ABSTRACT. Cascading failures in power grids, where failures propagate from one component to another, are a major cause of large-scale blackouts. With renewed interest in enhancing power grid resilience, it is even more critical to predict cascading failures so that effective mitigative actions can be identified. The existing cascading failure prediction methods lack high accuracy and fast computation time and often face challenges due to unbalanced or unrepresentative datasets. In this work, a comparative study of various data-driven methods for failure prediction with a shorter computation time is provided. The problem is formulated as a binary classification, where the input features, such as the loading levels of the transmission lines, are mapped to the output, which is the failure status of the transmission lines. To validate the effectiveness of the proposed methods, the IEEE 30-bus system is used as a test case, and the results confirm the viability of the compared methods for failure prediction. This study could guide future research in developing fast and accurate data-driven cascading failure models.

ABSTRACT. With the deep integration of inverter-based resources (IBR) at both the transmission and distribution levels, large-scale dynamic simulations have increasingly become a requirement to evaluate, validate, and verify new resources and their interaction with existing ones before their field deployment. Transient Stability Analysis (TSA) based modeling has been more favored for larger power system studies due to the computational burden and costs of extensive modeling associated with Electro Magnetic Transient (EMT) tools. This research aims to validate advanced EMT methods for modeling large-scale transmission and distribution systems. In addition to the conventional monolithic EMT simulation approach, two alternative setups are investigated: a co-simulation setup utilizing the Real Time Digital Simulator (RTDS) and Transient Stability Analysis Tool (TSAT) software, and a hybrid setup employing an equivalent model inside RTDS with a Dynamic Phasors (DP) interface between EMT and the equivalent model. The validation process involves rigorous assessments of steady-state and dynamic studies against the monolithic models. The findings demonstrate that both the co-simulation and hybrid simulation methods offer economic advantages for specific EMT modeling studies meanwhile dynamic interactions are still be considered for large-scale power systems, eliminating the need to model an entire network in the EMT platform.

ABSTRACT. Peak reduction is an important concern that can help reduce the growing stress on distribution grid and allow to defer investments in new capacity. However, the growing concern for customer privacy and comfort may impact the performance of load control for residential devices. Water heaters represent a convenient way of reducing peak due to their ability to store thermal energy for future use. In this paper, we developed a methodology to help utilities gain more insight with respect to the impact of load control efforts for shaving peak with no necessary information about the water heaters except the device status (on/off). To this end, we use a fleet of water heaters in a controlled residential neighborhood in Atlanta, GA. Our findings show that convergence in device status can serve as a proxy for peak shifting during hours of the evening peak.

ABSTRACT. Electricity prosumers are energy subsystems that not only consume, but also produce electricity. They are present in distribution level networks as traditional utility customers who have installed distributed energy resources. They are also present in transmission level networks as large conglomerates own both generation assets and large industrial loads. Previous work on the economics of prosumers has demonstrated that prosumers in a market have incentives to behave more competitively compared to producers and consumers in traditional markets. This paper further explores the behavior of prosumers and their response to market policies including the allocation of network losses and the impact of net metering. We extend a Cournot model of a dual prosumer and find that prosumers respond with higher supply quantities if network losses are allocated to the demand base. They respond with lower supply quantities if network losses are allocated to the supply base. Allocating network losses to the demand base also causes equilibrium prices to decline. Markets where prosumers first satisfy their own load and then sell the balance of electricity to the grid have lower quantities and higher prices than markets where prosumers buy and sell at locational marginal price.

ABSTRACT. Electricity market pricing and settlement are the key signals for real-time dispatch and long-term investment decisions. Regional transmission operators (RTOs) in the U.S. adopt uniform pricing scheme, which is based on the marginal costs of supplying an incremental MW of electric services. The marginal cost of an electric service is highly dependent on the constraints in the pricing models of RTOs. A slight difference in constraint modeling of pricing model on energy and ancillary services could result in drastically different market clearing prices (MCPs), cleared reserve quantities, and associated revenue. RTOs in the U.S. have various market designs and assumptions in ancillary services modeling in capacity sharing and reserve substitutes. This paper examines four combination models of capacity sharing and reserve substitutes and analyzes the associated market implications. The numerical results present that 1) cascading reserve requirements have direct impact on reserve pricing schemes 2) both cascading reserve requirements and sharing capacity have significant impact on reserve MCPs and locational marginal prices, and thus result in drastically different reserve revenue, energy revenue, generation cost, and generation profit.

