Energy storage capacity optimization for autonomy microgrid considering CHP and EV scheduling. (15th January 2018)

• Record Type: Journal Article
• Authors: Liu, Zifa; Chen, Yixiao; Zhuo, Ranqun; Jia, Hongjie
• Is Part Of: Applied energy. Volume 210(2018)
• Page Start: 1113
• Page End: 1125
• Publication Date: 2018-01-15
• Subjects: Microgrid -- Distributed energy resource -- Energy storage; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.07.002
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A sizing strategy of power sources and ESS in an autonomy MG is proposed. A two-layer HESS with three storage types is constructed based on response speed. Power differences among different time intervals are supplied by HESS. DSM and EV scheduling can result in significant saving in sizing of power resources. Abstract: 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.