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For Years, Experts have expressed growing concerns that critical infrastructures are vulnerable to cyberattacks. Remotely transmitted malware and viruses can compromise interconnected power systems, airports, hospitals, military systems, and any Internet-connected computers used to manage critical functions. As the world becomes more digitalized, integrating new communication technologies offers unlimited possibilities for the accumulation of information, creating a fundamental dependence on proper functioning in all areas of society. As such reliance could offer great opportunities for a smart and efficient world, it also poses significant threats to the operation of critical infrastructures and vital facilities. Specifically, safety issues from illegal acts that violate the integrity, availability, and confidentiality of information in cyberspace are expected to be the central theme in the digitalized world. Exploitation of cyberspace to advance political, social, and military agendas is of particular concern. [ABSTRACT FROM AUTHOR]

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As renewable energy sources are integrated into power systems, the complexity of their management increases. For the analysis of modern power systems, clustering algorithms have become indispensable tools. The representation of an electric power transmission system involves conceptualizing it as a graph—a mathematical structure depicting a set of nodes and edges. In the context of power systems, these nodes correspond to buses, and the edges represent branches. Weighting may be assigned to the edges to denote the strength of their connections. In this paper, spectral clustering and hierarchical agglomerative clustering have been employed to analyze the Italian transmission network. Spectral clustering relies on the eigenvalues and eigenvectors derived from the Laplacian matrix associated with the network. The hierarchical agglomerative clustering, based on Thévenin impedance to define the distances, merges the two closest clusters at each step. The primary objective of this research is to identify distinct areas within the network based solely on its topology. In pursuit of this goal, admittances and Thévenin impedances have been employed as weights in the graph representation. By utilizing these approaches, the research aims at uncovering meaningful structures and patterns within the power transmission system based on the inherent connectivity and strength of the network elements. The techniques have been used on two real network models to demonstrate the performance of the algorithms with different grid configurations.

Natural disasters and health pandemics have occurred more frequently in the 21st century. Research show that the large blackouts caused by natural disasters are more intractable, especially striking during health pandemics. In this sense, power grid resilience becomes a topical area both in industry and academia. The resilience can be enhanced by making the grid “Smarter”, utilizing advanced techniques such as distributed generation, microgrids, sensing, communication, and computing. In this paper, a structured review framework for decision support for smart distribution system (SDS) resilience against natural disasters during health pandemics is proposed. The challenges and negative impacts of natural disasters during health pandemics on SDS resilience are well illustrated. The stochastic impact assessment methods of natural disasters and human activities on SDSs are extensively discussed. The SDS planning and operation models against natural disasters during health pandemics are comprehensively reviewed. The strategic decision-making for resilient SDS against natural disasters during health pandemics are extensively surveyed. Future research directions are also presented.

Electric vehicles (EVs) are one important enabler for the evolution of sustainable transportation sector. However, one major barrier to the widespread adoption of EVs is their limited battery capacity and the excessive time required to fully charge depleted batteries. Although techniques such as charging with a rapid charger and battery swapping may partially alleviate such concerns, the corresponding high capital investment may not be profitable for areas with low EV adoption rates. Moreover, the diversified charging demands of randomly distributed EVs, both spatially and temporally, restrict high-quality services from current fixed charging stations (FCSs). To resolve such challenges, mobile charging services (MCSs) have been investigated as a supplemental charging method for EVs, wherein energy replenishment is provided by mobile charging vehicles (MCVs). Under the framework of the Internet of EVs (IoEV), this article describes the concept of MCSs and investigates their charging merits under some distinct charging scenarios, e.g., real-time response service and emergency rescue service. A case study concerning real-time response services is investigated to illustrate the influence of MCSs on the current charging system. Simulation results demonstrate the ability of MCSs to improve the quality of charging service by reducing the average service time and service acceptance ratio. Finally, technical challenges, future research issues, and possible methodologies regarding the MCSs for EVs are discussed. [ABSTRACT FROM AUTHOR]

Intelligent false data injection on load measurements can trigger false relay operation (FRO) of frequency-based protection relays, affecting the power system frequency and thus threatening the security of power systems. In this paper, we propose an optimization-based formal model to find the optimal false data injection attack (OFDIA) with the minimum required time leading to an FRO. The proposed model considers the dynamic behavior of the power system in an optimization framework to find the optimal size of attacks over multiple generators’ dispatching cycles to minimize the attack launch time. Using the proposed formal modeling, we study the impact of power system parameters, including inertia, governor’s droop and time constant, and the attacker’s accessibility to loads on the attack success and launch time. The results demonstrate that systems with low inertia are more vulnerable to FDIAs while systems with higher inertia are more secure as fewer generator protection relays are impacted by FRO. In addition, we show that securing more load meters can increase the time for launching an attack in the system. Moreover, our studies show that a combination of large values of the governor’s time constants and small values of the governor’s droops can raise the time of successful attacks, making the system more secure against FDIAs. [ABSTRACT FROM AUTHOR]

The virtual synchronous generator (VSG) control is a promising solution for the grid-forming converters to enhance the inertia of the converter-based power grid. The virtual inertia and output impedance of the VSG are the important behaviors to be focused. However, the virtual inertia of grid-forming converters may cause active power oscillation due to the interaction with the power inertia of grid through the low-frequency band grid impedance. Besides, the resonance can be also triggered by the interaction between middle-frequency impedance of power grid and output impedance of converter. In order to test the stability of grid-forming converter, accurate emulation of grid behaviors from more aspects, including inertia and impedance under wider frequency ranges, is becoming critical. This article proposes a grid emulation method to mimic the inertia and impedance characteristics of power grid, targeting for the stability test of grid-forming converter under multifrequency bands. A high-switching-frequency converter and a low-switching-frequency converter are adopted in the proposed emulator structure to achieve both high capability in respect to control bandwidth and power level. Moreover, the virtual impedance control is integrated with the VSG control to emulate the line impedance and inertia within relatively wider frequency ranges. The realization of virtual impedance is achieved without derivation terms nor low pass filter. Finally, the performance of the proposed emulator is verified by various simulations and experimental measurements. [ABSTRACT FROM AUTHOR]

