Flexibility in district heating and cooling systems (thermal networks in general) is an important means to cope with the intermittent generation of heat and electricity as the share of renewable energy sources (RES) increases. An important source of flexibility is the thermal energy storage present in district heating and cooling networks, found in the thermal inertia of buildings, storage units and the network itself. To unlock this flexibility and to use it effectively and efficiently, a suitable control strategy is required. In this context, this paper presents a possible definition of flexibility and its sources in a thermal network. It reviews techniques to quantify flexibility and shows the need for a more advanced control strategy; moreover, it discusses the challenges involved in developing such a control strategy. Also, the literature on advanced control in thermal networks is reviewed, by making a distinction between central, distributed and hybrid control. Finally, possible future research topics are identified based on the findings.@en

The design and operational optimisation of fourth generation district heating networks is a crucial step towards highly renewable energy systems of the future. In order to optimise such complex systems, a toolbox modesto (multi-objective district energy systems toolbox for optimisation) is being developed. Seasonal thermal energy storage is an essential technology to allow larger shares of renewable energy sources, yet large computational power is required for its representation in full-year operational optimisations, as a step towards district energy system optimal design. To decrease computational complexity, a technique with representative days able to include seasonal thermal energy storage systems is developed and validated. This methodology combines different part-solutions from literature, but also adds a novel aspect to safeguard the chronology of the optimisation problem. To validate the approach, the design optimisation of a fictitious solar district heating system with seasonal thermal energy storage is compared to different representative day optimisations in two steps. The operational optimisation is a linear optimisation problem, implemented using modesto; the design optimisation is built as a genetic algorithm, optimising the size of the storage and solar systems in the network. The validation exercise is done for the operational and for the design optimisation separately. This comparative study shows that modelling with representative days adequately mimics the behaviour for the presented case. Furthermore, a solution speed-up in the order of 10–30 times is shown for the representative optimisations with respect to the full year optimisation, in line with the reduction of the number of variables.@en

With the increasing integration of multiple energy carriers in district energy systems, an accurate simulation of the district energy demand becomes more crucial. To reduce the required computational power, the district energy demand is often quantified through a limited set of archetype buildings, representing the whole district. As the temporal behaviour is important to assess district energy systems, stochastic occupant models should be included in the simulation. However, obtaining representative set-point temperature profiles for archetype buildings is not straightforward, as all buildings respond differently to their demanded set-point temperature due to their thermal inertia. Hence, this paper proposes and compares three techniques to obtain the representative occupant behaviour for the archetype building, by focussing on 847 single-family dwellings. Including a smart occupant aggregation method allows to decrease the percentage error in annual energy demand for space heating between the full and the archetype simulation from around 7.5% to around 2%, depending on the building model and evaluation method.@en

This paper presents the first steps towards a District Energy Simulation Test (DESTEST), which is part of IBPSA Project 1. The goal is to develop a test sequel for district energy simulations, inspired by principles of the BESTEST. It aims at providing a means to validate District Energy System models. The description of the DESTEST cases and the simulation results of extensively verified models will be available as a reference for verification. By presenting the research plan, goal and first results, the district energy simulation community is informed about the project's intentions, offering a chance for feedback and collaboration.@en

Bypass valves in district heating substations are a compromise between efficiency and quality of service. On the one hand, they are required to ensure that each building (no matter the distance to the heat source) has warm water within an acceptable time. On the other hand, they form a short-circuit between the warm supply and cold return line and their use can increase the return temperature substantially. Therefore, a good control of these bypass valves is critical to limit the drawback of their use. In this context, this paper compares two commonly used control strategies (manual control and thermostat control) to a new theoretical benchmark that provides an upper boundary for the performance of bypass controllers. This theoretical benchmark ensures a just-in-time delivery of warm water by taking into account time delays in the network. In a simulation case study of a small neighbourhood in Genk, Belgium, the benchmark shows that substantial improvement regarding bypass control is possible.@en

4th generation district heating and cooling networks (shortly THERNETs) are often coined as a crucial technology to enable the transition towards low-carbon smart energy systems. Most importantly, they open perspectives for integration of low-grade residual heat from industry, renewable energy sources (such as geothermal heat and cold and solar thermal collectors), more efficient energy conversion units (such as collective heat pumps), while thermal energy storage (TES) systems increase system flexibility. In order to optimize design and control of such complex systems, a toolbox modesto (Multi-objective district energy systems toolbox for optimization) is under development. However, the representation of seasonal heat and cold storage systems on an annual basis requires large computational power. In an attempt to decrease computational cost, a technique with representative time slices (inspired by and combining aspects from optimization studies of electrical energy systems, unit commitment problems, thermal systems with short term energy storage and smaller scale industrial thermal systems with longer term energy storage) is developed and tested. The aim of this study is to investigate the applicability of such representative time periods to optimize seasonal TES systems in THERNETs. To this end a full year optimization is compared to one with representative time periods for a realistic case study that uses demand profiles from the city of Genk (Belgium) and energy system parameters from Marstal (Denmark). This comparative study shows that modelling with representative periods is sufficient to mimic the behaviour of a full year optimization. However, when curtailment of solar heat injection occurs, not all representations yield the same results. It was found that for the studied case, a selection of 12 representative weeks performs best, while all reduced optimizations result in a substantial reduction (speed-up of on average x4.8 to x7.7) of the calculation time.@en

In the quest to increase the share of renewable and residual energy sources in our energy system, and to reduce its greenhouse gas emissions, district heating networks and seasonal thermal energy storage have the potential to play a key role. Different studies prove the techno-economic potential of these technologies but, due to the added complexity, it is challenging to design and control such systems. This paper describes an integrated optimal design and control algorithm, which is applied to the design of a district heating network with solar thermal collectors, seasonal thermal energy storage and excess heat injection. The focus is mostly on the choice of the size and location of these technologies and less on the network layout optimisation. The algorithm uses a two-layer program, namely with a design optimisation layer implemented as a genetic algorithm and an optimal control evaluation layer implemented using the Python optimal control problem toolbox called modesto. This optimisation strategy is applied to the fictional district energy system case of the city of Genk in Belgium. We show that this algorithm can find optimal designs with respect to multiple objective functions and that even in the cheaper, less renewable solutions, seasonal thermal energy storage systems are installed in large quantities.@en

To aid in the integration of renewable and residual energy sources in the energy system, energy flexibility is required. By charging and discharging energy storage, energy flexibility can be created and heat demand and heat generation can be matched in time. One possible source of energy flexibility is the thermal capacity of the water in district heating network pipes. Effective use of this thermal energy storage requires efficient techniques to determine the available flexibility. The goal of this paper is to determine the required level of detail of a substation model to characterise network flexibility through simulation. The substation models differ in the assumptions that are made and range from a detailed, non-linear model to a simple, linear model. To analyse the results, we identify different phases occurring during a network flexibility activation. By determining if reduced models are as effective in reproducing important flexibility characteristics as more detailed and computationally expensive models, network flexibility characterisation can be simplified and sped up. Results show that the network flexibility can be adequately characterised even with very simple models, provided correct assumptions are made.@en