralized and decentralized energy generation leads to the overall lowest costs. However, the costs are distributed differently for the three scenarios: The DHG operator carries the cost for the energy hub and the DHG, while the end consumers have to carry the installation and operation cost for the BES. The LCOH are calculated by dividing the TAC by the total heating demand of 641 MWh_th/a (see Figure 4).

Additionally, the CRG that is achieved through temperature reduction can be calculated based on the approach shown in Formula (1). For the initial temperature reduction from scenario I to scenario II, the CRG can be calculated to

$${CRG}_{ScI-ScII}={\displaystyle \frac{390\mathrm{EUR}/\mathrm{MWh}-363\mathrm{EUR}/\mathrm{MWh}}{60°\mathrm{C}-50°\mathrm{C}}}={\displaystyle \frac{27\mathrm{EUR}/\mathrm{MWh}}{10°\mathrm{C}}}=2.7{\displaystyle \frac{\mathrm{EUR}/\mathrm{MWh}}{°\mathrm{C}}}$$

(2)

while the CRG for the reduction from scenario II to scenario III can be calculated to

$${CRG}_{ScII-ScIII}={\displaystyle \frac{363\mathrm{EUR}/\mathrm{MWh}-416\mathrm{EUR}/\mathrm{MWh}}{50°\mathrm{C}-43.5°\mathrm{C}}}={\displaystyle \frac{-53\mathrm{EUR}/\mathrm{MWh}}{6.5°\mathrm{C}}}=-8.15{\displaystyle \frac{\mathrm{EUR}/\mathrm{MWh}}{°\mathrm{C}}}$$

(3)

Therewith, the initial temperature reduction leads to a CRG of 2.7 EUR/MWh °C and the further reduction and flexibilization (the average value was assumed for the calculation), leads to a CRG of −8.15 EUR/MWh °C, meaning a cost increase.

3.3. Positive Energy District

In the following, it is evaluated whether the district is a PED under the different conditions of scenarios I–III. As described in Section 1.2.1, the most prominent feature of PEDs or similar concepts is the annual positive energy import–export balance. The energy balance over one year is presented graphically in Figure 11. For the considered use case, electricity is the only relevant form of energy being exchanged over the boundaries of the district. An observation of the juxtaposition of the annual electricity consumption and production of the use case at hand already clarifies the impossibility of it qualifying as a PED: while the annual total electricity produced by the PV system of the district is 116 MWh_el for all scenarios, the electricity consumption and conversion ranges from 277 to 390 MWh_el, overshooting the production by a factor of 2.4 to 3.4. In scenario I, the smallest amount of electricity is fed back into the superordinate grid, while the electricity import is rather high. Resultingly, the total electricity balance of scenario I demonstrates the lowest value over all scenarios. Scenario II imports the smallest amount of electricity, as less energy is needed for the central generation than scenario I, and less energy is needed for decentralized generation than scenario III.

The scenarios can be further differentiated according to their self-consumption, indicating the amount of the produced electricity that is consumed in the district. In Figure 11a, it can be observed that of the 116 MWh_el PV-production, 81 MWh_el are self-consumed and 35 MWh_el are fed back into the grid for scenario I. For the other scenarios, this balance shifts to less self-consumption and more grid feed-in. As the decentralized heat plants (IEWH) cannot be used to consume electricity produced by the PV plant (it uses the operating power directly from the grid), the self-consumption declines with more decentralized technology. This is visible in scenarios II and III.

4. Discussion

In the upcoming section, the results are discussed in more detail. For the discussion, aspects of this study in regard to the operating temperature, electrification, cost distribution, communal heat planning and positive energy districts are considered and put into context.

