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Baihao Qiao
School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China

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Journal article
Published: 01 December 2017 in Energies
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The intermittency of wind power and the large-scale integration of electric vehicles (EVs) bring new challenges to the reliability and economy of power system dispatching. In this paper, a novel multi-objective dynamic economic emission dispatch (DEED) model is proposed considering the EVs and uncertainties of wind power. The total fuel cost and pollutant emission are considered as the optimization objectives, and the vehicle to grid (V2G) power and the conventional generator output power are set as the decision variables. The stochastic wind power is derived by Weibull probability distribution function. Under the premise of meeting the system energy and user’s travel demand, the charging and discharging behavior of the EVs are dynamically managed. Moreover, we propose a two-step dynamic constraint processing strategy for decision variables based on penalty function, and, on this basis, the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) algorithm is improved. The proposed model and approach are verified by the 10-generator system. The results demonstrate that the proposed DEED model and the improved MOEA/D algorithm are effective and reasonable.

ACS Style

Boyang Qu; Baihao Qiao; Yongsheng Zhu; Jingjing Liang; Ling Wang. Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm. Energies 2017, 10, 1991 .

AMA Style

Boyang Qu, Baihao Qiao, Yongsheng Zhu, Jingjing Liang, Ling Wang. Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm. Energies. 2017; 10 (12):1991.

Chicago/Turabian Style

Boyang Qu; Baihao Qiao; Yongsheng Zhu; Jingjing Liang; Ling Wang. 2017. "Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm." Energies 10, no. 12: 1991.

Conference paper
Published: 24 June 2017 in Transactions on Petri Nets and Other Models of Concurrency XV
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In order to cope with the challenges brought by the large-scale Electric Vehicles (EVs) application to the power system dispatch, an dynamic economic emission dispatch model with the EVs is established. The vehicle to grid (V2G) power and conventional generator outputs of each dispatch period are set as the decision variables. The main optimization objectives are minimizing the total fuel cost and the pollution emission, so that the charging and discharging behavior of EVs is dynamically managed in the premise of meeting the demands of system energy and user travel. In this paper, the nondominated sorting genetic algorithm-II (NSGA-II) is used to solve such a model.

ACS Style

Boyang Qu; Baihao Qiao; Yongsheng Zhu; Yuechao Jiao; Junming Xiao; Xiaolei Wang. Using Multi-objective Evolutionary Algorithm to Solve Dynamic Environment and Economic Dispatch with EVs. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 31 -39.

AMA Style

Boyang Qu, Baihao Qiao, Yongsheng Zhu, Yuechao Jiao, Junming Xiao, Xiaolei Wang. Using Multi-objective Evolutionary Algorithm to Solve Dynamic Environment and Economic Dispatch with EVs. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; ():31-39.

Chicago/Turabian Style

Boyang Qu; Baihao Qiao; Yongsheng Zhu; Yuechao Jiao; Junming Xiao; Xiaolei Wang. 2017. "Using Multi-objective Evolutionary Algorithm to Solve Dynamic Environment and Economic Dispatch with EVs." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 31-39.