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Reliability-based Approaches to Quantify Maximum Permissible Penetration Level of Electric Vehicles in Power Systems
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The growing concern for environmental and pollution-related problems has led to increase the penetration of sustainable resources in both power and transportation systems. Although transitioning from fossil fuel-based vehicles to electric vehicles (EVs) in transportation systems helps in alleviating many environmental and pollution-related concerns associated with conventional cars, the inclusion of EVs represents a new type of electric load that requires allocating appropriate resources to maintain/improve power system reliability. Therefore, it is important to analyze the reliability of power systems with EVs for operation and planning perspectives. In order to maintain the reliability of a power system, load demand should be satisfied. If all or part of the load cannot be satisfied, load curtailments are needed to maintain system stability. The number of EVs is expected to exponentially increase in the coming years. This high-expected growth in the number of EVs will increase the probability of load curtailments of power systems. However, load curtailments can be avoided by incorporating demand response (DR) programs and optimum energy management systems. Existing methods to investigate the effect of DR programs on power system reliability do not include penalty terms, which is an important factor for effective participation of customers in DR programs. Thus, in addition to the existing economic models, a load curtailment model to emulate customers’ active participation in DR programs needs to be developed to improve the reliability of power systems. In addition, it is obvious that the continuous increase of EVs produce several challenges related to the variability of charging time, duration, and location for planning of future power system. Determining the amount of controlled charging of EV loads that can be incorporated in existing power systems is a promising factor in addressing these challenges. One of the promising solutions to deal with the challenges of accommodating EVs in power systems is that, with knowledge of load profiles of EVs, or behavior and preferences of EV drivers to charge their vehicles, and load profiles of conventional electric loads, the DR programs can reduce or even avoid the negative impacts of EVs on power system reliability. The work presented in this dissertation tabulates load profile of EVs in forms of hourly, daily, and weekly loads in percent of their maximum load. This load model considers drivers’ behavior and preferences, which include weekdays/weekends and seasonal charging patterns. Also, this work introduces two new metrics to measure the capability of a power system to accommodate EV loads without deteriorating its reliability. Moreover, this work proposes methods to increase or maximize the number of EVs that can be incorporated in power systems. The conducted work of this dissertation can be divided into three parts. Part I presents the concept of developing load profiles for the EVs. This part also describes the proposed methods to quantify EV loads in existing power systems under both controlled and uncontrolled charging. Moreover, this part provides algorithms to determine permissible penetration level of EVs at different regions (buses) of power systems. Part II of this dissertation describes several proposed smart charging/discharging strategies for EVs to increase their penetration level in power systems with existing resources. Part III presents a DR-based method to maximize the penetration level of EVs in power systems under uncontrolled charging scenario. In summary, the thesis presented here is that, with accurate modeling of EV load profiles based on drivers’ behavior and preferences, demand response (DR) programs and smart charging/discharging strategy will reduce negative impacts of EVs on power systems and defer expansion plans.