Automatic generation control (AGC) is paramount in maintaining the stability and operation of power grids. Its dependence on communication systems makes it vulnerable to various cyberphysical attacks. False data injection attacks (FDIA) are particularly difficult to detect and represent a major threat to AGC systems. This article proposes a novel spatio-temporal learning algorithm that can learn the normal dynamics of the power grid with AGC system to deal with this problem. The algorithm first uses a long short-term memory autoencoder to learn the normal dynamics. It then utilizes this unsupervised learned model in detecting the various possibilities of FDIA affecting the AGC system by evaluating the reconstruction residual of each measurements sample. The proposed algorithm is data-driven which makes it resilient against AGC's parameters uncertainties and modeling nonlinearities. The effectiveness of the developed algorithm is evaluated through test cases with various basic and stealth FDIAs. [ABSTRACT FROM AUTHOR]

Phase sequence exchange (PSE) is a recently developed emergency control technology in which power electronic devices are used to switch the three-phase sequence, thereby reducing the power angle of the generator by 120° and preventing the system from losing stability. This article proposes the installation of the PSE device in the fragile cut-set line of the power grid, and to use the local measurement information for control. This method can restore the stability of the system when the power grid oscillates, thereby avoiding the collapse of the island power grid after splitting due to oscillation. First, an improved real-time power angle calculation method is proposed. When a system loses synchronization and the power angle of a line reaches the threshold, the PSE device on the same cut-set line acts simultaneously to prevent the system from falling out-of-step and “splitting” from the cut set. A PSE prototype is used for experimental verification on the real-time digital simulator platform. The results demonstrate that the proposed power angle calculation method greatly reduces the error, and the proposed PSE control method can achieve online monitoring and real-time control, which achieves better control effects than conventional methods. [ABSTRACT FROM AUTHOR]

Driven by the demands of carbon footprint reduction, the conventional fossil-based generators are gradually being replaced by grid-forming inverter. One crucial aspect of grid-forming inverter is synchronization stability, which is not only related to the inverter power-frequency (P-f) dynamic but also influenced by power grid characteristics as well as grid impedances. From the perspective of a local inverter, it is difficult to know the detailed information. As a result, severe oscillations and even the loss of synchronization may occur and pose a great threat to the power grid. To overcome this problem, a black-box approach is presented in this article. Initially, a frequency-domain identification approach is proposed to acquire the power grid characteristic by the inverter itself. Based on the measurement results, the P-f dynamic of grid-forming inverter is explicitly reshaped to ensure the synchronization stability. The proposed methodology does not require any prior power system knowledge or the trivial modeling process. Finally, the effectiveness of the proposed method is verified by hardware experimental results. [ABSTRACT FROM AUTHOR]

Negative-sequence current is required to be intentionally injected to support the power grid or compensate the imbalanced load in asymmetrical faults utilizing STATCOM. However, the extreme compensation range of negative-sequence current injection becomes complicated when grid voltage becomes asymmetrical. This article presents a novel off-line strategy for negative-sequence current compensation under two typical kinds of asymmetrical grid faults — single-phase voltage drop and double-phase voltage drop, with the following two benefits simultaneously: maximizing negative current compensation capacity; avoiding iterations and reducing computation burdens. In this paper, the mechanism of DC voltage balancing among three phases in STATCOM is analyzed and a new identification method of negative-sequence current compensation range is explored out. To simplify the calculation, this paper takes the ratio of negative-sequence reactive (Iq-) and active (Id-) current as a variable (s). In this way, the relationship between the extreme compensation range, asymmetrical voltage sag depth of power grid h is drawn with MATLAB. Different from the previous iterative method, the off-line identification method is applied to avoid huge iterations. By fitting the curve, the precise ranges of negative-sequence current compensation under different asymmetrical grid faults are approximated as simple polynomials, thus significantly reducing the computation burdens. Simulation and experimental results verify the practicability of the proposed compensation method. [ABSTRACT FROM AUTHOR]

This paper widens the application of a testing protocol for overhead line insulators introduced by TERNA. Testing results relevant to three groups of Room Temperature Vulcanized pre-coated cap-and-pin insulators removed from High Voltage Alternate Current and Extra-High Voltage Alternate Current Italian lines after different years of service are reported and statistically processed; the third group is treated here for the first time and new results for the other two groups are included. Among the various tests prescribed in the protocol, focus is on the Quick Flashover test, with the aim to examine and compare the flashover voltages of all three groups as a function of service time. The dependence of flashover voltage on electric field is also analyzed and a model correlating flashover voltage with field and time is proposed for the first time. It is confirmed that the flashover voltages provided by the Quick Flashover test can be considered as a diagnostic property of Room Temperature Vulcanized insulators, as they drop with the ageing time and the electric field. [ABSTRACT FROM AUTHOR]

After any major blackout, recreating the exact scene is one of the crucial but foundation steps in postmortem analysis. This helps in identifying and understanding the exact causes and sequence of events to avoid such failures in the future. Rather, power system restoration demands critical skills which involves deployment of appropriate strategies based on information about various factors, notably the extent and duration of the blackout, location of black-started units, interconnections with neighbouring systems, generator capabilities, and selecting appropriate restoration paths. The actual execution of the restoration plan consists of many surprises of unique nature and a lot of learning’s to avoid future occurrences of such incidents. The restoration efforts and failure of Mumbai grid failure on October 12, 2020, are highlighted in this paper. The paper first proposes formulating the dynamic models to get a better insight of the restoration process in the event of critical issues during this incident. The various challenges faced in systematic recovery of such a large system and lessons learnt from it forms focus of the paper. The paper also discusses factors not covered in existing literature, such as weather, fuel availability, and water mass oscillations which plays an important role in the restoration. [ABSTRACT FROM AUTHOR]