ABSTRACT. Geomagnetically induced currents (GICs) in power systems are a potential source of introducing DC in transformers, resulting in undesirable occurrences of additional harmonics and higher temperatures. This paper reviews the methodology and results of case study that was performed on the transformer fleet of a 2000-bus synthetic grid on the geographic footprint of Texas. The thermal assessment technique identifies the transformers with potential thermal impacts using a first-order hotspot calculation method for the structural parts of the power transformer. The analysis is undertaken by modeling severe GMD events—NERC benchmark event and its derivatives—to assess how the transient hotspot behavior of a power transformer is related to various environmental conditions, such as electric field magnitude and direction, transformer neutral current, and storm duration.

ABSTRACT. Recently, there has been a major emphasis on developing data-driven approaches involving machine learning (ML) for high-speed static state estimation (SE) in power systems. The emphasis stems from the ability of ML to overcome difficulties associated with model-based approaches, such as handling of non-Gaussian measurement noise. However, topology changes pose a stiff challenge for performing ML-based SE because the training and test environments become different when such changes occur. This paper circumvents this challenge by formulating a graph neural network (GNN)-based time-synchronized state estimator that considers the physical connections of the power system during the training itself. The results obtained using the IEEE 118-bus system indicate that the GNN-based state estimator outperforms both the model-based linear state estimator and a data-driven deep neural network-based state estimator in the presence of non-Gaussian measurement noise and topology changes, respectively.

ABSTRACT. Conventional synchronous generators are gradually being replaced by inverter-based resources. Such transition introduces more complicated operation conditions and the reduction in system inertia imposes challenges for system operators on maintaining system rate-of-change-of-frequency (RoCoF) security. This paper presents a selectively linearized neural network (SLNN) based RoCoF-constrained unit commitment (SLNN-RCUC) model. A RoCoF predictor is first trained to predict the system wide highest locational RoCoF based on a high-fidelity simulation dataset. Instead of incorporating the complete neural network into unit commitment, a ReLU linearization method is implemented on a subset of selected neurons to improve the algorithm computational efficiency. The effectiveness of proposed SLNN-RCUC model is demonstrated on the IEEE 24-bus system by conducting time domain simulations using PSS/E.

ABSTRACT. Electric power system control, supervision, and protection actions require an accurate system dynamics model. The swing equation-based modeling approach does not properly capture the dynamics of a converter-dominated future power grid. Therefore, this research investigates the applicability of a computationally efficient and accurate neural ordinary differential equations (NODEs) approach to model and infer critical state information of the power system frequency dynamics. The designed NODEs framework proposes a new neural network architecture that models input dynamics missing from existing NODEs methods. By actively perturbing the system using a known logarithmic chirp signal, the proposed Python-based NODEs framework is trained using only measured system states and inputs without requiring detailed system information. The trained model accurately predicts future states under square and step excitation signals with a goodness of fit 99.7\% with an 18 times computational speedup than the detailed model in Simulink. The applicability of NODEs in modeling power system dynamics was validated and is a promising method to model future power grid dynamics integrated with large amounts of heterogeneous distributed energy resources.

