Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.728067
Title: Capability assessment of VAr support and demand response to transmission network using flexible tap changing techniques in distribution networks
Author: Guo, Yue
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2017
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Abstract:
Due to the increasing integration of renewable energy generations, the overvoltage and overload issues in transmission networks have become more significant, and they may occur at various locations. To mitigate the overvoltage issues, traditional solutions which often consider the installation of reactive power compensators such as shunt reactors, SVC, STATCOM may not be cost-effective. To mitigate the overload issues, traditional methods using direct or price-based demand control will affect customers’ electrical experience in that they are inconvenienced greatly. This thesis discusses the flexible tap changing techniques that utilise existing parallel transformers in distribution networks to provide reactive power absorption and demand response services for transmission systems. Among them, the tap stagger technique operates parallel transformers in small different tap positions, i.e. staggered taps, to result in more reactive power absorption from upstream networks. In addition, the tap changing technique changes voltages in the range of statutory limits through the adjustment of tap positions in order to change network demands without directly affecting customers. The aggregated reactive power absorption or demand response from many pairs of parallel transformers in distribution networks could be sufficient to provide VAr or demand support to transmission networks. Network capability studies have been carried out in OpenDSS simulation software to investigate the VAr absorption capability by using tap staggering technique and the demand reduction capability by using tap changing technique. The studies are based on two UK HV distribution networks (132-33kV) with 11 and 28 primary substations (33/11 or 6.6 kV) respectively, and the techniques are applied to parallel transformers in primary substations. Based on the results of the two networks, the capabilities of the whole ENW and the UK distribution networks have been estimated respectively by using linear estimation method. In addition, the VAr absorption capability of the tap stagger technique has been validated by using site trial data. The results show an average VAr absorption capability of 0.89MVAr for a primary substation, 315MVAr for ENW networks and about 2500MVAr for the UK at stagger level 4 and show an average demand reduction capability of 3.1% of the original demand at tap down level 3. The results of capability studies together with the validations results confirm that the flexible tap changing techniques are able to provide transmission networks with effective VAr support and demand response services. To assess network VAr absorption and demand response capability more precisely, this thesis also proposes an online load profile estimation method to estimate the load profiles of the network more accurately if not all substations in the network are monitored. The method uses Peak Load Share values, Euclidean Distance, and some load measurements to estimate load profiles. The method has been validated and compared with a traditional aggregation-based method. The results show an average estimation error of 13% ~ 23% in different conditions using the proposed method, and show an average estimation error reduction from about 47% (using the traditional method) to about 13% (using the proposed method). The results indicate that the developed method has a considerable improvement on the accuracy of load profile estimation.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.728067  DOI: Not available
Keywords: Ancillary Service ; Voltage/reactive power control ; Demand Response ; Tap Stagger
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