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.
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.
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
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.
Network flexibility is the use of the thermal capacity of water that is contained in the district heating network pipes to store energy and shift the heat load in time. Through optimal control, this network flexibility can aid in applications such as peak shaving and operational heat pump optimisation. Yet, optimal control requires perfect predictions and complete knowledge of the system characteristics. In reality, this is not the case and uncertainties exist. To obtain insight into the importance of these uncertainties, this paper studies the influence of imperfect knowledge of building parameters on the optimal network flexibility activation and its performance. It is found that for the optimisation of heat pump operation, building parameter uncertainties do not present large risks. For peak shaving, a more robust result can be achieved by activating more network flexibility than may be required.
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 justin-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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.