ABSTRACT. Power flow analysis plays a crucial role in examining the electricity flow within a power system network. By performing power flow calculations, the system’s steady-state variables, including voltage magnitude, phase angle at each bus, and active/reactive power flow across branches, can be determined. While the widely used DC power flow model offers speed and robustness, it may yield inaccurate line flow results for certain transmission lines. This issue becomes more critical when dealing with renewable energy sources such as wind farms, which are often located far from the main grid. Obtaining precise line flow results for these critical lines is vital for next operations. To address these challenges, data-driven approaches leverage historical grid profiles. In this paper, a graph neural network (GNN) model is trained using historical power system data to predict power flow outcomes. The GNN model enables rapid estimation of line flows. A comprehensive performance analysis is conducted, comparing the proposed GNN-based power flow model with the traditional DC power flow model, as well as deep neural network (DNN) and convolutional neural network (CNN). The results on test systems demonstrate that the proposed GNN-based power flow model provides more accurate solutions with high efficiency comparing to benchmark models.

ABSTRACT. Any change in load or trip of generator cause power mismatch in the system which is initially compensated by the kinetic energy from the inertial storage of system and then by the governor action. This governor action leads to a new equilibrium state with the deviation in frequency from the nominal value which may yield undesirable effect which may lead to large scale disturbances such as blackouts or cascading failures. So, to keep frequency at nominal value and also keeping the tieline flow at the scheduled value, Load Frequency Control is necessary. As the controller needs to be robust, we proposed a Load Frequency Controller based on LSTM Neural Network. To validate the performance of the proposed controller it is compared with traditional integral controller at different disturbance conditions like increment of load, decrement of load, removal of generating units and addition of bulk load at a bus. The results show that the proposed LSTM controller is able to capture the details of the dynamics of the traditional integral controller and can be used in place of the traditional controller in case of single are power system as well as two area power system.

ABSTRACT. Smart grid resource allocation (SGRA) is a large-scale demand response optimization problem to lower the electricity cost of customers by intelligently shifting their schedulable devices via an aggregator. The SGRA problem aims to maximize the aggregator profit while still considering the inconvenience and the availability constraints of each customer’s assets. A heuristic approach provides a reliable method to find near-optimal solutions to non-convex, complex, and multi-dimensional problems by reducing the computational time and not relying on gradients. To further improve the computational time of the heuristics, high-performance parallel computing concepts can be used to divide the formulated problem into smaller sub-problems. This research proposes a leader-follower based parallel Accelerated Particle Swarm Optimization (APSO) to find near-optimal solutions of SGRA using the OpenMP framework in MATLAB. The leader-follower topology allows the parallel execution of each particle, resulting in lower execution time. The proposed parallel APSO was both significantly faster (2 − 7×) than existing SGRA literature methods, and achieves similar aggregator profit, customer savings, and peak demand reduction.

ABSTRACT. Currently transmission line usage is varying compared to its historical usage. The shift in usage is caused by large scale renewables, and DERs. This transition, however, was not anticipated by the transmission owners before. Therefore, there is a need to develop a qualitative assessment tool to value lines based on the change in usage. This work developed factors to value line on usage based on market dynamics and operational wear and tear. A methodology, called Analytical Hierarchy Process (AHP) is used to combine these factors and provide a single value to transmission line. The value obtained through AHP will not have a reasonable meaning if economic values are not assessed. Hence, line valuation is followed by the economic assessment. This economic assessment is done by distributing the revenue to lines using AHP. This process of revenue distribution would be useful to multiple transmission owners owning lines in an area. The current practice of revenue distribution is also compared with the proposed methodology using case studies. The IEEE RTS-96 is used as a test system for this work.

ABSTRACT. The adverse effects of greenhouse gas emissions in electric generation have resulted in a push towards more sustainable and efficient systems. In order to address the environmental concerns that continue to arise, this study proposes a method for reducing marginal emissions of power systems by optimally allocating Distributed Flexible AC Transmission Systems (D-FACTS) in a network to reduce transmission congestion. This method optimally allocates D-FACTS modules in power systems by minimizing operating costs and global warming potential, and then the impact of D-FACTS on marginal emissions is evaluated. This method was applied to a test system that simulates the power grid of El Paso, TX, and the results show the integration of D-FACTS devices can reduce the marginal emissions in power systems by up to 16% in this system.

