Study of Power Distribution System Resilience in the Presence of E-Mobility Ecosystems

Study of Power Distribution System Resilience in the Presence of E-Mobility Ecosystems

Vandana Kumari, Sanjib Ganguly
Copyright: © 2024 |Pages: 30
DOI: 10.4018/979-8-3693-2611-4.ch007
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Abstract

Power distribution networks (PDNs) are most susceptible to extreme events, such as cyberattacks, hurricanes, floods, and wildfires, in a power system. Enhancing the resilience to withstand and recover after disruptive events is crucial for planners and operators of PDNs. The resilience-oriented planning and operation can enhance the resilience of PDNs. Furthermore, the significant EV integration, in recent years, provides a potential for improving resilience by restoration in PDNs. Considering the numerous studies regarding deployment of EVs for load restoration after disruptive events, this chapter provides the idea of resilience and the vulnerability of different components of PDNs due to various HILP (high impact, low probability) events. Furthermore, EV application to enhance the resilience of PDNs is reviewed with a detailed discussion of the methods, objectives, and solution strategies. In addition, a qualitative assessment of the studied literature is provided to enhance the comprehension of state-of-the-art resilience improvement methods of EV-enabled PDNs.
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Abbreviations

  • CACG: Column and constraint generation

  • DG: Distributed generation

  • DSM: Demand side management

  • DSO: Distribution system operator

  • ESS: Energy storage system

  • EV: Electric vehicle

  • HILP: High impact low probability

  • MEG: Mobile emergency generator

  • MESS: Mobile energy storage system

  • MILP: Mixed integer linear programming

  • MINLP: Mixed integer non-linear programming

  • MIQCP: Mixed integer quadratic constraint programming

  • MISNLP: Mixed integer stochastic nonlinear programming

  • MISOCP: Mixed integer second-order cone programming

  • MPS: Mobile power source

  • PDN: Power distribution network

  • NR: Network Reconfiguration

  • TN: Transportation Network

  • TSROP: Two stage robust optimization problem

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1. Introduction

In recent years, the rate and intensity of HILP events, such as earthquakes, hurricanes, tornadoes, floods, wildfires, and other natural or man-made attacks, have intensified worldwide. Table 1 lists the count of distinct natural calamities recorded in 1970 and 2022 (Ritchie et al., 2022). The combustion of fossil fuels, deforestation, and livestock farming are exerting growing influence on the earth's climate and temperature. This contributes substantial quantities of greenhouse gases to those that occur naturally in the atmosphere, intensifying the greenhouse effect and global warming. The primary cause of climate change is the greenhouse effect, where certain gases in the earth's atmosphere function similarly to the glass panels of a greenhouse. These glasses trap the sun's heat, preventing it from dissipating into space and thereby leading to global warming. The main factor behind the surge in the number of natural calamities is the changing climate (Panteli and Mancarella, 2015a). Natural disaster adversely affects both lives as well as critical infrastructures of society. The power system is among most vital infrastructures. The frequent natural disaster events cause grievous damage to power system infrastructure, resulting in widespread and extended power outages (Panteli et al., 2017). Therefore, to secure the power system from these unpredictable severe events and to keep light on even during and after the event, the power system's resilience comes into relevance. Power system resilience is the ability of the system to withstand disruptive events and recover quickly after the event (Gholami et al., 2018).

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