4.1. Operating Temperature

The operating temperature of the majority of Germany’s stock of existing DHGs is currently at a level of above 90 °C, and therewith most of it can still be considered 3GDHG [86,87]. However, the integration of RES becomes much easier as the DHG operating temperature is reduced [88]. In [71], exemplary calculations for various different RES (solar thermal, geothermal, and heat pump) and other heat sources can be found. For the DHG at hand, the inspection of three different operating temperatures with resulting dimensions of the components yields different techno-economic results. Figure 8 gives a valuable overview of the three scenarios which are defined by a gradual reduction in temperature. The initial temperature reduction from scenario I to II leads to decreased CO₂ emissions, primary energy demand, and LCOH. However, toward a further reduced and flexible temperature in scenario III, we see an increase in emissions, primary energy demand, and cost. In Section 3.2.3, the CRGs for the use case at hand are presented. They are 2.7 EUR/MWh/°C (for scenario I to scenario II) and −8.15 EUR/MWh/°C (for scenario II to scenario III) and reflect the initial cost decrease and subsequent increase with the respective temperature reductions. Refs. [71,88] investigate the parameter CRG as well. While [88] disintegrates CRGs into different technologies based on the reviewed literature, ref. [71] focuses on the possible cost savings of individual technologies based on thermodynamic modeling, e.g., for a HP, the modeled CRG is at 0.6 EUR/MWh/°C, for a DHG, at 0.07 EUR/MWh/°C, and for a buffer heat storage, at 0.05 EUR/MWh/°C.

The difference of operating with a flexible instead of a fixed temperature in scenario III is considered in the following: In an additional simulation dubbed scenario III.2, the existing use case is simulated with a fixed operating temperature of 43.5 °C. This is the average operating temperature of scenario III. The techno-economic results of this use case only differ from scenario III marginally; see Figure 12. However, the TAC of scenario III.2 exceeds those of scenario III by a margin. The most conspicuous difference is that the scenario III.2 utilizes a larger portion of electricity and therewith financial resources for the decentralized energy supply (BESs) than for the central energy supply (energy hub). This is based on the fact that with an ambient dependent flexible temperature, the centralized heat supply is increased during heating periods so that the need for less efficient decentralized re-heating grows smaller.

Another result of the temperature reduction is a decrease in heat losses in the DHG through a reduced temperature gradient from inside the DHG piping to the surrounding environment. This is indicated in Figure 9 with the values for heat losses and heat generated in the energy hub. For the scenarios, the relative heat losses are 24.77%, 20.61%, and 20.04% in order of the descending operating temperature. The relative heat losses are comparable to existing DHGs with similar operating temperatures [89]. The absolute heat losses amount to 211 MWh, 156 MWh, and 113 MWh, respectively. Next to grid topography, geometry, and insulation, which are identical for all considered scenarios, ref. [90] identifies the operating temperature and the heat density as influential factors for the relative heat losses. With a decrease in temperature, a decentralization of heat generation takes place, leading to a decreased heat density in the DHG, which in turn influences the relative heat losses. In the above mentioned scenario III.2, the relative heat losses are 22.34% and therewith higher than the relative heat losses for scenario II with a higher operating temperature. Apart from the reduced heat density in the DHG, this may be due to the fact that the piping, which was optimized in the second step of the nPro model and is equal for all scenarios, fits best for scenario II. The pipe diameter and other parameters are discrete values that cannot be optimized continuously for a use case.

Generally, the type, structure, and heat sources of a DHG depend on many different factors and must be evaluated and selected based on the individual case. The dimensioned 4GDHG represents an economical and efficient supply solution for the case study under the given conditions and assumptions. However, as costs rise with the temperature reduction from scenario II to scenario III, the transformation into a 5GDHG could be considered. In 5GDHG, even lower grid temperatures can be achieved by using a decentralized supply approach. Ref. [91] shows that although heat losses are minimized and the efficiency of the supply solution is increased when supplying heat with 5GDHG, higher investment, operating, and energy costs may emerge compared to 4GDHG. If the district also requires cooling in addition to heating, 5GDHG shows advantages over 4GDHG [21].

