Abstract
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 real-time 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.
Highlights
Promoting electric vehicles (EV) can accelerate fuel substitution and reduce vehicle exhaust emissions, which are of great significance for promoting energy conservation and emissions reduction and preventing air pollution [1,2,3,4]
This study presents a novel electric vehicle aggregator (EVA) framework that can reasonably dispatch EVs and provide auxiliary services to the power grid
We built a probability mass function (PMF) model based on plug-in time, parking duration time, and initial state of charge (SoC) information of EVs using the Monte-Carlo method to model EV charging behavior
Summary
Promoting EVs can accelerate fuel substitution and reduce vehicle exhaust emissions, which are of great significance for promoting energy conservation and emissions reduction and preventing air pollution [1,2,3,4]. According to the daily charging demand of the EV fleet, the EVA bids for energy in the day-ahead market It implements a real-time scheduling optimization strategy based on dayahead market transactions to maximize its profit without affecting EVs' charging targets [9,10,11]. The authors in [25] perform cluster analysis for different types of EVs and their travel times while combining market clearance limits and EV charging load limits to build a twolevel optimal bidding strategy model for EVs aggregators to minimize EVA costs. 3) Design of an optimal scheduling algorithm for a group of EVAs using a convex model in conjunction with data such as day-ahead energy market prices, real-time energy market prices, and EV characteristics This algorithm is designed to minimize EVA operation costs without affecting the charging requirements of EV users.
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