As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus, there is an imperative need to enhance grid emergency control to maintain system reliability and security. Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. However, existing DRL-based solutions have two main limitations: 1) they cannot handle well with a wide range of grid operation conditions, system parameters, and contingencies; 2) they generally lack the ability to fast adapt to new grid operation conditions, system parameters, and contingencies, limiting their applicability for real-world applications. In this paper, we mitigate these limitations by developing a novel deep meta-reinforcement learning (DMRL) algorithm. The DMRL combines the meta strategy optimization together with DRL, and trains policies modulated by a latent space that can quickly adapt to new scenarios. We test the developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast adaptation of the meta-trained DRL polices with latent variables to new operating conditions and scenarios using the proposed method, which achieves superior performance compared to the state-of-the-art DRL and model predictive control (MPC) methods. [ABSTRACT FROM AUTHOR]

Ensuring long-term voltage stability is critical for reliable operations of power grids. High share of distributed energy resources (DERs) can create complicated system operation modes that may invalidate the traditional long-term voltage stability analysis based on typical operation modes. To address this challenge, this paper investigates how to compute the shortest path to the voltage stability boundary in the DER aggregated load space with large dispersion. Instead of working in the Euclidean space, we establish the analysis and computations on the algebraic power flow manifold to better capture the curvature change of the shortest path along the direction of losing stability. A modified optimal control framework is presented for obtaining the ground-truth of the smooth shortest path on the manifold. To efficiently and accurately solve for the shortest path, we further leverage the geometric features of the power flow manifold and propose a tri-sectional approximation model that is scalable for large-scale systems. Several numerical examples, up to the 1354-bus system, with different DER penetration levels and high dimensional renewable power injection variations are evaluated. The simulation results demonstrate that the tri-sectional approximation achieves high accuracy and efficiency to approximate the shortest path to the voltage stability boundary. [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]

Network constraints play a key role in the price finding mechanism for European Power Markets, but historical data is very sparse and usually insufficient for many quantitative applications. We reconstruct the constraints data, known as the Power Transmission Distribution Factors (PTDFs) and Remaining Available Margins (RAMs), by first recovering the underlying time dependent signals known as the Generation Shift Keys (GSKs) and Phase Angles (PAs), and the electricity grid characteristics, via a mathematical optimisation problem. This is solved by exploiting marginal convexity in certain subspaces via alternating minimisation. The GSKs and PAs are then mapped to the PTDFs and RAMs, using the grid structure. Our reconstruction achieves in-sample relative errors of 22.3% and 15.9% for the PTDFs and RAMs respectively, while the out-of-sample ones are 25.4% and 18.1% respectively. We further show that our model outperforms the naive approach, and that the reconstructed GSKs and PAs recover specific structure. [ABSTRACT FROM AUTHOR]

This work presents a white-box modeling of the electromagnetic (EM) leakage from an integrated circuit (IC) to develop EM side-channel analysis (SCA)-aware design techniques. A new digital library cell layout design technique is proposed to minimize the EM leakage and is evaluated using a high-frequency structure simulator (HFSS)-based framework. Backed by our physics-based understanding of EM radiation, the proposed double-row power grid-based digital cell layout design shows $>5\times $ reduction in the EM SCA leakage compared to the traditional digital logic gate layout design. Furthermore, exploiting the magneto-quasistatic (MQS) regime of operation of the EM leakage from the CMOS circuits, the HFSS-based framework is utilized to develop a pre-silicon (Si) EM SCA evaluation technique to assess the vulnerability of cryptographic implementations against such attacks during the design phase itself. [ABSTRACT FROM AUTHOR]

This article proposes an improved hybrid parallel compensator (IHPC) for enhancing the power transfer capability of a photovoltaic (PV) grid-connected inverter. A thyristor switched capacitor (TSC) module is used in series with a conventional inductive-coupling voltage source inverter to reduce the dc-link voltage. Under a low dc-link voltage, the voltage across the insulated gate bipolar transistor (IGBT) will also be low, which will reduce the voltage stress of the IGBT, thereby improving the reliability and reducing the loss of the voltage resource inverter. First, the mechanism of reactive compensation for improving the PV inverter power transmission capability is analyzed. Then, the circuit configuration and operating principle of the IHPC are introduced. After that, an optimal control strategy for the IHPC is proposed to address the resonance problem caused by the TSC connected in series with the L filter and to improve the compensation performance of the IHPC. Finally, the effectiveness of the proposed IHPC and the control method is validated by the simulation and experiments. The results show that the IHPC with optimal control strategy can achieve continuous reactive power compensation under a low dc-link voltage, thereby enhance PV power transfer capability. [ABSTRACT FROM AUTHOR]

Due to vulnerabilities exposed to cyberattacks in the cyber physical power system, increasing concerns have been paid to its cybersecurity, especially on the so-called false data injection attack. Timely recovering true values of measurements and states after encountering cyber-attacks is of paramount importance for ensuring the subsequent controls and operations of the cyber physical power system. This article, for the first time, discovers a measurement data inertia effect, and uses this effect to deduce coarse values of preattack measurements as a preliminary work for data recovery. Then, based on the deduced coarse values and suggested state bounds, an optimization model is proposed to recover the measurements and states contaminated by attacks in-time. Moreover, an error criterion named interval error is proposed to assess the entire performance of the proposed recovery scheme. Extensive and comprehensive experiments are implemented on the IEEE 30-bus test benchmark to verify the feasibility and effectiveness of the proposed recovery scheme. The numerical studies reveal that the proposed method can achieve high accuracy and efficient timeliness for data recovery. [ABSTRACT FROM AUTHOR]

