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 language | English (US) |
|---|---|
| Article number | 8214260 |
| Pages (from-to) | 1928-1938 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Smart Grid |
| Volume | 10 |
| Issue number | 2 |
| DOIs | |
| State | Published - Mar 2019 |
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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 journal › Article
}
TY - JOUR
T1 - Real-time energy management in microgrids with reduced battery capacity requirements
AU - Li, Bingcong
AU - Chen, Tianyi
AU - Wang, Xin
AU - Giannakis, Georgios B.
PY - 2019/3
Y1 - 2019/3
N2 - 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.
AB - 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.
KW - Energy management
KW - Energy storages
KW - Smart microgrids
KW - Statistical learning
KW - Stochastic approximation
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UR - http://www.scopus.com/inward/citedby.url?scp=85038844815&partnerID=8YFLogxK
U2 - 10.1109/TSG.2017.2783894
DO - 10.1109/TSG.2017.2783894
M3 - Article
AN - SCOPUS:85038844815
VL - 10
SP - 1928
EP - 1938
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
SN - 1949-3053
IS - 2
M1 - 8214260
ER -