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Record W7071539453

Smart Operation of Four-Quadrant Electric Vehicle Chargers in Distribution Grids

2017· dissertation· en· W7071539453 on OpenAlexfundno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2017
Typedissertation
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsBattery (electricity)Electric vehicleSmart gridContext (archaeology)Electric-vehicle batteryGridVehicle-to-gridPower (physics)Control (management)
DOInot available

Abstract

fetched live from OpenAlex

Many policies and programs adopted in the context of climate change mitigation and substitution of fossil fuels are contributing to the continuous development and growth of Electric Vehicles (EVs) in urban mobility systems, reaching 1.26 million units on the roads through the end of 2015. Even though the increasing number of EVs will create problems in distribution systems, which can be mitigated using smart charging strategies, there will also be economic opportunities for EV owners to provide services to the grid while their vehicle are parked and plugged in, a concept known as Vehicle-to-Grid (V2G). Most of the studies on V2G have concentrated on the provision of services such as frequency regulation or spinning reserves, which may reduce the battery life because of the required extra charging/discharging cycles, and little attention has been paid to the possibility of providing reactive power control services to the grid by using the ac/dc converter and the dc link capacitor available in most advanced chargers, a practice that does not compromise the vehicle battery life. These kinds of chargers, which are known as four-quadrant EV chargers due to the capability of being operated in all quadrants of the P-Q plane, can be used in distribution networks to improve the power factor and help regulate voltage, thus facilitating larger EV penetrations, as discussed in this thesis. \n \nIn the first part of this thesis, a new average model of a single-phase, four-quadrant EV charger is developed. The steady-state and step responses of the proposed model for different P-Q requests, corresponding to the operation in the four quadrants of the P-Q plane, are used to validate its performance against a four-quadrant EV charger prototype. The model is shown to be useful for efficient time-domain simulations and studies that include a number of EV chargers, such as EV integration studies in Low-Voltage (LV) distribution networks. A practical case study is presented to demonstrate and test the performances of the four-quadrant charger and its model, investigating the voltage interactions of several chargers in an LV residential network during the provision of three vehicle-to-grid (V2G) strategies for active and reactive power. \n \nIn the second part, a novel three-stage algorithm to coordinate the operation of four-quadrant EV chargers with other volt/var control devices in Medium-Voltage (MV) and LV distribution feeders is proposed. The first stage of the algorithm is operated on a day-ahead basis and defines the Load Tap Changer (LTC) and capacitor schedules while minimizing the peak load associated with EVs in the distribution system. The second and third stages update their operation every five minutes, to fairly allocate the aggregated and individual EV loads in the MV and LV feeders, respectively, while minimizing active power losses and voltage deviations. The proposed technique is applied to CIGRE's North-American MV and LV benchmark systems to demonstrate its ability to properly allocate EV loads, and improve distribution system performance in terms of losses and voltage profiles.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.176
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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