Since 2004, Aston University has been delivering work-based learning (WBL) engineering degrees to key UK Energy sector employers, such as National Grid. National measures for widening participation in HE, such as the Degree Apprenticeship Levy, have led to significant changes in learning background diversity of WBL cohorts, consequently increasing student requirement for additional learning-support in HE Institutions (HEIs). To address these challenges, an intervention strategy was formulated in collaboration with Aston University's Learning Development Centre. Our methodology gradually embedded a provision of tailored learning-support sessions/ workshops in mathematics and effective communication skills within WBL curricula. Integrating this support has led to marked increases in student engagement,grade-attainment, and stakeholder satisfaction. This case study is pertinent to HE's current STEM sector focus on developing WBL programmes, where the flexible methodologies established here can serve as practical models for other HEIs in the delivery of 'in-employment' education, in response to the fastchanging workplace.
By 2020, smart meters will potentially provide the UK's distribution network operators (DNOs) with more detailed information about the real-time status of the low-voltage (LV) network. However, the smart meter data that the DNOs will receive has a number of limitations including the unavailability of some real-time smart meter data, aggregation of smart meter readings to preserve customer privacy, half-hourly averaging of customer demand/ generation readings, and the inability of smart meters to identify the connection phases. This research investigates how these limitations of the smart meter data can affect the estimation accuracy of technical losses and voltage levels in the LV network and the ways in which 1 min losses and correct phasing patterns can be determined despite the limitations in smart data.
Losses on low voltage networks are often substantial. For example, in the UK they have been estimated as being 4% of the energy supplied by low voltage networks. However, the breakdown of the losses to individual conductors and their split over time are poorly understood as generally only the peak demands and average loads over several months have been recorded. The introduction of domestic smart meters has the potential to change this. How domestic smart meter readings can be used to estimate the actual losses is analysed. In particular, the accuracy of using 30 minute readings compared with 1 minute readings, and how this accuracy could be improved, were investigated. This was achieved by assigning the data recorded by 100 smart meters with a time resolution of 1 minute to three test networks. Smart meter data from three sources were used in the investigation. It was found that 30 minute resolution data underestimated the losses by between 9% and 24%. By fitting an appropriate model to the data, it was possible to reduce the inaccuracy by approximately 50%. Having a smart meter time resolution of 10 minutes rather than 30 gave little improvement to the accuracy.
Access to smart meter data will enable electricity distribution companies to have a far clearer picture of the operation of their low voltage networks. This in turn will assist in the more active management of these networks. An important current knowledge gap is knowing for certain which phase each customer is connected to. Matching the loads from the smart meter with the loads measured on different phases at the substation has the capability to fill this gap. However, in the United Kingdom at the half hourly level only the loads from groups of meters will be available to the network operators. Therefore, a method is described for using this grouped data to assist with determining each customer's phase when the phase of most meters is correctly known. The method is analysed using the load readings from a data set of 96 smart meters. It successfully ranks the mixed phase groups very highly compared with the single phase groups.
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