Exploring the uncertainties of probabilistic LV network analysis

Tools

Frame, Damien Fleming and Ault, Graham; (2012) Exploring the uncertainties of probabilistic LV network analysis. In: CIRED 2012 Workshop Integration of Renewables into the Distribution Grid. IET, PRT. ISBN 978-1-84919-628-4 (https://doi.org/10.1049/cp.2012.0760)

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Abstract

The analysis of low voltage (LV) networks has received increasing attention in recent times as the emphasis on carbon reduction drives increased deployment of low carbon technology that will primarily interact with the LV network at the customer point of connection. Techniques have evolved over the last few decades from simple voltage drop calculations to more complex analysis. Where balanced conditions were often assumed and positive sequence models used, three-phase analysis of unbalanced conditions is now becoming more common for detailed studies at LV. However, at present in the UK, the need for this level of detail has been limited and the majority of analysis is conducted at 33/11kV using packages with standard single line model steady state analysis. Many Distribution Network Operators do not use a specific software package for LV studies and those that do will use a separate tool from that used on 33/11 kV studies . Planning and operating distribution networks with increased levels of low-carbon technology and customer participation is at the heart of many Smart Grid conversations and although opinions and definitions of the Smart Grid vary, all feature the requirement for increased visibility and understanding of the behaviour of these LV connected technologies and the need for new methods to plan and operate such networks. Research and development of the active control algorithms required, their operational implementation and the most appropriate planning frameworks for the Smart Grid requires detailed distribution system analysis that allows the effects of distributed energy resources and proposed control methodologies to be investigated at all levels of the network. For example, identifying the time and size of peak load on an LV feeder containing Electric Vehicles (EV), Heat Pumps, PV, combined heat and power generation (CHP) and electric water/space heating could be a considerable challenge and a planning approach that allows detailed analysis of phase unbalance and voltage/thermal constraints may be required. In addition, should there be varying degrees of control that could dispatch these DER in response to local or system events or market signals, additional uncertainty is added to the planning problem. Probabilistic, time-series load flow techniques are being increasingly used to provide the kind of analysis necessary to capture the stochastic nature of LV customer load profiles and DER behaviour. Rather than a worst case scenario analysis this approach provides information on the probabilities of attributes such as network constraints and system losses that could then form the basis of planning decisions. This paper reviews some recent approaches to and applications of probabilistic analysis of LV networks and considers the uncertainty introduced by the underlying assumptions. A specific case study analysis of EV penetration on a generic UK LV network is used to investigate the effect of key assumptions on the results of a probabilistic analysis. Common assumptions used to build LV network models from only positive sequence data are explored and varying approaches to generating synthetic load profiles are considered. The impact of these assumptions on the results of a probabilistic analysis is considered and the potential role of probabilistic analysis in distribution network planning is discussed.

ORCID iDs

Frame, Damien Fleming ORCID logoORCID: https://orcid.org/0000-0003-3236-2738 and Ault, Graham ORCID logoORCID: https://orcid.org/0000-0001-6369-4300;
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