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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]

One major reliability concern regarding renewable energy resources connected in the vicinity of series-compensated power transmission lines or to weak power grids is subsynchronous resonance. A particular type of interaction known as subsynchronous control interaction (SSCI), with a purely electrical nature, has the potential to grow very rapidly and can cause severe damage to power grid infrastructure. This article proposes an additional damping controller that allows mitigating the SSCI expeditiously. The proposed damping loop provides a very large impedance at the synchronous frequency and very low impedance at the subsynchronous range. More importantly, it can be integrated into the control loop of the battery energy storage systems (BESS) without imposing a negative impact on the routine performance of the BESS. As BESSs are becoming more popular in power grids, thanks to their significant impacts on reliability and power quality, it is anticipated that the proposed approach will have a great implementation opportunity. Hence, the proposed mitigation solution is implemented in a hybrid plant and tested with a radial test system and a section of the Electric Reliability Council of Texas power grid under a variety of operating conditions. The results show the efficiency and robustness of the mitigation solution even under different frequencies of oscillation and larges disturbances. [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]

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]

This paper proposes a two-stage energy management system (EMS) for power grids with massive integration of electric vehicles (EVs) and renewable energy resources. The first stage economic dispatch determines the optimal operating points of charging stations and battery swapping stations (BSS) for EVs under plug-in and battery swapping modes, respectively. The proposed stochastic model predictive control (SMPC) problem in this stage is characterized through a chance-constrained optimization formulation that can effectively capture the system and the forecast uncertainties. A distributed algorithm, the alternating direction method of multipliers (ADMM), is applied to accelerate the optimization computation through parallel computing. The second stage is aimed in coordinating the EV charging mechanisms to continuously follow the first-stage solutions, i.e., the target operating points, and meeting the EV customers’ charging demands captured via the Advanced Metering Infrastructure (AMI). The proposed solution offers a holistic control strategy for large-scale centralized power grids in which the aggregated individual parameters are predictable and the system dynamics do not vary sharply within a short time-interval. [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]

The increasing variability of renewables and volatile chronological net-load in power grids engenders significant risks of an uncertain sufficiency of flexible capacity. Although considerable advances in power grid flexibility assessment have been made, modeling the effect of temporal correlations associated with wind generations on the system flexibility provision capability has remained a challenge. This paper proposes a novel UC-time-scale security-constrained affinely robust formulation for wind-originated uncertainty sets in order to evaluate the system flexibility capacity over time. An efficient model based on duality theorem and affine policy is proposed to assess a secure region in response to uncertain wind generation scenarios. A framework using a combination of column and constraint generation and alternative direction algorithms is then developed to solve the proposed optimization model. The impacts of the sequential nature of wind generation, a type of dynamic uncertainty set, on the worst cases of generating units’ time-coupled ramping constraints are effectively captured to investigate how they contribute to the optimal allowable uncertainty set. Furthermore, the relationship between dynamic uncertainty set boundaries and the imposed re-dispatch costs are numerically investigated. Numerical experiments on the modified IEEE 73-bus test system reveals the efficacy of the suggested model and the proposed solution technique. [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]