Aiming at the scheduling problem for regional power grids under both outage and power uncertainties, this paper puts forward a multistage robust optimization (RO) method that ensures the reliable power supply in system operations. Firstly, a multistage RO model is proposed according to the practical scheduling request of the regional power grid. The startup and shutdown of slow-acting generators are optimized in the first stage. The optimal topology is then determined in the second stage in preparation to the outage uncertainty of grid-connected lines. In the third to 2+ ${T}$ stages, the scheduling plans of generation units in each time period are further formulated regarding the sequential realizations of renewable-load power uncertainties, which ensures the nonanticipativity of the obtained robust schedules. Secondly, a tailored and efficient transformation-decomposition algorithm is exploited for the solution of multistage RO problem. Based on the affine control function, the nested max-min optimization of the third to 2+ ${T}$ stages is explicitly transformed to a single-stage max-min model via the multi-to-one strategy. A column and constraint generation (C&CG) algorithm with looped alternative optimization procedure (AOP) handles the equivalent three-stage RO problem with binary recourses, thus avoiding numerous forward and backward iterations as well as speeding up the modeling solution. Numerical experiments of real-world regional power grids verify the effectiveness, superiorities and scalability of the proposed multistage RO scheduling method, which indicates great guidance for engineering applications. [ABSTRACT FROM AUTHOR]

Series compensations have been widely used in modern power systems to extend the power transfer capacity. Since more and more renewable power generation systems are installed in power systems, the grid-connected inverters (GCIs) used by the renewable power generations may interfere with series-compensated power lines, which introduce subsynchronous resonance (SSR) causing power system failure that has been observed in real cases. The phase-locked loop (PLL) of the GCI's control system is the main factor that can cause the SSR in the series-compensated power grid. To mitigate the impact of the PLL and obtain the stable operation of the power system, a q-axis voltage feedback method has been proposed in literature to damp the harmonic oscillations in the weak power grid where, however, the series compensation is not considered. Because the real q-axis voltage cannot be obtained, the q-axis voltage output from the PLL is used as the feedback variable in literature. However, due to the dynamic response of the PLL, the q-axis voltage output from the PLL is different from the real q-axis voltage, and the direct use of it for the damping of the SSR in the power grid with series compensations is invalid, which is investigated in this article. Besides, an observed q-axis voltage feedback method is proposed in this article to damp the SSR in the series-compensated power grid with GCIs being connected. By putting an observer in the q-axis voltage feedback path, the real q-axis voltage can be estimated and applied to damp the SSR. Simulation and experimental results have been provided to verify the effectiveness of the proposed observed q-axis voltage feedback method in the damping of the SSR. [ABSTRACT FROM AUTHOR]

This article analyzes the global stability of synchronous reference frame phase-locked loops (SRF-PLLs) from a large signal viewpoint. First, a large-signal model of SRF-PLL is accurately established, without applying any linearization method. Then, according to the phase portrait and Lyapunov argument, the global performance of SRF-PLL is discussed in the nonlinear frame. Compared with the small-signal analysis methods, the proposed analysis, not relying on the small-signal model and linearization method, provides a global discussion of the SRF-PLL performance. The contributions of this article are as follows. First, it is found that SRF-PLL has infinite equilibrium points, including stable points and saddle points. Second, it provides a way to divide the global region of SRF-PLL into many small regions. In each small region, the SRF-PLL only has one stable equilibrium point. And for any initial states $(\tilde{\theta }(t_0),\tilde{\omega }(t_0))$ in a small region, all states $(\tilde{\theta }(t),\tilde{\omega }(t)),t>t_0$ will remain in this small region, and SRF-PLL will converge to the unique stable equilibrium point of this small region. Third, by dividing the global region of SRF-PLL into many small regions, it is found that when the frequency of grids varies largely, the SRF-PLL will converge to a new equilibrium point that is far away from the original equilibrium point. It is the reason why the frequency convergence of SRF-PLL has many oscillations and SRF-PLL has a rather slow dynamic, when the frequency changes largely. The experimental results are provided to verify the proposed global stability analysis of SRF-PLL. [ABSTRACT FROM AUTHOR]

This article addresses the problem of proper flicker severity assessment by smart energy meters. Flicker severity occurs for a typical voltage “clipped cosine” distortion caused by operation of power electronic devices in the power grid. The voltage distortion caused only by higher harmonics does not cause voltage fluctuations, and therefore does not cause flicker. This article presents the research results for some models of advanced metering infrastructure (AMI) smart meters, for which irregularities are observed in the voltage variation severity assessment if the typical voltage distortion occurs in the power grid. Possible mistakes in implementation of flickermeter signal chain as defined by International Electrotechnical Commission (IEC) in smart meters that influence the measurement results have been explained. The analysis of the influence of typical voltage distortion on the indicator $P_{st}$ measurement has been carried out analytically and in numerical simulation studies using the modeled flickermeter signal chain. [ABSTRACT FROM AUTHOR]

With the increasing number of renewable energy plants connected to the power system, the gap grounding mode is often adopted by the high-voltage side neutral point of the interconnecting transformer of the renewable energy plants in order to improve the performance of the zero sequence protection. In practical engineering applications, the low-voltage side of the interconnecting transformer is connected with the renewable energy source (RES). It is necessary to analyze and evaluate the possible overvoltage at the high-voltage side neutral point when the power grid fails, as well as the damage brought to the stable operation of power grid. According to the operation characteristics of RES, the calculation method of neutral point overvoltage is proposed and the influence of different types of RES on neutral point overvoltage is analyzed when a single-phase grounding fault or a single-phase break-line fault (incomplete phase operation state) occurs in the power grid. Then the suggestions to prevent neutral overvoltage are proposed. The correctness of correlation analysis is verified by digital simulation and the conclusion can provide an important reference for the reasonable choice of the neutral grounding mode of the interconnecting transformer in renewable energy plant. [ABSTRACT FROM AUTHOR]

In remote areas, weak power grids, fewer grid access points, and power quality (PQ) problems dominated by excessive voltage unbalance (VU) and lower power factor (PF) have been prominent obstacles in single-phase 25 kV AC traction power supply systems. To achieve a long-distance power supply with a high PQ, a traction substation group connected to a weak remote power grid is introduced, where the electrical structure is composed of multiple slave traction substations using single-phase traction transformers (TTs), a master traction substation (MTS) using a combination of TTs and a power flow controller (PFC) adopting a modular multilevel converter (MMC). Then, a unified mathematical model of the proposed system suitable for different TT wiring types revealing the effect of asymmetrical grids on the PFC is established. The negative sequence current (NSC) caused by multiple traction stations at the point of common coupling (PCC) and the PF can be improved effectively based on power conservation theory. In addition, to reduce the compensation capacity, an optimization compensation strategy is proposed. Finally, the effectiveness of the proposed algorithm is verified by simulation and a case study. The installed capacity of the PFC is 18.5% lower than that of traditional compensation. [ABSTRACT FROM AUTHOR]

