This paper develops an effective two-stage stochastic post-hurricane recovery framework to improve networked microgrid resilience using mobile emergency resources (MERs) and a proposed reconfiguration strategy. In the first stage, network reconfiguration actively alters the local power flow path and provides opportunities for restoring critical loads, thus reducing the energy not supplied to electric consumers. The optimal schedule determined in the first stage problem is also used to determine the islanded loads that need MERs for restoration. In the second stage, truck-mounted MERs will deliver power to islanded loads, observing the shortest path and post-hurricane transportation infrastructure constraints. Dijkstra’s algorithm is used to produce the shortest path and avoid possible out-of-service roads. In order to model the uncertainties of the problem, a stochastic framework based on unscented transform (UT) is employed. The proposed problem is formulated as a two-stage stochastic single-objective optimization problem maximizing system resilience. Simulation results on a test networked microgrid demonstrate the effectiveness and satisfying performance of the proposed model.

Physics-based models for battery temperature prediction are often not suitable for online applications due to the large number of fitted parameters, low fidelity results from parameter inaccuracy and unaccounted model dynamics, limited high quality experimental data, and slow convergence of predictions from unknown initial conditions. On the other hand, data-driven models require much less computation power but a large dataset to learn the time dependent behavior. This paper proposes a physics-informed neural network (PINN) to take full advantage of both physics-based and data-driven models. Four comparative studies were performed to investigate the effectiveness of including chamber temperature with two different activation functions, the optimal number of neurons and hidden layers, the dependance on reversible heat generation, and the performance of long short-term memory (LSTM)-PINN model. The results show that the LSTM-PINN with chamber temperature as one of the inputs delivers better prediction accuracy. The LSTM-PINN with an exponential activation function for the chamber temperature has a more accurate prediction for the direct current fast charge (DCFC) test profile and similar prediction accuracy for the grade load (GL) 100 test profile. The root mean square errors (RMSEs) of the LSTM-PINN are 0.57°C for DCFC and 0.52°C for GL 100, respectively. In addition, having 55 neurons and 4 hidden layers gives the lowest prediction error. Furthermore, the improvement by having reversible heat generation is negligible. At last, the LSTM-PINN model has less prediction error than the LSTM model, especially when the battery temperature range is enormous.

Historically, the power system has relied on synchronous generators (SGs) to provide inertia and maintain grid stability. However, because of the increased integration of power-electronics-interfaced renewable energy sources, the grid’s stability has been challenged in the last decade due to a lack of inertia. Currently, the system predominantly uses grid-following (GFL) converters, built on the assumption that inertial sources regulate the system stability. Such an assumption does not hold for the low-inertia grids of the future. Grid-forming (GFM) converters, which mimic the traditional synchronous machinery’s functionalities, have been identified as a potential solution to support the low-inertia grids. The performance analysis of GFM converters for small-signal instability can be found in the literature, but large-signal instability is still an open research question. Moreover, various topologies and configurations of GFM converters have been proposed. Still, no comparative study combining all GFC configurations from the perspective of large-signal stability issues can be found. This paper combines and compares all the existing GFM control schemes from the perspective of large-signal stability issues to pave the way for future research and development of GFM converters for large-signal stability analysis and stabilization of the future low-inertia grids.

The physics-informed neural network (PINN) has drawn much attention as it can reduce training data size and eliminate the need for physics equation identification. This paper presents the implementation of a PINN with adaptive normalization in the loss function to predict lithium-ion battery cell temperature. In particular, the PINN was trained with the actual battery test data, and a lumped capacitance lithium-ion battery thermal relationship was applied to the loss function with the addition of a pre-layer and connection layer to the neural network architecture. The PINN architecture shows the most accurate battery temperature prediction compared with the fully connected neural network (FCN) and its variants evaluated in this study. The proposed PINN architecture has a mean square prediction error of 0.05 °C with a limited number of training data and without battery thermal model identification.

A novel carrier-based unipolar-sinusoidal pulse width modulation (SPWM)-oriented modulation technique with synchronous rectification for isolated bidirectional single-stage high-frequency-ac link dc-ac converters using SiC MOSFET is presented in this paper. The dc-ac converter is composed of a full-bridge (FB) inverter cascaded with a cycloconverter through a high-frequency transformer. A carrier-based unipolar-SPWM-oriented modulation technique with synchronous rectification is proposed to realize zero-voltage-switching (ZVS) for the FB inverter and zero-current or zero-voltage-switching (ZVS/ZCS) for the cycloconverter in all load ranges, and to suppress the voltage spikes introduced by the transformer leakage inductance as well. In order to increase the switching frequency, efficiency, and power density, this paper proposes to utilize SiC MOSFETs for the converter. Synchronous rectification is implemented to further increase the converter efficiency. With the novel modulation technique, there are two switches in the cycloconverter that are continuously on at each interval, which eliminates on-fourth of the switching loss. A simulation model and a 400 VDC-240 VAC, 1.2=kW prototype have been developed to validate the effectiveness and performance of the proposed unipolar soft-switching modulation technique and SiC converter.

The adoption of electric vehicles (EVs) is increasing due to governmental policies focused on curbing climate change. EV batteries are retired when they are no longer suitable for energy-intensive EV operations. A large number of EV batteries are expected to be retired in the next 5–10 years. These retired batteries have 70–80% average capacity left. Second-life use of these battery packs has the potential to address the increasing energy storage system (ESS) demand for the grid and also to create a circular economy for EV batteries. The needs of modern grids for frequency regulation, power smoothing, and peak shaving can be met using retired batteries. Moreover, these batteries can also be employed for revenue generation for energy arbitrage (EA). While there are articles reviewing the general applications of retired batteries, this paper presents a comprehensive review of the research work on applications of the second-life batteries (SLBs) specific to the power grid and SLB degradation. The power electronics interface and battery management systems for the SLB are also thoroughly reviewed.