Energy storage capacity optimization for autonomy microgrid considering CHP and EV scheduling

被引:183
作者
Liu, Zifa [1 ]
Chen, Yixiao [2 ]
Zhuo, Ranqun [1 ]
Jia, Hongjie [3 ]
机构
[1] North China Elect Power Univ, Sch Elect & Elect Engn, Beijing 102206, Peoples R China
[2] State Grid Zhengzhou Power Supply Co, Zhengzhou 450052, Henan, Peoples R China
[3] Tianjin Univ, Tianjin 300072, Peoples R China
基金
中国国家自然科学基金;
关键词
Microgrid; Distributed energy resource; Energy storage; PARTICLE SWARM OPTIMIZATION; RENEWABLE ENERGY; ECONOMIC-DISPATCH; SYSTEM; GENERATION; STRATEGY; DESIGN;
D O I
10.1016/j.apenergy.2017.07.002
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Microgrid is universally accepted as a new approach to solve the global energy problem. In a microgrid, the optimal sizing of energy storage is necessary to ensure reliability and improve economic efficiency. Its sizing results are impacted by uncertainty on natural resources, energy storage as well as load, and it is hard to coordinate these factors. Therefore, microgrid needs more improved strategies for optimal sizing. In this paper, we present a power source sizing strategy with integrated consideration of characteristics of distributed generations, energy storage and loads. Distributed generations consist of wind turbine, photovoltaic panels, combined heat and power generation (CHP) as well as electric vehicles. A two layer hybrid energy storage system with three storage types (i.e. super capacitor, li-ion battery, lead acid battery) is constructed based on their power density, energy density, response speed and lifetime, as well as load classification. Power load differences among different time intervals which are supplied by different types of storage leads to allocation of energy storage. An objective function is established based on life cycle cost (LCC) theory, which includes construction cost, operation maintenance cost, recycling profit, environment cost, and energy shortage compensation. Three scenarios, in which particle swarm optimization (PSO) is used for the optimal sizing, modeling and results calculating. From the simulations results analysis, it is found that the proposed model and strategy are feasible and practical. (C) 2017 Published by Elsevier Ltd.
引用
收藏
页码:1113 / 1125
页数:13
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