Real-time energy management in microgrids with reduced battery capacity requirements

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

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Energy storage units hold promise to transform the electric power industry, since they can supply power to end customers during peak demand times, and operate as customers upon a power surplus. This paper studies online energy management with renewable energy resources and energy storage units. For the problem at hand, the popular approaches rely on stochastic dual (sub)gradient (SDG) iterations for a chosen stepsize μ, which generally require battery capacity O(1/μ) to guarantee an O(μ)-optimal solution. With the goal of achieving optimal energy cost with considerably reduced battery capacity requirements, an online learning-aided management (OLAM) scheme is introduced for energy management, which incorporates statistical learning advances into real-time energy management. To facilitate real-time implementation of the proposed scheme, the alternating direction method of multipliers method is also leveraged to solve the involved subproblems in a distributed fashion. It is analytically established that OLAM incurs an O(μ) optimality gap, while only requiring battery capacity O(log 2 (μ)/μ). Simulations on the IEEE power grid benchmark corroborate that OLAM incurs similar average cost relative to that of SDG, while requiring markedly lower battery capacity.

Original languageEnglish (US)
Article number8214260
Pages (from-to)1928-1938
Number of pages11
JournalIEEE Transactions on Smart Grid
Volume10
Issue number2
DOIs
StatePublished - Mar 2019

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Energy management
Energy storage
Renewable energy resources
Costs
Industry

Keywords

  • Energy management
  • Energy storages
  • Smart microgrids
  • Statistical learning
  • Stochastic approximation

Cite this

Real-time energy management in microgrids with reduced battery capacity requirements. / Li, Bingcong; Chen, Tianyi; Wang, Xin; Giannakis, Georgios B.

In: IEEE Transactions on Smart Grid, Vol. 10, No. 2, 8214260, 03.2019, p. 1928-1938.

Research output: Contribution to journalArticle

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