A genetic algorithm approach for modelling low voltage network demands. (1st October 2017)

• Record Type: Journal Article
• Authors: Giasemidis, Georgios; Haben, Stephen; Lee, Tamsin; Singleton, Colin; Grindrod, Peter
• Is Part Of: Applied energy. Volume 203(2017)
• Page Start: 463
• Page End: 473
• Publication Date: 2017-10-01
• Subjects: Low voltage networks -- Load demand modelling -- Genetic algorithm -- Buddying; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.06.057
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A genetic algorithm approach for modelling network electricity demand. Method provides real profiles to unmonitored customers, simple to implement by DNOs. Results on the accuracy of the profiles and the peak errors on real feeders. Results more accurate than a comparable Monte Carlo approach at the feeder level. Important relationships which can be used in planning and decision making by DNOs. 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.