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This paper proposes a modeling approach for characterizing the probability of wildfire ignition caused by faults on power distribution systems. The proposed model serves as a starting point in research literature to illustrate, from an analytical perspective, the many factors that influence wildfire ignitions in power distribution systems. This paper presents the series of events that leads to power-system-related wildfire ignitions, and characterizes the wildfire ignition probability as a combination of the probability that a fault occurs along a power distribution line segment, and the probability that the fault results in the sustained ignition of a vegetation fuel bed surrounding the line. The proposed model integrates a variety of data including environmental conditions, power system protection settings, and power system line flows. A case study is performed on a test 33-bus distribution system using observed historical weather data from a high-threat fire district in California. The California case study is utilized to investigate the effects of three primary factors (wind speed, line congestion, and protection settings) on wildfire ignition probability. [ABSTRACT FROM AUTHOR]

Some of the deadliest wildfires in the U.S., such as California’s 2018 wildfires, have been ignited by power systems. In an effort to prevent and minimize the ignition of wildfires, or control them if ignited, energy companies have developed wildfire mitigation plans. This paper provides energy companies and power system operators, engineers, researchers, and suppliers an overview of the state-of-the-art studies that address key topics in these wildfire mitigation plans and compares the wildfire mitigation plans of several energy companies. The key topics include grid design and system hardening, asset management and inspection, situational awareness and forecasting, operational response, vegetation management, public safety power shutoff, and risk-spend efficiency. This paper also presents a comparison of several energy companies’ decision-making criteria for initiating a public safety power shutoff. Finally, we discuss opportunities for future research studies that could help energy companies prevent wildfire ignitions. [ABSTRACT FROM AUTHOR]

Channel allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is appealing to devise a blended strategy to perform effective resource sharing. In this paper, we exploit deep learning techniques predict vehicles’ mobility patterns. Then we propose an architecture consisting of centralized decision making and distributed channel allocation to maximize the spectrum efficiency of all vehicles involved. To achieve this, we leverage two deep reinforcement learning techniques, namely deep Q-network (DQN) and advantage actor-critic (A2C) techniques. In addition, given the time varying nature of the user mobility, we further incorporate the long short-term memory (LSTM) into DQN and A2C techniques. The combined system tracks user mobility, varying demands and channel conditions and adapt resource allocation dynamically. We verify the performance of the proposed methods through extensive simulations and prove the effectiveness of the proposed LSTM-DQN and LSTM-A2C algorithms using real data obtained from California state transportation department. [ABSTRACT FROM AUTHOR]

Penetration of distributed energy resources (DERs) such as wind, solar and battery units are rapidly increasing in distribution power system (DPS). In view of power grid reliability and efficiency, cyber and physical secure of these units are highly important since it can directly impact power system operations. This paper investigates the dynamic behavior of power distribution during cyber, physical and natural strikes on reactive power control in DER units. Various fault scenarios including modifications in voltage-reactive power (Volt-Var) curve pattern such as slope of the curve, dead band; and changes in capacitive or/and inductive reactive power delivery to the power grid are presented. Further, fault scenarios are investigated with the presence of dynamic VAR (volt-ampere reactive) compensation (DVC) units in the system. A southern California 140-bus commercial distribution power system is selected as a test system and faults are analyzed in both Matlab/Simulink and ETAP simulation platforms. The results evidence that the modifications in reactive power control values at DER units significantly affects the voltage quality/security of the system and affect the number of switching actions of voltage regulators (VRs). In contrast, DPS connected with DVCs improves voltage quality and reduces the number of switching actions of VRs, also voltage quality/security is maintained during the faults. [ABSTRACT FROM AUTHOR]

This paper presents a methodological framework to evaluate the resilience, with the primary focus on performance degradation, of levee-protected electric power networks to flooding in a changing climate. To this end, a multi-disciplinary framework is established by integrating climate science, hydrology, and electric power network analysis. The framework quantifies the effect of climate change on flood hazard levels in a levee-protected area and the subsequent changes in the resilience of the electric power network. In the first step, the changes in the exposure of levee-protected regions to flooding hazard in a warming climate are calculated. Then, the probabilities of failure of power network components due to flooding under current and projected future climate are determined. Finally, the power system resilience index is used to assess the system resiliency for pre-flooding (baseline), historic flooding, and two projected future flooding scenarios. For demonstration, the proposed framework is applied to a levee-protected area in Northern California. The IEEE 118-bus standard test system is employed to represent the power network in the study area. Results reveal that climate change can considerably decrease the system resilience of the levee-protected electric power network. The findings of this study can contribute towards more resilient power network systems under a changing climate. [ABSTRACT FROM AUTHOR]

