A simulation-based probabilistic risk assessment of electric vehicles control strategies accounting for renewable energy sources

Roberto Rocchetta, Edoardo Patelli

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Distributed power Generator systems (DGs) and Renewable Energy Sources (RES) are promis-ing technologies, which have been proven to be beneficial for the power grid in severalways, e.g. improve voltage profile, reduce pollutant emissions and decrease power losses.Although an increasing renewable energy penetration may be beneficial, power produced bythose devices is affected by high uncertainty. Because of this, the use of renewable energycan/might lead to reduced reliability of power supply as well as increase system nonlinearityand complexity, posing new challenges for the future grid. Possibility of control chargeand discharge of the Electric Vehicles (EV) batteries, makes EVs an interesting technology,which may help improving the renewable energy penetration and prove ancillary service tothe customers. In the last years, several works have been focused on optimal Vehicle to Grid(V2G) and Grid to Vehicle (G2V) control states, with respect to different objectives and goals.Nevertheless, risk assessment frameworks to compare EVs control schemes seem in need offurther investigations. In this paper, a framework for EVs control strategies risk assessmentand comparison is presented. The framework allows to account uncertainties due to intrinsicrandomness in the environmental conditions and operative states of the system. A contingencyanalysis is performed and line overload and cascading index are obtained. A study case, themodified IEEE 24 nodes reliability test system, is used as test system for the framework.Risk analysis and contingency ranking have been performed for three different EVs controlstrategies. Results show that this framework can be effectively used as platform for EVs controlstrategies comparisons account for uncertainty.

(18) (PDF) A Simulation-Based Probabilistic Risk Assessment of Electric Vehicles Control Strategies Accounting for Renewable Energy Sources. Available from: https://www.researchgate.net/publication/283490703_A_Simulation-Based_Probabilistic_Risk_Assessment_of_Electric_Vehicles_Control_Strategies_Accounting_for_Renewable_Energy_Sources [accessed Dec 12 2019].
Original languageEnglish
Title of host publicationIn Proceedings of the 13th International Probabilistic Workshop (IPW 2015)
Number of pages16
DOIs
Publication statusPublished - 26 Oct 2015
Externally publishedYes
Event13th international probabilistic workshop - Liverpool, United Kingdom
Duration: 4 Nov 20156 Nov 2015

Conference

Conference13th international probabilistic workshop
CountryUnited Kingdom
CityLiverpool
Period4/11/156/11/15

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