Frequency Regulation Strategy Considering User Behavior Uncertainty of Electric Vehicles

As the integration of renewable energy accelerates, the challenge of maintaining grid frequency stability becomes increasingly significant. Traditional frequency regulation strategy relies on large generators, which are not adapted to the current conditions due to their slow response time. In recent...

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Veröffentlicht in:2024 6th International Conference on Power and Energy Technology (ICPET) S. 1226 - 1231
Hauptverfasser: Hao, Jiao, Zhao, Wenmeng, Zhang, Zongbao, Hu, Xinying, Li, Zepeng, Wu, Qiu-Wei
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 12.07.2024
Schlagworte:
chance constraints programming (CCP)
electric vehicle (EV)
Electric vehicles
Frequency control
frequency regulation
Optimization models
Particle swarm optimization
particle swarm optimization (PSO)
Power system stability
Renewable energy sources
Stability analysis
Stochastic processes
Time factors
Uncertainty
user behavior uncertainty
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Zusammenfassung:As the integration of renewable energy accelerates, the challenge of maintaining grid frequency stability becomes increasingly significant. Traditional frequency regulation strategy relies on large generators, which are not adapted to the current conditions due to their slow response time. In recent years, the rapid growth in the number of electric vehicles (EVs) has provided significant frequency regulation potential. However, the inherent uncertainty in user behavior complicates the integration of EVs into frequency regulation frameworks. This paper addresses these challenges by proposing a frequency regulation strategy considering user behavior uncertainty of EVs. Initially, three major uncertainties in user behavior of EVs are modeled. Subsequently, a stochastic optimization model based on chance constraints is established to optimize the participation of EVs in frequency regulation. A particle swarm optimization (PSO) algorithm enhanced by random simulation techniques is employed to solve the proposed model. Finally, the proposed strategy is tested on a modified IEEE 33-bus test system. The results demonstrate that the proposed strategy effectively quantifies the uncertainties of user behavior and makes the frequency regulation strategy for EVs more realistic.
DOI:10.1109/ICPET62369.2024.10941407