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There is an increasingly rich literature on the decarbonisation of heat and the evolution of heat networks. This paper investigates whether a novel fifth Generation District Heating and Cooling Network (5GDHC) could be retrofitted to an existing National Health Service (NHS) hospital campus for the purpose of heating and cooling. The building load was simulated and input into a custom-written script to carry out a series of parametric studies and optimise design options. The model was calibrated against site data available from hospital facilities management. The research found that it is feasible to use a 5GDHC consisting of a large single mass of water to utilise inter-seasonal thermal storage. A natural water resource such as an aquifer was not required. The model tested sizing options and found that larger thermal storage, heat pumps and chillers reduce operating costs and improve flexibility. The paper closes with a discussion of the practical factors in retrofitting 5GDHC networks to a densely occupied and highly constrained campus environment. The findings are novel in further describing the circumstances for which 5GDHC networks are suitable. [ABSTRACT FROM AUTHOR]

This paper investigates the energy performance and sizing criteria of 5th generation district heating and cooling (5GDHC) networks as a function of the heat distribution temperature in the building systems connected to the district network. An energy simulation model of a 5GDHC network was developed in Modelica for a case study located in Denmark. Calculations were carried out for four different building heating systems. Simulation results show that reducing the heat distribution temperatures from 70 °C to 23 °C leads to around 40% annual electric energy savings (from 10.4 kWh/m2 to 6.2 kWh/m2) for the operation of the heat pumps. Heat distribution temperatures of 23 °C cause higher water mass flow rates through the network, leading to annual electric energy consumption for the circulation pumps that are almost doubled (from 0.16 kWh/m2 to 0.3 kWh/m2) compared to the reference case at 70 °C. Furthermore, the paper discusses how the results obtained from the Danish case study can be generalized and applied to other cases using a simplified mathematical approach. It is found that about 1.5% of electric energy savings can be achieved for each temperature degree reduction in the heat distribution system.

There is an increasingly rich literature on the decarbonisation of heat and the evolution of heat networks. This paper investigates whether a novel fifth Generation District Heating and Cooling Network (5GDHC) could be retrofitted to an existing National Health Service (NHS) hospital campus for the purpose of heating and cooling. The building load was simulated and input into a custom-written script to carry out a series of parametric studies and optimise design options. The model was calibrated against site data available from hospital facilities management. The research found that it is feasible to use a 5GDHC consisting of a large single mass of water to utilise inter-seasonal thermal storage. A natural water resource such as an aquifer was not required. The model tested sizing options and found that larger thermal storage, heat pumps and chillers reduce operating costs and improve flexibility. The paper closes with a discussion of the practical factors in retrofitting 5GDHC networks to a densely occupied and highly constrained campus environment. The findings are novel in further describing the circumstances for which 5GDHC networks are suitable.

Peak shaving, demand response, fast fault detection, emissions and costs reduction are some of the main objectives to meet in advanced district heating and cooling (DHC) systems. In order to enhance the operation of infrastructures, challenges such as supply temperature reduction and load uncertainty with the development of algorithms and technologies are growing. Therefore, traditional control strategies and diagnosis approaches cannot achieve these goals. Accordingly, to address these shortcomings, researchers have developed plenty of innovative methods based on their applications and features. The main purpose of this paper is to review recent publications that include both hard and soft computing implementations such as model predictive control and machine learning algorithms with applications also on both fourth and fifth generation district heating and cooling networks. After introducing traditional approaches, the innovative techniques, accomplished results and overview of the main strengths and weaknesses have been discussed together with a description of the main capabilities of some commercial platforms. [ABSTRACT FROM AUTHOR]

