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Degradation and Operation-Aware Framework for the Optimal Siting, Sizing and Technology Selection of Battery Storage

Lookup NU author(s): Dr Timur Saifutdinov, Professor Haris Patsios, Professor Petr Vorobev, Dr David Greenwood, Professor Janusz Bialek, Professor Phil Taylor

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2020.

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Abstract

This paper addresses the problem of optimal siting, sizing, and technology selection of Energy Storage System (ESS) considering degradation arising from state of charge and Depth of Discharge (DoD). The capacity lost irreversibly due to degradation provides the optimizer with a more accurate and realistic view of the capacity available throughout the asset's entire lifetime as it depends on the actual operating profiles and particular degradation mechanisms. When taking into account the ESS's degradation, the optimization problem becomes nonconvex, therefore no standard solver can guarantee the globally optimal solution. To overcome this, the optimization problem has been reformulated to a Mixed Integer Convex Programming (MICP) problem by substituting continuous variables that cause nonconvexity with discrete ones. The resulting MICP problem has been solved using the Branch-and-Bound algorithm along with convex programming, which performs an efficient search and guarantees the globally optimal solution. We found that the optimal battery use does not necesseraly correspond to it reaching its End of Life state at the end of the service lifetime, which is the result of nonlinear degradation mechanicms from both idling and cycling. Finally, the proposed methodology allows formulating computationally tractable stochastic optimization problem to account for future network scenarios.

Publication metadata

Author(s): Sayfutdinov T, patsios H, Vorobev P, Gryazina E, Greenwood D, Bialek J, Taylor P

Publication type: Article

Publication status: Published

Journal: IEEE Transactions of Sustainable Energy

Year: 2020

Volume: 11

Issue: 4

Pages: 2130-2140

Print publication date: 01/10/2020

Online publication date: 31/10/2019

Acceptance date: 31/10/2019

Date deposited: 28/11/2019

ISSN (print): 1949-3029

ISSN (electronic): 1949-3037

Publisher: IEEE

URL: https://doi.org/10.1109/TSTE.2019.2950723

DOI: 10.1109/TSTE.2019.2950723

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