Multi-objective Hierarchical Optimal Scheduling of Microgrids with V2G Price Incentives

被引:1
|
作者
Luo, Cheng [1 ]
Cheng, Ruofa [1 ]
Wei, Tianci [1 ]
Mao, Zhixin [2 ]
机构
[1] Nanchang Aviat Univ, Sch Informat Engn, Nanchang, Peoples R China
[2] Jiangxi Elect Power Construct CHINAPOWER, Nanchang, Peoples R China
关键词
price incentives; hierarchical optimization; multi-objective; improvedparticle swarm;
D O I
10.1109/AEEES56888.2023.10114297
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
For the stable and economic operation of EVs connected to the microgrid, not only the EV orderly charging model is established, but also the price incentive mechanism is proposed in the usual period to motivate the EV users to participate in the microgrid power balance dispatch. Firstly, a time-sharing tariff mechanism is used to combine the orderly charging behavior for microgrid dispatching in the valley and peak hours, and the EV charging and discharging power participating in the dispatching is solved with the objective of maximizing the comprehensive satisfaction of EV layer users; secondly, a price incentive mechanism is used to promote the participation of EV users in microgrid dispatching in the ordinary hours, and the maximum benefit of EV users and the minimum interaction power between the microgrid and the main network are used as the objectives to solve for the EV charging and discharging power participating in the dispatching. Finally, the EV charging and discharging plan is passed to the microgrid layer, which adjusts the power output of controllable distributed power sources within the microgrid layer with the objectives of minimizing system cost and interaction power. For this multi-objective model, a modified particle swarm algorithm is used to solve it, and the simulation results show that the model containing the price incentive mechanism is conducive to promoting mutual benefits between the EV layer and the micro-grid layer, which verifies the effectiveness of the proposed method.
引用
收藏
页码:1010 / 1015
页数:6
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