ABSTRACT. The world’s electric grids are susceptible to geomagnetic phenomenon such as disturbances created by coronal mass ejections (CMEs) from the sun, which create geomagnetically induce currents (GICs) in the systems. It is important to study events such as geomagnetic disturbances (GMDs) to ensure the electric grids’ resiliency against such events. In this work, test patterns of time varying electric fields are proposed to assist with the study of GMDs and other electromagnetic phenomena which may affect the stability of the power grids. Formulation of mitigation strategies against electromagnetic events which may cause major grid issues, such as system voltage collapse and transformer overheating is the focus of this work.

ABSTRACT. Corona effects are one of the most important parameters to take into consideration when designing an overhead transmission line. Corona discharges cause power loss which should be considered during transmission line design. Unconventional high surge impedance loading (HSIL) lines have subconductors placed anywhere in space and have no bundle symmetry. However, they have shown to have the potential to produce greater natural power than conventional lines and conventional HSIL lines. For unconventional HSIL lines, existing empirical formulae and methods to estimate corona effects are thus not applicable, as they are all based on symmetrical bundle arrangements. This paper addresses this technical gap and discusses novel methods to estimate corona loss in both fair and foul weather conditions. It is seen that the unconventional lines under discussion undergo much greater corona loss than the conventional and the conventional HSIL lines.

ABSTRACT. With high penetration of inverter-based renewable energy sources (IBRES) power system dynamics began to occur within shorter time frames. However, existing monitoring tools that rely on phasor measurements lack sufficient resolution to monitor such fast transients in the grid. To address this issue, this paper proposes a weighted least absolute value (WLAV) estimation-based monitoring tool that uses the same discrete samples of measured voltages and currents that are used by the phasor measurement units (PMU). Such samples are in general not made available to the users by the PMUs but instead they are processed for a full or fraction of a cycle to obtain the positive sequence phasors. Given that these discrete samples are readily available, there is little reason not to use them for tracking fast voltage and current transients introduced by inverter-based renewable energy sources. Performance of the proposed estimator is illustrated using a small IEEE 30-bus system modeled using appropriate discrete-time components and simulated measurements with Gaussian noise as well as gross errors.

ABSTRACT. Today’s scientific software in the power system domain highly relies on CPU-based multi-core clusters. However, the rise of heterogeneous computing requires that parallel applications be executed on various hardware from different vendors. Thus, efficient solutions are emerging using compiler directives, communication protocols, high-level libraries, and execution policies. Power system dynamic simulation (DS) is critical to predicting and identifying real-time physical stability constraints with inevitable system component failures. This paper explores a multiprocessing heterogeneous solution for parallel DS. We design a single-source code realized through Message Passing Interface (MPI) and array computing at a high level. The performance test of a 30-second simulation on the largest 36864-bus-12288-generator system indicates that the program run with pure CPU-based parallelism finishes the computation in 14.6 seconds on a personal computer, and the program's CPU+GPU acceleration (hybrid switch) reaches more speedup with 2.33 seconds on a cluster node. The proposed solution significantly improves computational performance with high portability and adaptiveness to various hardware environments.

ABSTRACT. This paper progresses on the development of the discrete electromechanical oscillation control (DEOC). The DEOC approach is based on the step-wisely control of electronically-interfaced resources' (EIR) power output and aims to significantly reduce the amplitude of multiple oscillatory modes in power systems. The theoretical formulation of the problem and the proposed solution is described. This work addresses the issues of a nonlinear grid representation and favorable reduction of control actions from EIRs, as well as their impact on the DEOC performance. Simulations on a 9-bus system validate the effectiveness of the proposed control even when highly load scenarios are considered.

