Charging/Discharging strategy for electric vehicles based on bi-level programming problem: San Francisco case study

M. Bagheri Tookanlou; M. Marzband; J. Kyyrä; A. Al Sumaiti; K. Al. Hosani

doi:10.1109/CPE-POWERENG48600.2020.9161485

Charging/Discharging strategy for electric vehicles based on bi-level programming problem: San Francisco case study

M. Bagheri Tookanlou, M. Marzband, J. Kyyrä, A. Al Sumaiti, K. Al. Hosani

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

5 Citations (Scopus)

Abstract

The increasing market share of electric vehicles (EVs) leads to determine a proper strategy for charging/discharging EV batteries such that rewards of all agents including EV charging stations (EVCSs) and EV owners (EVOs) that participate in charging/discharging EV batteries are guaranteed. In this study, an economical and technical strategy is developed. It focuses on finding proper EVCSs by EVOs and determining optimal day-ahead electricity prices traded between all agents such that the rewards of EVCSs and EVOs are met simultaneously. This optimal charging/discharging decision making and optimal day-ahead electricity prices are determined by bilevel programming problem (BLPP). The outer level corresponds to the optimization problem of EVCSs and the inner level belongs to EVOs. Salp swarm optimization (SSO) algorithm is utilized to solve BLPP. Based on determination of minimum distance travelled by EVOs and optimal day-ahead electricity prices offered by EVCSs, the rewards of EVCSs and EVOs are analysed during charging/discharging period. For simulation purposes, a case study based on San Francisco in US is presented to visualize and validate the modelling results. Six EVCSs are installed in San Francisco for charging/discharging 247 EVs during 24 hours of a typical day. Simulation results show that under implementing the proposed charging/discharging strategy, the total cost of EVOs decreases by 17.8% and total revenue of EVCSs increases 18.2%, in comparison with not considering the proposed strategy.

Original language	English
Title of host publication	Proceedings of the IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020
Publisher	IEEE
Pages	24-29
Number of pages	6
ISBN (Electronic)	9781728142180
DOIs	https://doi.org/10.1109/CPE-POWERENG48600.2020.9161485
Publication status	Published - Jul 2020
MoE publication type	A4 Conference publication
Event	IEEE International Conference on Compatibility, Power Electronics and Power Engineering - Virtual, Online Duration: 8 Jul 2020 → 10 Jul 2020 Conference number: 14

Conference

Conference	IEEE International Conference on Compatibility, Power Electronics and Power Engineering
Abbreviated title	CPE-POWERENG
City	Virtual, Online
Period	08/07/2020 → 10/07/2020
Other	Virtual Conference

Keywords

Bi-level programming problem
Charging/discharging decision
Electric vehicles
Electricity pricing

Access to Document

10.1109/CPE-POWERENG48600.2020.9161485

Cite this

Bagheri Tookanlou, M., Marzband, M., Kyyrä, J., Al Sumaiti, A., & Al. Hosani, K. (2020). Charging/Discharging strategy for electric vehicles based on bi-level programming problem: San Francisco case study. In Proceedings of the IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020 (pp. 24-29). Article 9161485 IEEE. https://doi.org/10.1109/CPE-POWERENG48600.2020.9161485

@inproceedings{f2a8f9db907a4802bc9cea76b817b914,

title = "Charging/Discharging strategy for electric vehicles based on bi-level programming problem: San Francisco case study",

abstract = "The increasing market share of electric vehicles (EVs) leads to determine a proper strategy for charging/discharging EV batteries such that rewards of all agents including EV charging stations (EVCSs) and EV owners (EVOs) that participate in charging/discharging EV batteries are guaranteed. In this study, an economical and technical strategy is developed. It focuses on finding proper EVCSs by EVOs and determining optimal day-ahead electricity prices traded between all agents such that the rewards of EVCSs and EVOs are met simultaneously. This optimal charging/discharging decision making and optimal day-ahead electricity prices are determined by bilevel programming problem (BLPP). The outer level corresponds to the optimization problem of EVCSs and the inner level belongs to EVOs. Salp swarm optimization (SSO) algorithm is utilized to solve BLPP. Based on determination of minimum distance travelled by EVOs and optimal day-ahead electricity prices offered by EVCSs, the rewards of EVCSs and EVOs are analysed during charging/discharging period. For simulation purposes, a case study based on San Francisco in US is presented to visualize and validate the modelling results. Six EVCSs are installed in San Francisco for charging/discharging 247 EVs during 24 hours of a typical day. Simulation results show that under implementing the proposed charging/discharging strategy, the total cost of EVOs decreases by 17.8% and total revenue of EVCSs increases 18.2%, in comparison with not considering the proposed strategy.",

keywords = "Bi-level programming problem, Charging/discharging decision, Electric vehicles, Electricity pricing",

author = "{Bagheri Tookanlou}, M. and M. Marzband and J. Kyyr{\"a} and {Al Sumaiti}, A. and {Al. Hosani}, K.",

year = "2020",

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doi = "10.1109/CPE-POWERENG48600.2020.9161485",