Due to limited amplitude and controlled phase of current supplied by inverter-interfaced renewable power plants (IIRPPs), the IIRPP-side distance protection of lines connected to IIRPPs fails to detect the fault location accurately, so it may malfunction. The composite sequence network of a line connected to an IIRPP during asymmetrical faults is analyzed, and an adaptive distance protection based on the analytical model of additional impedance is proposed in this study. Based on open circuit property of negative-sequence network at the IIRPP-side, the equivalent impedance of power grid and current flowing through fault point are calculated in real-time using local measurements, which are substituted into the analytical model of additional impedance to calculate fault location. In the case of negative-sequence reactive current injection from IIRPPs during asymmetrical faults, the error of calculating fault point current from local measurements is analyzed and corrected to ensure reliability of the proposed protection. The proposed protection alleviates the effect of fault resistance in a system with weak sources. In addition, the proposed protection can adapt to different grid codes (GCs), the operation mode change of the power grid, and the capacity change of the IIRPP. PSCAD/EMTDC test results verify the effectiveness of the proposed protection. [ABSTRACT FROM AUTHOR]

The modular multilevel matrix converter (M3C) is the high-power three-phase to three-phase AC/AC converter of the next generation. However, when the M3C is applied for power grid interconnection, with equal or close input and output frequency, the current distribution in its nine arms becomes significantly uneven. Furthermore, the current distribution pattern is directly influenced by the randomly changing input-output angle-difference, forcing each arm to be designed based on the highest-possible current, which significantly increases the required semiconductor rating and converter cost. To address this issue, a novel current reallocation is proposed to attenuate the highest-possible arm current of M3C. The equal-frequency operation of M3C is first modeled and analyzed, and the operation sweet-spot where each arm stays in natural energy balance is located. Then, the current reallocation is analytically derived for sweet-spot operation. By injecting proper circulating current, the highest-possible arm current is reduced by 13.4%, and the required circulating current component is analytically derived. The implementation method into the classic control structure of M3C is provided. Performance of the proposed strategy is validated by a simulation benchmark established in MATLAB/Simulink environment in the context of interconnection between two 220 kV/50 Hz power grids. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable resources via power-electronic converters is turning the modern power grid into a multi-converter system (MCS). In an MCS, most renewable resources currently use grid-following converters (GFLCs) for grid synchronization. The increasing integration of renewable resources via GFLCs can cause small-signal stability problems, especially under weak grid conditions. One way to prevent the stability issue is limiting the capacity of GFLCs in an MCS. Thus, it is important to assess the maximal capacity of GFLCs in the MCS while considering small signal stability constraints (SSSCs). This assessment is challenging due to 1) the complexity of assessing the small signal stability resulting from the complicated interaction between the power network and a large number of GFLCs, especially for a heterogeneous GFLCs with unknown inner parameters; 2) the difficulty of finding the optimal solution to the relevant nonlinear optimization problem for maximal capacity assessment. To address these challenges, this paper proposes a semi-definite programming (SDP)-based method to assess the maximal capacity of GFLCs with SSSCs. In the proposed method, we first formulate the SSSCs based on the generalized short-circuit ratio (gSCR), which is a grid strength metric that can significantly reduce the complexity of quantifying small signal stability in an MCS. Then, we convert the formulated gSCR-based nonlinear optimization problem into an SDP that can conveniently find the optimal solution to the maximal capacity of GFLCs and their optimal allocations in the MCS. The efficacy of the proposed method is demonstrated on a 39-bus test system and a practical wind power system. [ABSTRACT FROM AUTHOR]

The development of electric vehicle (EV) industry is facing a series of issues, among which the efficient charging of multiple EVs needs solving desperately. This paper investigates the coordinated charging of multiple EVs with the aim of reducing the charging cost, ensuring a high battery state of charge (SoC), and avoiding the transformer overload. To this end, we first formulate the EV coordinated charging problem with the above multiple objectives as a Markov Decision Process (MDP) and then propose a multi-agent deep reinforcement learning (DRL)-based algorithm. In the proposed algorithm, a novel interaction model, i.e., communication neural network (CommNet) model, is adopted to realize the distributed computation of global information (namely the electricity price, the transformer load, and the total charging cost of multiple EVs). Moreover, different from the most existing works which make specific constraints on the size, the location, or the topology of the distribution network, what we need in the proposed method is only the transformer load. Besides, due to the use of long and short-term memory (LSTM) for price prediction, the proposed algorithm can flexibly deal with various uncertain price mechanisms. Finally, simulations are presented to verify the effectiveness and practicability of the proposed algorithm in a residential charging area. [ABSTRACT FROM AUTHOR]

Inferring the network topology from the dynamics of interacting units constitutes a topical challenge that drives research on its theory and applications across physics, mathematics, biology, and engineering. Most current inference methods rely on time series data recorded from all dynamical variables in the system. In applications, often only some of these time series are accessible, while other units or variables of all units are hidden, i.e. inaccessible or unobserved. For instance, in AC power grids, frequency measurements often are easily available whereas determining the phase relations among the oscillatory units requires much more effort. Here, we propose a network inference method that allows to reconstruct the full network topology even if all units exhibit hidden variables. We illustrate the approach in terms of a basic AC power grid model with two variables per node, the local phase angle and the local instantaneous frequency. Based solely on frequency measurements, we infer the underlying network topology as well as the relative phases that are inaccessible to measurement. The presented method may be enhanced to include systems with more complex coupling functions and additional parameters such as losses in power grid models. These results may thus contribute towards developing and applying novel network inference approaches in engineering, biology and beyond.

