Thesis (Ph.D.)--The George Washington University, 2020
Includes bibliographical references
A power grid transformation is needed to integrate large-scale variable renewable energies (VREs) and electric vehicles (EVs), in order to address the environmental concerns. Organizations and governments have set ambitious targets for the integration of these emerging resources into the modern power grids to build, plan, and operate a clean and sustainable energy landscape. This dissertation proposes an integrated control and energy management scheme for power grids with massive integration of VREs and EVs. We firstly propose new EV charging station (EVCS) control scheme and a holistic approach to evaluate the electrical safety of the large-scale EVCSs. Our approach mainly focuses on several topics on the operational safety of EVCS primarily concerning: (1) the facility degradation which could potentially result in a compromised EVSE reliability performance and EVCS protection failure; (2) the cyber-attack challenges when the smart charging and the communication between EVCSs and electric utilities are enabled; and (3) the potential mismatch between the renewable output and EVCS demand, which could trigger the system stability challenges during normal operation and inability to supply the critical EV loads during outages. A two-stage energy management system (EMS) for power grids is proposed. The first stage economic dispatch determines the optimal operating points of charging stations and battery swapping stations (BSS) for EVs under plug-in and battery swapping modes, respectively. The proposed stochastic model predictive control (SMPC) problem in this stage is characterized through a chance-constrained optimization formulation that can effectively capture the system and the forecast uncertainties. A distributed algorithm, the alternating direction method of multipliers (ADMM), is applied to accelerate the optimization computation through parallel computing. The second stage is aimed in coordinating the EV charging mechanisms to continuously follow the first-stage solutions, i.e., the target operating points, and meeting the EV customers' charging demands captured via the Advanced Metering Infrastructure (AMI). The proposed solution offers a holistic control strategy for large-scale centralized power grids in which the aggregated individual parameters are predictable and the system dynamics do not vary sharply within a short time-interval. Based on this new control and energy management schemes, we propose a new datadriven approach for EV charging load modeling. We first introduce a mathematical model that characterizes the flexibility of EV charging demand. Advanced simulation procedures are then proposed to identify the parameters of different EV load models and simulate EV charging demand under different electricity market realizations. The proposed EV load modeling approach can simulate different EV operation schedules, charging levels, and customer participation as a benchmark system. Eventually, a restoration approach for EVCSs is also proposed to utilize the flexibility of the aggregated EV loads to enhance the power grid resilience against extremes. A framework is also introduced to offer adaptive operation strategies for the EVCS operators. As a result, the system can effectively manage the EVCS under different penetration levels of EVs, considering both normal operating conditions and restoration processes during interruptions and emergencies. The proposed adaptive operation mechanism could bring significant advantages to the operation and control of smart power grids with high penetration of renewables and EVs when facing different operating conditions
Electronic reproduction. Ann Arbor, Mich. : ProQuest, 2021
