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This paper presents a suite of analytics that are proposed to be embedded in next-generation smart sensors in electric power grids. The proposed analytics take the electrical signals as the input and unlock the full potential in signal processing and machine learning for real-time event detection and classification. Meanwhile, a robust synchrophasor estimation mechanism is housed within the proposed sensor technology that will be triggered following a detected event and guides on the adaptive selection of the best-fit (most accurate) synchrophasor estimation algorithms at all times. Embedding such analytics within the sensor and closer to where the waveforms are captured, the proposed distributed intelligence solution technology mitigates the potential risks to communication failures and latencies as well as malicious cyber threats. Our experiments demonstrate that the introduced scheme achieves improved quality of measurements with a promising event detection and classification accuracy, collectively resulting in enhanced online situational awareness in modern power grids. [ABSTRACT FROM AUTHOR]

Safeguarding the nation’s electrical infrastructure and personnel against natural and man-made disasters, and ensuring a continuous, reliable, and resilient supply of energy are among the top priorities for the electric industry. In recent years, wildfires have been remarkably threatening the safety and security of electric power grids demanding innovative frameworks for ensured resilience. Our proposed approach focuses on mitigation of wildfire disruptions that, once occur, can jeopardize the well-being of electrical equipment and the safety of the personnel. The proposed framework first offers a comprehensive wildfire characterization package that can spatiotemporally monitor and analyze the wildfire behaviors, i.e., wildfire intensity, arrival time, and binding paths from ignition points to electrical equipment. This allows power system operators to make proactive decisions before the fire approaches the electrical elements, e.g., power distribution lines. Next, a decision support tool for wildfire management in power grids is proposed such that various local generation resources,—i.e., distributed renewable energy resources and energy storage systems, can be effectively employed to mitigate the wildfire impacts on the power grid. The proposed integrated solution technology ensures a significant reduction in power outages and enhances the safety and resilience of the power grid and the operating personnel. [ABSTRACT FROM AUTHOR]

Wildfires have been growingly recognized as a prominent threat in regions with high temperatures during the summer. Power distribution systems, especially those passing through forest regions, are exposed and highly vulnerable to wildfires. This article provides a general formulation to enhance the operational resilience of power distribution networks equipped with renewable energy resources, e.g., wind and solar energy, micro turbines as well as energy storage systems when exposed to progressive wildfires. The wildfire incident is characterized comprehensively and the dynamic heat balance equations of power distribution branches are used to model the impacts of wildfires on overhead line conductors. A mixed-integer quadratic optimization formulation is applied to optimally operate and coordinate all local energy resources to reduce load outages and enhance the system resilience. The applied framework is evaluated on the IEEE 33-node test system. Comprehensive sensitivity analyses are conducted to assess the efficacy of the applied framework, where the numerical results reveal the resilient operation of power distribution networks in the face of wildfire emergencies. [ABSTRACT FROM AUTHOR]

The legacy Optimal Power Flow (OPF) dispatch in electric power grids with high proliferation of renewables can be at risk due to the lack of awareness on major uncertainties, and sudden changes in renewable outputs. This may, in turn, result in conditions where transmission line power flows are significantly exceeded, and subsequent automatic protective actions take place. This letter presents a new generalized joint chance-constrained model for the OPF problem that effectively captures the stochasticity in renewable power generation in the system. In dealing with the complexity, and non-convexity of the proposed optimization model with probabilistic guarantees, we propose a novel tractable Boolean method to transform the model into an equivalent deterministic mixed-integer linear problem, which can be solved quickly, and efficiently by off-the-shelf solvers. Numerical results verify the effectiveness of the proposed model, and the suggested Boolean methodology. [ABSTRACT FROM AUTHOR]

