Human-centric data-driven optimization and recommendation in EV-interfaced grid at city scale: poster abstract
Human-centric data-driven optimization and recommendation in EV-interfaced grid at city scale: poster abstract
复制标题
城市规模电动汽车接口网格中以人为中心的数据驱动优化和推荐：海报摘要
DOI：
10.1145/3563357.3567752
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Jiang, Xiaofan
中科院分区：
文献类型：
--
作者：
Nie, Jingping;Hu, Lanxiang;Liu, Yian;Fan, Yuang;Preindl, Matthias;Jiang, Xiaofan
电动汽车和电动汽车充电器的快速发展引入了许多影响电网的因素。电动汽车充电和充电调度也给电动汽车驾驶员和电网运营商带来了挑战。在这项工作中，我们提出了一个以人为中心，数据驱动，城市规模，多变量优化方法的电动汽车接口的网格。这种方法考虑了用户的历史驾驶和充电习惯、用户偏好、电动汽车特性、城市规模的移动性、电动汽车充电器的可用性和价格以及电网容量。用户偏好包括成本和充电时间之间的权衡，以及参与不同节能计划的激励。我们利用深度强化学习（DRL）为电动汽车驾驶员提供建议，并在提高电网性能的同时优化他们的福利。
The fast development of electric vehicles (EV) and EV chargers introduces many factors that affect the grid. EV charging and charge scheduling also bring challenges to EV drivers and grid operators. In this work, we propose a human-centric, data-driven, city-scale, multivariate optimization approach for the EV-interfaced grid. This approach takes into account user historical driving and charging habits, user preferences, EV characteristics, city-scale mobility, EV charger availability and price, and grid capacity. The user preferences include the trade-off between cost and time to charge, as well as incentives to participate in different energy-saving programs. We leverage deep reinforcement learning (DRL) to make recommendations to EV drivers and optimize their welfare while enhancing grid performance.
具有标准化电网服务的电动汽车接口微电网的高性能最佳潮流估计
影响因子：
4.4
作者：
Jingping Nie;Liwei Zhou;Margaret Frances Kaye;C. Silveira;Afam Nwokolo;Xiaofan Jiang;M. Preindl
通讯作者：
M. Preindl
基于深度强化学习的微电网优化潮流与电网服务实施的方法
DOI：
10.1109/itec53557.2022.9813862
发表时间：
2022
期刊：
2022 IEEE Transportation Electrification Conference & Expo (ITEC)
影响因子：
--
作者：
Jingping Nie;Yanchen Liu;Liwei Zhou;Xiaofan Jiang;M. Preindl
通讯作者：
M. Preindl