ABSTRACT. For large-scale interconnected power systems that cover large geographical areas, certain electrical studies are required so that appropriate decisions ensure system reliability and low cost. For such studies, it is often neither practical nor necessary to model in detail the entire power system, which is increasingly complex due to a more diverse range of grid assets to choose from in both short and long-term planning. The goal of this paper is to present a methodology to reduce the order of large-scale power networks based on spectral graph theory given that current methods for static network reduction are not scalable. A brief analysis of some spectral clustering properties to determine which graph Laplacian matrix should be used and why is included. The analysis shows that the utilization of the normalized graph Laplacian is more advantageous for clustering purposes. Techniques are proposed to approximate cost functions for the aggregated generators. This is done via linear regression. The reduced-order model obtained with the proposed methodology has an accuracy above 94% and solves the scalability issue commonly present in other reduction methods. If the utilization of the reduced-order model is either constrained to load levels above mid-peak demand, or cost functions of aggregated units are approximated via a piece-wise quadratic approach, then the error distribution is in the order of 0.001.

ABSTRACT. Historically, the power grid has relied on conventional fossil, nuclear, and hydro-power generations, which used large rotating machines that provided ample inertia. However, as the grid transforms with the increasing penetration of inverter- based resources (IBR) like wind, solar photovoltaic, and battery storage systems, which lack inherent inertia, there is a growing need to reassess the role of inertia in the future grid. This shift raises concerns about maintaining system stability and security. In recent years, there has been a significant focus on the importance of inertia estimates in various areas such as robust frequency control, managing the integration of IBR, enhancing system reliability through fast frequency response analysis, and designing synthetic inertia as an ancillary service. This paper aims to provide insight into the impact of various measurement- based inertia estimation approaches on the evolving power grid. The estimation methods are classified into two categories: large disturbance-based methods and ambient data-based methods. This categorization highlights the strengths and weaknesses of different approaches, emphasizing the importance of the various approaches of inertia estimation.

ABSTRACT. Power flow analysis is a key to operate and design an electric power system. However, analyzing a complex electrical power system is a computationally time-intensive activity. This paper assesses the implementation of Newton Raphson based power flow formulation in the heterogeneous computing environment (GPU+CPU) for speeding up the computation process. To this end, the computationally intensive Newton Raphson algorithm involved in power flow calculations is discussed along with the modalities in their implementation on sequential as well as parallel processing environments. The performance comparison of computation using sequential and parallel processing is then presented with the purpose to identify the bottleneck in speeding up calculation using GPU. The results show that solving the system of linear equations becomes the main bottleneck for speeding up the Newton Raphson based Power flow method in CPU+GPU environment.

ABSTRACT. In the field of Electromagnetic transient (EMT) simulation and transient stability analysis, solving the network equation constitutes a significant portion of the computational workload. Traditional approaches, such as LU decomposition, suffer from inherent sequential nature, making parallelization challenging. On the other hand, matrix multiplication methods, although parallelizable, can be computationally expensive for large-scale systems. This paper introduces an efficient solver that leverages both coarse-grained and fine-grained parallelism to address these challenges. The proposed approach achieves coarse-grained parallelism by partitioning the network into branches connected by cut nodes. Additionally, it employs a parallel forward/backward substitution algorithm based on recursive node reordering to achieve fine-grained parallelism. Experiment on the IEEE 69-bus system shows our approach is 2.7 times faster than sparse LU and 42% faster than parallel BBDF method with LU solver. Moreover, on a synthetic large-scale system, our method is 14 times faster than sparse LU and up to 75% faster than BBDF method with LU solver. These findings highlight the efficiency and scalability of the proposed approach.

ABSTRACT. As the adoption of smart home management devices grows, residential consumers become increasingly responsive to electricity prices. However, this price responsiveness cannot continue indefinitely as prices increase. Modeling this saturation effect is crucial to prevent demand response from becoming too costly, especially for consumers. This paper proposes an optimization model to design price response events that ensure energy equity by considering the income status and response saturation effect of each consumer. The proposed method uses energy burden to measure energy equity and builds a piecewise linear model to express the response saturation effect of each consumer. We formulate the tariff design problem as a mixed integer non-linear optimization model, which achieves a demand reduction target while minimizing energy burden and sharing the economic expense proportionally. We use real-world datasets in a case study to obtain personalized electricity prices, energy consumption, and energy burden for each consumer. We find that personalized tariffs effectively reduce energy burdens. By comparing the results with and without saturation effects, we conclude that modeling saturation effects can reduce energy burdens in demand reduction response events.

