A genetic algorithm approach for modelling low voltage network demands

Abstract

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.

Predicting key features of a substation without monitoring

“…Lastly, we showed how our predictions for mean peak value, and mean half-hourly load, can be used within a buddying framework [12]. Aggregating across assigned smart meter profiles on a substation, we matched the substation behaviour accurately, even when compared with alternative methods which require substation monitoring.…”

“…This information alone helps DNOs plan and manage, for example, knowing the average and peak load at a substation enables a DNO to make informed decisions regarding load management via storage devices [8]. Secondly, we demonstrate that this method can be used within a so-called 'buddying framework' [12] to provide a realistic half-hourly load profile for a single household.…”

“…However, DNOs can install high resolution monitoring at the substation level and thus understand the aggregated demand, and potentially infer the customer demands [12]. Using a genetic algorithm, Giasemidis et al [12] assign a smart meter profile to domestic customers based on demand at the substation, and households' quarterly meter readings. Their genetic algorithm assigns properties on a substation to a particular smart meter profiles in a manner that minimises the difference between the estimated aggregated demand, and actual aggregated demand (from substation monitoring), and it also minimises the difference between the actual daily load (from quarterly meter readings) and the daily load of the assigned smart meter profile.…”

“…Therefore, it is an important tool for a DNO, since even if smart meter data can be purchased, it is likely to be expensive [16]. However, DNOs can install high resolution monitoring at the substation level and thus understand the aggregated demand, and potentially infer the customer demands [12]. Using a genetic algorithm, Giasemidis et al [12] assign a smart meter profile to domestic customers based on demand at the substation, and households' quarterly meter readings.…”

“…The aggregate load from this estimated population is calculated and compared to the actual load at the substation. The estimate and actual load is only compared at two points (whereas [12] aim to match every half hour over several weeks). That is, the fitness function proposed here minimises the mean squared error (RMSE) of the estimated mean peak value, and the mean half-hourly load,…”