Near-optimal Online Algorithms for Joint Pricing and Scheduling in EV Charging Networks
Near-optimal Online Algorithms for Joint Pricing and Scheduling in EV Charging Networks
复制标题
电动汽车充电网络中联合定价和调度的近最优在线算法
DOI：
10.1145/3575813.3576878
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Hajiesmaili, Mohammad
中科院分区：
文献类型：
--
作者：
Bostandoost, Roozbeh;Sun, Bo;Joe-Wong, Carlee;Hajiesmaili, Mohammad
随着交通电气化的快速加速，公共充电站正在成为智能可持续城市中提供按需电动汽车（EV）充电服务的重要基础设施。随着越来越多的消费者寻求利用公共充电服务，这些服务的定价和调度将成为调解充电资源竞争的重要补充工具。然而，由于电动汽车到达的在线性质，确定正确的价格是困难的。研究了充电容量有限且能量成本时变的电动汽车充电网络运营商的联合定价和调度问题。收到充电请求后，运营商会提供一个价格，电动汽车根据其自身的价值和公布的价格决定是否接受该报价。如果EV接受报价，则运营商然后调度实时充电过程以满足充电请求。我们提出了一个在线定价算法，可以确定张贴的价格和电动汽车充电时间表，以最大限度地提高社会福利，即，电动汽车的总价值减去充电站的能源成本。理论上，我们证明了所设计的算法可以实现一个订单最优的竞争比下的竞争分析框架。实际上，我们使用真实的EV充电数据在实验中显示了我们的算法的经验性能优于其他基准算法。
With the rapid acceleration of transportation electrification, public charging stations are becoming vital infrastructure in smart sustainable cities to provide on-demand electric vehicle (EV) charging services. As more consumers seek to utilize public charging services, the pricing and scheduling of such services will become vital, complementary tools to mediate competition for charging resources. However, determining the right prices to charge is difficult due to the online nature of EV arrivals. This paper studies a joint pricing and scheduling problem for the operator of EV charging networks with limited charging capacity and time-varying energy costs. Upon receiving a charging request, the operator offers a price, and the EV decides whether to accept the offer based on its own value and the posted price. The operator then schedules the real-time charging process to satisfy the charging request if the EV admits the offer. We propose an online pricing algorithm that can determine the posted price and EV charging schedule to maximize social welfare, i.e., the total value of EVs minus the energy cost of charging stations. Theoretically, we prove the devised algorithm can achieve an order-optimal competitive ratio under the competitive analysis framework. Practically, we show the empirical performance of our algorithm outperforms other benchmark algorithms in experiments using real EV charging data.
登录
查看更多内容
DOI：
10.1145/2602044.2602053
发表时间：
2014-06
期刊：
Proceedings of the 5th international conference on Future energy systems
影响因子：
--
作者：
Zizhan Zheng;N. Shroff
通讯作者：
Zizhan Zheng;N. Shroff
DOI：
10.1145/3428336
发表时间：
2020-10
期刊：
Proceedings of the ACM on Measurement and Analysis of Computing Systems
影响因子：
--
作者：
Bo Sun;Ali Zeynali;Tongxin Li;M. Hajiesmaili;A. Wierman;D. Tsang
通讯作者：
Bo Sun;Ali Zeynali;Tongxin Li;M. Hajiesmaili;A. Wierman;D. Tsang
自适应充电网络：智能电动汽车充电框架
影响因子：
9.6
作者：
Lee, Zachary J.;Lee, George;Lee, Ted;Jin, Cheng;Lee, Rand;Low, Zhi;Chang, Daniel;Ortega, Christine;Low, Steven H.
通讯作者：
Low, Steven H.
DOI：
10.1609/aaai.v35i12.17294
发表时间：
2020-12
期刊：
ArXiv
影响因子：
--
作者：
Ali Zeynali;Bo Sun;M. Hajiesmaili;A. Wierman
通讯作者：
Ali Zeynali;Bo Sun;M. Hajiesmaili;A. Wierman
影响因子：
2.3
作者：
Shashank Goyal;D. Gupta
通讯作者：
D. Gupta