ABSTRACT. This paper presents an improved generic demand-side model that captures the aggregated effect of smart buildings and a large population of prosumers. The model is formulated as a bilevel program. The upper level is cast as a unit commitment problem to emulate the market outcome. In contrast, the lower level maximizes the aggregate self-consumption of prosumers. The coupling between the two levels is done through prosumer demand instead of the electricity price. The formulations are validated and compared on the PJM 5-bus system.

ABSTRACT. In the real-time electricity market, look-ahead security constrained economic dispatch (SCED) plays a substantial role in ensuring the sound operation of the electricity spot market with the high penetration of renewable energy. It is crucial to appreciate the effect of unit operation constraints on locational marginal prices (LMPs) in real-time electricity market. To this end, this paper establishes the look-ahead SCED model, derives the LMP formulae under the look-ahead SCED optimization model, and anatomizes the impact of unit operation constraints on the LMP and its components. These formulae and analysis can help market participants to evaluate their own benefits based on their own unit physical parameters. Finally, the correctness of the analysis in this paper is verified via a 3-bus system.

ABSTRACT. The fact that power generation is one of the main sources of producing emissions and extra greenhouse gasses such as carbon dioxide and methane in the earth's atmosphere, requires power section procedures to be optimized. We have utilized a newly developed marketing tool to forecast the day-ahead order for smart grid systems. The open-source FB-Prophet forecasting model captures the main factors involved in electricity demand to forecast the power demand for the same period next day. Trend, seasonality, and holidays of the previous four years of Tennessee region power demand (TVA) is taken as a case study to apply and verify the model in the fifth year. This user-friendly tool is able to receive the grid characteristics and demand-related data as input and provide the user with day-ahead order amount. Optimizing the day-ahead order amount consequently minimizes the real-time power generation costs, use of fossil fuels, and low efficient real-time power generation emission.

ABSTRACT. Distribution system state estimation (DSSE) methods have been developed for real-time monitoring of distribution systems. Some common topology changes, like disconnection of capacitor banks due to internal failures, are not monitored or known and creates an incorrect system model for DSSE. Capacitor banks degrade over time and can experience internal failures that cause them to become disconnected. This impacts the performance of DSSE and creates incorrect estimation results. Bad data detection methods can be used for detection of such topology errors, however are limited in identification. The main contribution of this paper is the development of a search method for the identification of capacitor bank failures in a system once detection occurs. Test results on a sample IEEE distribution feeder have been provided to illustrate the effectiveness of the search method.

ABSTRACT. In this paper, we propose a distributed continuous-time optimal power flow (OPF) model, with DC power flow constraints, for a multi-area transmission network. The model exploits the unique properties of variational optimization, function space representation, and the alternating direction method of multipliers (ADMM) to enable continuous-time power exchange between adjacent areas. More specifically, the centralized multi-area OPF is formulated as a variational optimization problem with continuous-time load and decision variables (power generation, voltage phase angles, line/tieline power flows), which is then converted to a conventional optimization problem by projecting the load and decision trajectories into the Bernstein function space, and is decomposed to function space-based OPF sub-problems of individual areas using ADMM. The numerical results of implementing the proposed model on a synthesized three-area network indicate convergence to the centralized continuous-time OPF solution and showcase computational efficiency and the efficient sharing of ramping resources among areas.

