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Those who rely on electricity-dependent durable medical equipment (DME) often struggle to use their medical devices during prolonged power outages. With the increasing frequency of natural disasters and the growing use of electricity-dependent home medical devices, in addition to the continued integration of home-level renewable energy and mobile storage systems such as electric vehicles with vehicle-to-home (V2H) capabilities, home energy management systems (HEMS) must prioritize life-essential medical loads during extended power outages. This work integrates electricity-dependent DME into home energy management optimization. An oxygen concentrator and a hemodialysis machine are used as examples of medical devices with high power demands and distinct usage patterns. The HEMS model is formulated as a mixed-integer linear program (MILP) to minimize the total weighted load curtailment and thermal discomfort during extended outage scenarios. The results demonstrate that the HEMS is effective in sustaining DME operation. 

100% inverter-based renewable units are becoming more prevalent, introducing new challenges in the protection of microgrids that incorporate these resources. This is particularly due to low fault currents and bidirectional flows. Previous work has studied the protection of microgrids with high penetration of inverter-interfaced distributed generators; however, very few have studied the protection of a 100% inverter-based microgrid. This work proposes machine learning (ML)–based protection solutions using local electrical measurements that consider implementation challenges and effectively combine short-circuit fault detection and type identification. A decision tree method is used to analyze a wide range of fault scenarios. PSCAD/EMTDC simulation environment is used to create a dataset for training and testing the proposed method. The effectiveness of the proposed methods is examined under seven distinct fault types, each featuring varying fault resistance, in a 100% inverter-based microgrid consisting of four inverters. 

Comprehending the impact of wildfire smoke on photovoltaic (PV) systems is of utmost importance in ensuring the dependability and consistency of power systems, particularly due to the growing prevalence of PV installations and the occurrence of wildfires. Nevertheless, this issue has not received extensive investigation within the current literature. A major obstacle in studying this phenomenon lies in accurately quantifying the impact of smoke. Conventional techniques such as aerosol optical depth (AOD) and PM 2.5 are inadequate for accurately assessing the influence of wildfire smoke on PV systems due to the complex interplay of smoke elevation, dynamics, and nonlinear effects on the solar spectral irradiance. To address this challenge, a new methodology is developed in this research that employs the optical properties of wildfire smoke. This approach utilizes the spectral response (SR) of PV devices to estimate the theoretical reduction in PV power output. The findings of this study enable precise measurement of the power output reduction caused by wildfire smoke for different types of PV cells. This newly devised method can be adopted for power system operation and planning to ensure the stability and reliability of power grids. Additionally, this study highlights the need to consider different PV cell technologies in regions at high risk of wildfires to minimize the power reduction caused by wildfire smoke.