Coordinated charging and discharging control of electric vehicles to manage supply voltages in distribution networks: Assessing the customer benefit
| dc.contributor.author | Nimalsiri, Nanduni | |
| dc.contributor.author | Ratnam, Elizabeth | |
| dc.contributor.author | Mediwaththe, Chathurika | |
| dc.contributor.author | Smith, David | |
| dc.contributor.author | Halgamuge, Saman | |
| dc.date.accessioned | 2024-03-14T00:43:23Z | |
| dc.date.issued | 2021 | |
| dc.date.updated | 2022-11-13T07:16:25Z | |
| dc.description.abstract | Increased worldwide uptake of Electric Vehicles (EVs) accentuates the need for developing coordinated EV charging and discharging methods that mitigate detrimental and sustained under-voltage and over-voltage conditions in distribution networks. In this paper, a centrally coordinated EV charge-discharge scheduling method is proposed, referred to as Network-aware EV Charging (and Discharging) N-EVC(D), that takes into account both EV customer economics and distribution grid constraints. Specifically, N-EVC(D) is designed to maintain quasi-steady-state feeder voltages within statutory power quality limits, while minimizing EV customer operational costs associated with: (1) purchasing (or otherwise being compensated for delivering) electricity on a time-of-use tariff; and (2) battery degradation due to frequent charging and discharging. The optimization problem for N-EVC(D) is formulated as a quadratic program, with voltage constraints to limit voltage variability across a radial distribution feeder, and individual EV constraints to satisfy heterogeneous EV charge requirements. In N-EVC(D), each grid-connected EV follows an operator-specified battery schedule that is obtained by solving the proposed quadratic program. A receding horizon implementation is also proposed to support near-real-time N-EVC(D) operations while accommodating non-deterministic EV arrivals and departures. The benefits of N-EVC(D) are assessed by means of numerical simulations carried out on an IEEE test feeder populated with a real-world dataset of residential load collected from households within an Australian distribution network. The simulation results confirm that N-EVC(D) mitigates non-compliant voltage deviations that would otherwise occur when voltage constraints are not enforced. Compared to uncoordinated EV charging, N-EVC(D) offers a 92% ? 111% reduction in the operational costs incurred by EV customers. | en_AU |
| dc.description.sponsorship | Nanduni I. Nimalsiri acknowledges the financial support of an Australian National University (ANU) Postgraduate Research Scholarship and a CSIRO Data61 PhD Scholarship. | en_AU |
| dc.format.mimetype | application/pdf | en_AU |
| dc.identifier.issn | 0306-2619 | en_AU |
| dc.identifier.uri | http://hdl.handle.net/1885/315985 | |
| dc.language.iso | en_AU | en_AU |
| dc.publisher | Pergamon Press | en_AU |
| dc.rights | Crown Copyright © 2021 Published by Elsevier Ltd. | en_AU |
| dc.source | Applied Energy | en_AU |
| dc.subject | Electric vehicles | en_AU |
| dc.subject | Quadratic program | en_AU |
| dc.subject | Receding horizon | en_AU |
| dc.subject | Supply voltages | en_AU |
| dc.subject | Time-of-use | en_AU |
| dc.subject | Vehicle-to-grid | en_AU |
| dc.title | Coordinated charging and discharging control of electric vehicles to manage supply voltages in distribution networks: Assessing the customer benefit | en_AU |
| dc.type | Journal article | en_AU |
| local.bibliographicCitation.lastpage | 12 | en_AU |
| local.bibliographicCitation.startpage | 1 | en_AU |
| local.contributor.affiliation | Nimalsiri, Nanduni, College of Engineering, Computing and Cybernetics, ANU | en_AU |
| local.contributor.affiliation | Ratnam, Elizabeth, College of Engineering, Computing and Cybernetics, ANU | en_AU |
| local.contributor.affiliation | Mediwaththe, Chathurika, College of Engineering, Computing and Cybernetics, ANU | en_AU |
| local.contributor.affiliation | Smith, David, College of Engineering, Computing and Cybernetics, ANU | en_AU |
| local.contributor.affiliation | Halgamuge, Saman, College of Engineering, Computing and Cybernetics, ANU | en_AU |
| local.contributor.authoruid | Nimalsiri, Nanduni, u6562870 | en_AU |
| local.contributor.authoruid | Ratnam, Elizabeth, u6837405 | en_AU |
| local.contributor.authoruid | Mediwaththe, Chathurika, u1049257 | en_AU |
| local.contributor.authoruid | Smith, David, u4593644 | en_AU |
| local.contributor.authoruid | Halgamuge, Saman, u1029002 | en_AU |
| local.description.embargo | 2099-12-31 | |
| local.description.notes | Imported from ARIES | en_AU |
| local.identifier.absfor | 490304 - Optimisation | en_AU |
| local.identifier.absfor | 400805 - Electrical energy transmission, networks and systems | en_AU |
| local.identifier.ariespublication | a383154xPUB20360 | en_AU |
| local.identifier.citationvolume | 291 | en_AU |
| local.identifier.doi | 10.1016/j.apenergy.2021.116857 | en_AU |
| local.identifier.thomsonID | WOS:000640380200003 | |
| local.publisher.url | https://www.elsevier.com/ | en_AU |
| local.type.status | Published Version | en_AU |
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