An Online Scheduling Algorithm for a Community Energy Storage System
An Online Scheduling Algorithm for a Community Energy Storage System
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DOI：
10.1109/tsg.2022.3179251
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发表时间：
2021-10
影响因子：
9.6
通讯作者：
Nathaniel Tucker;M. Alizadeh
中科院分区：
文献类型：
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作者：
Nathaniel Tucker;M. Alizadeh
在本文中，我们考虑了一个社区储能（CES）系统，该系统由各种电力消费者共享，他们希望在给定的时间跨度内对CES进行充电和放电。我们研究的问题所面临的经理这样的CES谁必须安排充电，放电，和容量预留众多的用户。此外，我们考虑了CES充电/放电请求以在线方式到达并且CES管理者必须立即分配充电功率和能量容量以满足请求或完全拒绝请求的情况。消费电子产品经理的目标是最大限度地提高消费电子产品的所有用户获得的总价值，同时考虑消费电子产品的操作限制。我们讨论了一个算法，名为COmmunityEnergyScheduling，作为一个定价机制的基础上在线的原始对偶优化作为CES经理的问题的解决方案。在线算法实时估计双变量（价格），以便在请求到达时立即分配或拒绝请求。此外，所提出的方法促进充电和放电取消以减少CES在流行时间的使用，并且能够处理源自用户净负载模式的随机性和天气不确定性的充电/放电请求的固有随机性。此外，我们能够表明，该算法能够处理任何adversarially选择的请求序列，并将始终产生总福利的离线最佳福利的$\frac {1}{\alpha }$的一个因素。
In this paper, we consider a community energy storage (CES) system that is shared by various electricity consumers who want to charge and discharge the CES throughout a given time span. We study the problem facing the manager of such a CES who must schedule the charging, discharging, and capacity reservations for numerous users. Moreover, we consider the case where requests to charge/discharge the CES arrive in an online fashion and the CES manager must immediately allocate charging power and energy capacity to fulfill the request or reject the request altogether. The objective of the CES manager is to maximize the total value gained by all of the users of the CES while accounting for the operational constraints of the CES. We discuss an algorithm titled COmmunityEnergyScheduling that acts as a pricing mechanism based on online primal-dual optimization as a solution to the CES manager’s problem. The online algorithm estimates the dual variables (prices) in real-time to allow for requests to be allocated or rejected immediately as they arrive. Furthermore, the proposed method promotes charging and discharging cancellations to reduce the CES’s usage at popular times and is able to handle the inherent stochastic nature of the requests to charge/discharge stemming from randomness in users’ net load patterns and weather uncertainties. Additionally, we are able to show that the algorithm is able to handle any adversarially chosen request sequence and will always yield total welfare within a factor of $\frac {1}{\alpha }$ of the offline optimal welfare.