Coordinated Charging Scheduling for Electric Vehicles and Optimal Tuning of the Controller for Frequency Regulation under Uncertain Environment

被引：4

作者：

Das, Sourav ^{[1
]}

Acharjee, Parimal ^{[1
]}

Bhattacharya, Aniruddha ^{[1
]}

机构：

[1] Natl Inst Technol Durgapur, Dept Elect Engn, Durgapur, India

来源：

2021 IEEE INDUSTRY APPLICATIONS SOCIETY ANNUAL MEETING (IAS) | 2021年

关键词：

Charging Scheduling; Electric Vehicles; Frequency Regulations; Vehicle-to-Grid (V2G); WIND POWER; ENERGY; MANAGEMENT;

D O I：

10.1109/IAS48185.2021.9677300

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper proposes a Multilevel Coordinated Controlled Charging Algorithm (MLCCA), by which the cost for Electric Vehicles (EV) charging can be minimized and as well as the Dynamic Frequency Regulation (DFR) under uncertain environment can be done by minimizing the Area Control Error Signal (ACES) by optimizing the control parameters of PID controllers in two area interconnected power system. Along with the EVs, Conventional FR resources ( CFR) and Battery Energy Storage (BES) has been integrated in priority basis while performing the DFR as per the operating states. The coordinated charging of EVs has adopted both Grid to Vehicle (G2V) and Vehicle to Grid (V2G) mode of operation. The uncertainties related with the driving cycles has been tackled by 2m Point Estimation (2m-PEM). To establish the effectiveness of EV/BES as FR sources, along with CFR, dynamic simulation has been performed for AGC of two area interconnected system.

引用

页数：6

共 15 条

[1] Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation [J].