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Bagheri Tookanlou, M, Marzband, M, Kyyrä, J, Al Sumaiti, A & Al. Hosani, K 2020, Charging/Discharging strategy for electric vehicles based on bi-level programming problem: San Francisco case study. in Proceedings of the IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020., 9161485, IEEE, pp. 24-29, IEEE International Conference on Compatibility, Power Electronics and Power Engineering, Virtual, Online, 08/07/2020. https://doi.org/10.1109/CPE-POWERENG48600.2020.9161485

Charging/Discharging strategy for electric vehicles based on bi-level programming problem: San Francisco case study. / Bagheri Tookanlou, M.; Marzband, M.; Kyyrä, J. et al.
Proceedings of the IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020. IEEE, 2020. p. 24-29 9161485.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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N2 - The increasing market share of electric vehicles (EVs) leads to determine a proper strategy for charging/discharging EV batteries such that rewards of all agents including EV charging stations (EVCSs) and EV owners (EVOs) that participate in charging/discharging EV batteries are guaranteed. In this study, an economical and technical strategy is developed. It focuses on finding proper EVCSs by EVOs and determining optimal day-ahead electricity prices traded between all agents such that the rewards of EVCSs and EVOs are met simultaneously. This optimal charging/discharging decision making and optimal day-ahead electricity prices are determined by bilevel programming problem (BLPP). The outer level corresponds to the optimization problem of EVCSs and the inner level belongs to EVOs. Salp swarm optimization (SSO) algorithm is utilized to solve BLPP. Based on determination of minimum distance travelled by EVOs and optimal day-ahead electricity prices offered by EVCSs, the rewards of EVCSs and EVOs are analysed during charging/discharging period. For simulation purposes, a case study based on San Francisco in US is presented to visualize and validate the modelling results. Six EVCSs are installed in San Francisco for charging/discharging 247 EVs during 24 hours of a typical day. Simulation results show that under implementing the proposed charging/discharging strategy, the total cost of EVOs decreases by 17.8% and total revenue of EVCSs increases 18.2%, in comparison with not considering the proposed strategy.

AB - The increasing market share of electric vehicles (EVs) leads to determine a proper strategy for charging/discharging EV batteries such that rewards of all agents including EV charging stations (EVCSs) and EV owners (EVOs) that participate in charging/discharging EV batteries are guaranteed. In this study, an economical and technical strategy is developed. It focuses on finding proper EVCSs by EVOs and determining optimal day-ahead electricity prices traded between all agents such that the rewards of EVCSs and EVOs are met simultaneously. This optimal charging/discharging decision making and optimal day-ahead electricity prices are determined by bilevel programming problem (BLPP). The outer level corresponds to the optimization problem of EVCSs and the inner level belongs to EVOs. Salp swarm optimization (SSO) algorithm is utilized to solve BLPP. Based on determination of minimum distance travelled by EVOs and optimal day-ahead electricity prices offered by EVCSs, the rewards of EVCSs and EVOs are analysed during charging/discharging period. For simulation purposes, a case study based on San Francisco in US is presented to visualize and validate the modelling results. Six EVCSs are installed in San Francisco for charging/discharging 247 EVs during 24 hours of a typical day. Simulation results show that under implementing the proposed charging/discharging strategy, the total cost of EVOs decreases by 17.8% and total revenue of EVCSs increases 18.2%, in comparison with not considering the proposed strategy.

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KW - Charging/discharging decision

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KW - Electricity pricing

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Bagheri Tookanlou M, Marzband M, Kyyrä J, Al Sumaiti A, Al. Hosani K. Charging/Discharging strategy for electric vehicles based on bi-level programming problem: San Francisco case study. In Proceedings of the IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020. IEEE. 2020. p. 24-29. 9161485 doi: 10.1109/CPE-POWERENG48600.2020.9161485

Charging/Discharging strategy for electric vehicles based on bi-level programming problem: San Francisco case study

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