Research on real-time dispatching strategy based on comprehensive adjustable capacity assessment of Electric Vehicles

With the substantial increase in the number of electric vehicles, small changes in the traditional excitation parameters often lead to unanticipated fluctuations in the load curve, thus affecting the regulation performance of peak shaving and valley filling. Meanwhile, the existing charging and discharging scheduling does not comprehensively consider the responsiveness and preference of EVs to be dispatched, making it difficult to reasonably mobilise their regulation capability. Therefore, this paper proposes an evaluation and selection model that comprehensively considers the adjustable capability of EVs. Firstly, this paper establishes a strategy optimization framework for EVs to participate in real-time peak and valley regulation based on multi-timescale collaboration with rules and aggregators in the real-time phase. Secondly, this paper establishes a comprehensive adjustable capacity assessment model for EVs based on the vehicle-pile fusion adjustable capacity model, charging demand laxity model, personal preference model, and historical participation model, and adopts the subjective and objective empowerment evaluation model based on AHP-anti-entropy method to establish a dynamic scheduling selection strategy based on aggregators. Finally, this paper verifies the effectiveness of the algorithm to enhance the optimization effect of charging and discharging strategy in realtime phase through examples.