Current sustainability challenges place entire communities at the centre of the energy revolution, rather than individual buildings. The need to develop energy-efficient and low-carbon economies lies at the heart of fifth-generation district heating and cooling (5GDHC) networks. The potential of these networks is represented by a lower working temperature close to the ground temperature, usually between 10 and 25 °C. Thanks to this feature, the network presents optimal conditions to be used as a heat source for reversible heat pumps. 5GDHC networks provide users with a fundamental active role, giving the possibility of extracting and releasing energy into the thermal network, while producing heating and cooling simultaneously. The integration of renewable sources and reduced heat losses are added values to be taken into consideration. Given the limited application of 5GDHC networks, this paper aims to evaluate the energy advantages of coupling several users within a bidirectional fifth-generation network based on the integration of reversible heat pumps with on-site production by renewable sources and waste energy recovery. An integrated model of 5GDHC network with buildings was developed in order to meet the energy needs of a neighbourhood made up of users with different energy profiles throughout a whole year. To enhance future applications, the energy performance of the 5GDHC network model was studied through a simulation tool. An hourly numerical calculation tool was developed to simulate the behaviour of the network and the users’ response over an annual period, estimating the energy shared between users during both the heating and cooling seasons. The results obtained are presented not only in terms of energy exchanged in the network, but also in the form of performance indexes of the individual users, identifying advantageous combinations between the users for future design developments of these systems. The outcomes of this paper highlight the integration of established technologies into fifth-generation thermal networks, with a view to a future development of nearly Zero Energy Districts (nZED).

Sweden has an ambitious plan to fully decarbonise district heating by 2030 and to contribute with negative emissions of greenhouse gases in 2050. The vagaries of the energy market associated with climate, political, and social changes entail cross-sectoral integration that can fulfill these national targets. Fifth-generation district heating and cooling (5GDHC) is a relatively new concept of district energy systems that features a simultaneous supply of heating and cooling using power-to-heat technologies. This paper presents best practices for developing 5GDHC systems in Sweden to reach a consensus view on these systems among all stakeholders. A mixed-method combining best practice and roadmapping workshops has been used to disseminate mixed knowledge and experience from middle agents representing industry professionals and practitioners. Four successful implementations of 5GDHC systems are demonstrated and the important learned lessons are shared. The best practices are outlined for system planning, system modeling and simulation, prevailing business models for energy communities, and system monitoring. A roadmap from the middle agents’ point of view is composed and can be utilised to establish industry standards and common regulatory frameworks.

A 5th generation district heating (5GDH) system consists of a low-temperature network used as a heat source for de-centralized heat pumps to serve heating demand. Until now, there is a lack of studies looking into the economic aspect of implementing the 5GDH concept. The performance characteristics, system dynamics, and economic feasibility of the 5GDH system are insufficiently investigated in cold climates. This paper aims to bridge the research gap by performing the techno-economic analysis of a 5GDH system using a case study based in Tallinn, Estonia. A detailed thermo-hydraulic simulation model is constructed in TRNSYS and Fluidit Heat. In addition, the uncertainty and sensitivities on the economic performance are analysed using Monte Carlo method implemented in Python. The study further analyses the effectiveness of using solar power technologies in reducing the cost of heating. For designed boundary conditions, the system can deliver heat at levelised cost of heating (LCOH) of 80 €/MWh. Integration of photovoltaic up to a limited capacity results in 1 % reduction when compared to the base case LCOH. The economic benefit of photovoltaic thermal is lower compared to photovoltaic. This study can provide a benchmark for the application of 5GDH systems in heating dominated regions. [ABSTRACT FROM AUTHOR]

• A 5th-generation district heating and cooling system in business district is studied. • Design and operation strategies are based on load analysis and load forecasting. • Load predictions are based on machine learning algorithms and historical loads. • Larger flow rate in water loop than that on source-side makes system more efficient. • Source-network-load-storage collaboration in a district energy system is explored. The fifth-generation heating and cooling system (5GDHC) is a bidirectional low temperature network, which is a good solution for decarbonization and energy-saving in building cooling and heating. Because of its complex configurations, 5GDHC requires specific advanced control strategies for different scenarios. This paper aims to investigate 5GDHC with multiple sources in the business district in hot-summer and cold-winter zone through modelica simulation. The analysis and forecasting of district loads were used to study the design and operation strategies of 5GDHC. A suitable heat pump performance curve was presented for 5GDHC after comparing several heat pumps. And the larger the total flow in the network, the more beneficial it is for users. In this study, 5GDHC for the business district performed best when it adopted the strategy of continuous operation of the ground heat exchanger, nighttime operation of the surface-water coil, and supplemental cooling of the cooling tower upon it is needed, resulting in a 16.6% reduction in CO 2 emissions compared to the strategy that all sources operated continuously. Besides, carbon emission analysis showed the importance of balancing operational cost with carbon emission. This study will help optimize the design and operation of 5GDHC in business district scenarios. [ABSTRACT FROM AUTHOR]

