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Modeling and optimal scheduling of battery energy storage systems in electric power distribution networks.

Authors :
Mehrjerdi, Hasan
Hemmati, Reza
Source :
Journal of Cleaner Production. Oct2019, Vol. 234, p810-821. 12p.
Publication Year :
2019

Abstract

Thanks to the unique features, deployment of battery energy storage systems in distribution systems is ever-increased. Therefore, new models are needed to capture the real-life characteristics. Beside active power, the battery energy storage system can exchange reactive power with the grid due to the inverter-based connection. Although some previous works have considered this issue, a detailed linear model suitable for the realistic large scale distribution systems is not addressed adequately. In this context, this paper proposes a mixed integer linear programming model for optimal battery energy storage system operation in distribution networks. The proposed model considers various parts of the battery energy storage system including battery pack, inverter, and transformer in addition to linear modeling of the reactive power and apparent power flow limit. Moreover, a linear power flow model is used to calculate voltage magnitudes and power losses with high accuracy. The proposed model is applied to the IEEE 33-bus test case and the results prove the accuracy and efficiency of the proposed model. The results demonstrate that considering reactive capability of the batteries offers new benefits including voltage profile improvement, decreasing reactive power flow in the network, reducing network losses, and releasing network and substation capacity. • Model BESS considering the parts including battery pack, inverter, and transformer. • Model BESS reactive power contribution based on the 4-quadratnt capability curve. • Mixed Integer Linear Programming model for optimal BESS operation. • Considering voltage magnitudes and network losses with precise linear equations. • Achieving diverse benefits besides conventional load leveling by proper modeling. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09596526
Volume :
234
Database :
Academic Search Index
Journal :
Journal of Cleaner Production
Publication Type :
Academic Journal
Accession number :
137683105
Full Text :
https://doi.org/10.1016/j.jclepro.2019.06.195