This paper first presents an adaptive switching frequency modulation that optimizes the efficiency of the cascaded buck-boost (CBB) converter. Such bidirectional dc–dc CBB topology is typically deployed in PV-battery-device systems. A precise loss-model that accounts for component nonlinearities is developed to determine power-loss distribution. According to the loss model, optimal switching frequencies are selected that correspond to the lowest total loss in both the buck and the boost modes of the operation. Then, a small volume low-profile planar-nanocrystalline inductor is designed to further increase efficiency and power density. Finite element analysis method is used to evaluate the inductor design. A digital control system is designed to periodically adjust the optimal switching frequencies to modulate the pulsewidth modulation duty cycle and to take into account any load and line regulations. The objective of this paper is to achieve ultra-high efficiency (over 99%) for a well-known CBB converter. A 100 W CBB converter prototype was built to verify the proposed algorithm and designed nanocrystalline inductor. A California Energy Commission weighted efficiency of 98.87% with a peak efficiency of 99.31% was achieved for the buck mode and 98.58% with a peak efficiency of 99.25% was achieved for the boost mode, both at nominal voltage. Also, a power density of 3.67 kW/L was reported. [ABSTRACT FROM AUTHOR]

Traditionally voltage control in distribution power system (DPS) is performed through voltage regulating devices (VRDs) including on load tap changers (OLTCs), step voltage regulators (SVRs), and switched capacitor banks (SCBs). The recent IEEE 1547-2018 from March 2018 requires inverter fed distributed energy resources (DERs) to contribute reactive power to support the grid voltage. To accommodate VAR from DERs, well-organized control algorithm is required to use in this mode to avoid grid oscillations and unintended switching operations of VRDs. This paper presents two voltage control strategies (i) static voltage control considering voltage-reactive power mode (IEEE 1547-2018), (ii) dynamic and extensive voltage control with maximum utilization of DER capacity and system stability. Further, effective time-graded control is implemented between VRDs and DER units to reduce the simultaneous and negative operation. The proposed voltage control strategies are tested in a realistic 140-bus southern California distribution power system through extensive time-domain simulation studies. The results show that voltage quality in a distribution system is effectively achieved through the proposed voltage control strategies with a significantly reduction in the number of switching operations of VRDs. In addition, proposed voltage control strategies increase reliability and security of the DPS during unexpected failures. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic model is proposed for a joint statistical description of solar photovoltaic (PV) power and outdoor temperature. The underlying correlation emerges from solar irradiance that is responsible in part for both the variability in solar PV power and temperature. The proposed model can be used to capture the uncertainty in solar PV power and its correlation with the electric power consumption of thermostatically controlled loads. First, a model for solar PV power that explicitly incorporates the stochasticity due to clouds via a regime-switching process between the three classes of sunny, overcast and partly cloudy is proposed. Then, the relationship between temperature and solar power is postulated using a second-order Volterra model. This joint modeling is leveraged to develop a joint probabilistic forecasting method for solar PV power and temperature. Real-world datasets that include solar PV power and temperature measurements in California are analyzed and the effectiveness of the joint model in providing probabilistic forecasts is verified. The proposed forecasting methodology outperforms several reference methods thus portraying that the underlying correlation between temperature and solar PV power is well defined and only requires a simple lower-complexity sampling space. [ABSTRACT FROM AUTHOR]

In this paper, a highly efficient isolated resonant converter with a novel modulation method is proposed for delivering power from photovoltaic (PV) modules to the dc microgrid. The proposed modulation method allows the converter to boost low input voltages and regulate a wide input voltage range. The converter design is based on a series resonant converter (SRC) that operates at the resonant frequency to achieve highest efficiency under nominal input voltage condition. Under shadowed or low irradiance conditions of PV panels, the converter will operate with the proposed “double-pulse duty cycle” modulation method to step up the voltage for the dc microgrid connection. With the proposed modulation method, the output switches serve for both synchronous rectification and voltage boost function. This method enables a higher voltage boost ratio than the SRC without adding additional switches while operating at the resonant frequency. A 300-W hardware prototype with gallium-nitride devices is built to verify the performance of the proposed converter and modulation method. The converter achieved a peak efficiency of 98.9% and a California Energy Commission weighted efficiency of 98.7% under nominal input voltage condition. [ABSTRACT FROM AUTHOR]

