TY - CHAP
T1 - Utilizing Wind Farms and Electric Vehicles to Mitigate the First and Second Frequency Dips in Power Systems
AU - Hosseini, Seyed Amir
AU - Peyghami, Saeed
PY - 2024/8/1
Y1 - 2024/8/1
N2 - In this paper, we address the challenge of mitigating the impact of low system inertia by harnessing the synergies between electric vehicles (EVs) and wind farms (WFs) in modern power system frequency regulation. We introduce a virtual inertia controller, compliant with the IEC 61851 standard, designed to leverage the contributions of EVs to frequency regulation. This controller measures the rate-of-change-of-frequency (ROCOF) and computes an optimal EV current set point accordingly. To address concerns regarding the impact of frequency regulation on EV owners, we propose a stochastic allocation method for distributing the regulation tasks among EVs parked at different locations. Furthermore, we present an algorithm for wind turbines participation in frequency control. This algorithm dynamically adjusts the wind turbines’ output power based on wind velocity, reaching a maximum acceptable value, and stabilizing it. We develop a method to determine this maximum acceptable power increment, considering wind turbine constraints. The algorithm employs two PID controllers for the support and recovery phases. During the recovery phase, we identify and address a second frequency dip, leveraging EV collaboration to mitigate this adverse event. To assess the performance of our proposed algorithms and controllers, we conduct extensive simulation studies using the modified IEEE 39-bus test power system. Our results validate the effectiveness of these methods in providing robust system frequency support.
AB - In this paper, we address the challenge of mitigating the impact of low system inertia by harnessing the synergies between electric vehicles (EVs) and wind farms (WFs) in modern power system frequency regulation. We introduce a virtual inertia controller, compliant with the IEC 61851 standard, designed to leverage the contributions of EVs to frequency regulation. This controller measures the rate-of-change-of-frequency (ROCOF) and computes an optimal EV current set point accordingly. To address concerns regarding the impact of frequency regulation on EV owners, we propose a stochastic allocation method for distributing the regulation tasks among EVs parked at different locations. Furthermore, we present an algorithm for wind turbines participation in frequency control. This algorithm dynamically adjusts the wind turbines’ output power based on wind velocity, reaching a maximum acceptable value, and stabilizing it. We develop a method to determine this maximum acceptable power increment, considering wind turbine constraints. The algorithm employs two PID controllers for the support and recovery phases. During the recovery phase, we identify and address a second frequency dip, leveraging EV collaboration to mitigate this adverse event. To assess the performance of our proposed algorithms and controllers, we conduct extensive simulation studies using the modified IEEE 39-bus test power system. Our results validate the effectiveness of these methods in providing robust system frequency support.
KW - Electric vehicle
KW - First and second frequency dips
KW - Frequency regulation
KW - Modern power system
KW - Wind farms
UR - http://www.scopus.com/inward/record.url?scp=85201404045&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-62042-3_5
DO - 10.1007/978-3-031-62042-3_5
M3 - Book chapter
SN - 978-3-031-62041-6
SN - 978-3-031-62044-7
VL - 1
T3 - Green Energy and Technology
SP - 37
EP - 45
BT - Future Directions in Energy Engineering
A2 - Wang, Xiaolin
PB - Springer
ER -