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Voltage-led Load Management in UK Distribution Networks

Ballanti, Andrea

[Thesis]. Manchester, UK: The University of Manchester; 2018.

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Abstract

The growing uptake of wind and photovoltaic technologies requires further sources of system-level flexibility to avoid or defer significant investments. The ability to control, to some extent, customer demand (load management, LM) is one of these sources of flexibility. However, the direct involvement of a large number of customers makes the scalability of such approach a major challenge. A mostly unexplored solution to overcome the challenges of managing thousands or millions of customers is to leverage the positive correlation between voltage and demand. More precisely, Distribution Network Operators (DNOs) can control existing regulation devices to reduce customer voltages and so triggering a reduction in demand. This scheme, hereafter called voltage-led LM, avoids the direct involvement of customers overcoming one of the major barriers of traditional LM solutions. To understand whether this approach can be of any significance, a methodology able to quantify such reduction in demand need to be developed. However, the few methodologies available in the literature neglect the interactions across voltage levels and their influence on the benefits of the scheme. Moreover, time-varying demand profiles and load models are not always considered. Finally, the impact that the widespread adoption of distributed energy resources might have, is also neglected. This thesis addressed these gaps by developing a four-stage approach in which the time-varying volume of demand reduction that the scheme can unlock is quantified considering for the first time the influences among all voltage levels in distribution network. To reduce the complexity each voltage level is analysed separately whilst maintaining the corresponding dependencies. The methodology, also able to extrapolate the results at national scale, can quantify the impact that the uptake of residential scale PV units might have on the scheme. The methodology is demonstrated with a real UK case study where 10-min resolution time-series daily and seasonal analysis are performed. For the first time real network models across the whole distribution network, from 132 kV to 400 V, have been adopted. The interactions across voltage levels, the adoption of realistic load models, the variety of network models and the use of a time-varying approach, all aspects simultaneously considered for the first time in a case study, have shown to play a key role in the quantification. In Great Britain the scheme is expected to provide a significant volume of flexibility of around 1.8GW (60 GW of peak demand). The presence of PV, at least in the short term, has shown to have only a marginally effect on the benefits unlocked by the voltage-led LM scheme, making such scheme promising even in a low carbon future.

Bibliographic metadata

Type of resource:
Content type:
Form of thesis:
Type of submission:
Degree type:
Doctor of Philosophy
Degree programme:
PhD Electrical & Electronic Engineering (42 month)
Publication date:
Location:
Manchester, UK
Total pages:
235
Abstract:
The growing uptake of wind and photovoltaic technologies requires further sources of system-level flexibility to avoid or defer significant investments. The ability to control, to some extent, customer demand (load management, LM) is one of these sources of flexibility. However, the direct involvement of a large number of customers makes the scalability of such approach a major challenge. A mostly unexplored solution to overcome the challenges of managing thousands or millions of customers is to leverage the positive correlation between voltage and demand. More precisely, Distribution Network Operators (DNOs) can control existing regulation devices to reduce customer voltages and so triggering a reduction in demand. This scheme, hereafter called voltage-led LM, avoids the direct involvement of customers overcoming one of the major barriers of traditional LM solutions. To understand whether this approach can be of any significance, a methodology able to quantify such reduction in demand need to be developed. However, the few methodologies available in the literature neglect the interactions across voltage levels and their influence on the benefits of the scheme. Moreover, time-varying demand profiles and load models are not always considered. Finally, the impact that the widespread adoption of distributed energy resources might have, is also neglected. This thesis addressed these gaps by developing a four-stage approach in which the time-varying volume of demand reduction that the scheme can unlock is quantified considering for the first time the influences among all voltage levels in distribution network. To reduce the complexity each voltage level is analysed separately whilst maintaining the corresponding dependencies. The methodology, also able to extrapolate the results at national scale, can quantify the impact that the uptake of residential scale PV units might have on the scheme. The methodology is demonstrated with a real UK case study where 10-min resolution time-series daily and seasonal analysis are performed. For the first time real network models across the whole distribution network, from 132 kV to 400 V, have been adopted. The interactions across voltage levels, the adoption of realistic load models, the variety of network models and the use of a time-varying approach, all aspects simultaneously considered for the first time in a case study, have shown to play a key role in the quantification. In Great Britain the scheme is expected to provide a significant volume of flexibility of around 1.8GW (60 GW of peak demand). The presence of PV, at least in the short term, has shown to have only a marginally effect on the benefits unlocked by the voltage-led LM scheme, making such scheme promising even in a low carbon future.
Thesis main supervisor(s):
Thesis co-supervisor(s):
Language:
en

Institutional metadata

University researcher(s):

Record metadata

Manchester eScholar ID:
uk-ac-man-scw:313333
Created by:
Ballanti, Andrea
Created:
6th February, 2018, 11:28:34
Last modified by:
Ballanti, Andrea
Last modified:
2nd March, 2018, 10:30:43

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