As a new type of transportation, electric vehicles (EV) can effectively adjust the supply and demand balance of power systems using their vehicle-to-grid (V2G) characteristics. To better promote the participation of EV resources in the energy market and interact with power systems, we propose a novel framework of an electric vehicle aggregator (EVA) that can aggregate schedulable EVs within its jurisdiction to provide auxiliary services for the power grid. Due to EV charging behavior's uncertain nature, we employ a probability mass function (PMF) based model to provide more accurate forecasts of future EV behaviors. To reduce EVA operation costs and maximize the travel utility for EV users participating in this service, we develop an EVA optimization schedule model that combines a day-ahead optimization schedule and realtime optimization schedule. Finally, we create three case studies to verify the results of the proposed method. Matlab is used to simulate and analyze each case study concerning uncoordinated charging, coordinated charging while considering day-ahead optimization schedules, and an ensemble of coordinated charging activities that consider the day-ahead optimization schedule and real-time optimization schedule. Through comparative analysis, it is verified that the proposed strategy can effectively reduce EVAs' operating costs and meet the travel requirements of EV users. The impact of different degrees of error of EV plug-out information on the proposed method is also analyzed. INDEX TERMS Electric vehicles, vehicle-to-grid, electric vehicle aggregator, day-ahead optimization schedule, real-time optimization schedule
Existing literature on planning for electric vehicle charging stations (EVCS) fails to consider uncertain factors in power systems, such as load fluctuations and the impact of EV integration. Consequently, using deterministic power flow (DPF) algorithms for EVCS planning is unreliable. To address this, we propose a probabilistic model for EV charging loads and introduce a novel dynamic system voltage stability (DSVS) index. We then present an effective optimization model for EVCS site and size planning using stochastic power flow (SPF). Our model aims to maximize capital gains on investment costs of EVCS, minimize yearly EV users' average wait time and distance to charge costs and minimize the DSVS index. To simplify the problem, we use the super-efficiency data envelopment analysis (SEDEA) method to determine objective weights and transform the multi-objective optimization problem into a single-objective one. Finally, we jointly solve the model using the voronoi diagram and adaptive differential evolution optimization algorithm (ADEOA). We verify the effectiveness of our proposed method using a case study with the IEEE 33-node distribution network topology diagram and a planning area diagram.
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