Cyber-enabled operation is needed for smart distribution system implementation. The interaction of cyber and power components will affect system reliability. This paper focuses on developing an analytical reliability model for fault detection, isolation, and service restoration for smart distribution feeders. The impact of end-to-end outage probability of data communication along with sending, receiving, and relaying communication node failures is incorporated into the model. Vulnerability of system to cyber attack as an emerging cause of reliability degradation is also investigated. An optimal placement of fault detectors and switching devices are determined in this work to improve reliability. The sum of customer service interruption cost and investment cost is considered as an objective function to be minimized. Bus 2 of the Roy Billinton test system and a typical 27-node distribution network are used to illustrate the role of communication infrastructure malfunction on the planning problem in a distribution feeder. Results and discussions show the necessity of incorporating communication infrastructure failure into the power distribution system planning problem.

Fundamental shifts in the structure and generation profile of electrical grids are occurring amidst increased demand for resilience. These two simultaneous trends create the need for new planning and operational practices for modern grids that account for the compounding uncertainties inherent in both resilience assessment and increasing contribution of variable inverter-based renewable energy sources. This work reviews the research work addressing the changing generation profile, state-of-the-art practices to address resilience, and research works at the intersection of these two topics in regards to electrical grids. The contribution of this work is to highlight the ongoing research in power system resilience and integration of variable inverter-based renewable energy sources in electrical grids, and to identify areas of current and further study at this intersection. Areas of research identified at this intersection include cyber-physical analysis of solar, wind, and distributed energy resources, microgrids, network evolution and observability, substation automation and self-healing, and probabilistic planning and operation methods.

The optimal energy management of microgrids in the face of uncertainty is a challenging task. It requires practical modeling of the microgrid along with an efficient decision-making tool to effectively improve the economical operation and power quality. This paper presents a robust optimization framework for the day-ahead operation planning of unbalanced three-phase microgrids. The operation planning problem is formulated in detail as a mixed-integer nonlinear programming (MINLP) model. This model includes the operational constraints pertaining to voltage unbalance, synchronous generator (SG) unbalance operation, and power unbalance at the point of common coupling (PCC). Linearization techniques are proposed to transform this problem into a mixed-integer linear programming (MILP) model for which efficient solvers are available. Two-stage robust optimization (TSRO) is used to obtain a robust operation planning decision against realizations of uncertain renewable generation, real-time market price, and voltage-dependent load parameters. This problem is solved using the column and constraint generation (C&CG) algorithm. The IEEE 37-bus and a 60-bus feeders are used to validate the performance of the robust solution.

This study presents a data-driven analysis of a Level-3 EV charging station on a major interstate highway in a northern rural region of the United States, focusing on adherence to the National Electric Vehicle Infrastructure (NEVI) formula program. Utilizing two years of 15-minute smart meter data, this paper presents the power demand dynamics and characteristics of this EV charging station. This study investigates charging trends and behaviors over various time frames, including monthly, seasonal, and weekday periods. The Charging Behavior (CB) index, a novel metric, is developed to quantify and compare charging behavior patterns. Besides, this study investigates the correlation between the power demand of the charging station and the corresponding traffic counts. The result shows that the correlation is particularly relevant in seasonal variations in northern rural areas. The findings from this comprehensive analysis are vital for stakeholders in the power sector, aiding in the strategic planning and optimization of EV charging infrastructure under NEVI compliance, especially in remote regions that have the four-season climate. The study's insights are also crucial for researchers focusing on the sustainable integration of EV infrastructure into existing power grids, addressing both operational planning and demand management.

Major power outages have risen over the last two decades, largely due to more extreme weather conditions. However, there is a lack of knowledge on the distribution of power outages and its relationship to social vulnerability and co-occurring hazards. We examined the associations between localized outages and social vulnerability factors (demographic characteristics), controlling for environmental factors (weather), in Washington State between 2018–2021. We additionally analyzed the validity of PowerOutage.us data compared to federal datasets. The population included 27 counties served by 14 electric utilities. We developed a continuous measure of daily outage burden using PowerOutage.us data and operationalized social vulnerability using four factors: poverty level, unemployment, disability, and limited English proficiency. We applied zero-altered lognormal generalized additive mixed-effects models to characterize the relationship between social vulnerability and daily power outage burden, controlling for daily minimum temperature, maximum wind speed, and precipitation, from 2018 to 2021 in Washington State. We found that social vulnerability factors have non-linear relationships with outages. Wind and precipitation are consistent drivers of outage occurrence and duration. There are seasonal effects that vary by county-utility area. Both PowerOutage.us and federal datasets have missing and inaccurate outage data. This is the first study evaluating differential exposure to localized outages as related to social vulnerability that has accounted for weather and temporal correlation. There is a lack of transparency into power outage distribution for those most vulnerable to climate impacts, despite known contributions by electric utilities to climate change. For effective public health surveillance of power outages and transparency, outage data should be made available at finer spatial resolution and temporal scales and/or utilities should be required to report differential exposure to power outages for socially vulnerable populations.