Coordinated Stochastic Scheduling for Improving Wind Power Adsorption in Electric Vehicles-Wind Integrated Power Systems by Multi-objective Optimization Approach

Yuanzheng Li; Zhixian Ni; Tianyang Zhao; Minghui Yu; Yun Liu; Lei Wu

doi:10.1109/IAS.2019.8912450

Coordinated Stochastic Scheduling for Improving Wind Power Adsorption in Electric Vehicles-Wind Integrated Power Systems by Multi-objective Optimization Approach

Yuanzheng Li
, Zhixian Ni
, Tianyang Zhao
, Minghui Yu
, Yun Liu
, Lei Wu

Department of Electrical and Computer Engineering

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

5 Scopus citations

Abstract

Electric vehicles (EVs) and renewable energy (RE), such as wind power, have been widely utilized to meet the sustainable development of our society. To this end, researches on operation performance of the EV-wind integrated power system are important. This paper proposes a coordinated stochastic scheduling model based on a multi-objective optimization approach, which aims to improve wind power adsorption while considering energy conservation and emission reduction of thermal generators. Besides, to conduct comprehensive investigation among these multiple objectives, we formulate the coordinated stochastic scheduling model as a multi-objective optimization problem. Then, a multi-objective optimization algorithm based on a parameter adaptive differential evolution is proposed to solve this problem. Simulation results based on a modified Midwestern US power system verify that the proposed scheduling model could reveal the relationship among multiple objectives, and the integration of EVs can improve wind power adsorption and cost effectiveness of the power system.

Original language	English
Title of host publication	2019 IEEE Industry Applications Society Annual Meeting, IAS 2019
ISBN (Electronic)	9781538645390
DOIs	https://doi.org/10.1109/IAS.2019.8912450
State	Published - Sep 2019
Event	2019 IEEE Industry Applications Society Annual Meeting, IAS 2019 - Baltimore, United States Duration: 29 Sep 2019 → 3 Oct 2019

Publication series

Name	2019 IEEE Industry Applications Society Annual Meeting, IAS 2019

Conference

Conference	2019 IEEE Industry Applications Society Annual Meeting, IAS 2019
Country/Territory	United States
City	Baltimore
Period	29/09/19 → 3/10/19

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Wind power
electric vehicles
multi-objective optimization
stochastic scheduling model
uncertainty

Access to Document

10.1109/IAS.2019.8912450

Cite this

Li, Y., Ni, Z., Zhao, T., Yu, M., Liu, Y., & Wu, L. (2019). Coordinated Stochastic Scheduling for Improving Wind Power Adsorption in Electric Vehicles-Wind Integrated Power Systems by Multi-objective Optimization Approach. In 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019 Article 8912450 (2019 IEEE Industry Applications Society Annual Meeting, IAS 2019). https://doi.org/10.1109/IAS.2019.8912450

@inproceedings{95185313ce2046739cd6ab33bcaadb0f,

title = "Coordinated Stochastic Scheduling for Improving Wind Power Adsorption in Electric Vehicles-Wind Integrated Power Systems by Multi-objective Optimization Approach",

abstract = "Electric vehicles (EVs) and renewable energy (RE), such as wind power, have been widely utilized to meet the sustainable development of our society. To this end, researches on operation performance of the EV-wind integrated power system are important. This paper proposes a coordinated stochastic scheduling model based on a multi-objective optimization approach, which aims to improve wind power adsorption while considering energy conservation and emission reduction of thermal generators. Besides, to conduct comprehensive investigation among these multiple objectives, we formulate the coordinated stochastic scheduling model as a multi-objective optimization problem. Then, a multi-objective optimization algorithm based on a parameter adaptive differential evolution is proposed to solve this problem. Simulation results based on a modified Midwestern US power system verify that the proposed scheduling model could reveal the relationship among multiple objectives, and the integration of EVs can improve wind power adsorption and cost effectiveness of the power system.",

keywords = "Wind power, electric vehicles, multi-objective optimization, stochastic scheduling model, uncertainty",

author = "Yuanzheng Li and Zhixian Ni and Tianyang Zhao and Minghui Yu and Yun Liu and Lei Wu",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019 ; Conference date: 29-09-2019 Through 03-10-2019",