4.2. Electrification

Beyond the previously discussed advantages, the reduction in DH operating temperatures simplifies the integration of heat sources that are based on electricity. In the considered use case, as well as in other studies, an increase in the COP of the air/water HP can be achieved by decreasing the DHG temperature [19]. HPs which utilize ground, water, or waste heat as a source, and therewith rely on relatively constant temperatures throughout the year, can also profit from a decreased operating temperature. These HPs achieve higher COPs in seasons with low ambient air temperatures and a high heat demand throughout the year compared to air/water HPs. This further minimizes the electricity requirements and costs incurred for the heat supply and increases the efficiency of the supply solution [19]. For this case study, the effect of the temperature reduction on the efficiency of the heat pump can be investigated concretely. In Figure 9, the produced heating energy by the energy hub based on an electric input is shown. The respective annual COPs of the energy hub for the scenarios are displayed in parentheses. The values are 228%, 338%, and 352% in order of descending operating temperature. The energy hub consists of a HP, an electric heater, and a heat storage. The COP deviation over the scenarios is explained, on the one hand, by the different COPs of the HP due to differing operating temperatures and, on the other hand, by different heat generation shares of the electric heater with lower efficiency.

The use of P2H systems in the heat supply results in a higher load on the electricity grid. To counteract this load and decarbonize electricity generation, the expansion of electricity grids and RES is essential at a systemic level. The use of fluctuating RES leads to surpluses and shortfalls in the electricity supply. In addition to the expansion of electricity grids and storage systems, the flexible use of P2H systems in the districts heating supply offers another way of counteracting the fluctuation of RES [92]. Ref. [93] shows that P2H systems can also be used at the regional and local levels to regulate the electricity grids. The marketing and utilization of P2H systems on the balancing energy market is therefore not only suitable for large-scale district heating supply, as described by [94,95] but can also be implemented economically on a small-scale district level [93]. Furthermore, it is possible to use the day-ahead spot market to supply the P2H plants. When spot market prices are low, the electricity is used by the P2H plants to supply heat directly to the district or store it temporarily in a heat storage. The thermal inertia of the DHG can also be used to provide a certain flexibility to an increasingly volatile electricity market. As shown in this use case, an economic optimization of the system operation with flexible prices is possible.

The electricity demand of the P2H systems can be covered by RESs installed in the district. This minimizes electricity procurement costs and emissions or eliminates them in the case of a completely self-sufficient supply. In the case study, PV systems on the roofs of the energy hub, SFRs and MFRs cover part of the electricity demand. In order to increase the capacity of the PV systems and cover a higher proportion of the electricity demand self-sufficiently, it is possible to use additional open spaces for PV and integrate other RESs or even an electricity storage. If all RESs were connected to the district via its own local power grid, also known as a microgrid, not only the power requirements of the energy hub but also the consumer’s power requirements could be covered [96]. As no microgrid is installed in the use case, the installed RES only feeds into the energy hub; the electricity demand of the end consumers (e.g., decentralized heating units) is only satisfied by the superposed grid. In cases of an existing microgrid and the operation of RES and P2H systems in combination, smart metering is necessary. This means an intelligent control and data collection of the systems and the electricity and heating grid is installed with the aim to optimize operation and make the district solution as efficient as possible [97].

4.3. Cost Distribution and LCOH

In scenarios II and III, DHW is proportionally supplied by both the DHG and the IEWH due to the lower operating temperature. While reducing the temperature improves the HP efficiency and lowers operational costs for the DHG operator, it increases household electricity costs drawn from the grid, imposing greater financial burdens on end consumers. The distribution of costs varies across the scenarios. The DHG operator bears expenses related to the energy hub and grid infrastructure, while households are responsible for installation and operational costs of their building systems. In scenario I, households contribute only 7% of the total costs, compared to 14% in scenario II and 30% in scenario III. The economic preferences for DHG operators and end consumers depend significantly on the tariff structure and the party responsible for energy system installation, which may shift to the DHG operator in some cases. Thus, while a basic annual cost analysis provides a useful overview, it may not fully capture the nuanced economic dynamics of these scenarios.

