Date of Award
2019
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Physics and Astronomy
Committee Chair
Massimiliano Bonamente
Committee Member
James A. Miller
Committee Member
Ming Sun
Subject(s)
Plug-in electric vehicles--Power supply--Management--Mathematical models
Abstract
The transportation sector is responsible for almost one-third of energy consumption and greenhouse gas emissions. To mitigate the above-mentioned issues, plug-in electric vehicles (PEV) are being encouraged by governments and environmentalists to replace internal combustion engine (ICE) vehicles, since PEVs can be charged by renewable energy sources. Based on the predictions performed by the research organizations, the PEV sales will surpass the ICE car sales in the next two decades. Thus, the PEV drivers will consume a considerable portion of electricity soon. However, the uncontrolled and simultaneous charging of PEVs can put the power system under stress. Therefore, the charging time of PEVs needs to be optimally managed by the system operator by introducing a variety of incentives to the drivers. In this thesis, to consider the cooperation likelihood of drivers with the power system operator, the drivers’ responsiveness probability is modelled with respect to the value of incentive, the drivers’ social class (low-income, moderate income, and high-income), and the real driving routes in San Francisco, CA. Moreover, different PEV penetration levels (low, moderate, and high) and various PEV types (Tesla Model S, BMW i3, and Volkswagen e-up) are considered and studied in the problem. In this thesis, Monte Carlo Markov Chain (MCMC) is applied to estimate the hourly probability distribution function (corresponding to the mean value and confidence band) of state of charge (SOC) of PEV fleet. The main goal of the thesis is to optimally manage the charging time of PEVs in San Francisco, CA to minimize the operation cost of electrical distribution network penetrated by the renewable energy sources. Herein, a stochastic model predictive control (SMPC) is used in the optimization process of problem to address the variability and uncertainty issues of PEVs’ SOC and renewables’ power. In addition, quantum-inspired simulated annealing (QISA) algorithm is applied to solve the optimization problem. Several sensitivity analyses are performed to study the effects of input parameters on the output parameters of MCMC as well as the problem results.
Recommended Citation
Rahmani-Andebili, Mehdi, "Charging management of plug-in electric vehicles applying Monte Carlo Markov Chain and quantum-inspired optimization algorithm" (2019). Theses. 308.
https://louis.uah.edu/uah-theses/308