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Title: Steady-state analysis of power distribution system with dynamic charging electric vehicles
Authors: Wang, Chuan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Source: Wang, C. (2020). Steady-state analysis of power distribution system with dynamic charging electric vehicles. Master's thesis, Nanyang Technological University, Singapore.
Abstract: Increasing concerns about global carbon emission and the shortage of fossil fuels have shifted people’s attention from traditional cars towards electric vehicles (EVs). For EVs, charging is of great importance and can be largely classified into wireless and plug-in types. Wireless dynamic charging technologies are gradually replacing the plug-in ones as they allow EVs being charged while they are in motion. In this way, those EVs become a new type of loads - the moving loads. They may change their locations constantly in the network. A large number of EVs charging to the grid creates a significant challenge to the stability and quality of the overall power system, like affecting the voltage control. To analyze the steady-state problem of dynamic charging, voltage profiles along the distribution system and probabilistic load flow problems should be both considered. To study the effect of these moving loads on power distribution grids, this work first focuses on the steady-state analysis of electrified roads equipped with wireless dynamic charging. In particular, the voltage profile and the long-term voltage stability of the electrified roads are considered. We introduce a simple dynamic charging EV system and the power flow problem for the steady-state study. MATPOWER is used to construct the steady-state voltage profile. We analyze the problem for several different conditions including moving directions, power consumptions, charging efficiencies, photovoltaic panels integration, and reactive power compensation with capacitor banks. Unusual shapes of the voltage profile are observed such as the half-leaf veins for a one-way road and the harp-like shape for a two-way road. Voltage swings are also detected when the vehicles move in the two-way road configuration. Such new observations will contribute to the voltage regulation of the distribution system. We also consider the long-term voltage stability of electrified roads where one can calculate the maximum length of a road for a fixed fleet of EVs and the maximum number of vehicles for a fixed road. A new method is introduced to use the continuation power flow (CPF) to find the critical length and number of allowed EVs. Varying segment sizes of the distribution system affect the results of both the maximum length of the road and the maximum number of vehicles. The voltage collapse phenomenon when the vehicles move beyond the maximum allowed length will also be analyzed. In addition to the above deterministic analysis, we consider the probabilistic steady-state problem. In particular, we propose a non-parametric probabilistic load flow (NP-PLF) technique based on Gaussian Process (GP). The technique can provide “semi-explicit” power flow solutions by implementing a learning step and a testing step. The proposed NP-PLF leverages upon the GP upper confidence bound (GP-UCB) sampling algorithm. The salient features of this NP-PLF method are: i) applicable for power flow problem having power injection uncertainty with unknown class of distribution; ii) providing probabilistic learning bound (PLB) which controls the error and convergence; iii) capable of handling intermittent distributed generation as well as load uncertainties; and iv) applicable to both balanced and unbalanced power flow with different type and size of systems. The simulation results performed on the IEEE 33-bus, IEEE 30-bus, and IEEE 118-bus systems show that the proposed method is able to learn the state variable function in the input subspace using a small number of training samples. Further, the testing with different distributions indicates that more complete statistical information can be obtained for the probabilistic power flow problem.
DOI: 10.32657/10356/140291
Schools: School of Electrical and Electronic Engineering 
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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