  Distribution network operators (DNOs) are increasingly concerned about the
impact of low carbon technologies on the low voltage (LV) networks. More
advanced metering infrastructures provide numerous opportunities for more
accurate load flow analysis of the LV networks. However, such data may not be
readily available for DNOs and in any case is likely to be expensive. Modelling
tools are required which can provide realistic, yet accurate, load profiles as
input for a network modelling tool, without needing access to large amounts of
monitored customer data. In this paper we outline some simple methods for
accurately modelling a large number of unmonitored residential customers at the
LV level. We do this by a process we call buddying, which models unmonitored
customers by assigning them load profiles from a limited sample of monitored
customers who have smart meters. Hence the presented method requires access to
only a relatively small amount of domestic customers' data. The method is
efficiently optimised using a genetic algorithm to minimise a weighted cost
function between matching the substation data and the individual mean daily
demands. Hence we can show the effectiveness of substation monitoring in LV
network modelling. Using real LV network modelling, we show that our methods
perform significantly better than a comparative Monte Carlo approach, and
provide a description of the peak demand behaviour.
