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.
Bibliographical noteFunding Information:
Manuscript received August 13, 2017; revised November 9, 2017; accepted December 9, 2017. Date of publication December 15, 2017; date of current version February 18, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61671154, in part by the National Key Research and Development Program of China under Grant 2017YFB0403402, and in part by the U.S. NSF under Grant 1509005, Grant 1508993, and Grant 1711471. Paper no. TSG-01170-2017.
This work was supported in part by the National Natural Science Foundation of China under Grant 61671154, in part by the National Key Research and Development Program of China under Grant 2017YFB0403402, and in part by the U.S. NSF under Grant 1509005, Grant 1508993, and Grant 1711471. Paper no. TSG-01170-2017.
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- Energy management
- Energy storages
- Smart microgrids
- Statistical learning
- Stochastic approximation