Abstract
Load modelling plays a key role in assessing peak load reduction and has attracted renewed interest recently due to increasing diversity and uncertainty in load characteristic resulted by emergence of new types of loads and more penetrations of distributed renewable generations onto electricity networks. This paper presents a methodology for the development of a static load model based load characteristic profile in medium voltage distribution networks from year-long field measurements. High-resolution load monitoring devices with the 1 s sampling rate installed at 60 primary substations in the U.K. distribution network are used to collect the data for the purpose of load modelling. Load models at these 60 substations comprising domestic, commercial/industrial, and mixed-type load demand are presented in the paper together with relevant model parameters. Field trials (which involve triggering transformer taps to initiate voltage changes) were performed throughout the year on 15 selected substations aimed at load profile validation. The developed load profiles take into account seasonal, weekly, and daily variations of P-V and Q-V characteristics. Representative 24 h (0.5 h) load matrices are developed for all 60 substations and provide an insight into the operational flexibility and network resilience to voltage variations.
Original language | English |
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Pages (from-to) | 1848-1859 |
Number of pages | 12 |
Journal | IEEE Transactions on Power Systems |
Volume | 33 |
Issue number | 2 |
Early online date | 17 Aug 2017 |
DOIs | |
Publication status | Published - Mar 2018 |
Keywords
- distribution networks
- load characteristic profile
- measurement based load modelling
- smart grid trials
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Increasing renewable energy and reducing customer bills: using managed connections and flexible demand response controls in the electricity network to support decarbonisation with the minimum infrastructure investment.
Li, H. (Participant), Martinez-Cesena, E. (Participant), Milanovic, J. (Participant), Wang, Z. (Participant), Ochoa, L. (Participant), Mancarella, P. (Participant) & Jones, C. (Participant)
Impact: Environmental, Economic