year = "2019",

month = sep,

doi = "10.1109/IAS.2019.8912450",

language = "English",

series = "2019 IEEE Industry Applications Society Annual Meeting, IAS 2019",

booktitle = "2019 IEEE Industry Applications Society Annual Meeting, IAS 2019",

}

Li, Y, Ni, Z, Zhao, T, Yu, M, Liu, Y & Wu, L 2019, Coordinated Stochastic Scheduling for Improving Wind Power Adsorption in Electric Vehicles-Wind Integrated Power Systems by Multi-objective Optimization Approach. in 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019., 8912450, 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019, 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019, Baltimore, United States, 29/09/19. https://doi.org/10.1109/IAS.2019.8912450

Coordinated Stochastic Scheduling for Improving Wind Power Adsorption in Electric Vehicles-Wind Integrated Power Systems by Multi-objective Optimization Approach. / Li, Yuanzheng; Ni, Zhixian; Zhao, Tianyang et al.
2019 IEEE Industry Applications Society Annual Meeting, IAS 2019. 2019. 8912450 (2019 IEEE Industry Applications Society Annual Meeting, IAS 2019).

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

TY - GEN

T1 - Coordinated Stochastic Scheduling for Improving Wind Power Adsorption in Electric Vehicles-Wind Integrated Power Systems by Multi-objective Optimization Approach

AU - Li, Yuanzheng

AU - Ni, Zhixian

AU - Zhao, Tianyang

AU - Yu, Minghui

AU - Liu, Yun

AU - Wu, Lei

PY - 2019/9

Y1 - 2019/9

N2 - Electric vehicles (EVs) and renewable energy (RE), such as wind power, have been widely utilized to meet the sustainable development of our society. To this end, researches on operation performance of the EV-wind integrated power system are important. This paper proposes a coordinated stochastic scheduling model based on a multi-objective optimization approach, which aims to improve wind power adsorption while considering energy conservation and emission reduction of thermal generators. Besides, to conduct comprehensive investigation among these multiple objectives, we formulate the coordinated stochastic scheduling model as a multi-objective optimization problem. Then, a multi-objective optimization algorithm based on a parameter adaptive differential evolution is proposed to solve this problem. Simulation results based on a modified Midwestern US power system verify that the proposed scheduling model could reveal the relationship among multiple objectives, and the integration of EVs can improve wind power adsorption and cost effectiveness of the power system.

AB - Electric vehicles (EVs) and renewable energy (RE), such as wind power, have been widely utilized to meet the sustainable development of our society. To this end, researches on operation performance of the EV-wind integrated power system are important. This paper proposes a coordinated stochastic scheduling model based on a multi-objective optimization approach, which aims to improve wind power adsorption while considering energy conservation and emission reduction of thermal generators. Besides, to conduct comprehensive investigation among these multiple objectives, we formulate the coordinated stochastic scheduling model as a multi-objective optimization problem. Then, a multi-objective optimization algorithm based on a parameter adaptive differential evolution is proposed to solve this problem. Simulation results based on a modified Midwestern US power system verify that the proposed scheduling model could reveal the relationship among multiple objectives, and the integration of EVs can improve wind power adsorption and cost effectiveness of the power system.

KW - Wind power

KW - electric vehicles

KW - multi-objective optimization

KW - stochastic scheduling model

KW - uncertainty

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UR - https://www.scopus.com/pages/publications/85076720007#tab=citedBy

U2 - 10.1109/IAS.2019.8912450

DO - 10.1109/IAS.2019.8912450

M3 - Conference contribution

AN - SCOPUS:85076720007

T3 - 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019

BT - 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019

T2 - 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019

Y2 - 29 September 2019 through 3 October 2019

ER -

Li Y, Ni Z, Zhao T, Yu M, Liu Y, Wu L. Coordinated Stochastic Scheduling for Improving Wind Power Adsorption in Electric Vehicles-Wind Integrated Power Systems by Multi-objective Optimization Approach. In 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019. 2019. 8912450. (2019 IEEE Industry Applications Society Annual Meeting, IAS 2019). doi: 10.1109/IAS.2019.8912450

Coordinated Stochastic Scheduling for Improving Wind Power Adsorption in Electric Vehicles-Wind Integrated Power Systems by Multi-objective Optimization Approach

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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