Data-Driven Predictive Control for Ensuring Grid Security with High Penetration of Wind Energy

In order to achieve net-zero carbon emissions in the UK, electricity generation must complete the transition from fossil fuel-based production to Renewable Energy Sources (RES). The resulting increase in penetration of RES makes operation of the power grid more challenging. Reliance on the weather for wind and solar generation introduces uncertainties in supply capability. In traditional power plants, the inertia of rotating turbines helps maintain grid stability. However, RES use power electronics known as inverters to provide the required voltage and frequency. Inverters do not provide inertia to the power grid which makes it more susceptible to instability. The planned research is focused on addressing the challenges of grid stability and of matching supply to consumer demand through the development of novel control approaches and network architectures.
A new paradigm known as direct data-driven control has begun to gain traction in the research community. System inputs are found directly from gathered data rather than by estimating an approximate model using data and then determining the optimal inputs using model predictions of system behaviour. This results in theoretical improvements in performance.
To date, only a small number of direct data-driven control strategies applied to power system scenarios have been simulated to the author's knowledge. Potential applications for this approach range from controlling signals in power converters to managing a wind farm. The proposed research aims to assess the suitability of direct data-driven methods for controlling power networks and to ensuring grid stability with a high penetration of RES. The research plan involves carrying out simulations of power networks managed by a data-driven controller. Multiple network scenarios will be simulated to assess the suitability of the approach and the optimal controller design. The research aims to improve understanding of which methods may best cope with uncertainties and nonlinearities in the system as this is currently an open question. A further goal is to test the direct data-driven control strategy on a small-scale microgrid within a laboratory setting.
One marker of the net-zero transition is the increasing prevalence of distributed electricity generation through local, small-scale production methods such as roof-mounted solar cells. This offers greater operational flexibility compared to the traditional centralized approach but requires a significant improvement in control and communications technology to take full advantage of its benefits.
A concept known as the holonic approach has emerged that may deliver such an improvement. A holonic network is composed of holons forming a holarchy; it is analogous to a hierarchy. A holon is both part of a system and a system in itself; a smart home manages power demands within a home whilst existing as an element of the district power grid. The holarchy is able to adapt during operation to balance competing demands and manage network faults. Limited research has been published concerning such an implementation for power networks and almost no research has been published to the author's knowledge regarding the ability of holons to automatically adapt to changes in the network.
The proposed research aims to address these gaps and develop the approach towards an implementable architecture for managing power networks. A further aim is to develop theory outlining the fundamental behaviour of a holarchy that could be used in designing a holarchy for specific use cases. The direct data-driven control and holonic approaches could be combined by using operational data to optimize the network structure and determine appropriate control inputs.
Both direct data-driven control and the holonic approach show potential to support the development of today's power networks to the networks of the future that will deliver a net-zero power grid; a crucial part of the transition to a sustainable global e