Data-driven distributed control and optimisation for multi-energy demand management in local energy communities and microgrids

Multi-energy systems use multiple energy carriers (electricity, heat, gas,…) to supply end-users with energy services such as space heating, hot water, lighting, electric vehicle charging, etc. These energy carriers are coupled to each other via energy conversion units (coupled heat power generation, heat pumps, electric and gas boilers, etc.). The ability to supply the energy service from different carriers allows for operational flexibility, which can be used to optimise objective functions such as minimising primary energy use, minimising cost, maximising profits for different actors, etc. The operational freedom to exploit the flexibility only becomes larger if storage devices (hot water storage tanks, batteries, seasonal thermal storage, etc.) are added. Such storage devices are often shared between different households in a residential context, or between different end users in a more industrial context, leading to local energy communities and microgrids. At this level, also renewable energy sources (photovoltaics or wind turbines) are added to reduce grid dependence. Thanks to the addition of information and communication technology much sensor data is available about the status of the grids and its connected devices (three-phase voltages and currents for the electrical grids, temperatures and flows for the heat grids, state-of-charge of batteries and storage tanks, energy requirements of controllable devices, such as electric vehicle charging requirements or hot water use profiles, etc.), and many devices in the system can be controlled – resulting in smart, multi-energy systems. Due to the uncertainties related to user behaviour, weather change or energy prices, it is challenging to do planning and operations of the controllable devices in these local energy communities and microgrids. Thanks to the large amounts of sensor data (both in real-time and historic), it is possible to forecast the uncertainties (using machine learning techniques) and to use data-driven control methods for the controllable devices, and for system optimisation. A combination of the local flexibility control and distributed control methods is needed to deal with the hierarchical requirements in a microgrid, needing a balance between demand and supply over all energy carriers. Such distributed control can be completely decentralised with multi-agent systems, or be hierarchically structured in a more classical approach. Finally, this control must incorporate multiple time dimensions (seconds, minutes, hours, days, months), due to the different time constants involved in electrical and thermal balancing of generation, load and storage devices, leading to multi-scale, multi-energy systems. This leads to the central research question in this PhD thesis, i.e. how to use data-driven, distributed methods to control the controllable storage, generation and end-use devices, such that optimal energy services are delivered in a microgrid context.