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Title: Gaining insight into the smart grid by analysing smart metering data
Author: Chen, Qipeng
ISNI:       0000 0004 6059 3733
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2016
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By 2020, the majority of EU and US consumers will have smart meters installed. This thesis considers the exploitation of the potential to analyse smart metering data to help gain insight into the medium voltage electrical power systems' operating conditions, the low voltage electrical' power systems' topology information and consumers' power consumption habits. Distribution system state estimation can estimate a medium voltage system's electrical quantities. Its inputs involve transformers' loads that can be given by the aggregation of smart metering data. However, such loads have errors due to the lack of synchronisation among smart meters, the power loss in low voltage systems and the delay of meter data collection, so the subsequent impact of these three issues on the performance of the distribution system state estimation in MV systems are analysed, in order to determine whether they are barriers to gaining insight into these systems' operating conditions. The results show that: the first issue does not show a clear affect; the impact of the second issue is obvious; and the third issue significantly degrades the state estimation performance. To increase the insight into a low voltage system's topology, a phase identification method is designed and a topology identification technique is studied. The benefit of topology identification for state estimation is further assessed - limited benefit is shown, especially when the smart metering data used has low accuracy. The utility companies can provide specialised services to guide their electricity consumers to correctly control their own power demand, so power systems' energy efficiency may be increased. This requires the knowledge about consumers' power consumption habits. Therefore, in this work such knowledge is discovered from consumers' smart metering, socio-demographic and other data by three rule induction techniques. It is shown how, for example, those consumers with high potential for peak demand shifting can be targeted.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available