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.|
|Number of pages||5|
|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
|Name||2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings|
|Other||5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017|
|Period||11/14/17 → 11/16/17|
Bibliographical noteFunding 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.
© 2017 IEEE.
- Energy management
- Energy storages
- Smart microgrids
- Statistical learning
- Stochastic approximation