In this paper, we propose a data-driven methodology to identify the optimal placement of sensors in a multi-zone building. The proposed methodology is based on statistical tests that study the (in) dependence of measurements from various available sensors. The tests advice on a set of most dissimilar sensors to be retained, as they would convey the maximum information. The method starts with an initial setup that can provide measurements of every building zone to carry out this study; any of these sensors can be removed eventually to decrease costs in normal operation. The method has the advantages of being purely data driven and computationally efficient, as against several methods proposed in the scientific literature, that operate under the premise that detailed building models are available, to evaluate the number/position of the required sensors. This property makes the method scale to different buildings, in an expert free manner. The methodology can help towards better characterization of a building for optimal control and monitoring applications. It is validated against a widely used method – Kalman filtering with Grey-box models, using two different case studies. In both cases, the proposed approach agrees with the results using grey box models, suggesting that the method is reliable, while being quick and efficient.

@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

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

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

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

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

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

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

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

The two spaces office module is usually considered as a representative case-study to analyse the energetic improvement in office buildings. In this kind of buildings, the use of a model predictive control (MPC) scheme for the climate system control provides energy savings over 15% in comparison to classic control policies. This paper focuses on the influence of solar radiation on the climate control of the office module under Belgian weather conditions. Considering MPC as main climate control, it proposes a novel distributed enhanced control for the blind system (BS) that takes into account part of the predictive information of the MPC. In addition to the savings that are usually achieved by MPC, it adds a potential 15% improvement in global energy use with respect to the usually proposed BS hysteresis control. Moreover, from the simulation results it can be concluded that the thermal comfort is also improved. The proposed BS scheme increases the energy use ratio between the thermally activated building system (TABS) and air-handling unit (AHU); therefore increasing the use of TABS and allowing economic savings, due to the use of more cost-effective thermal equipment.

@en

Optimal design and operation of district heating networks require accurate, but simple models to allow fast simulation. This paper describes the analytical derivation of such a model based on existing work regarding heat loss and dynamic temperature profile calculations in literature. The most important addition from this work is the incorporation and investigation of heat transfer from supply to return side in double pipes, something which is often (over)simplified in more commonly used models. The paper presents the mathematical derivations and the assumptions made in detail for the case of steady-state heat losses in double pipes. The resulting model is carefully examined in a parameter study, from which a number of interesting conclusions can be drawn. Firstly, the heat losses are found to be nearly independent of the mass flow rate in the range of mass flow rates usually encountered in thermal network pipes. The remaining heat loss calculation is simply based on temperature levels and thermal resistance factors, determined by the pipe dimensions and materials. Furthermore, it is found that heat losses from supply to return side should be incorporated in the analysis for better accuracy of the results, even more so with the increasing popularity of twin pipes with common insulation.

@en

Simulation and optimisation of district heating and cooling networks requires efficient and realistic models of the individual network elements in order to correctly represent heat losses or gains, temperature propagation and pressure drops. Due to more recent thermal networks incorporating meshing decentralised heat and cold sources, the system often has to deal with variable temperatures and mass flow rates, with flow reversal occurring more frequently. This paper presents the mathematical derivation and software implementation in Modelica of a thermo-hydraulic model for thermal networks that meets the above requirements and compares it to both experimental data and a commonly used model. Good correspondence between experimental data from a controlled test set-up and simulations using the presented model was found. Compared to measurement data from a real district heating network, the simulation results led to a larger error than in the controlled test set-up, but the general trend is still approximated closely and the model yields results similar to a pipe model from the Modelica Standard Library. However, the presented model simulates 1.7 (for low number of volumes) to 68 (for highly discretized pipes) times faster than a conventional model for a realistic test case. A working implementation of the presented model is made openly available within the IBPSA Modelica Library. The model is robust in the sense that grid size and time step do not need to be adapted to the flow rate, as is the case in finite volume models.

@en

Due to its inherent large thermal inertia, concrete core activation (CCA) could assist in active demand response schemes. By shifting the injection or extraction of thermal power in time, demand peaks that normally occur more or less simultaneously in clusters of buildings can be spread out in time. Furthermore, thanks to the low temperature differences allowed with respect to the room temperature, CCA is ideally combined with technologies that have increasing renewable potential, such as heat pumps, low temperature district heating and high temperature district cooling. This paper illustrates the flexibility potential of concrete core activation through the exploitation of its thermal energy storage capacity by dynamic simulations. A validated RC thermal model of a CCA is coupled with a detailed building model, including user occupancy and weather disturbances. The flexibility indicator is calculated based on a method presented in the literature, but using an extended version that allows application to more complex systems, including heat losses. Balancing the building between minimal and maximal temperature, the thermal power needed to heat or cool the building can be modulated up- or downward with respect to the reference energy use. The method is applied to various building types with different insulation levels, in order to map the flexibility potential of CCA heating compared to buildings heated with radiators.

@en