Time series forecasting remains a central challenge problem in almost all scientific disciplines. We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system using Dynamic Mode Decomposition (DMD) in time delay coordinates. Central to this approach is the insight that grid load, like many observables on complex real-world systems, has an “almost-periodic” character, i.e., a continuous Fourier spectrum punctuated by dominant peaks, which capture regular (e.g., daily or weekly) recurrences in the dynamics. The forecasting method presented takes advantage of this property by (i) regressing to a deterministic linear model whose eigenspectrum maps onto those peaks, and (ii) simultaneously learning a stochastic Gaussian process regression (GPR) process to actuate this system. Our forecasting algorithm is compared against state-of-the-art forecasting techniques not using additional explanatory variables and is shown to produce superior performance. We further show that this joint approach offers marked improvement over existing methods that use purely DMD or GPR. Moreover, its use of linear intrinsic dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid. Load forecasting is an essential challenge in power systems engineering, with major implications for real-time control, pricing, maintenance, and security decisions.

Given sensor readings over time from a power grid, how can we accurately detect when an anomaly occurs? A key part of achieving this goal is to use the network of power grid sensors to quickly detect, in real-time, when any unusual events, whether natural faults or malicious, occur on the power grid. Existing bad-data detectors in the industry lack the sophistication to robustly detect broad types of anomalies, especially those due to emerging cyber-attacks, since they operate on a single measurement snapshot of the grid at a time. New ML methods are more widely applicable, but generally do not consider the impact of topology change on sensor measurements and thus cannot accommodate regular topology adjustments in historical data. Hence, we propose DynWatch, a domain knowledge based and topology-aware algorithm for anomaly detection using sensors placed on a dynamic grid. Our approach is accurate, outperforming existing approaches by 20 $\%$ or more (F-measure) in experiments; and fast, averaging less than 1.7 ms per time tick per sensor on a 60K+ branch case using a laptop computer, and scaling linearly with the size of the graph. [ABSTRACT FROM AUTHOR]

Power system substations have intelligent electronic devices (IEDs) that collect data and control other devices. As the bridge between the physical and cyber parts of the power system, IEDs capture some key system behaviors. Since adversaries can modify the system’s behavior, physical and cyber data can be used to infer characteristics about the adversary. In this article, we present alert and control logic for hardware-based power system defense using the physical data and communication status in substation IEDs for cyber threat detection, cyber-physical contingency detection and response, and physical contingency identification and response. The proposed alert and control logic routines are implemented in an industrial real-time automation controller using IEC 61131-3 in the resilient energy systems lab testbed. The goal is to help operators identify adversaries and protect the power grid in a cyber-physical environment. The effectiveness and accuracy of logic schemes are validated under different adversarial scenarios. Comparing the proposed schemes with an intrusion detection system, Snort, our results also suggest the benefits of using cyber and physical data to identify threats. The results also suggest the use of such hardware-based schemes with software algorithms in a next-generation cyber-physical energy management system, which can implement automatic control actions to protect power grids and its physical equipment against cyber threats. [ABSTRACT FROM AUTHOR]

This work investigates the impact of phase-locked loop (PLL) parameters on the stability of a wind-integrated weak power grid. The analysis is based on a weak grid model that integrates a Type-4 wind turbine generator with the grid represented by a lumped group model of a thermal power station with a steam governor and exciter system through a variable impedance network. This model is classically used for a weak grid study representing a real-life system. First, the impact of proportional and integral gain constants of the PLL on stability and oscillation characteristics is discussed in detail. Based on the analysis, an adaptive gain scheduling PI-based PLL architecture is proposed. The advantages of the proposed architecture are studied based on rigorous changes in the grid impedances and the wind dynamics. The results show that the proposed architecture significantly improves grid stability and damping when compared with conventional PLL designs. [ABSTRACT FROM AUTHOR]

The escalating demand on electric vehicles (EVs) has enhanced the necessity of adequate charging infrastructure, especially in residential areas. This article proposes a smart charging approach for off-board EVs chargers in home-energy-hub (HEH) applications along with dc sources such as photovoltaic and battery storage (BS). The proposed method facilitates smart charging and discharging of EVs to obtain both vehicle-to-x and x-to-vehicle operations focusing on the domestic applications integrated with renewable and storage elements. Furthermore, the optimal state-of-charge (SOC) profiles for BS and EV in the HEHs system is defined by the extended Bellman-Ford-Moor algorithm (BFMA). This modified BFMA utilizes the forecasted data such as solar irradiation, electricity tariff, and power consumption to gain economic benefits in HEHs with respect to the user and EV requirements. Moreover, the plugging time, duration, and initial/final SOC are fluctuating at each connection due to the stochastic nature of EV conditions and user settings. This study presents a laboratory implementation of two-level hierarchical energy management system for HEHs with plug-in electric vehicles. In fact, the primary level includes power converters controller, while the proposed algorithm is implemented in the secondary level. Finally, the simulation and experimental results confirm the effectiveness of the proposed analysis regarding the interaction of HEHs and power grid with EVs behavior. [ABSTRACT FROM AUTHOR]

As we move from traditional power grids to smart grids, new threats arise. We focus on cybersecurity in power grids, highlighting the threats to and vulnerabilities of those cyberphysical systems and presenting future solutions. [ABSTRACT FROM AUTHOR]

The traditional system frequency response (SFR) model is not applicable to the study of the frequency response of contemporary power grid due to the response of the large capacity (single unit up to 500 MW) type-II doubly-fed variable speed pumped storage (VSPS) unit is not considered. This article proposes an improved reduced-order SFR model for the power grid integrated with the large-scale type-II VSPS. proposed models Compared with the traditional SFR model, this model takes into account the active power coupling effect of this type of VSPS, and the computational burden increase of the model is small due to the model of VSPS has been reduced to the greatest extent. Therefore, the SFR model can be used by the system operators to rapidly calculate and evaluate the frequency response of contemporary power grid. Firstly, a reduced-order frequency response transfer function model of the Type-II VSPS is established by using the state-space model and small-signal incremental method. Compared with the higher-order nonlinear model of the Type-II VSPS, the state variables and computation burden of the established model is dramatically decreased. Secondly, an improved SFR model for the power grid integrated with the type-II VSPS is presented, and then the reduced-order analytical solution for the frequency response of the grid is put forward based on the parameter weighted aggregation method. The improved SFR model is more realistic and can better express the frequency response characteristics of the contemporary power grid. The proposed SFR model have demonstrated advantages in solving speed and efficiency when contrast it with the higher-order nonlinear model of the grid. Finally, the simulation and experimental results are respectively compared with the analytical solution of the reduced-order models, and the results have demonstrated the effectiveness of the. [ABSTRACT FROM AUTHOR]

