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|Title:||Integration of electric vehicles into power grid||Authors:||Kandasamy, Nandha Kumar||Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2016||Source:||Kandasamy, N. K. (2016). Integration of electric vehicles into power grid. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||Electric Vehicles (EVs) are becoming a promising solution to the environmental problems caused by the gasoline powered internal combustion engine automobiles. The major challenges to the deployment of EVs are high initial cost, limited range, increased peak demand, etc. But with good charging infrastructure which is integrated into smart grid, most of the disadvantages can be eliminated. Moreover, the EV batteries can be considered as distributed storage and used for various smart grid applications. For integration of EVs into smart grid, the requirements and feasibility of different charging methodologies for EVs are to be investigated. Based on the investigation, economic and efficient models for EV charging along with the associated charging strategies are to be derived for efficient integration. The impacts of different charging methods on battery life and distribution system peak demand are the two most important factors to be considered while deriving the models. When the EVs are plugged into the grid for charging, all the operations including charging of their batteries and auxiliary functions such as grid support using Vehicle to Grid (V2G) and demand response management (DRM) are to be coordinated. This thesis presents a dynamic scheduling algorithm using novel priority criteria for integrating EVs into smart grid. The proposed dynamic scheduling algorithm is used for various other applications such as V2G capacity estimation and DRM. The scheduling algorithm is also used for verifying the methodology for estimating the number of EVs that can be supported by typical smart grid clusters with distributed energy resources. A novel data driven load model is used for determining the charging profiles of the EVs. Matlab based simulation models are developed to implement the proposed methods and illustrative case studies are used to evaluate the performance of the proposed system under a given set of conditions. The proposed dynamic scheduling algorithm uses model based scheduling approach to overcome the disadvantages of existing scheduling methods. The load models used are stochastic building load demand (without EVs) and predicted EV charging profiles. Predicting the charging profiles of EVs connected to a building incorporated with a Building Energy Management System (BEMS) will improve the energy efficiency of the building. The predicted charging profiles along with the stochastic load data can be used for calculating V2G capacity and for performing load/source scheduling. Data driven modeling has significant advantages in predicting the charging profiles of EVs, hence an Artificial Neural Network (ANN) based model is proposed for predicting the charging profiles of EVs connected to a building. The ANN model considers the previous charging profiles, initial State of Charge (SOC) and final SOC for predicting the charging profiles of EVs. Appropriate dynamic priority criteria required specifically for EV scheduling is also proposed. The algorithm is applied for both time coordinated EV charging and power coordinated EV charging. The objective function of the algorithm is to minimize the variance in priority values of the connected EVs. Minimizing the variance in priority values of the EVs will reduce the variations in fairness given to the EVs. The impact of different priority criteria and different combination of priority criteria on the fairness and chargeability of the EVs are also studied. Furthermore, the need for using weighted priority criteria is demonstrated. The proposed dynamic scheduling algorithm is also applied to the real-time V2G capacity estimation for a group of vehicles. Using scheduling for V2G capacity estimation is a novel approach and has significant advantages. The V2G capacity that a group of vehicles can provide is estimated by evaluating the chargeability of the EVs to desired final SOC. Using scheduling for determining the V2G capacity will have a greater significance as the accuracy of the estimation is not affected by the time at which the estimation is carried out. A comparison of the proposed method with other methods which do not employ scheduling for the V2G capacity estimation is presented to demonstrate the advantages. The proposed dynamic scheduling algorithm is also applied for novel pre-emptive DRM of EVs in a building. The load shedding of the EVs is carried based on the priority values given by the proposed dynamic priority criteria. The chargeability of the EVs is ensured before deploying the EVs for DRM. The dynamic priority based pre-emptive DRM of EVs is implemented as part of intelligent pre-emptive DRM in a building which ensures that contracted capacity or demand limit (CC/DL) is not exceeded and at the same time reduction of energy consumption in the building is achieved. A methodology which uses stochastic calculations (Monte-Carlo simulations) and linear programming for the estimating the optimal number of EVs that can be deployed under a high-rise building in Singapore is presented in this thesis. The proposed dynamic scheduling algorithm is used to validate the accuracy of the estimation. Load demand data for five years and load model of EVs are used for the validation. Although there are various obstacles for the deployment of EVs, unavailability of charging infrastructure is the biggest obstacle. Hence, the cost-benefit analysis for installation of charging stations in buildings is also discussed by varying different parameters. It is important to ensure that the number of EVs integrated into the smart grid does not bring any adverse effect. A novel method for determining the limits based on the hot-spot temperature of distribution transformer is also proposed.||URI:||http://hdl.handle.net/10356/68144||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
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