In this study, the LCOH for scenarios I to III was calculated to 390 EUR/MWh_th, 363 EUR/MWh_th, and 416 EUR/MWh_th, respectively. These values can be compared to other studies with similar goals. Some studies that considered decarbonization ambitions and DHGs were investigated: A German case study calculates scenarios with LCOH between 140 and 165 EUR/MWh_th, for one particular scenario largely supplied by HPs. The authors critically comment on high levies and fees for electricity powered HPs, impeding their economic feasibility [98]. Another German case study finds LCOH in a range of 20 to 85 EUR/MWh_th, the former being biomass or electricity-based scenarios and the last one being the final DH customer price of Munich at the time [99]. A Finnish case study calculates LCOH between 32 and 55 EUR/MWh_th for the integration of solar heat and thermal energy storages into DH systems, while a Swiss case study finds LCOH between 195 and 325 EUR/MWh_th for biomass, geothermal, and electricity-powered scenarios [100,101]. A Danish study comparing ultra-low and low temperature DHGs sourced from heat pumps or excess heat finds a wide variety of LCOH between slightly below 50 and above 200 EUR/MWh_th [89]. Depending on the DH system conditions (existing, newly built, and different heat densities) and the investigated scenarios (different energy sources and national contexts), a wide range of LCOH can be found in these publications. The LCOH considered in this study is higher than the investigated studies. This can have many reasons, the most prominent being higher energy procurement prices, the greenfield approach taken for this case study, and the relatively small district that was considered.

4.4. Communal Heat Planning

In Section 1.2.4, the federal regulatory framework for heat planning is presented. The district’s building structure is described in more detail in Section 2.2.1. The examined district is a new development area and consists only of new buildings. According to the recent regulatory framework, it therefore has to meet higher standards than new or existing buildings in an existing area with respect to energy efficiency (GEG) and heating systems (WPG). According to the GEG, new buildings must meet the KfW55 standard, which means 55% of the energy losses of a KfW reference building. The obligation of the building owner with respect to the heating system is dependent on the heat plan that has been carried out for the respective area in which the building in question is located. A district heating grid is planned together with the new development area in this use case, and the supply option is therefore already set a priori. The building owner is therefore not responsible for sypplying a heating option with at least 65% renewable energy share. Instead, the operator of the DHG, in this case the local utility, is responsible for the decarbonization of the energy supply by lowering the primary energy factor. The DHG in this study uses a heat pump with electricity from PV-plants and the superposed grid. An electrically operated heat pump as well as a direct electric heater (like the IEWH) are two of several solutions that meet the requirement of the 65% rule by default. Furthermore, the primary energy factor of the supplied electricity has to be reduced in the coming years as stipulated by the climate change act.

Furthermore, the considered DHG could be integrated into the existing DHG of the city surrounding the new development area. Through a larger amount of total customers, a more leveled demand overall, meaning fewer relative peaks over the year, might be achieved. Additionally, the flexibility potential through the coupling of electricity and heating sector (see Section 4.2) grows as the heating demand becomes larger. However, hydraulic problems of integration may arise, as different operating temperatures of 3GDHG and 4GDHG could lead to complications in the coupling of the grids. An exemplary case of this can be found in Vienna, where the DHG consists of a primary and secondary grid [102]. In this case, the secondary grid is only used for distribution and not generation. Ref. [103] suggests two variants for an integration of low temperature DHG into the overlaying grid. However, the low-temperature DHG also only functions as a distribution grid in this case; no heat generation and transport to the overlaying grid from the subsystem is intended.

