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Battery energy storage systems are widely used for renewable power generation and electric transportation systems. Bidirectional DC-DC converters (BDCs) are key components in such systems, enabling bidirectional power flow in battery charging and discharging modes. BDCs can be categorized into isolated and non-isolated. Non-isolated BDCs have lower volume, weight, and power losses, suitable for compact structures without needing galvanic isolation. In this paper, a comprehensive literature review is conducted for non-isolated BDCs, covering soft switching, current ripple reduction, high voltage gain and resiliency techniques. Soft switching aims to reduce switching losses and improve efficiency, including auxiliary circuits and non-auxiliary methods, such as interleaved structures, phase-shift modulation, and synchronous rectification. Current ripple reduction focuses on capacitive loop configurations, interleaved structures, and coupled inductor-based methods. Batteries are low-voltage power sources, BDCs can increase the output voltage to a level required by the applications through an appropriate voltage gain, and high voltage gain techniques include capacitor-based, magnetic-based, and combined networks, and mixed structures. Resiliency is explored to ensure reliable operations under adverse conditions. This review provides valuable insights into developing more efficient, reliable, and high-performance BDCs, addressing the evolving demands of modern energy systems. Future research directions in non-isolated BDCs are recommended in this paper. 

Understanding the dynamics of fire propagation is essential in improving predictive models and developing effective fire management strategies. This study applies computer vision techniques to complement traditional fire behaviour modelling. We employ the Segment Anything Model to achieve the accurate segmentation of experimental fire videos, enabling the frame-by-frame segmentation of fire perimeters, quantification of the rate of spread in multiple directions, and explicit analysis of slope effects. Our laboratory experiments reveal that the ROS increases exponentially with slope, but with coefficients differing from those prescribed in the Canadian Fire Behaviour Prediction System, reflecting differences in field conditions. Complementary field data from prescribed burns in coniferous fuels (C-7) further demonstrate that slope effects vary under operational conditions, suggesting field-dependent dynamics not fully captured by existing deterministic models. Our experiments show that, even under controlled laboratory conditions, substantial variability in spread rate is observed, underscoring the inherent stochasticity of fire spread. Together, these findings highlight the value of vision-based perimeter extraction in generating precise spread data and reinforce the need for probabilistic modelling approaches that explicitly account for uncertainty and emergent dynamics in fire behaviour. 

Public Safety Power Shutoffs (PSPS) are a critical yet disruptive wildfire mitigation strategy used by electric utilities to reduce ignition risk during periods of elevated fire danger. However, current PSPS decisions often lack transparency and consistency, prompting the need for data-driven tools to better understand utility behavior. This paper presents a Support Vector Machine (SVM) framework to model and interpret PSPS decision-making using post-event wildfire reports. Forecast-based weather and fire behavior features are used as model inputs to represent decision-relevant variables reported by utilities. The model is calibrated using Platt scaling for probabilistic interpretability and adapted across utilities using importance- weighted domain adaptation to address feature distribution shifts. A post-hoc clustering segments PSPS events into wildfire risk zones based on ignition risk metrics excluded from model train- ing. Results demonstrate that the proposed framework supports interpretable, transferable analysis of PSPS decisions, offering insight into utility practices and informing more transparent de- energization planning. 

Network protector units (NPUs) are crucial parts of the protection of secondary networks to effectively isolate faults occurring on the primary feeders. When a fault occurs on the primary feeder, there is a path of the fault current going through the service transformers that causes a negative flow of current on the NPU connected to the faulted feeder. Conventionally, NPUs rely on the direction of current with respect to the voltage to detect faults and make a correct trip decision. However, the conventional NPU logic does not allow the reverse power flow caused by distributed energy resources installed on secondary networks. The communication-assisted direct transfer trip logic for NPUs can be used to address this challenge. However, the communication-assisted scheme is exposed to some vulnerabilities arising from the disruption or corruption of the communicated data that can endanger the reliable operation of NPUs. This paper evaluates the impact of the malfunction of the communication system on the operation of communication-assisted NPU logic. To this end, the impact of packet modification and denial-of-service cyberattacks on the communication-assisted scheme are evaluated. The evaluation was performed using a hardware-in-the-loop (HIL) co-simulation testbed that includes both real-time power system and communication network digital simulators. This paper evaluates the impact of the cyberattacks for different fault scenarios and provides a list of recommendations to improve the reliability of communication-assisted NPU protection. 

Accurate long-term electricity load forecasting is critical for energy planning, infrastructure development, and risk management, especially under increasing uncertainty from climate and economic shifts. This study proposes a multi-resolution probabilistic load forecasting framework that leverages temporal hierarchies to generate coherent forecasts at hourly, daily, monthly, and yearly levels. The model integrates climate and economic indicators and employs tailored forecasting techniques at each resolution, including XGBoost and ARIMAX. Initially incoherent forecasts across time scales are reconciled using advanced methods such as Ordinary Least Squares (OLS), Weighted Least Squares with Series Variance Scaling (WLS_V), and Structural Scaling (WLS_S) to ensure consistency. Using historical data from Alberta, Canada, the proposed approach improves the accuracy of deterministic forecasts and enhances the reliability of probabilistic forecasts, particularly when using the OLS reconciliation method. These results highlight the value of temporal hierarchy structures in producing high-resolution long-horizon load forecasts, providing actionable insights for utilities and policymakers involved in long-term energy planning and system optimization.