Automating the Verification of the Low Voltage Network Cables and Topologies

Low Voltage (LV) networks are increasingly required to cope with challenges they were not designed for, requiring for more active network management (ANM). Crucially, ANM solutions require the availability of accurate network information. In practice, available data on LV networks can be incomplete, a problem often overlooked in prior ANM research. For example, in the U.K. and many developed countries, the lifetime of distribution networks assets spans several decades, with some of the available asset data gathered and maintained over many years. This can often lead to incomplete cable data being available to network operators. To overcome this, we propose a novel machine learning technique to autonomously approximate the missing cable information in LV networks. Our proposed algorithm uses a tree-based search methodology, which approximates the missing cable's cross section area (XSA) data based on rules engineers used when designing the LV networks. We validate our approach using a large database of real LV networks, where some of the cables' XSA are treated as unknown and used as ground truth to evaluate the accuracy of the predictions. Moreover, we propose a mechanism that scores the confidence level of the prediction, information which is then presented to the human network planners

Models for efficient control and fair sharing of assets in energy communities

In recent times, energy communities have gained significant interest. These communities empower citizen prosumers by leveraging their own renewable energy generation and storage assets to manage their energy requirements and engage in the broader energy market. Such communities offer a promising solution for sustainable energy systems, promoting renewable integration and active user involvement. Within energy communities, members can engage in energy trading and invest in shared assets like production units, energy storage, and network infrastructure. However, efficiently controlling these assets in real-time and equitably distributing energy outputs among diverse members with varying needs remains a vital challenge. Addressing this concern is of both research and practical importance. It is essential to consider technical constraints like local low-voltage network characteristics and power ratings during this process. To tackle these challenges, this thesis presents a model that examines the techno-economic benefits of community-owned versus individually-owned energy assets, accounting for physical asset degradation and network constraints. Employing cooperative game theory principles, the thesis proposes a redistribution model for community benefits based on the marginal contribution of each household. This redistribution mechanism utilizes the concept of marginal value from coalitional game theory and distributed AI (specifically the multi-agent system). Study results demonstrate that the proposed marginal cost redistribution mechanism is fairer and more computationally manageable than existing state-of-the-art methods, thus providing a scalable approach for economic sharing of joint assets in community energy systems. However, integration of centrally shared community-owned energy assets may face limitations due to network/grid constraints. To address this issue, the thesis proposes a novel framework for a local peer-to-community (P2C) market mechanism as an alternative solution to investing in community-owned assets. The dynamics of the P2C market mechanism are studied for three different types of P2C sellers with non-uniform pricing schemes and tested across various community settings (comprising a mix of prosumers and consumers) and different rates of renewable energy adoption. All proposed models are validated and applied to a real case study from a large-scale smart energy demonstration project in the UK, using a substantial dataset of real renewable generation and demand. This practical case study provides confidence in the robustness of the experimental comparison results presented in the thesis.Engineering and Physical Sciences Council (EPSRC) Doctoral Training Programme (DTP) grant (EP/R513040/1