Optimal Sizing of Battery Energy Storage Systems Driven by Clustered Load Profiles

2021 ◽  
Author(s):  
Pasquale De Falco ◽  
Fabio Mottola ◽  
Daniela Proto
Keyword(s):  
Energy Storage ◽  
Storage Systems ◽  
Energy Storage Systems ◽  
Battery Energy Storage ◽  
Optimal Sizing ◽  
Battery Energy Storage Systems ◽  
Battery Energy

Optimal Sizing and Control of Battery Energy Storage Systems for Hybrid Turboelectric Aircraft

2020 ◽  
Author(s):  
Aaronn Sergent ◽  
Michael Ramunno ◽  
Matilde D'Arpino ◽  
Marcello Canova ◽  
Christopher Perullo
Keyword(s):  
Energy Storage ◽  
Storage Systems ◽  
Energy Storage Systems ◽  
Battery Energy Storage ◽  
Optimal Sizing ◽  
Battery Energy Storage Systems ◽  
Battery Energy ◽  
And Control

scholarly journals Optimal Sizing of Battery Energy Storage Systems Considering Cooperative Operation with Microgrid Components

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7442
Author(s):  
Hirotaka Takano ◽  
Ryosuke Hayashi ◽  
Hiroshi Asano ◽  
Tadahiro Goda
Keyword(s):  
Energy Storage ◽  
Solution Method ◽  
Storage Systems ◽  
Optimal Size ◽  
Energy Storage Systems ◽  
Battery Energy Storage ◽  
Optimal Sizing ◽  
Battery Energy Storage Systems ◽  
Battery Energy ◽  
Combined Algorithm

Battery energy storage systems (BESSs) are key components in efficiently managing the electric power supply and demand in microgrids. However, the BESSs have issues in their investment costs and operating lifetime, and thus, the optimal sizing of the BESSs is one of the crucial requirements in design and management of the microgrids. This paper presents a problem framework and its solution method that calculates the optimal size of the BESSs in a microgrid, considering their cooperative operations with the other components. The proposed framework is formulated as a bi-level optimization problem; however, based on the Karush–Kuhn–Tucker approach, it is regarded as a type of operation scheduling problem. As a result, the techniques developed for determining the operation schedule become applicable. In this paper, a combined algorithm of binary particle swarm optimization and quadratic programming is selected as the basis of the solution method. The validity of the authors’ proposal is verified through numerical simulations and discussion of their results.

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