Fast and widespread uptake of low-carbon technologies (LCTs) - such as electric vehicles and heat pumps - is necessary for decarbonising the UK's energy consumption, but presents significant challenges for the country's power systems. Needed in advance of this are costly upgrades to the nation's electricity distribution networks and policies that decrease demand during periods of peak consumption. Determining where network infrastructure is most needed, and how to most effectively mitigate costly network activity, for accelerating the decarbonisation of the UK's energy consumption is necessary for reaching the country's 2035 and 2050 goals. However, there is significant uncertainty on the rate of uptake, nature, and location of the LCTs being installed - hindering our ability to estimate future energy consumption behaviour. To account for this uncertainty, a probabilistic approach is taken to model network changes and consumer behaviour in order to inform planning, pricing, and investment. AIMS, OBJECTIVES & BENEFITS In this research project, I plan to probabilistically model power systems - and individuals interacting with it - via a hierarchical Bayesian model. This approach seeks to use empirical data to train and improve the scientific models governing power system simulations. With this simulation platform, the effect of different technologies, consumer behaviours, and policies are to be modelled. The aim of this is to determine strategic investments for boosting the transition to LCTs. Additionally, we aim to identify the potential for strategic network pricing methodology, so that consumer behaviours can be influenced to optimally utilise renewable energy with existing electricity infrastructure. The benefits of these findings have the potential to bring cost savings to energy consumers, while accelerating the transition to renewables. RESEARCH COUNCIL RELEVANCE This project is being undertaken as part of the Accountable, Responsible and Transparent AI (ART-AI) - a UK Research and Innovation (UKRI) funded Centre for Doctoral Training (CDT). UKRI, with its strategic investment in artificial intelligence research, seeks to support the use of artificial intelligence advances for "application-driven research and innovation in discovery science and in areas such as health, the environment, agriculture, security, and government policy". In particular, according to the UKRI's "Transforming Our World With AI" report, they are looking to encourage the adoption of AI technologies to "manage smart energy networks, tackle climate change and deliver net zero CO2 targets". This project is strongly aligned with these aims, by seeking to answer pressing questions about the installation of LCTs and the design of smart energy networks that influence their usage. Furthermore, this project seeks to specifically conduct AI research in a responsible manner, while establishing interpretable machine learning techniques. As decisions must be made for power systems with technologies, behaviour, and phenomena that will not be representable from existing power system data, this project is using model-based methods so that scientifically-determined rules and human judgement are used (as well as data). This approach should therefore result in conclusions that properly account for the anticipated uncertainty associated with power systems, and avoid the overconfident and misguided predictions that could result from purely data-driven approaches. If successful, the methods used may be instructive for increasing the robustness of AI-based research to distributional shifts. Additionally, it is important that this research, as it looks to inform the design of consumer electricity pricing schemes, be transparent and auditable. By eschewing black-box techniques, and instead building models with interpretable variables, unjust correlations between different household attributes and network usage costs can be easily determined.