Search

With low voltage (LV) distribution networks increasingly being re-purposed beyond their original design specifications to accommodate low carbon technologies, the ability to accurately calculate their actual spare capacity is critical. Traditionally, within the Great Britain (GB) power system, there has been limited monitoring of LV distribution networks, making this difficult. This paper proposes a method for estimating spare capacity of unmonitored LV networks using demand data from customer Smart Meters. In particular, the proposed method infers existing LV network capacity, as well as losses, across scenarios where only a limited number of customers have Smart Meters installed. Typical daily load profiles across customers with Smart Meters are learned using a Dirichlet sampled Gaussian mixture model (GMM). Learned profiles are then applied to all unmetered customers to estimate network parameters. Method accuracy is assessed by comparing estimations with simulated, fully observed, LV network models. The method is also compared to benchmark models for establishing unobserved demand profiles. Overall, results in the paper show that the proposed method outperforms benchmark models in terms of accurately assessing substation headroom, particularly in scenarios where only 10–50% of customers have Smart Meters installed. [ABSTRACT FROM AUTHOR]

Proposed future UK energy supply infrastructures harbour ambitions for achieving a sustainable and reliable energy network, which can potentially be realised through a decentralised and flexible approach to electricity generation. Such an energy system will be expected to deliver these objectives through the widespread use of distributed energy resources, thereby contributing to the international aim of a low carbon future. Existing research in the area of distributed energy resource integration has developed the energy hub methodology [Geidl M, Koeppel G, Favre-Perrod P, et al. The energy hub – a powerful concept for future energy systems. In: Proceedings of the third annual Carnegie Mellon conference on the electricity industry, Tepper School of Business, Carnegie Mellon University, 13–14 March 2007, pp.1–10.] to formally model flows within an energy network, abstracting generation from multiple energy carriers (e.g. electricity, wind and natural gas) and allowing a modular energy network construction that facilitates optimal placement of distributed energy resources within each hub. This article will present the energy hub approach to modelling a decentralised energy network, illustrated with two case studies from the East and West coasts of Scotland. The resulting analysis will show the impact of two scenarios with different energy mixes available whilst reflecting the subtle differences in demands across areas with different weather profiles. This is a fundamental progression of existing distributed energy resource research to date in that individual characteristics and costs of a mixture of technologies are modelled. Such analysis will be pivotal in planning future networks to ensure that they function optimally and also in directing policy to encourage consumers to adopt a different approach to energy generation. [ABSTRACT FROM AUTHOR]

Power distribution networks are increasingly challenged by ageing plant, environmental extremes and previously unforeseen operational factors. The combination of high loading and weather conditions is responsible for large numbers of recurring faults in legacy plants which have an impact on service quality. Owing to their scale and dispersed nature, it is prohibitively expensive to intensively monitor distribution networks to capture the electrical context these disruptions occur in, making it difficult to forestall recurring faults. In this paper, localised weather data are shown to support fault prediction on distribution networks. Operational data are temporally aligned with meteorological observations to identify recurring fault causes with the potentially complex relation between them learned from historical fault records. Five years of data from a UK Distribution Network Operator is used to demonstrate the approach at both HV and LV distribution network levels with results showing the ability to predict the occurrence of a weather related fault at a given substation considering only meteorological observations. Unifying a diverse range of previously identified fault relations in a single ensemble model and accompanying the predicted network conditions with an uncertainty measure would allow a network operator to manage their network more effectively in the long term and take evasive action for imminent events over shorter timescales. [ABSTRACT FROM AUTHOR]