Energy-Storage Modeling: State-of-the-Art and Future Research Directions

Ramteen Sioshansi; Paul Denholm; Juan Arteaga; Sarah Awara; Shubhrajit Bhattacharjee; Audun Botterud; Wesley Cole; Andres Cortes; Anderson de Queiroz; Joseph DeCarolis; Zhenhuan Ding; Nicholas DiOrio; Yury Dvorkin; Udi Helman; Jeremiah Johnson; Ioannis Konstantelos; Trieu Mai; Hrvoje Pandzic; Daniel Sodano; Gordon Stephen; Alva Svoboda; Hamidreza Zareipour; Ziang Zhang

doi:10.1109/tpwrs.2021.3104768

Energy-Storage Modeling: State-of-the-Art and Future Research Directions

Ramteen Sioshansi, Paul Denholm, Juan Arteaga, Sarah Awara, Shubhrajit Bhattacharjee, Audun Botterud, Wesley Cole, Andres Cortes, Anderson de Queiroz, Joseph DeCarolis, Zhenhuan Ding, Nicholas DiOrio, Yury Dvorkin, Udi Helman, Jeremiah Johnson, Ioannis Konstantelos, Trieu Mai, Hrvoje Pandzic, Daniel Sodano, Gordon StephenAlva Svoboda, Hamidreza Zareipour, Ziang Zhang

Energy Security, Resilience and Integration

Research output: Contribution to journal › Article › peer-review

67 Scopus Citations

Abstract

Given its physical characteristics and the range of services that it can provide, energy storage raises unique modeling challenges. This paper summarizes capabilities that operational, planning, and resource-adequacy models that include energy storage should have and surveys gaps in extant models. Existing models that represent energy storage differ in fidelity of representing the balance of the power system and energy-storage applications. Modeling results are sensitive to these differences. The importance of capturing chronology can raise challenges in energy-storage modeling. Some models 'decouple' individual operating periods from one another, allowing for natural decomposition and rendering the models relatively computationally tractable. Energy storage complicates such a modeling approach. Improving the representation of the balance of the system can have major effects in capturing energy-storage costs and benefits.

Original language	American English
Pages (from-to)	860-875
Number of pages	16
Journal	IEEE Transactions on Power Systems
Volume	37
Issue number	2
DOIs	https://doi.org/10.1109/tpwrs.2021.3104768 https://doi.org/10.1109/TPWRS.2021.3104768
State	Published - 1 Mar 2022

Bibliographical note

Publisher Copyright:
© 1969-2012 IEEE.

NREL Publication Number

NREL/JA-5C00-76865

Keywords

Energy storage
modeling
power system economics
power system expansion planning
power system operations

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Sioshansi, R., Denholm, P., Arteaga, J., Awara, S., Bhattacharjee, S., Botterud, A., Cole, W., Cortes, A., de Queiroz, A., DeCarolis, J., Ding, Z., DiOrio, N., Dvorkin, Y., Helman, U., Johnson, J., Konstantelos, I., Mai, T., Pandzic, H., Sodano, D., ... Zhang, Z. (2022). Energy-Storage Modeling: State-of-the-Art and Future Research Directions. IEEE Transactions on Power Systems, 37(2), 860-875. https://doi.org/10.1109/tpwrs.2021.3104768, https://doi.org/10.1109/TPWRS.2021.3104768

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abstract = "Given its physical characteristics and the range of services that it can provide, energy storage raises unique modeling challenges. This paper summarizes capabilities that operational, planning, and resource-adequacy models that include energy storage should have and surveys gaps in extant models. Existing models that represent energy storage differ in fidelity of representing the balance of the power system and energy-storage applications. Modeling results are sensitive to these differences. The importance of capturing chronology can raise challenges in energy-storage modeling. Some models 'decouple' individual operating periods from one another, allowing for natural decomposition and rendering the models relatively computationally tractable. Energy storage complicates such a modeling approach. Improving the representation of the balance of the system can have major effects in capturing energy-storage costs and benefits.",

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Sioshansi, R, Denholm, P, Arteaga, J, Awara, S, Bhattacharjee, S, Botterud, A, Cole, W, Cortes, A, de Queiroz, A, DeCarolis, J, Ding, Z, DiOrio, N, Dvorkin, Y, Helman, U, Johnson, J, Konstantelos, I, Mai, T, Pandzic, H, Sodano, D, Stephen, G, Svoboda, A, Zareipour, H & Zhang, Z 2022, 'Energy-Storage Modeling: State-of-the-Art and Future Research Directions', IEEE Transactions on Power Systems, vol. 37, no. 2, pp. 860-875. https://doi.org/10.1109/tpwrs.2021.3104768, https://doi.org/10.1109/TPWRS.2021.3104768

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AU - Helman, Udi

AU - Johnson, Jeremiah

AU - Konstantelos, Ioannis

AU - Mai, Trieu

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AU - Sodano, Daniel

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AU - Zareipour, Hamidreza

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AB - Given its physical characteristics and the range of services that it can provide, energy storage raises unique modeling challenges. This paper summarizes capabilities that operational, planning, and resource-adequacy models that include energy storage should have and surveys gaps in extant models. Existing models that represent energy storage differ in fidelity of representing the balance of the power system and energy-storage applications. Modeling results are sensitive to these differences. The importance of capturing chronology can raise challenges in energy-storage modeling. Some models 'decouple' individual operating periods from one another, allowing for natural decomposition and rendering the models relatively computationally tractable. Energy storage complicates such a modeling approach. Improving the representation of the balance of the system can have major effects in capturing energy-storage costs and benefits.

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Energy-Storage Modeling: State-of-the-Art and Future Research Directions

Abstract

Bibliographical note

NREL Publication Number

Keywords

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