Microwave radiometers measure weak thermal emission from the Earth, which is broadband in nature. Radio frequency interference (RFI) originates from active transmitters and is typically narrow band, directional, and continuous or intermittent. The Global Precipitation Measurement (GPM) Microwave Imager (GMI) has seen RFI caused by ocean reflections from direct broadcast and communication satellites in the shared 18.7-GHz allocated band. This paper focuses on the use of a complex signal kurtosis algorithm to detect direct broadcast satellite (DBS) signals at 18.7 GHz. An experiment was conducted in August 2017 at the Harvest oil platform, located about 10 km off the coast of central California. Data were collected for direct and ocean reflected DBS transmissions in the K- and Ku-bands from a commercial geostationary satellite. Results are presented for the complex kurtosis performance for a five-channel quadrature phase-shift keying (QPSK) signal versus the seven-channel case. As the spectrum becomes more occupied, detector performance decreases. Filtering of RFI in the fully occupied spectrum is very difficult, and detection using the complex kurtosis detector is only possible for very large interference-to-noise ratio (INR) values at −5 dB and higher. This corresponds to over 100 K in a real system such as GMI; therefore, other detection approaches might be more appropriate. [ABSTRACT FROM AUTHOR]

Convergence bidding, a.k.a., virtual bidding, has been widely adopted in wholesale electricity markets in recent years. It provides opportunities for market participants to arbitrage on the difference between the day-ahead market locational marginal prices and the real-time market locational marginal prices. Given the fact that convergence bids (CBs) have a significant impact on the operation of electricity markets, it is important to understand how market participants strategically select their CBs in real-world electricity markets. We address this open problem with focus on the electricity market that is operated by the California Independent System Operator (ISO). In this regard, we use the publicly available electricity market data to learn, characterize, and evaluate different types of convergence bidding strategies that are currently used by market participants. Our analysis includes developing a data-driven reverse engineering method that we apply to three years of real-world California ISO market data. Our analysis involves feature selection and density-based data clustering. It results in identifying three main clusters of CB strategies in the California ISO market. Different characteristics and the performance of each cluster of strategies are analyzed. Interestingly, we unmask a common real-world strategy that does not match any of the existing strategic convergence bidding methods in the literature. Next, we build upon the lessons learned from the advantages and disadvantages of the existing real-world strategies in order to propose a new CB strategy that can significantly outperform them. Our analysis includes developing a new strategy for convergence bidding. The new strategy has three steps: net profit maximization by capturing price spikes, dynamic node labeling, and strategy selection algorithm. We show through case studies that the annual net profit for the most lucrative market participants can increase by over 40% if the proposed convergence bidding strategy is used. [ABSTRACT FROM AUTHOR]

As compared with conventional methods for measuring ocean surface currents, spaceborne synthetic aperture radar (SAR) offers cloud-penetrating ocean-current observation capability at high spatial resolution. While some studies have shown the potential of SAR for studying ocean surface currents through feature tracking, they have only analyzed a few images to demonstrate the basic measurement technique, and no concise general technique has been developed. This paper shows the application of the maximum cross correlation (MCC) method to generate ocean surface currents from nearly two years of available sequential spaceborne C-band SAR imagery from the Envisat ASAR and ERS-2 Advanced Microwave Instrument SAR sensors over the coastal California Current System. The data processing strategies are discussed in detail, and results are compared with HF radar measured currents. One-dimensional wavenumber spectra of the SAR-derived surface currents agree with the k-2 power law, as predicted by sub mesoscale resolution models. Comparisons with HF radar currents show encouraging results with MCC SAR vectors oriented slightly counterclockwise relative to HF radar vectors. MCC SAR surface currents are found to have larger magnitudes than HF radar currents (≈11 cm/s), which may be due to the fact that SAR penetrates only a few centimetres into the ocean surface whereas HF radar currents are averaged over the top 1 m of the ocean surface. The larger part of this magnitude difference is contained in the along-shore component, which can be attributed to higher HF radar accuracy in the direct radial cross-shore measurements as compared with along-shore components derived from multiple cross-shore radial measurements. [ABSTRACT FROM PUBLISHER]

Recent experimental campaigns provided the opportunity to measure radio wave propagation and atmospheric conditions with the $X$ -band beacon-receiver (XBBR) array system. The system consists of vertical arrays of transmitters and receivers for measuring the $X$ -band propagation. Measurements near the sea surface can be used to obtain information regarding the refractivity profile of the lower atmosphere. Since ducted propagation acts as a leaky waveguide, the vertical array elements in various transmit and receive height combinations effectively observe differing combinations of the modal components propagating in the duct, the use of multiple combinations improves the estimation of duct properties. The aforementioned measurement campaigns occurred near the coast of southern California; the SoCal 2013 experiment and the Scripps Pier Campaign. During both campaigns, the propagation loss recorded at each of the receivers from each of the transmitters, standardized by the total received power, was compared to variable terrain radio parabolic equation model predictions in order to estimate the evaporation duct height (EDH). Point meteorological data were recorded and used with the Navy Atmospheric Vertical Surface Layer Model to obtain in situ measurements of the EDH. Comparisons show strong correlation between EDH values inferred from XBBR measurements and meteorological information. [ABSTRACT FROM PUBLISHER]