Renewable energy source (RES)-powered base stations have received tremendous research interest in recent years because they can expand network coverage without building a power grid. This paper proposes a novel user association (UA), resource allocation (RA), and dynamic power control (PC) scheme to maximize the $\alpha $ -fairness in RES-assisted small cell networks. The $\alpha $ -fairness is a general notion that flexibly adjusts the balance between the throughput, proportional fairness, and max-min fairness according to $\alpha $. Nevertheless, none of the existing studies has proposed UA, RA, and PC to maximize the $\alpha $ -fairness due to its NP-hardness. Furthermore, fixed-policy-based PC designs cannot consider time-varying environments (e.g., energy harvesting models and wireless channels) of the RES-assisted networks. We first provide a Lagrangian duality-based algorithm to solve the UA and RA problem for a fixed PC. Next, we propose a dynamic PC scheme based on deep reinforcement learning (DRL) that chooses the best PC considering the time-varying environments. However, because the UA and RA algorithm executed in each step of the dynamic PC requires a long computation time, we aim to accelerate the computation of the UA and RA with DRL. Inspired by the Lagrangian duality, we design a DRL-based UA and RA with a low-dimensional continuous variable by relaxing the UA variable, the cardinality of which increases exponentially with the number of base stations and users. The simulation results show that the proposed scheme achieves a 100 times shorter computation time than the optimization-based schemes by computing only two neural networks. In particular, although there have been numerous studies on the proportional fairness maximization, the proposed scheme outperforms the optimization-based schemes in the throughput, proportional fairness, and max-min fairness metrics. [ABSTRACT FROM AUTHOR]

Primitive grid synchronization systems of power converters were based on a zero-crossing detection (ZCD) algorithm, which is an open-loop synchronization (OLS) system. ZCD algorithms, however, could cause some stability problems for power converters. Therefore, they were gradually replaced by closed-loop synchronization systems. Recently, in parallel with developing closed-loop synchronization systems, designing efficient OLS systems has received much attention. This article aims to provide an overview of recent developments in designing OLS systems, analyze the systems’ advantages/disadvantages, and formulate their design procedure into some general yet simple steps. [ABSTRACT FROM AUTHOR]

Displacement current-based energy harvesters have great potential for driving self-powered sensors in power systems. Although there have been some preliminary developments, this is still a technology under construction, and many new contributions have been reported in recent years. [ABSTRACT FROM AUTHOR]

This article proposes a synthesis of a novel dynamic incentive mechanism and optimal control for moral hazard in power grids with real-time power balancing markets. For the liberalization of the balancing markets, it is an indispensable requirement to integrate dynamic subsystems controlled by individual agents into a stabilized power grid via an incentive mechanism. Meanwhile, without the proper use of the agents’ private information, high-speed market-clearing achieving an appropriate social objective cannot be ensured. Consequently, a market mechanism is needed that contains some dynamic incentive mechanisms for eliciting their private information and proper controls under moral hazard, which is called incentivizing market in this article. Drawing on dynamic contract theory in microeconomics, a novel method for designing the incentivizing market is proposed on the basis of the integration of economic models and a dynamic grid model. Conventional contract problems have been analyzed for both static and dynamic systems whose control inputs are operated directly by an independent system operator. However, these analyses do not accord with the design for the dynamic incentive mechanism in the incentivizing market. The approach in this article requires adapting the dynamic contract problems to the market. To achieve this objective, the fundamental formulas for optimal design are established and the basic properties of the designed market based on a model-based approach are clarified. We next propose optimal pricing and control synthesis for social-objective designs. The performance of our proposed method is illustrated through simulations with an Institute of Electrical Engineers of Japan (IEEJ) East 30-machine system. [ABSTRACT FROM AUTHOR]

Noncooperative game-theoretic methods have been widely utilized in system-level engineering applications as they are capable of aggregating interests, information, and behaviors of many independent, self-interested entities with conflicting objectives via payoffs. Another major advantage is the existence of adaptive learning dynamics that involve realistic, constrained decision making, and converge to Nash equilibrium. If players are not fully rational (e.g., constrained or regulated), such convergence is generally not guaranteed except in some special games, such as potential games (PGs). Within each PG, there exists a potential function that connects the global objective with players’ payoffs and actions such as optimizers of the global objective align with Nash equilibrium. Therefore, PGs have been widely utilized in many engineering domains and recently to power grids. This article summarizes the state-of-the-art existing literature on applying various PG models in power grids and provides an overlook of potential models and algorithms that have not received sufficient attention but possess unique capabilities to solve existing challenges in power grids. [ABSTRACT FROM AUTHOR]

Despite the reliability of modern power systems, large blackouts due to cascading failures (CFs) do occur in power grids with enormous economic and societal costs. In this article, CFs in power grids are theoretically modeled proposing a Markov decision process (MDP) framework with the aim of developing optimal load-shedding (LS) policies to mitigate CFs. The embedded Markov chain of the MDP, established earlier to capture the dynamics of CFs, features a reduced state-space and state-dependent transition probabilities. We introduce appropriate actions affecting the dynamics of CFs and associated costs. Optimal LS policies are computed that minimize the expected cumulative cost associated with CFs. Numerical simulations on the IEEE 118 and IEEE 300 bus systems show that the actions derived by the MDP result in minimum total cost of CFs, compared to fixed and random policies. Moreover, the optimality of derived policies is validated by a CF simulation based on dc power flow for the IEEE 118 bus system. Therefore, such actions developed by the proposed theoretical MDP framework can serve as a baseline for devising optimal LS strategies to mitigate CFs in power grids. [ABSTRACT FROM AUTHOR]

