As awareness of human footprint grows, solutions are investigated to reduce greenhouse gas emissions (GHG). To reduce the carbon intensity of electricity production, a massive deployment of renewable energy sources (RESs) has become a mandatory goal. A significant challenge behind the deployment of RESs is the frequency regulation of such systems due to the high penetration of inverters based on energy storage systems (ESS). To overcome this issue, this manuscript proposes, as the main contribution, to use a virtual inertia emulator-based model predictive control. MPC is an optimization-based control strategy that aims at finding the optimal control actions of a system by predicting its future behaviors. This control solution is compared to a proportional (P) and a proportional integrator (PI) controller. The results of this study show that the MPC controller reduce the minimum ESS capacity by 55% and 2%, and the ESS energy throughput by 86% and 36 % compared to a P and PI controller. Those results suggest that the MPC controller can help to diminish the CAPEX and ESS aging compared to a P or PI controller.

This paper describes the development of a dedicated electromagnetic interference (EMI) characterization platform for the evaluation of wide bandgap-based converters in ungrounded architectures of the type likely to be employed on the future shipboard systems. This platform is designed to support the characterization of wide-bandgap-based converters operating at switching frequencies of hundreds of kilohertz; with power levels up to 100 kVA and with expected emissions up to 100 MHz. To illustrate the capabilities of this platform, the conducted emissions of a SiC-based half-bridge converter are evaluated (with a focus on common-mode (CM) behavior), and a parametric study is conducted which considers variations in the impedance between the half-bridge module base plate and a representative grounding structure. The focus of this work is not only the half-bridge converter which serves as the preliminary test subject, but also rather the custom metrological setup used for characterization, which is designed to discover sensitivities to resonant paths so that the design guidelines for peripheral structures and EMI mitigating components can be developed in the future studies. One major contribution of this work is the discovery that the symmetry of the parasitic capacitances within the converter's multichip power module is an important factor which significantly influences the CM conducted emissions of the half-bridge structure, which serves as a building block for more complex converter topologies.

Power management strategies have impacts on fuel economy, greenhouse gasses (GHG) emission, as well as effects on the durability of power-train components. This is why different off-line and real-time optimal control approaches are being developed. However, real-time control seems to be more attractive than off-line control because it can be directly implemented for managing power and energy flows inside an actual vehicle. One interesting illustration of these power management strategies is the model predictive control (MPC) based algorithm. Inside a MPC, a cost function is optimized while system constraints are validated in real time. The MPC algorithm relies on dynamic models of the vehicle and the battery. The complexity and accuracy of the battery model are usually neglected to benefit the development of new cost functions or better MPC algorithms. The contribution of this manuscript consists of developing and evaluating a high-fidelity battery model of a plug-in hybrid electric vehicle (PHEV) that has been used for MPC. Via empirical work and simulation, the impact of a high-fidelity battery model has been evaluated and compared to a simpler model in the context of MPC. It is proven that the new battery model reduces the absolute voltage, state of charge (SoC), and battery power loss error by a factor of 3.2, 1.9 and 2.1 on average respectively, compared to the simpler battery model.

Estimation of a lithium battery electrical impedance can provide relevant information regarding its characteristics. Currently, electrochemical impedance spectroscopy (EIS) constitutes the most recognized and accepted method. Although highly precise and robust, EIS is usually performed during laboratory testing and is not suitable for any on-board application, such as in battery electric vehicles (BEVs) because it is an instrumentally and computationally heavy method. To address this issue and on-line system applications, this manuscript describes, as a main contribution, a passive method for battery impedance estimation in the time domain that involves the voltage and current profile induced by the battery through its ordinary operation without injecting a small excitation signal. This method has been tested on the same battery with different passive voltage and current profile and has been validated by achieving similar results. Compared to the original idea presented in the published conference paper, this manuscript explains, in detail, the previously developed method of transforming the battery impedance from the frequency domain to time domain. Moreover, this impedance measurement is used to estimate more robustly the battery state of charge (SoC) through Kalman filters. In the original published conference paper, only an extended Kalman filter (EKF) was applied. However, in this manuscript, an EKF and an unscented Kalman filter (UKF) are used and their performances are compared.

Transient stability simulation of a large-scale and interconnected electric power system involves solving a large set of differential algebraic equations (DAEs) every simulation time-step. With the ever-growing size and complexity of power grid, dynamic simulation becomes more time-consuming and computationally difficult using conventional sequential simulation techniques. To cope with this challenge, this paper aims to develop a fully distributed approach intended for implementation on High Performance Computer (HPC) clusters. A novel, relaxation-based domain decomposition algorithm known as Parallel-General-Norton with Multiple-port Equivalent (PGNME) is proposed as the core technique of a two-stage decomposition approach to divide the overall dynamic simulation problem into a set of subproblems that can be solved concurrently to exploit parallelism and scalability. While the convergence property has traditionally been a concern for relaxation-based decomposition, an estimation mechanism based on multiple-port network equivalent is adopted as the preconditioner to enhance the convergence of the proposed algorithm. The proposed algorithm is illustrated using rigorous mathematics and validated both in terms of speed-up and capability. Moreover, a complexity analysis is performed to support the observation that PGNME scales well when the size of the subproblems are sufficiently large.