Many urban areas in Germany do not yet have a DHG. However, some German communities might plan to implement DHGs in light of the recent adjustments of the regulatory framework (see Section 1.2.4). These communities have to designate heat supply areas, possibly district heating areas. The new development areas have the chance to be planned as DHGs of a newer generation with initially lower operating temperatures than the German average. In contrast, many municipalities operating DHG with a high temperature (3GDHG) tend to struggle to decrease the operating temperature, as their equipment (substations, pipe insulation, etc.) is optimized for much higher temperatures. In some cases, a replacement of the old infrastructure may be necessary. Ref. [88] states that 75% heat loss reduction is possible when designing a DHG for 4GDHG in comparison to a conventional design. When considering an existing grid with temperature reduction, the heat loss reduction in the DHG is only at 35%.

4.5. Positive Energy District

Some prerequisites that a PED has to meet according to a wide variety of sources are explained in Section 1.2.1. One of the most basic KPIs in this context is the annual energy balance of a PED, implying an energy surplus when positive, and an energy shortfall when negative. In Section 3.3, this balance is calculated for all considered scenarios. None of them meet the PED condition of a positive energy balance. This is graphically displayed in Figure 11.

To actually meet the conditions of a PED, the possibility to expand RE generation for this district exists in the future. More fundamentally, the question of the boundaries of a PED can be raised anew at this point. As [18] suggest, the boundaries of a PED do not necessarily have to be purely based on geography. Alternatively, a ‘virtual’ PED can be defined, linking a PED by the connection of its energy system instead of physical proximity. Given this broader definition, the DHG operator could transform the district at hand to a PED by connecting another renewable energy source—e.g., a geo- or solarthermal plant, even at a distance—to the district. There is no direct financial incentive defined by a political framework to actually meet the mentioned conditions beyond the technical and financial advantages that result from a positive energy balance. However, some organizations support the implementation of PEDs, like the ‘Implementation Working Group on positive energy districts and neighbourhoods for sustainable urban development’ and ‘JPI Urban Europe’ [16].

4.6. Limitations and Further Research

The results are based on calculations with the software tool nPro and were performed using a case study. Simplifying assumptions were made, and heuristic calculation methods and interpolation were used to obtain the results. For the district, only the heat supply, the electricity demand of the DHG pumps, and the operating electricity demand were considered in more detail. The weather data, energy procurement costs, emissions and primary energy requirements of the electricity mix were based on average, historical values, and time series.

The assumed regulatory framework conditions are partly based on planned changes in the German energy law framework. However, they are highly dependent on political decisions and the national context. Therefore, an accurate prediction for the use, integration and costs of P2H systems in districts depends on many factors and is characterized by numerous unpredictabilities, which can only be partially taken into account by the simulation and analysis. The transfer to other national contexts requires an adjustment of the assumed regulatory framework. Additionally, the work at hand is a case study: Specific assumptions were made in regards to building and district size and configuration as well as the design of the DHG-like operating temperature, heat source, etc. Due to the specific nature of this study, the possibility to make generally valid statements is impeded.

In addition to demand simulation, measured consumption data or real standard load profiles can also be used to determine energy requirements. Detailed calculations are necessary for more precise dimensioning of the DHG and determination of the DHG costs. The approach chosen in this work therefore only serves to pre-dimension the DHG and approximate the costs. This means that in reality, there may be deviations in the heat and pressure losses. For the system operation of the energy hub, maintenance work or technical defects were neglected, leading to the system’s temporary inoperability, which can lead to delayed start-up times. Fixed efficiencies were defined for the systems, and a table with COPs was used to determine the operation of the heat pumps. Therefore, there may be deviations in the operation and efficiency of the systems. In order to validate the knowledge gained in this work, a comparison with practical examples is recommended, and real district supply solutions should be analyzed to compare the results of the simulation.