Low temperature district heating and cooling networks (5GDHC) in combination with very shallow geothermal energy potentials enable the complete renewable heating and cooling supply of settlements up to entire city districts. With the help of 5GDHC, heating and cooling can be distributed at a low temperature level with almost no distribution losses and made useable to consumers via decentralized heat pumps (HP). Numerous renewable heat sources, from wastewater heat exchangers and low-temperature industrial waste heat to borehole heat exchangers and large-scale geothermal collector systems (LSC), can be used for these networks. The use of large-scale geothermal collector systems also offers the opportunity to shift heating and cooling loads seasonally, contributing to flexibility in the heating network. In addition, the soil can be cooled below freezing point due to the strong regeneration caused by the solar irradiation. Multilayer geothermal collector systems can be used to deliberately generate excessive cooling of individual areas in order to provide cooling energy for residential buildings, office complexes or industrial applications. Planning these systems requires expertise and understanding regarding the interaction of these technologies in the overall system. This paper provides a summary of experience in planning 5GDHC with large-scale geothermal collector systems as well as other renewable heat sources. [ABSTRACT FROM AUTHOR]

In the planning phase of district energy systems, optimization models based on mathematical programming are a widely used approach. Most optimization models determine the optimal energy system design based on a single representative year. However, this approach is unable to consider changing economic and technological boundary conditions over the system's lifetime. As a result, these one-period models fall short for districts with 5th generation district heating and cooling (5GDHC) networks, which are typically developed over long time periods and are built in multiple construction phases. In this paper, we present two multi-period optimization approaches for designing 5GDHC districts: The first approach is a forward-looking model which determines the optimal investment pathway by using a perfect foresight of future parameter developments. In the second approach, a one-period model is solved repeatedly for every investment period without knowledge of future parameter developments. Both approaches are compared to a one-period model for a real-world 5GDHC district in Germany. In comparison with a one-period design approach, the forward-looking method leads to total cost savings of up to 17 % and the sequential method of up to 11 %. By using a forward-looking model, gas-fired technologies are sized smaller while the capacity of electricity-driven technologies in the energy hub as well as photovoltaic modules and thermal energy storages in buildings increases compared to a one-period model. The case study shows that a multi-period modeling approach is an important addition to design optimization models for 5GDHC networks and can have a significant impact on the optimal design. [ABSTRACT FROM AUTHOR]

District heating and cooling are considered effective solutions to decarbonise the energy use in the building sector. The latest generation of district heating and cooling also increases the potential of integrating heat pumps and chillers in each building substation. The benefits of such integration are the reduction of network temperature and distribution losses; the recovery of waste heat through a bidirectional network; and the decentralised production of heating and cooling. Sizing the network depends mainly on the heat flows between connected buildings. The substation performance and technical installations determine these heat flows. We present in this paper Modelica-based simulations of two design cases for substations. The first design case involves installed heat pump, chiller, and circulation pumps. Alternatively, the second design enables the heat pump to provide direct cooling through a heat exchanger. The models for these installations were developed using the Modelica language to perform continuous-time simulations. The performance in each design case was evaluated in terms of seasonal coefficient of performance and total electric energy use. An analysis on a cluster of 11 buildings suggests that the addition of the direct cooling heat exchanger can save up to 10% of the total annual electric energy use. Additional savings can be achieved by optimising the building supply temperatures and the district network temperature.

