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
N1 - Publisher Copyright:
© 2019 IEEE.
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
UR - http://www.scopus.com/inward/record.url?scp=85076720007&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076720007&partnerID=8YFLogxK
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 -