计算机科学
背景(考古学)
智能电网
微电网
后悔
调度(生产过程)
分布式计算
计算机安全
人工智能
工程类
机器学习
运营管理
生物
电气工程
古生物学
控制(管理)
作者
Yichen Liu,Pan Zhou,Lei Yang,Yulei Wu,Zichuan Xu,Kai Liu,Xiumin Wang
出处
期刊:IEEE transactions on emerging topics in computational intelligence
[Institute of Electrical and Electronics Engineers]
日期:2022-06-01
卷期号:6 (3): 462-478
被引量:12
标识
DOI:10.1109/tetci.2021.3085964
摘要
Electric Vehicles (EVs) are beginning to play a key role in the fast developing area of Internet of Things (IoT). Numerous results have shown the feasibility of vehicle-to-building (V2B) mode of charge/discharge, where EVs are considered as dynamically configurable dispersed energy storage units. When properly incorporated into the building energy system, EVs are able to provide ancillary services to the power grid during high demand periods or outage situations. The arising challenge is how to act intelligent behaviors in complex and changing microgrid environments. With the aim of minimizing the cost and maximizing satisfaction degree, this paper, unlike previous works, jointly considers the building energy need and the safety/willingness of EVs to find and dispatch the optimal vehicle to conduct auxiliary or supportive actions. To realize that, we propose an intelligent Privacy-preserving Context-based Online EV Dispatching System (PCOEDS), using a tree-based structure which supports the ever-increasing big metering datasets with context-awareness. Moreover, privacy preservation and security protection on both sides of the the energy transmission process are well guaranteed in our work. Theoretical results validate that our intelligent dispatching system achieves sublinear regret and differential privacy, which outperforms other online learning method when applied to a huge city-level dataset.
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