Abstract
In order to improve low voltage (LV) network visibility without extensive monitoring and integrate low carbon technologies (LCTs) in a cost-effective manner, this paper proposes a novel three-stage network load profiling method. It uses real-time information monitored from selective representative areas to develop network templates. The three stages are: clustering, classification and scaling. It can be used to identify the loading conditions of unmonitored LV systems with similar fixed data to those monitored LV substations. In the clustering stage, hierarchical clustering and K-means are used to cluster substations into groups based on the shape of the monitored load profiles. The classification tool designed with multinomial logistic regression maps an unmonitored LV substation into the most probable templates by using routinely available fixed data. Finally, clusterwise weighted constrained regression is employed to estimate peak for individual LV substations and the developed templates. The three-stage profiling is demonstrated on a practical system in the U.K. under the umbrella of a smart grid trail project. Ten LV templates are developed by using the metered data from 800 monitored LV substations. A comprehensive comparison between the estimated peaks using the three-stage process and the metered peaks suggests that the methodology can achieve superior accuracy. This is part I of the paper, introducing clustering and classification. The scaling (peak estimation) process will be introduced in part II of the paper.
Original language | English |
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Article number | 6981990 |
Pages (from-to) | 3036-3044 |
Number of pages | 8 |
Journal | IEEE Transactions on Power Systems |
Volume | 30 |
Issue number | 6 |
Early online date | 10 Dec 2014 |
DOIs | |
Publication status | Published - 30 Nov 2015 |
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Chenghong Gu
- Department of Electronic & Electrical Engineering - Professor
- Centre for Sustainable Energy Systems (SES)
- Centre for Climate Adaptation & Environment Research (CAER)
- Centre for Regenerative Design & Engineering for a Net Positive World (RENEW)
- IAAPS: Propulsion and Mobility
Person: Research & Teaching, Core staff, Affiliate staff
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Furong Li
- Department of Electronic & Electrical Engineering - Professor
- Centre for Doctoral Training in Decarbonisation of the Built Environment (dCarb)
- Centre for Sustainable Energy Systems (SES)
- IAAPS: Propulsion and Mobility
- Institute of Sustainability and Climate Change
Person: Research & Teaching, Core staff, Affiliate staff