Real-time energy management with improved cost-capacity tradeoff

Bingcong Li; Tianyi Chen; Xin Wang; Georgios B Giannakis

doi:10.1109/GlobalSIP.2017.8309120

Real-time energy management with improved cost-capacity tradeoff

Bingcong Li, Tianyi Chen, Xin Wang, Georgios B Giannakis

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

The present paper studies online energy management for smart microgrids with the presence of renewable energy resources and energy storages. For the problem at hand, the recent popular approach relies on the Stochastic Dual subGradient (SDG) method. Although SDG enjoys efficient implementation and provable convergence, it generally requires the battery capacity O(1/μ) to guarantee an O(μ)-optimal solution. To overcome this limitation, we develop an Online Learning-Aided Management (OLAM) scheme for energy management, which incorporates the statistical learning advances into realtime energy management. To facilitate real-time implementation of the proposed scheme, the alternating direction method of multipliers (ADMM) method is also leveraged to solve the involved subproblems in a distributed fashion. It is analytically established that the proposed OLAM incurs an O(μ) optimality gap, while only requiring the battery with capacity O(log²(μ)√μ). Numerical tests corroborate that OLAM incurs slightly lower average cost than that of SDG, when requiring battery with significantly lower capacity.

Original language	English (US)
Title of host publication	2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1045-1049
Number of pages	5
ISBN (Electronic)	9781509059904
DOIs	https://doi.org/10.1109/GlobalSIP.2017.8309120
State	Published - Mar 7 2018
Event	5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Montreal, Canada Duration: Nov 14 2017 → Nov 16 2017

Publication series

Name	2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
Volume	2018-January

Other

Other	5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017
Country/Territory	Canada
City	Montreal
Period	11/14/17 → 11/16/17

Bibliographical note

Funding Information:
Work in this paper was supported by the National Natural Science Foundation of China No. 61671154, the Innovation Program of Shanghai Municipal Education Commission; US NSF 1509005, 1508993, 1423316, and 1442686.

Publisher Copyright:
© 2017 IEEE.

Keywords

Energy management
Energy storages
Smart microgrids
Statistical learning
Stochastic approximation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Publisher link

10.1109/GlobalSIP.2017.8309120

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Cite this

Li, B., Chen, T., Wang, X., & Giannakis, G. B. (2018). Real-time energy management with improved cost-capacity tradeoff. In 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings (pp. 1045-1049). (2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2017.8309120

Real-time energy management with improved cost-capacity tradeoff. / Li, Bingcong; Chen, Tianyi; Wang, Xin et al.
2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1045-1049 (2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Li, B, Chen, T, Wang, X & Giannakis, GB 2018, Real-time energy management with improved cost-capacity tradeoff. in 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings. 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1045-1049, 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017, Montreal, Canada, 11/14/17. https://doi.org/10.1109/GlobalSIP.2017.8309120

Li B, Chen T, Wang X, Giannakis GB. Real-time energy management with improved cost-capacity tradeoff. In 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1045-1049. (2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings). doi: 10.1109/GlobalSIP.2017.8309120

Li, Bingcong ; Chen, Tianyi ; Wang, Xin et al. / Real-time energy management with improved cost-capacity tradeoff. 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1045-1049 (2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings).

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AB - The present paper studies online energy management for smart microgrids with the presence of renewable energy resources and energy storages. For the problem at hand, the recent popular approach relies on the Stochastic Dual subGradient (SDG) method. Although SDG enjoys efficient implementation and provable convergence, it generally requires the battery capacity O(1/μ) to guarantee an O(μ)-optimal solution. To overcome this limitation, we develop an Online Learning-Aided Management (OLAM) scheme for energy management, which incorporates the statistical learning advances into realtime energy management. To facilitate real-time implementation of the proposed scheme, the alternating direction method of multipliers (ADMM) method is also leveraged to solve the involved subproblems in a distributed fashion. It is analytically established that the proposed OLAM incurs an O(μ) optimality gap, while only requiring the battery with capacity O(log2(μ)√μ). Numerical tests corroborate that OLAM incurs slightly lower average cost than that of SDG, when requiring battery with significantly lower capacity.

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Real-time energy management with improved cost-capacity tradeoff

Abstract

Publication series

Other

Bibliographical note

Keywords

UN SDGs

Publisher link

Find It

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