This paper presents challenges in the control of 5GDHC networks and proposes an approach for the robust control design of these networks. Temperatures are low and temperature differences are small in 5GDHC networks, in order to minimise heat losses in the distribution network and directly utilise low-temperature heat sources. Therefore, the robust control design of substations is crucial for stable and efficient operation of 5GDHC networks. We proposed the key steps to obtain a consistent control design that can be verified and tested prior to commissioning of the building substations and network. The design approach was elaborated in a case study in an existing neighbourhood in Haarlem, the Netherlands, constructed in the 1930s to 1970s. This neighbourhood will become independent of natural gas using PVT-rooftop panels, individual heat pumps (HP), a 2-pipe, bi-directional energy flow network operating at ultra-low temperatures and ATES system as a seasonal and daily storage facility.

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Buildings can deliver short-term thermal energy storage by utilising the thermal capacity of the building construction and/or by activating the water tanks included in the heating/cooling installation. The flexibility potential of demand management using decentralized thermal energy storage has been quantified in many theoretical modelling studies, and it is considered an essential technology for an affordable energy transition. We have investigated the drivers and barriers to the adoption of demand management in buildings in district heating and cooling systems via a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis and presented 17 elements that shape the current and future application of this concept. The results indicate that the application of the DR concept has left the theoretical studies and moved towards real-life applications. Yet, there is a lack of feasible business models and regulatory frameworks supporting the large-scale application of the concept. Utilities and their customers do not fully understand the benefits of the DR concept; therefore they are reluctant to adopt it outside of the research projects where the test environment is fully controlled and with limited impact and timeline. Therefore, the regulatory framework must be adjusted to allow DHC operators to develop new business models and DR tariffs that will incentivise the customers to deliver flexibility to the system without compromising their comfort and everyday practices and increasing energy poverty.

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The energy required for space heating amounts to approximately 68% of the total energy demand of existing buildings in Europe. The heat requirement of a building, and thus its carbon emission, can be lowered by optimizing the supply and return temperature of the heating system. A lower supply temperature enables a wider variety of transition pathways towards sustainable heating with reduced carbon emissions. However, the minimum supply temperature that guarantees acceptable indoor temperatures in existing dwellings during design weather conditions is still unknown. In this study, we determine the minimum supply temperature by fitting a 2 R–2C model to hourly measurement data. The measurement data is obtained from a representative set of 220 existing gas-fired dwellings in the Netherlands. The heating system of each dwelling was equipped with a pulse flowmeter and temperature sensors on both the supply and return side. Additionally, data was collected from the thermostat in the main living room and the gas boiler. The data was supplemented with weather data from a nearby weather station. The data-driven model shows that the minimum supply temperature can be lower than 55 °C for 60% of the dwellings during design weather conditions (i.e., −10 °C in the Netherlands). Moreover, the minimum supply temperature is poorly correlated with general building properties, such as the building typology, construction period or specific annual space heating demand (kWh/(m²yr)). On the contrary, the ratio between the required and installed heat output of the radiators in the heating system is a promising parameter to predict the minimum design supply temperature of an individual dwelling that guarantees an acceptable indoor temperature during design weather conditions.

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The energy required for space heating amounts to approximately 70% of the total energy demand of existing buildings in Europe. Decarbonization of the heat demand of existing buildings is one of the grand challenges in the energy transition and sustainable district heating is a promising solution in many neighborhoods. The construction cost of district heating networks (DHNs) in an existing neighborhood is associated with large uncertainties and therefore investment risk. We have hypothesized that the CAPEX of DHNs is determined by spatial parameters that characterize the subsurface space scarcity. We have investigated this hypothesis by collecting construction cost data of 66 recently finished projects in close collaboration with Dutch district heating companies. Furthermore, we have collected construction cost data on replacement of 68 water distribution and 52 gas distribution networks. The replacement costs of these critical infrastructures might be affected by similar spatial parameters. The analysis of the district heating data reveals that the unit construction costs (€/m routing) are affected by the presence of water bodies, the road width, the zip code and the age of the neighborhood. The combined analysis with data from three infrastructures reveals that the unit construction costs (€/m routing) are affected by the presence of water bodies, soil contamination, the age of the neighborhood, the relative footprint of buildings and the zip code. The relevance of the zip code in this analysis is an indication that local regulations may have a significant impact on construction costs. Given the large number of drivers for the DHN construction costs, the datasets are too small for a statistically reliable construction cost model. We conclude that it is practically impossible to develop a project database of recently executed DH construction works with sufficient variation on all relevant characteristics. Future work will focus on automated generation of an activity-based construction cost model, using multiple GIS-layers. The improved construction cost model will be validated against traditional construction costing methods by 4 or 5 contractors.@en

