Energy management for integrated energy systems based on stochastic optimization

被引：0
|
作者：
Ji Z. [1 ,2 ]
Huang X. [1 ,2 ]
Zhang Z. [1 ,2 ]
Sun H. [1 ,2 ]
Zhao J. [3 ]
Li J. [4 ]
机构：
[1] School of Electrical Engineering, Southeast University, Nanjing
[2] Key Laboratory of Jiangsu Province Smart Grid Technology and Equipment, Nanjing
[3] State Grid Suzhou Power Supply Company, Suzhou
[4] School of Electrical Engineering, Nanjing Institute of Technology, Nanjing
来源：
Huang, Xueliang (xlhuang@seu.edu.cn) | 2018年 / Southeast University卷 / 48期
关键词：
Benders decomposition; Electric vehicle coordinated charging; Integrated energy system; Stochastic optimization;
D O I：
10.3969/j.issn.1001-0505.2018.01.008
中图分类号：
学科分类号：
摘要：
To rise the economical efficiency of integrated energy system and reduce peak electricity loads by the widespread use of electric vehicles, a new energy management strategy is presented based on a stochastic optimization and a parallel solving algorithm to achieve fast solution. The receding horizon based energy management model in a stochastic programming framework is established after the formulation of an integrated energy system with constraints, and is implemented with a coordinating charging strategy for electric vehicles. To reduce the time cost of online operation, a scenario generation and reduction method is used to apply suitable predicted scenario sets, and the Benders decomposition technique is adopted to execute parallel computing. The simulation results show that the proposed method achieves significantly decrease of the operation cost than those without stochastic optimization. Compared with a stochastic method for neither scenario reduction nor Benders decomposition, computing time is notably declined by little operational cost to increase. © 2018, Editorial Department of Journal of Southeast University. All right reserved.
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页码：45 / 53
页数：8
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