ABSTRACT. The presence of power electronic loads can significantly impact the damping of harmonic propagation on an electric power system. This is of relevance in harmonic analysis when considering power quality issues from nonlinear loads such as arc furnaces and when calculating signal attenuation in power line communications (PLC). On account of recent European Union (EU) regulations, existing passive rectifiers are increasingly replaced with power factor corrected (PFC) rectifiers. Simulation results have suggested that the input impedance of these devices is not only affected by their circuit topology but also the design of the control system. Due to the potential for resonances at certain frequencies, sufficient models are necessary to perform accurate harmonic power flow studies. This paper presents experimental results of power factor corrected rectifiers and provides a lumped circuit model for use in harmonic analysis. The experimental results were performed in a laboratory using a commercially available PFC power supply unit supplying controllable electronic loads. The lumped circuit model can be applied in existing power system analysis software to perform harmonic studies.

ABSTRACT. Secure, reliable and economic operation of power system requires having good quality models of power plants in the system. So, the power plant models need periodic validation and calibration of their parameters. Traditional approach of validation which involves taking generators offline and staged testing is expensive and time consuming. Another cost-effective approach of model validation which is based on online measurements of disturbance data has been in increasing application recently. In this paper, we present an open-source model validation tool which uses disturbance data for parameter estimation of dynamic models. The paper compares the performance of three optimization algorithms, Nelder-Mead, Broyden-Fletcher-Goldfarb- Shanno, and Differential Evolution for parameter estimation of a grid forming inverter model while demonstrating the application of the tool.

ABSTRACT. In this paper, a comparison between electric vehicles (EVs) fixed and mobile charging methods in terms of consumer and producer perspectives is presented. From the consumer’s perspective, a comparison between fixed charging stations and virtual mobile charging stations is performed, considering consumer convenience and consumer expenses. From the producer’s perspective, it is performed by employing the levelized cost of energy (LCOE) index. For consumer convenience, the total charging time is considered, and a mathematical model considering the total charging time cost for fixed charging and the delivery cost for mobile charging methods is presented. The LCOE criterion is used to make a comparison between fixed charging and mobile charging methods for different utilization rates by using a virtual charging station model. In addition, a comparison between fixed charging stations with and without land costs is carried out using the LCOE, and the same comparison is made for mobile charging stations. A case study in Jordan is presented to illustrate the proposed methodology. Results show that applying the mobile charging method in Jordan is more costly compared to the fixed charging method in the presence of the current policies.

ABSTRACT. The negative impact of measurement time skew on the static state estimation of the power grid has been exacerbated by increasing variation of system operating conditions. To mitigate the time skew problem, this paper proposes a regression model forecasting (RMF) method to forecast the time-skewed measurements, along with a confidence interval estimation (CIE) method to determine the weights associated with the forecasted measurements. The proposed RMF-CIE method is compared against several benchmark methods through Monte-Carlo simulation on the IEEE 16-machine, 68-bus model. It was observed that the proposed RMF-CIE consistently achieved more accurate state estimation on average. In addition, it was found that its estimation accuracy increases with the decrease of the skew time and variation levels.

ABSTRACT. Frequency is a very critical and sensitive parameter in an electrical power system. Any variation in a power system can be traced back to a change in the electric power system’s frequency. Moreover, with the rapid development of power electronics, the security, reliability, stability, and operational efficiency of electric power systems has deteriorated dramatically due to the injection of harmonic components in the power system. Therefore, the need to control the frequency by first determining the frequency in an electrical power system is needed. The method proposed here for the estimation of power system frequency and its harmonics is based on Rank Revealing QR Factorization method (RRQR) in conjunction with Multiple Signal Classification (MUSIC) algorithm. The experimental results will demonstrate the effectiveness of the proposed method in detecting the fundamental frequency, even and odd harmonic frequencies, and interharmonics against the classical approach of the Fast Fourier Transform (FFT) algorithm.

ABSTRACT. Numerous techniques have been implemented to calculate the synchronous machines' inertia constant and accurately assess it. Many of them use machine model simulation. These offer a means of knowing the inertia value a machine contributes to the grid to assist with frequency regulation. This paper provides a novel strategy for estimating the synchronous machine's inertia in real time using PMU measurements. This proposal is based on the Teager-Kaiser energy operator used to identify the time of disturbances and the ARMAX method for estimating inertia.

