  The increasing use and spread of low carbon technologies are expected to
cause new patterns in electric demand and set novel challenges to a
distribution network operator (DNO). In this study, we build upon a recently
introduced method, called "buddying", which simulates low voltage (LV) networks
of both residential and non-domestic (e.g. shops, offices, schools, hospitals,
etc.) customers through optimization (via a genetic algorithm) of demands based
on limited monitored and customer data. The algorithm assigns a limited but
diverse number of monitored households (the "buddies") to the unmonitored
customers on a network. We study and compare two algorithms, one where
substation monitoring data is available and a second where no substation
information is used. Despite the roll out of monitoring equipment at domestic
properties and/or substations, less data is available for commercial customers.
This study focuses on substations with commercial customers most of which have
no monitored `buddy', in which case a profile must be created. Due to the
volatile nature of the low voltage networks, uncertainty bounds are crucial for
operational purposes. We introduce and demonstrate two techniques for modelling
the confidence bounds on the modelled LV networks. The first method uses
probabilistic forecast methods based on substation monitoring; the second only
uses a simple bootstrap of the sample of monitored customers but has the
advantage of not requiring monitoring at the substation. These modelling tools,
buddying and uncertainty bounds, can give further insight to a DNO to better
plan and manage the network when limited information is available.
