This study aims to check the performance of two distinct configurations system: a photovoltaic-thermal (PVT) system with an ice buffer storage tank figure [1] and a conventional PV system with an air heat exchanger on the back, figure [3]. The ice buffer storage tank is used to store excess heat from the PVT collector and supporting the heat pump that supplies domestic hot water and space heating. The air heat exchanger is used to harvest heat from the air. The reason for choosing these two configurations is because they are simple to install and cost-effective. The simulation results will show the energy gain, the efficiency and might help on calculating the payback period of both systems.
This report has been established within the Flexiheat project. Flexiheat has focused on increasing flexibility in district heating systems. The intelligent district heating network is a dynamic network: an open network where different waste heat and renewable energy sources are connected, that has multiple producers and groups of consumers and facilitates the connection between different energy infrastructures (gas, heat and electricity). Eventually this will lead to an optimal deployment of the available heat sources and an increased cost-efficiency of district heating. Flexiheat aims to develop new concepts for these intelligent, flexible district heating networks. One of the strategies is to allow third party access to the network. A smart control system is developed to manage the heat flows across the network. This system makes use of dynamic pricing. In this exploration the concept of third party access in relation to the Flexiheat project will be discussed. The development of new business and price models based on the Flexiheat approach has led to an analysis of possible alternative price models for consumers.
As electric loads in residential areas increase as a result of developments in the areas of electric vehicles, heat pumps and solar panels, among others, it is becoming increasingly likely that problems will develop in the electricity distribution grid. This research will analyse different solutions to such problems to determine Using a model developed as part of this project, we will simulate various cases to determine under which circumstances load balancing at a community-level is more (cost) effective than alternative solutions (e.g. grid reinforcement and/or household batteries).
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
