Energy flexibility in buildings through thermal storage devices: Modelling and optimal control

Energy consumption worldwide has increased dramatically in recent times, driven by the unrelenting nature of development in modern societies. A large share of the energy consumption is dominated by fossil–fuel based energy sources, which are responsible for more than three–fourths of the global Greenhouse Gas (GHG) emissions. These GHGs have a severe negative impact on both our health and the environment: they contribute to air–pollution and are the main drivers behind climate change. It is an urgent and critical need of the present to decarbonise our energy system to reduce GHG emissions and avoid the worst effects of climate change. To rapidly decarbonise the energy system, a key strategy is to transition quickly to low–carbon Renewable Energy Sources (RESs) by shifting the sources of energy away from carbon–rich fossil fuels.

Owing to advances in technology, reduction in costs and supportive energy policy, the renewables share in the energy mix has increased steadily over the past decade. To further support the transition, an increasing number of countries have adopted net–zero emission targets that aim towards a future energy system with 100% RES supply. However, the integration of these intermittent RESs into the electricity system creates major technical challenges for the operational reliability of the electricity grid. Their non–dispatchable and volatile nature creates supply demand imbalances which must be managed to maintain the security of the energy system.

An effective way to integrate the stochastic RESs is to generate additional flexibility in the energy system. Power–to–Heat (P2H), which refers to the coupling between the power and the heating sectors, offers a strategic pathway to increase the flexibility of the power system. It involves the usage of electric–driven heat pumps and Thermal Energy Storage (TES) technologies to facilitate the flexible use of electricity for heating. TESs reduce the discrepancy between supply and demand by providing a means to store the temporary excess electricity from RESs in the form of thermal energy. This characteristic feature enables them to provide a significant amount of operational flexibility to the electricity power system.

Buildings are responsible for almost a third of the total global final energy consumption. Building thermal demand, which is composed primarily of the two end–uses of Domestic Hot Water and space–heating, accounts for a major share (80%) of the total building energy demand. A significant portion of this thermal demand can be made flexible i.e. it can be shifted in time, especially over the short–term, without adversely affecting the thermal comfort. Therefore, the building sector is identified as a significant and promising source of energy flexibility.

Demand Response (DR) approaches aim to harness the potential energy flexibility of buildings through the intelligent management of the building thermal demand. DR seeks to influence both the pattern and magnitude of energy consumption of buildings through various measures, such as peak–shaving and load–shifting, to strategically provide energy flexibility to the grid. An appropriate control strategy, aided by an accurate building energy model and an effective optimisation algorithm, is required to effectively implement and automate these DR measures in building heating systems.

The focus of research in this thesis is to investigate the design and development of intelligent DR solutions to enable buildings equipped with TES devices to actively interact with the electric power system. The broader aim of the research is to enable buildings to be grid–responsive so as to support the increasing penetration of RESs by enhancing the reliability and resilience of the energy system by providing energy flexibility to the electricity grid.

The research focus was further deconstructed into multiple research areas and they were addressed through in–depth investigations of five specific research objectives: (i.) To develop control oriented numerical models that are reliable and computation-ally–tractable. (ii.) To develop data–driven prediction models to reliably estimate the thermal energy demand of buildings under different contexts. (iii.) To develop Optimal Control strategies to intelligently utilise the energy storage capability of TES devices to activate and maximise the energy flexibility of building energy systems under different contexts. (iv.) To comprehensively quantify, assess and evaluate the energy flexibility provided by the developed Optimal Control strategies. (v.) To quantify and compare the energy flexibility capability of a sensible Thermal Energy Storage (TES) and a Latent Heat Thermal Energy Storage (LHTES) to assess their DR capability in relative terms.