A comparative study for a representative case district in the Netherlands with around 2500 dwellings has been executed with the purpose of identifying the best solution for a Low Temperature District Heating (LTDH) with Low Temperature Geothermal Heat (LTGH) as the main heat source. The district is presently connected to the Dutch gas network. Its heat demand and building typologies are used as departing points. The comparison is based on 3 key performances indicators (KPI’s): CO2 emissions, levelized costs of energy (LCOE) and peak electricity use. The following LTDH designs have been considered: Central heat pump and collective peak supply at 70 ⁰C / 50 ⁰C; Central heat pump and decentral peak supply at 70 ⁰C / 50 ⁰C; Decentralized heat pumps using 30 ⁰C supply temperature. Individual air source heat pumps for space heating and electric boilers for domestic hot water purposes are taken as reference. The LTDH concept with decentralized heat pumps saves 19.5 %, 16.8 % and 36 % on the CO2 emission, LCOE, and electricity use in peak load hours compared to the reference concept.@en

Future district heating systems (DHS) will be supplied by renewable sources, most of which are limited in temperature and flow rate. Therefore, operational optimization of DHS is required to maximize the use of renewable sources and minimize (fossil) peak loads. In this paper, we present a robust and fast model-predictive control approach to use the thermal mass of buildings as a daily storage without violating temperature constraints. The novelty of this paper includes two elements. First, the focus on an operational control strategy that explicitly accounts for temperature-limited renewable sources, like a geothermal source. Secondly, the optimization problem is formulated as a (nearly) convex optimization problem, which is required for adoption of model-predictive control in practice. The examples show that the peak heating demand can be reduced by 50%, if the thermal inertia of the buildings is used and the heating setpoints are adapted. Furthermore, the operational optimization finds the proper balance between benefits of pre-heating using renewable sources with limited capacity and costs of additional heat losses due to pre-heating.

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This study presents convex modeling of drainage pumps so that real-time control systems can be implemented to minimize their energy use. A convex model is built based on pump curves and then used in mixed-integer optimization to allow pumps to be turned on or off. It is implemented as an extension to the open source software package RTC-Tools. The formulation is such that the continuous relaxations of the mixed-integer problem are convex, hence branch-and-bound techniques may be used to find a global optimum. The formulation can be used for variable-speed and constant-speed pumps. There are several possible applications, such as optimization of polder systems, pumped-storage systems, or certain water distribution networks. Finally, an example of the drainage pump is presented to compare the method to current methods and show that energy can be saved by using the proposed method.

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Gully pots are essential assets designed to relief the downstream system by trapping solids and attached pollutants suspended in runoff. This study applied a methodology to develop a quantitative gully pot sedimentation and blockage model. To this end, sediment bed level time series from 300 gully pots, spanning 15 months, were collected. A generalised linear mixed modelling (GLMM) approach was applied to model and quantify the accumulation of solids in gully pots and to identify relevant physical and catchment properties that influence the complex trapping processes. Results show that the retaining efficiency decreases as sediment bed levels increase. Two typical silting evolutions were identified. Approximately 5% of all gully pots experienced progressive silting, eventually resulting in a blockage. The other gully pots show stabilising sediment bed levels. The depth of the sand trap, elapsed time since cleaning and the road type were identified to be the main properties discriminating progressive accumulation from stabilising sediment bed levels. Furthermore, sediment bed levels exhibit no residual spatial correlation, indicating that the vulnerability to a blockage is reduced as adjacent gully pots provide a form of redundancy. The findings may aid to improve maintenance strategies in order to safeguard the performance of gully pots.

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The performance of lateral house connections can have a substantial impact on the overall level of service provided by sewer systems. However, knowledge on the failure probability of these components remains scarce. This paper analyses field data to determine failure rates. A distinction is made between different mechanisms that contribute to the overall failure probability of lateral house connections. Root causes that promote the occurrence of these failure mechanisms are derived from a literature review. Results of a trend analysis shows a time constant failure rate, which exceeds blockage rates reported for main sewers. Fat, oil, and grease deposits are the dominant failure mechanism for both a specific case study in Rotterdam and the Netherlands. Literature suggests misuse and the structural condition as main root causes for this failure mechanism. The sheer number of failures associated with lateral house connections suggests that these components should be taken into account in sewer asset management.@en