Resilient Operation of Electric Vehicles considering Grid Resiliency and Uncertainties

Author:

Armaghan Seyed Mostafa1,Asgharzadeh Fatemeh2,Yousefi-Khanghah Babak2ORCID,Ashrafi Hamid Reza3

Affiliation:

1. Faculty of Electrical Engineering, Department of Power, Amirkabir University of Technology, Tehran, Iran

2. Department of Electrical Engineering, Tabriz University, Tabriz, Iran

3. Department of Electrical Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran

Abstract

This paper proposes a novel short-time optimization method based on a resiliency enhancement strategy for a smart distribution network during adverse weather conditions. The key idea is to integrate various electric vehicles (EVs) with different behaviour patterns and other distributed energy resources into emergency tools of distribution network operation such as topology reconfiguration and grid-supported services. In this regard, possible management programs for three types of EVs (with different levels of control potentials), energy resources, reconfiguration, and demand response programs impacts on boosting resiliency are investigated. The framework is organized on coordination and benefit sharing among distribution system operator (DSO), private sector, and EV owners to reduce possible side effects of failures that could be incepted by extreme weather conditions. A Monte Carlo-based stochastic simulation for modelling uncertainties such as EVs movement, state of change, arrival/departure time to charge stations, and weather-based failures is devised. In order to evaluate resiliency, metrics based on a multiphase method are analysed. The performance of the proposed method on extra benefit obtaining for DSO and private sector and also resiliency enhancement in disturbance progress, postdisturbance degraded, and restorative state are carried out on IEEE-33 bus test system, and obtained results are analysed in detail.

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Modeling and Simulation

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