District heating and cooling (DHC) networks, and in particular, the fifth generation of DHC networks, offer great potential in increasing the overall system efficiency and reducing CO2 emissions in the heating and cooling of urban districts. Due to the growing complexity of these energy systems, the use of new planning methods, such as the use of dynamic simulation models based on Modelica, becomes more important. However, especially with large, complex thermal networks, there is a high effort for manual model construction and parameterization. For this reason, we present a framework for automated model generation of DHC networks based on simulation models in Modelica written in Python. The core function of the Python framework is to transform a graph representation of a district heating network into a dynamic simulation model. The authors briefly describe the workflow and demonstrate its applicability with three different use cases. We investigate the impact of different design decisions, e.g., comparing the difference between central and decentral pumps as well as a combination of both in one network. In addition, we present the results of evaluating the impact of different network temperature levels or pipe insulation compared to the overall energy supplied to the network, leading to the conclusion that the presented framework is capable of reducing the manual effort for performing DHC network simulations with Modelica and allows to easily perform parameter studies in an early planning phases in the future. [ABSTRACT FROM AUTHOR]

In order to realize an energy efficient and emission-free heat and cold supply in urban areas, 5th Generation District Heating and Cooling (5GDHC) networks are a promising technology. In 5GDHC networks, the control of the network temperature is crucial since it affects the efficiency of connected heat pumps and chillers, the heat losses (or gains) of the network, as well as the integration of waste heat or free cooling. Due to the large number of opposing effects, the optimal control of network temperatures is a challenging task. In this paper, a mixed-integer linear program (MILP) is proposed for short-term optimization of the network temperature in 5GDHC systems. The model comprises an air-source heat pump, compression chiller and thermal storage in a central generation unit as well as heat pumps, chillers, electric boilers and thermal storages in buildings. Furthermore, the model considers the thermal inertia of the water mass in the network which functions as additional thermal storage. The optimization model is real-time capable and designed to be deployed in a model-predictive control. In a case study, the optimization approach leads to cost savings in two of three investigated months (by 10 % and 60 % respectively) compared to a reference operation strategy (free floating network temperature). • Operation optimization model for 5th generation district heating and cooling grids. • Network temperature levels are discretized resulting in an MILP formulation. • Performance comparison with free-floating temperature control. • Investigation of storage tank and thermal inertia of network for demand balancing. • Optimization model results in lower costs compared to free-floating temperature. [Display omitted] [ABSTRACT FROM AUTHOR]

• A novel metric (DOC) for bidirectional low temperature networks (BLTNs). • DOC describes the balancing potential of heating and cooling demands in a district. • DOC helps to identify building clusters with complementary demand profiles. • Correlations between DOC and KPIs are elaborated based on 63 demand scenarios. • Performance comparison of BLTNs with state-of-the-art building energy systems. Bidirectional low temperature networks (BLTNs) are a promising technology to drive decarbonization of the heating and cooling sector. The energy efficiency of a BLTN strongly depends on the heating and cooling demands of the connected buildings and their simultaneity. In order to evaluate the efficiency of a BLTN, a novel metric, called Demand Overlap Coefficient (DOC), is presented in this paper. The DOC can be calculated for individual buildings or a district and describes which proportion of heating and cooling demands can be balanced inside buildings and between buildings. The DOC is calculated with time series for heating and cooling demands, taking special account of their simultaneity. For a real-world use case in Germany, it is shown that 25% of heating and cooling demands can be balanced in buildings and 45% can be balanced by the BLTN between buildings. The DOC is evaluated for 63 demand scenarios. Correlations show that for districts with a DOC larger than 0.45, BLTNs have lower annualized costs than a state-of-the-art heating and cooling system. BLTNs have higher exergy efficiencies for districts with DOCs larger than 0.3. With the derived correlations, the DOC serves as a practical metric in the planning process of BLTNs in order to identify and assemble building clusters with complementary demand profiles. [ABSTRACT FROM AUTHOR]

Peak shaving, demand response, fast fault detection, emissions and costs reduction are some of the main objectives to meet in advanced district heating and cooling (DHC) systems. In order to enhance the operation of infrastructures, challenges such as supply temperature reduction and load uncertainty with the development of algorithms and technologies are growing. Therefore, traditional control strategies and diagnosis approaches cannot achieve these goals. Accordingly, to address these shortcomings, researchers have developed plenty of innovative methods based on their applications and features. The main purpose of this paper is to review recent publications that include both hard and soft computing implementations such as model predictive control and machine learning algorithms with applications also on both fourth and fifth generation district heating and cooling networks. After introducing traditional approaches, the innovative techniques, accomplished results and overview of the main strengths and weaknesses have been discussed together with a description of the main capabilities of some commercial platforms.

District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid. [ABSTRACT FROM AUTHOR]