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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. 

WAVEFORMS ARE THE MOST GRANULAR ANDauthentic representation of voltage and current in power systems. With the latest advancements in power system measurement technologies, it is now possible to obtain time-synchronized waveform measurements, i.e., synchrowaveforms, from different locations of a power system. The measurement technology to obtain synchro-waveforms is referred to as a waveform measurement unit (WMU). WMUs can capture the most inconspicuous disturbances that are overlooked by other types of time-synchronized sensors, such as phasor measurement units (PMUs). WMUs also monitor system dynamics at much higher frequencies as well as much lower frequencies than the fundamental components of voltage and current that are commonly monitored by PMUs. Thus, synchro-waveforms introduce a ew frontier to advance power system and equipment monitoring and control, with direct applications in situational awareness, system dynamics tracking, incipient fault detection and identification, condition monitoring, and so on. They also play a critical role in monitoring inverter-based resources (IBR) due to the high-frequency switching characteristics of IBRs. Accordingly, in this article, we provide a high-level overview of this new and emerging technology and its implications, discussing the latest advancements in the new field of synchro waveforms, including basic principles, real-world examples, potentials in data analytics, and innovative applications 

Using real-world data from Waveform Measurement Units (WMUs), this letter proposes novel data-driven methods to model the dynamic response of inverter-based resource (IBR) to the high-frequency disturbances that occur in practice in power systems. WMUs are an emerging class of smart grid sensors. They can capture the fast sub-cycle dynamics in power systems, which are overlooked by phasor measurement units (PMUs). After extracting the differential voltage and current waveforms from the raw waveform data, we develop multiple methods that include data-driven model library construction and proper model selection. One class of methods is proposed in frequency domain, which is based on modal analysis. Another class of methods is proposed in time domain, which is based on regression analysis of time-series. Experimental results based on real-world WMU data demonstrate the of performance the proposed methods. 

This paper, for the first time, investigates the operation and impact of convergence bids (CBs) during blackouts . First, the amount of load shedding in the real-time market (RTM) is modeled as a function of the amount of the cleared CBs in the day-ahead market (DAM). The sign of the slope of this function is proposed as a metric to determine if a CB exacerbates or heals the power outages. Next, a series of mathematical theorems are developed to characterize this new metric under different network conditions. It is proved that, when there is no congestion in the DAM, the metric is always greater than or equal to zero. When there is congestion in the DAM, the metric can be positive or negative. Using numerical case studies, we show that, this metric in fact most often is positive. Therefore, supply CBs almost always hurt the system during blackouts while demand CBs almost always help the system. Furthermore, the impact of load shedding on the profit of CBs is also analyzed. It is shown that, load shedding usually creates advantage for supply CBs and disadvantage for demand CBs in their profit. The implications of these results are discussed. We also analyze the real-world market data from the California Independent System Operator (ISO) during the blackouts in August 2020. It is shown that, the decision by the California ISO to suspend CBs during this event matches the mathematical and numerical results in this paper. 

Continuous streaming of synchro-waveforms, i.e., time-synchronized waveform measurements, can provide a comprehensive record of the status of the power system. The key to unmask the value of such massive data recording is to extract the most informative aspects of the data. In this paper, we develop and test new methods to detect and characterize subcycle events in continuous streaming of synchro-waveforms. The measurements in this study are collected by the authors in a practical test-bed in California. The measurements are made at low-voltage circuits under two different substations, using GridSweep devices with GPS time stamping. Over 40 billion data points were collected during one month. Several practical challenges are addressed, including the computational complexity due to the enormous size of data, the need for realignment between waveform samples and cycles, and the challenges in extracting differential waveforms to reveal the event signatures. 

Convergence bidding, has been adopted in recent years by most Independent System Operators (ISOs) in the United States as a relatively new market mechanism to enhance market efficiency. Convergence bidding affects many aspects of the operation of the electricity markets and there is currently a gap in the literature on understanding how the market participants strategically select their convergence bids in practice. To address this open Problem, in this paper, we study three years of real-world market data from the California ISO energy market. First, we provide a data - driven overview of all submitted convergence bids (CBs) and analyze the performance of each individual convergence bidder based on the number of their submitted CBs, the number of locations that they placed the CBs, the percentage of submitted supply or demand and CBs, the amount of cleared CBs, and their gained profit or loss. Next, we scrutinize the bidding strategies of the 13 largest market players that account for 75 % of all CBs in. the California ISO market. We identify quantitative features to characterize and distinguish their different convergence bidding strategies. This analysis results in revealing three different classes of CB strategies that are used in practice. We identify the differences between. these strategic bidding classes and compare their advantages and disadvantages. We also explain how some of the most active market participants are using bidding strategies that do not any of the strategic bidding methods that currently exist in the literature.