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

Roberto Rocchetta; Edoardo Patelli

doi:10.3850/978-981-09-7963-8_088

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 proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	In Proceedings of the 13th International Probabilistic Workshop (IPW 2015)
Number of pages	16
DOIs	https://doi.org/10.3850/978-981-09-7963-8_088
Publication status	Published - 26 Oct 2015
Externally published	Yes
Event	13th international probabilistic workshop - Liverpool, United Kingdom Duration: 4 Nov 2015 → 6 Nov 2015

Conference

Conference	13th international probabilistic workshop
Country/Territory	United Kingdom
City	Liverpool
Period	4/11/15 → 6/11/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3850/978-981-09-7963-8_088

Cite this

@inproceedings{ce04b919649a4764a0bcddb2b466b9ea,

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

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].",

author = "Roberto Rocchetta and Edoardo Patelli",

year = "2015",

month = oct,

day = "26",

doi = "10.3850/978-981-09-7963-8_088",

language = "English",

booktitle = "In Proceedings of the 13th International Probabilistic Workshop (IPW 2015)",

note = "13th international probabilistic workshop ; Conference date: 04-11-2015 Through 06-11-2015",

}

Rocchetta, R & Patelli, E 2015, A simulation-based probabilistic risk assessment of electric vehicles control strategies accounting for renewable energy sources. in In Proceedings of the 13th International Probabilistic Workshop (IPW 2015)., ipw15, 13th international probabilistic workshop, Liverpool, United Kingdom, 4/11/15. https://doi.org/10.3850/978-981-09-7963-8_088

A simulation-based probabilistic risk assessment of electric vehicles control strategies accounting for renewable energy sources. / Rocchetta, Roberto; Patelli, Edoardo.
In Proceedings of the 13th International Probabilistic Workshop (IPW 2015). 2015. ipw15.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - A simulation-based probabilistic risk assessment of electric vehicles control strategies accounting for renewable energy sources

AU - Rocchetta, Roberto

AU - Patelli, Edoardo

PY - 2015/10/26

Y1 - 2015/10/26

N2 - 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].

AB - 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].

U2 - 10.3850/978-981-09-7963-8_088

DO - 10.3850/978-981-09-7963-8_088

M3 - Conference contribution

BT - In Proceedings of the 13th International Probabilistic Workshop (IPW 2015)

T2 - 13th international probabilistic workshop

Y2 - 4 November 2015 through 6 November 2015

ER -

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

Abstract

Conference

UN SDGs

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