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Communication-based load allocation and restoration to improve efficiency and resilience in smart grids integrated with renewables and electric vehicles

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posted on 2022-03-28, 13:18 authored by Pouya Jamborsalamati
Recent developments in the area of advanced information and communication technologies offer provisions to power utilities to run an elegant load allocation and restoration in smart grids. Communication-enhanced platforms enable higher situational awareness with more grid components getting actively involved in load allocation and restoration, which results in higher network flexibilities and lower grid demand. For instance, multi-functional Electric Vehicle (EV) chargers, in aggregated level, can enable new ancillary services for operators to use (e.g. EV-assisted Volt-Var Control (VVC)). In addition, service restoration after a High-Impact Low-Probability (HILP) event in the grid could be improved by modern grid components, such as EVs and Distributed Energy Resources (DERs), to be dispatched during the restoration. This results in higher restoration rapidity and robustness in future grids. Communication-based load allocation and restoration can improve various phases of the power system behavior from planning (preventive activities in pre-event period) to operation (corrective activities in post-event time interval). This Ph.D. study encompasses chapters on both planning and operation stages. A resilience-oriented multi-objective decision-making framework has been designed in this work to plan for higher structural resilience in the grid subject to HILP events.Unlike the widely accepted standard metrics for reliability assessment in power distribution systems (e.g. system average interruption duration index (SAIDI), system average interruption frequency index (SAIFI), energy not supplied (ENS) etc.), a resilience index which quantifies resilience features such as preparedness, robustness, and restorative/disruptive rapidity is missing. A novel multi-dimensional resilience metric is proposed in this Ph.D.study to be adopted by power utilities to evaluate resilience of the grid and optimize the aforementioned characteristics of power system behavior. For operational resilience, a communication-based EV-assisted load restoration system is designed and implemented in this Ph.D. work. Unlike the past studies, the proposed solution harnesses (a) the imported power and flexibility from the neighboring networks, (b)Distributed Energy Resources (DERs), and (c) aggregated vehicle to grid (V2G) capacity in all steps of restoration when facing an extreme HILP incident with multiple faults. The proposed real-time SR mechanism is implemented using the RTDS Hardware In the Loop (HIL) platform and the contribution of each SR resource was numerically quantified by the developed resilience metric in previous chapter. The proposed solution ensures an enhanced feeder-level resourcefulness that can contribute to agile response and efficient recovery. This is primarily achieved by a strategic deployment of major modern resources (with focus on EVs contribution) during a sequence of multiple faults -- abstract.

History

Table of Contents

1. Introduction -- 2. Literature review -- 3. MQTT-Based load allocation for grid demand reduction in smart neighborhoods considering unreliable communication links -- 4. Load restoration planning to improve resilience in power distribution networks: a multi-objective decision support -- 5. Enhancing power grid resilience through an IEC61850-based EV-assisted load restoration -- 6. Conclusion and future work -- References -- Appendix: Miscellaneous applications of the proposed IOT platform in Chapter 3.

Notes

Theoretical thesis. Bibliography: pages 113-130

Awarding Institution

Macquarie University

Degree Type

Thesis PhD

Degree

PhD, Macquarie University, Faculty of Science and Engineering, School of Engineering

Department, Centre or School

School of Engineering

Year of Award

2020

Principal Supervisor

Jahangir Hossain

Rights

Copyright Pouya Jamborsalamati 2020. Copyright disclaimer: http://mq.edu.au/library/copyright

Language

English

Extent

1 online resource (xx, 135 pages) illustrations

Former Identifiers

mq:71524 http://hdl.handle.net/1959.14/1275261