Real-time energy management with improved cost-capacity tradeoff
Real-time energy management with improved cost-capacity tradeoff
复制标题
DOI：
10.1109/globalsip.2017.8309120
复制
发表时间：
2017-11
期刊：
影响因子：
--
通讯作者：
Bingcong Li;Tianyi Chen;X. Wang;G. Giannakis
中科院分区：
文献类型：
--
作者：
Bingcong Li;Tianyi Chen;X. Wang;G. Giannakis
本文研究了可再生能源和储能的智能微电网的在线能量管理问题。对于手头的问题，最近流行的方法依赖于随机对偶次梯度（SDG）方法。虽然SDG具有高效的实现和可证明的收敛性，但它通常需要电池容量O（1/μ）来保证O（μ）最优解。为了克服这一限制，我们开发了一个在线学习辅助管理（OLAM）计划的能源管理，它结合了统计学习的进步，实时能源管理。为了便于实时实现所提出的计划，交替方向的乘法器（ADMM）方法也被用来解决所涉及的子问题，在一个分布式的方式。分析表明，所提出的OLAM产生O（μ）最优性差距，而仅需要具有O（log 2（μ）<$μ）容量的电池。数值测试证实，当需要容量明显较低的电池时，OLAM的平均成本略低于SDG。
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.