Different electric vehicle (EV) charging algorithms result in different EV charging load profiles, that if aggregated, influence the power grid operation. The existing EV charging demand models are either based on the charging status upon EV arrival or smart charging algorithms reinforced with particular charging methods and/or charging levels. This article proposes a new data-driven approach for EV charging load modeling. We first introduce a mathematical model that characterizes the flexibility of EV charging demand. Advanced simulation procedures are then proposed to identify the parameters of different EV load models and simulate EV charging demand under different electricity market realizations. The proposed EV load modeling approach can simulate different EV operation schedules, charging levels, and customer participation as a benchmark system. The proposed framework will also provide informative guidelines to transmission system operators for EV charging infrastructure planning in modern power systems. [ABSTRACT FROM AUTHOR]

The operation of the electricity delivery infrastructure is environmentally driven and vulnerable to a wide range of high-impact low-probability (HILP) hazards. Among different classes of HILP disasters, earthquakes are one of the most unpredictable hazards which may lead to widespread disruptions of mission-critical services and infrastructures. This article introduces a comprehensive framework for modeling and characterization of seismic hazards, vulnerability assessment of electric systems to the earthquake, and corrective actions and mitigation strategies ensuring operational resilience. The Monte Carlo simulation is employed to produce a realistically large set of possible earthquake scenarios to capture the stochastic nature of seismic hazards. An inclusive approach is then introduced based on the fundamental principles of fragility curves to assess the vulnerability of power generation facilities in the face of HILP earthquakes. A new seismic risk metric is suggested that takes into account both hazard and vulnerability probabilities, as well as the financial consequences due to postquake disruptions in power generation stations. Along with the generation redispatch strategy as a conventional mitigation solution following a nontrivial contingency, a new mitigation strategy centered on corrective network topology control is formulated to maximize the load outage recovery following HILP disruptions. The proposed decision support tool enables a swift restoration and improved resilience in dealing with the aftermath of the HILP earthquakes. Efficacy of the proposed framework is numerically analyzed and verified on both the IEEE 57-bus and IEEE 118-bus test systems. [ABSTRACT FROM AUTHOR]

Mobile power sources (MPSs), including mobile emergency generators, truck-mounted mobile energy storage systems, and electric vehicles, have great potentials to be employed as grid-support resources during power grid emergency operating conditions to supply the critical loads and enhance the resilience of distribution system (DS) via a swift disaster restoration. We here investigate the MPS dispatch (i.e., routing and scheduling) in coordination with DS dynamic network reconfiguration. We propose a two-stage restoration scheme to facilitate the DS restoration following the high-impact low-probability (HILP) seismic disasters. In the first stage, a seismic hazard is simulated through a Monte Carlo simulation engine to estimate the unavailability of power distribution branches under a suite of seismic force scenarios. In the second stage, a mixed-integer nonlinear programming (MINLP) optimization model is formulated for DS restoration that cooptimizes the routing and scheduling of MPSs and DS dynamic network reconfiguration. The MINLP model is then linearized to a mixed-integer linear programming model to reduce the computation complexity, where the seismic-resilience recovery strategies are generated at different timescales. The efficacy of the proposed method is evaluated on the IEEE 33-node test system and the results verify a significant reduction in the load outages and an improved power system resilience to HILP earthquakes. [ABSTRACT FROM AUTHOR]

Power grid operation continuously undergoes state transitions caused by internal and external uncertainties, e.g., equipment failures and weather-driven faults, among others. This prompts an observation of different types of waveforms at the measurement points (substations) in power systems. Modern power systems utilize phasor measurement units (PMUs) and intelligent electronic devices embedded with PMU functionality to capture the corresponding peculiarities through synchrophasor measurements. However, existing PMU devices are equipped with only one synchrophasor estimation algorithm (SEA) and are, thus, not always robust to handle different types of signals across the network. This article proposes a PMU-embedded framework that ensures real-time grid surveillance and potentially enables adaptive selection of preinstalled SEAs in the PMU. Therefore, it ensures high-fidelity measurements at all times and irrespective of the input signals. Our proposed framework consists of: 1) a pseudocontinuous quadrature wavelet transform which generates the featured scalograms and 2) a convolutional neural network for event classification based on the extracted features in the scalograms. Our experiments demonstrate that the proposed framework achieves high classification accuracy on multiple types of prevailing events in power grids, through which an enhanced grid-scale situational awareness in real time can be realized. [ABSTRACT FROM AUTHOR]