For further considerations, it could be investigated how P2H systems can be more easily integrated into districts and how DHG need to be dimensioned in order to make the supply solution more efficient and reduce costs. The utilization of different heat sources and HP types, the combination of CHP and P2H systems, and the implementation of RES should be an integral part of the research. In addition to the heat supply, the power supply of districts should also be considered in order to show what role sector coupling can play in the district. The digitization of supply, smart metering, and the use of microgrids and intelligent DHG to decarbonize districts and supply them more efficiently are also relevant.

Other DHG types, pipe systems, or energy and media flow directions may be more favorable for other case studies. The determination of the grid temperature, the pipe material, and the insulation can also vary and offer different advantages and disadvantages depending on the design. The comparison of 4GDHG and 5GDHG and the further development of DHG to minimize heat losses and costs and increase efficiency could also be part of future scientific work. To accelerate the integration of P2H systems, the future technical and regulatory development has to be assessed. In particular, the increased load on the electricity grid due to the greater electrification of the heat supply presents a challenge. Therefore, the flexible use of P2H systems on the balancing energy market and day-ahead spot market in order to counteract fluctuating RES offers many research opportunities in connection with districts.

With a progressing temperature reduction beyond that in scenario III, a transition toward 5GDHG becomes more sensible and could be investigated in more detail. Herein, decentralized heat pumps deliver heat at the necessary temperature level with a higher efficiency than IEWHs.

5. Conclusions

This German case study is concerned with the supply of heating energy in the context of a district. Herein, the focus is on P2H-technology for the generation and DH-technology for the distribution of the energy supply while aiming to achieve a decent degree of energy autonomy (concept of PED). In DHGs, the reduction in the operating temperature has multiple goals, to decrease emissions and cost while increasing the system efficiency. The aim of this study is to investigate and quantify the effects of a reduced operating temperature. To that end, an energy system model called nPro is applied to a use case, concretely an exemplary district of 40 SFRs and 4 MFRs. Therein, three scenarios with different operating temperatures for the DHG are considered, namely, 60 °C, 50 °C, and a flexible shift between 40 °C and 50 °C. The research question concretely aims to explore the effects of a temperature reduction and flexibilization under consideration of decentralized electrified heating. Therein, economic, technical and ecological dimensions are considered. As measurements, the following KPIs are applied: LCOH, CRG, and TAC for the economic; the primary energy demand for the technical; and the CO₂ emissions for the ecological dimensions. The summarized results are presented in Section 3.2 and are displayed in Figure 8 for this case study.

It can be derived from the results of this specific case study that the initial decrease in the operating temperature from 60 °C to 50 °C has a positive effect on the CO₂ emissions, primary energy demand and cost, reducing all. However, as the operating temperature is further reduced and flexibilized, an emission, energy demand, and cost increase takes place. While the necessary cost and primary energy for central components decrease, an increase in the decentralized energy demand is necessary to reach the required DHW temperature. This leads to higher TAC and CO₂ emissions overall. Although this study illustrates that there is a turning point in economic feasibility when reducing the operating temperature, the quantification of an exact temperature is beyond the scope of this study. The reduction in the operating temperature has a positive effect on the operation of the HP and the heat losses of the DHG. While the annual COP increases, the heat losses of the DHG decrease. These effects are more pronounced for the initial temperature reduction (60 °C to 50 °C) than for the flexibilization (50 °C to 40–50 °C). While some electricity is produced on the premise of the district via PV-plants, the annual energy balance remains negative over all considered scenarios as the energy demand overshoots the production. Furthermore, the degree of self-consumption decreases with a reduction in the operating temperature. This is due to a connected increase in decentralized heat generation, which is sourced from the overlaying grid instead of the locally produced electricity for technical reasons.

In general, this study shows that temperature reduction or flexibilization of the DHG operating temperature in combination with a decentralized re-heating can lead to cost decreases. However, it is also shows, that depending on the case study at hand, a multitude of effects come into play that go beyond a simple causality between temperature reduction and cost reduction.