A Benders decomposition approach to transmission expansion planning considering energy storage

MacRae, C, Ernst, A and Ozlen, M 2016, 'A Benders decomposition approach to transmission expansion planning considering energy storage', Energy, vol. 112, pp. 795-803.


Document type: Journal Article
Collection: Journal Articles

Title A Benders decomposition approach to transmission expansion planning considering energy storage
Author(s) MacRae, C
Ernst, A
Ozlen, M
Year 2016
Journal name Energy
Volume number 112
Start page 795
End page 803
Total pages 9
Publisher Elsevier Ltd
Abstract We describe an electricity transmission network expansion and energy storage planning model (TESP) that determines the location and capacity of energy storage systems (ESS) in the network for the purposes of demand shifting and transmission upgrade deferral. This problem is significantly harder than the standard network expansion models that are typically considered literature as the benefit of storage can only be understood by including multiple time periods in the model. The addition of the time dimension leads to much larger mixed integer linear programming problems. We address this increase in size and complexity of the optimization problem by developing a Benders decomposition approach for the TESP. The model is tested against the well known Garver's 6-bus, IEEE 25-bus, and Brazilian 46-bus test systems under two different demand scenarios; the first is characterized by a short period of peak demand, the second by a long period. Benders decomposition is shown to be an effective means to render the problem more tractable when compared to the standard mixed integer linear programming approach. We find that installation of ESS is an effective means of transmission upgrade deferral. However storage is unlikely to be installed where circuit installation is of comparatively low cost.
Subject Operations Research
Keyword(s) Power transmission
Energy storage
Optimization
DOI - identifier 10.1016/j.energy.2016.06.080
Copyright notice © 2016 Elsevier Ltd. All rights reserved.
ISSN 0360-5442
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