Geomagnetically induced currents (GICs) in power grids are mainly caused by geomagnetic disturbances especially during solar storms. Such currents can potentially cause negative impacts on power grid equipment and even damage the power transformers resulting in a significant risk of blackouts. Therefore, monitoring GICs in power systems and developing solutions to mitigate their impacts before rising to a certain threatening level is urgently in need. Monitoring GICs is, however, quite a challenge and costly, as they usually appear in forms of dc components in the high voltage transmission lines, which are barely accessible through transformers. By examining the measured currents from the current transformers, this article proposes a framework to detect GICs in power transmission systems through a hybrid time-frequency analysis combined with machine learning technology. Simulated results verify that the proposed approach can promisingly estimate GICs in power systems during a variety of grid operating conditions. [ABSTRACT FROM AUTHOR]

With the increasing trend in the integration and deployment of distributed energy resources, the real-time assessment of power system transient stability is becoming more and more challenging. This study presents an enhanced sensitivity-based decentralised transient stability assessment scheme, which first virtually decomposes the large-scale power network into several smaller areas. Then, a new sensitivity-based scheme is developed to derive and quantify the kinetic energy-based stability assessment index. This index allows taking into account the effects of both AC generators and power electronic-based generator units. In addition, thanks to the new sensitivity-based scheme, the transient stability can be assessed in a decentralised manner using only the boundary buses in each segmented area, which relaxes the need for massive data availability and transfer across the network and enables its applicability for online applications in large-scale power systems. Simulation results on the 48-machine IEEE 140-bus and 544-machine 2000-bus synthetic power grid test systems demonstrate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

This article presents a probabilistic formulation and solution technique for the application of dc optimal power flow-based network topology control through transmission line switching strategies. Efficient utilization of the point estimation method (PEM) is pursued to model the system uncertainties, i.e., the stochastic load profile and the intermittent renewable generation. In order to address the computational effectiveness of the suggested probabilistic methodology, the PEM formulation is harnessed by a scenario reduction approach to capture the correlations of the system uncertainties, thereby achieving a more robust and faster operation solution for day-ahead and real-time applications. The proposed approach is applied to a modified IEEE 118-bus test system, where it demonstrates its attractive performance under different test scenarios. To further verify the efficiency and scalability of the proposed algorithms on large-scale systems, the proposed analytics are applied to 200-bus synthetic grid of central Illinois. AC feasibility and transient stability checks are performed on both test systems and the results are extensively analyzed. [ABSTRACT FROM AUTHOR]

Flexible ramping product (FRP) has been recently launched in a number of electricity markets to enhance the power grid flexibility and accommodate additional deals of uncertainty introduced by renewable energy resources. The FRP procures additional ramp capacities provided by the dispatchable generating units and consumers in the real-time market (RTM) with the aim to enhance the ability of supply to closely follow the net-load at the next immediate time-interval. An important challenge in widespread adoption of this technology is a key question: Does the available energy capacity of the RTM participants contain sufficient ramp capacity to tackle the net-load uncertainties? Indeed, shortage of the systems ramp capacity may not only jeopardize its flexibility, but also may cause drastic fluctuations in the RTM energy prices. To address this challenge, this paper proposes a novel optimization framework to harness the procured spinning reserve services in the day-ahead market (DAM) for FRP provision in the RTM while meeting the system reliability requirements. On this basis, the FRP will be simultaneously procured from the RTM energy and DAM spinning reserve capacities. Furthermore, a new payment mechanism is proposed to compensate the portion of spinning reserve utilized for FRP procurement in the RTM. The proposed framework is simulated on the IEEE 118-bus test system and the results verify its effectiveness in modern electricity markets. [ABSTRACT FROM AUTHOR]