Over recent years, we have seen a significant rise in electric mobility to overcome the anthropogenic emissions by conventional gasoline vehicles. However, the prerequisite of smoothening of peaks and imbalances through bidirectional charging is concatenated with the undesired impacts on reliability and security of power system operation when there are probable intrusions in the eXtreme Fast Charging (XFC) station, hence destabilizing the charging networks. So this paper applies STRIDE based threat modeling to analyze and identify multiple potential threats endured by the cyber-physical system (CPS) of XFC station by using weighted attack defense tree. Potential mitigation strategies are then suggested for the identified severe threats. In addition, this paper also develops a stochastic probabilistic tool, the Hidden Markov Model (HMM) for modeling the security attacks for a given range of identified attack vectors and hence employing an appropriate defense strategy against the malicious hacker. Also, a weighted attack defense tree has been developed to generate various attack scenarios. In the end, the results of the proposed work are substantiated and validated if it is able to considerably improve overall charging efficiency and cyber-physical security of the charging station network. [ABSTRACT FROM AUTHOR]

Over the years, electric vehicles (EVs) have gained extensive attention and popularity worldwide. The ownership of EVs has been rapidly growing globally. The increasing EV charging (EVC) demand creates growth of electric energy demand and imposes additional stress on the power grid. To meet the required EVC demand, it is necessary to construct sufficient EV supply equipment (EVSE). The construction of EVSE is a key prerequisite for the wider deployment of EVs. Although EVC stations (CSs) have gained a default position for EV infrastructure, battery-swapping systems (BSSs) have also drawn considerable attention. In this article, we first introduce the system architectural design of BSSs. Then we present four kinds of BSS designs. The lessons learned from the practice of BSS implementation and operation in China are provided. BSS standardization is a determining issue in BSS development. State-of-the-art BSS standardization development is summarized, while key technical challenges and opportunities of BSSs are listed. [ABSTRACT FROM AUTHOR]

Buried power rail (BPR) and back-side power delivery grid have been proposed as solutions to scaling challenges that arise beyond the 5-nm technology node, mainly to lower IR drop and further shrink area. This article demonstrates a holistic evaluation of this technology and its variants at the microprocessor level. This is carried out by taking an Arm Cortex-A53 design through the standard-VLSI physical design implementation flow on Imec’s iN6 node, equivalent to the industry 3-nm technology node, which features the buried power technology. The power, performance, area, on-chip IR drop, and off-chip voltage droop metrics are benchmarked, and implications on power gating are explored. An extensive Design-Technology-Co-Optimization (DTCO) study of the back-side power grid is presented to enhance the decoupling capacitance by sweeping associated technology parameters showcasing further optimization opportunities in manufacturing. The conclusions of this work highlight that the front-side (FS) power delivery network (PDN) with buried rails achieves a 25% lower on-chip IR drop and 17% lower off-chip voltage droop (power supply noise) resulting in 21% lower guard band voltage. On the other hand, the back-side power grid with BPRs achieves 85% lower on-chip IR drop and 30% off-chip voltage droop resulting in 60% lower guard band voltage. In addition, the impact of BPRs, and back-side power grids on power gated designs are evaluated. [ABSTRACT FROM AUTHOR]

Parameter Identification plays an important role in electric power transmission systems. Existing approaches for parameter identification tasks typically have two limitations: (1) They generally ignored development trend of historical data, and did not mine characteristics of corresponding power grid branches. (2) They did not consider the constraints of power grid topology, and treated different branches independently. Therefore, they could not characterize correlations between the center node and its neighborhoods. To overcome these limitations, this work proposes a multi-task graph convolutional neural network (MT-GCN) which utilizes the graph convolutional network (GCN) and the fully convolutional network (FCN) as building blocks for parameter identification. Specially, GCN can extract the structure information to enhance local feature extraction. FCN is a decoding module following GCN module, and it is used to identify the parameters of each branch according to its characteristics. Compared with previous methods, the proposed method is significantly improved in accuracy. Besides, this method is robust to measurement noise and errors, and can cope with multiple conditions in real power transmission systems. [ABSTRACT FROM AUTHOR]

The characteristics of the short circuit current of renewable energy sources are quite different from those of traditional generators. When applied to tie lines and collector lines of renewable energy sources, the traditional directional elements may not operate correctly. A new method of fault direction identification suitable for inverter power sources and doubly fed power sources based on the fault features of renewable energy sources is proposed. The fault type is judged by sequence voltage. The fault direction is identified by comparing the amplitude between the sequence currents and the phase angle between the sequence voltage and the sequence current, without distinguishing whether the crowbar of the DFIG is triggered. The results of digital simulations and experiments show that this method is not affected by the types of renewable energy sources connected to the power grid and can correctly judge the fault direction in different fault types, which has good engineering application value. [ABSTRACT FROM AUTHOR]

Resonant circuits are commonly used in inductive power transfer (IPT) systems for the charging of electric vehicles because of the high power efficiency. Transient behaviors of the resonant circuits, which play a significant role in the design and analysis of IPT systems, are cumbersome to model analytically because of the high-order. This article develops a reduced-order continuous dynamic model based on the energy interactions among the resonant tanks. By applying the proposed energy balancing method (EBM), the order of the dynamic model is reduced to half of the number of the passive components in the resonant circuits. To show the accuracy of the EBM, the dynamics of a series–series (SS) compensated IPT system are modeled using Laplace phasor transformation (LPT) and EBM separately and the results are compared. The order of the EBM is found to be one-fourth of that of the LPT method. The sensitivity of the EBM to the switching frequency is discussed when the zero voltage switching turn-on operation is attained. Besides, to prove the advantage of reducing the order of the dynamic model, model predictive controls (MPCs) based on EBM and LPT are developed. The transient performances of the MPC controllers are simulated and the control inputs are applied to an experimental setup. Finally, experiments are conducted to verify the accuracy of the proposed EBM under zero and nonzero conditions and the effectiveness of the developed MPC controller. [ABSTRACT FROM AUTHOR]