LAPSE:2020.0720
Published Article
LAPSE:2020.0720
Identification of the Most Effective Point of Connection for Battery Energy Storage Systems Focusing on Power System Frequency Response Improvement
Thiago Pieroni, Daniel Dotta
June 23, 2020
With the massive penetration of intermittent generation (wind and solar), the reduction of Electrical Power Systems’ (EPSs) inertial frequency response represents a new challenge. One alternative to deal with this scenario may be the application of a Battery Energy Storage System (BESS). However, the main constraint for the massive deployment of BESSs is the high acquisition cost of these storage systems which in some situations, can preclude their use in transmission systems. The main goal of this paper is to propose a systematic procedure to include BESSs in power system aiming to improve the power system frequency response using full linear models and geometric measures. In this work, a generic battery model is developed in a two-area test system with assumed high wind penetration and full conventional generators models. The full power system is linearized, and the geometric measures of controllability associated with of the frequency regulation mode are estimated. Then, these results are used to identify the most effective point of connection for a BESS aiming at the improvement of the power system frequency response. Nonlinear time-domain simulations are carried out to evaluate and validate the results.
Keywords
battery energy storage systems (BESS), controllability, frequency control, frequency regulation mode, wind generation
Suggested Citation
Pieroni T, Dotta D. Identification of the Most Effective Point of Connection for Battery Energy Storage Systems Focusing on Power System Frequency Response Improvement. (2020). LAPSE:2020.0720
Author Affiliations
Pieroni T: Department of Systems and Energy, University of Campinas, Campinas 13083-852, Brazil
Dotta D: Department of Systems and Energy, University of Campinas, Campinas 13083-852, Brazil [ORCID]
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Journal Name
Energies
Volume
11
Issue
4
Article Number
E763
Year
2018
Publication Date
2018-03-28
Published Version
ISSN
1996-1073
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Original Submission
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PII: en11040763, Publication Type: Journal Article
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LAPSE:2020.0720
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doi:10.3390/en11040763
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Jun 23, 2020
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CC BY 4.0
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Jun 23, 2020
 
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Calvin Tsay
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