The Cerro Prieto geothermal field (CPGF) lies at the step-over between the imperial and the Cerro Prieto faults in northern Baja California, Mexico. While tectonically this is the most active section of the southern San Andreas Fault system, the spatial and temporal deformation in the area is poorly resolved by the sparse global positioning system (GPS) network coverage. Moreover, interferograms from satellite observations spanning more than a few months are decorrelated due to the extensive agricultural activity in this region. Here we investigate the use of frequent, short temporal baseline interferograms offered by the new Sentinel-1A satellite to recover two components of deformation time series across these faults. Following previous studies, we developed a purely geometric approach for image alignment that achieves better than 1/200 pixel alignment needed for accurate phase recovery. We construct interferometric synthetic aperture radar time series using a coherence-based small baseline subset method with atmospheric corrections by means of common-point stacking. We did not apply enhanced spectral diversity because the burst discontinuities are generally small (<1.4 mm) and can be effectively captured during the atmospheric corrections. With these algorithms, the subsidence at CPGF is clearly resolved. The maximum subsidence rate of 160 mm/yr, due to extraction of geothermal fluids and heat, dominates the ~40 mm/yr deformation across the proximal ends of the imperial, the Cerro Prieto, and the indiviso faults. [ABSTRACT FROM PUBLISHER]

Given the frequency of power outages caused by recent crises, including high-impact natural disasters, power and energy supply must be more resilient and cost effective. Owing to this necessity, this article presents a grid-connected multicarrier energy microgrid, which can provide continuous energy for both electrical and heating load demands at a low cost. The model employs a demand response (DR) scheme based on time of use and integrates with the proposed microgrid. Several energy sources—photovoltaics, batteries, a combined heat and power unit, and associated dispatch strategy—are optimized using the deterministic mixed-integer linear programming algorithm. The proposed model is tested in a hospital of California, USA. The results show the benefits of the DR program in terms of techno-economic output when compared to the system without its implementation. Several simulation results also demonstrate the model’s effectiveness and resiliency in maintaining a continuous energy supply in the midst of power outages at various times of the year while lowering costs. Furthermore, a comparative study with the existing methods is performed, which shows the effectiveness of the proposed technique as well. [ABSTRACT FROM AUTHOR]

Sea surface nitrate (SSN) is an important parameter to characterize physical and biogeochemical processes, particularly to quantify oceanic new primary production, yet its remote estimation from satellite has been difficult due to the complex relationships between environmental variables and SSN. In the central and southern sections of the California Current System (CSCCS), this challenge is attempted through modeling, validation, and extensive tests in different oceanic scenarios. Specifically, using extensive SSN datasets collected by many cruises spanning 40 years (1978–2018) and Moderate Resolution Imaging Spectroradiometer (MODIS) estimated sea surface temperature (SST) and chlorophyll-a (Chl), a stacking random forest (SRF) model of SSN has been developed and validated with a spatial resolution of ~4 km. The model showed an overall performance of root mean square difference (RMSD) $=0.83\,\,\mu $ mol/kg, with coefficient of determination ($R^{2}) =0.87$ , mean bias $= -0.11\,\,\mu $ mol/kg, and mean ratio = 1.15 for SSN ranging between 0.05 and $19.90~\mu $ mol/kg ($N =1034$). Furthermore, tests of the model with its original parameterization for the upwelling period, oceanic period, and winter period all showed satisfactory performance with an overall RMSD of 1.95 $\mu $ mol/kg. The sensitivity of the SRF model to uncertainties of MODIS SST and Chl was examined, with induced uncertainties of $\le 2.22~\mu $ mol/kg. The extensive evaluation and sensitivity tests indicated the robustness of the SRF model in estimating SSN in the study area of the CSCCS, and it could serve as a robust approach for other regions once sufficient in situ SSN data are available for model calibration. [ABSTRACT FROM AUTHOR]

Transmission faults caused by recent wildfires in California have induced the disconnection of utility-scale converters in photovoltaic (PV) power plants. Postmortem investigations reported that tripping commands were caused by phase-locked loops (PLLs) and dc-side dynamics, which are typically unmodeled in classical transient stability studies. Since existing simulation packages rely on simplified models that neglect these dynamics, they have a limited capability to predict converter behavior during faults. To address this shortcoming, we set forth a positive-sequence model for PV power plants that is derived from physics and controls first principles. As seen on utility-scale three-phase converters, the model includes PLLs, dc-side dynamics, and closed-loop controllers. Instances of the developed model are integrated into illustrative power systems containing conventional generators. Numerical simulations of the obtained multi-machine multi-converter power systems are assessed via a suitable set of stability and performance metrics. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]