As one of the most promising pathways in the transition period towards the low carbon economy, a large scale implementation of electric vehicles (EV) is expected in the near future. Concentration of EV charging in a small area or within a short time will dramatically affect the load demand profile, especially the peak load in the distribution network. As a result, distribution transformers are facing hazards of shortened lifetime due to extra loads, and direct failures caused by potential overloads. Considering the large number of distribution transformers and the massive investment involved, the adaptability of the population of distribution transformers under future EV scenarios should be assessed. In this thesis, an assessment strategy for the future adaptability of distribution transformer population under EV scenarios is introduced. Assessing the adaptability is to understand the impact of the hot-spot temperature, loss-of-life, expected lifetime and failure probability of each individual in the distribution transformer population. Determination of hot-spot temperature of distribution transformers is essential for the assessment. In order to achieve accurate prediction of hot-spot temperatures under EV scenarios, thermal parameters should be refined for individual distribution transformers so that their thermal characteristics can be reflected more accurately than using the generic values recommended for all distribution transformers in the IEC loading guide. Two methods for the refinement are proposed in this thesis. One method is to curve-fit hot-spot temperatures measured in the extended heat run test; and the other is to calculate each parameter with developed equations in the loading guide with standard heat run test results. The assessment strategy is introduced and demonstrated on a group of selected distribution transformers from the population under three EV scenarios, i.e. Business as usual (BAU), High-range and Extreme-range scenarios, which represent 0%, 32% and 58.9% EV penetration levels respectively. Results show that EV charging would be less concerned on the acceleration of thermal ageing than the direct failure due to breakdown caused by decrease of dielectric strength in an event of bubbling. Since the peak load and hot-spot temperature under EV scenarios only last for a short time and would be compensated by low values during the rest time of a day, which eventually leads to a moderate thermal ageing. Occasionally, severe over-ageing can be caused by extremely high hot-spot temperatures, and the lifetime will be reduced to an unacceptable level. However, on such occasions, hot-spot temperatures would be high enough to trigger bubbling and reduce the dielectric strength of transformer's insulation system to a level that is incapable of undertaking the voltage stress, which eventually causes breakdown of transformers. In terms of the failure probability, results show that no distribution transformers are facing failure risks due to bubbling under BAU scenario. Failure starts under High-range scenario. If transformers possessing a failure probability over 50% are identified as high risk, then 13% of investigated transformers are at high risk under High-range scenario, while it increases to 39% under Extreme-range scenario. It is found that the failure probability is dominantly controlled by the peak load, other factors such as transformer age and installation conditions are less influential. A threshold peak load of around 1.5 p.u. is observed that distinguishes transformers in high risk from others under Extreme-range scenario. This observation could be applied to assist the asset management under future EV scenarios that the peak load of distribution transformers should be restricted below 1.5 p.u. to prevent potential failure due to bubbling.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:727856 |
| Date | January 2016 |
| Creators | Gao, Yuan |
| Publisher | University of Manchester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.research.manchester.ac.uk/portal/en/theses/assessment-of-future-adaptability-of-distribution-transformer-population-under-ev-scenarios(f2aafdab-2161-4968-9568-2550a80d673e).html |
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