Online Control and Near-Optimal Algorithm for Distributed Energy Storage Sharing in Smart Grid
Online Control and Near-Optimal Algorithm for Distributed Energy Storage Sharing in Smart Grid
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DOI：
10.1109/tsg.2019.2957426
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发表时间：
2020-05
影响因子：
9.6
通讯作者：
Weifeng Zhong;Kan Xie;Yi Liu;Chao Yang;Shengli Xie;Yan Zhang
中科院分区：
文献类型：
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作者：
Weifeng Zhong;Kan Xie;Yi Liu;Chao Yang;Shengli Xie;Yan Zhang
提出了一种分布式储能共享系统实时能量管理的在线控制方法。一个新的ES共享的情况下，被认为是物理ES（PES）的能力重新分配给用户，使每个用户管理自己的虚拟ES（VES），而不知道详细的操作的PES。为了对专家系统共享系统进行真实的实时优化，提出了一种基于李雅普诺夫优化框架的在线算法。在线算法的优点是它只根据当前系统状态的实现做出决策，而不必预测未来不确定的系统状态，如电价，用户负荷和可再生能源发电。在性能分析中，证明了在线解决方案是可行的，并具有可证明的性能保证。在此基础上，提出了一种离线参数优化选择方法，以保证在线控制性能。针对隐私保护的实际需要，提出了一种基于交替方向乘子法（ADMM）的分布式在线控制方法。在分布式实现中，允许用户在本地管理他们的VES，而无需将他们的私有数据发送给任何人。在仿真中，使用电价、家庭负荷和家庭可再生能源发电的实际实时数据。结果表明，所提出的分布式在线控制方法可以提供一个接近最优的解决方案，与其他基准。
This paper proposes an online control approach for real-time energy management of distributed energy storage (ES) sharing. A new ES sharing scenario is considered, in which the capacities of physical ESs (PESs) are reallocated to users, so that each user manages its own virtual ES (VES) without knowing detailed operations of the PESs. To optimize the ES sharing system in real time, an online algorithm is developed based on Lyapunov optimization framework. The advantage of the online algorithm is that it makes decisions only based on the realization of current system states, without having to predict future uncertain system states such as electricity price, user load, and renewable generation. In performance analysis, it is proven that the online solution is feasible and has a provable performance guarantee. Based on the analysis, an approach for optimal offline parameter selection is proposed to guarantee the online control performance. For practical need of privacy protection, a distributed implementation of the online control is proposed via alternating direction method of multipliers (ADMM). In the distributed implementation, users are allowed to manage their VESs locally without sending their private data to anyone. In simulation, actual real-time data of electricity price, home load, and home renewable generation is used. Results show that the proposed distributed online control approach can provide a near-optimal solution, compared with other benchmarks.