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

ABSTRACT. This paper aims to develop and optimize an efficient residential demand response (DR) strategy that ensures reliable voltage supply to customers, reduces network losses, and minimizes load switching. To maintain comfort levels, this strategy considers compensation costs for voltage management and prioritizes consumer preferences. The network voltage management involves utilizing the modified binary teaching learning-based optimization algorithm (BTLBO) to determine the optimal combination of household appliances for switching. Additionally, the solar and wind power model is trained using the Levenberg Marquardt Artificial Neural Network (LM-ANN) technique. The proposed method is evaluated on a modified IEEE 33-bus distribution system with significant overload and high integration of renewable energy sources. Two worst-case scenarios are examined to improve network voltage magnitude during DR operations. Simulation results demonstrate that the proposed approach, incorporating direct load control, effectively improves network voltage and addresses the challenges associated with high renewable energy integration.

ABSTRACT. Extreme weather events can cause power outages anywhere, but quite extensively along the U.S. southeastern coastline. Accurate outage prediction before a hurricane landfall is essential for reducing the impacts from distribution outage management and restoration planning perspectives. An outage prediction model (OPM) is developed to predict outages associated with substations based on data collated from multiple sources using tree-based ensemble machine learning regression algorithm. For this study, publicly available data as well as actual outage data from a major utility in the U.S. are employed. Performance validation of outage prediction models is done using several extreme weather events data over the past decade. Results confirm enhancement in accuracy for power outage predictions over baseline approaches.

ABSTRACT. Network reduction is useful in addressing the computational intensity of expansion planning methods. In this paper, a network reduction method is proposed to reduce system size while preserving important power flows. The focus of this study is on two aspects of the proposed method, namely, zone identification and generation bus aggregation. For zone identification, a minimum spanning tree algorithm is used, and its performance on the IEEE 118 case is compared with another method published in the literature called multi-cut. The remaining steps in the reduction method will be addressed in future work.

ABSTRACT. Recent natural and man-made disasters leading to sustained power outage has urged the system planners and policy makers for improving grid resiliency. This requires innovation across diverse facets ranging from better anticipation of such events to post event response. This work focuses on the problem of supporting the critical loads while the system is undergoing such an event. As a preliminary step towards the big picture, this work aims to develop a load criticality-based model to operable microgrid formation. A new firefly search inspired metaheuristic algorithm is developed in this work for microgrid formation with the objective of maximizing the critical load restoration with minimal switching operation. Numerical analysis is conducted on a modified Iowa state 240-bus distribution system and the results show the scalability and critical load restoration capabilities of the proposed algorithm.

ABSTRACT. As the electric power grid is brought up to date with state-of-the-art devices connected to the internet, pertinent information needs to reliably make its way from the edge to the cloud for system operators to visualize grid health. Conversely, control commands also need to be sent from the cloud to individual devices on the grid in order to maintain grid stability and safety, especially during unbalanced cases. This paper presents an affordable, brand-agnostic solution to grid data aggregation and communication using off-the-shelf products pieced together with IEC 61131-3 open source language for Programmable Logic Controllers (PLCs). A Phoenix Contact PLC along with some ancillary devices used to provide wireless communication capabilities were used to build an Edge Intelligent Device (EID) which interfaces with edge devices like solar inverters using Modbus protocol and the cloud using MQTT with an LTE backend for internet connection. The advantage of the EID over other similar devices is affordability and it is highly customizable. The EID was tested in both simulated and real-world applications: over 750 solar inverters were simulated in OPAL-RT and their data were transmitted to the cloud and visualized with Grafana in intervals under 30 seconds; data from a field testbed generating real data from solar panels and inverters, as well as another inverter built in-house powered by a grid simulator, was also transmitted through the EID. The paper will describe how the EID was built and coded, how the test beds were created, and show the results in Grafana.

ABSTRACT. The accuracy of distribution system state estimation may be significantly impacted by the existence of bad measurements and unexpected topology errors. This paper proposes a data-driven Graph Convolutional Network (GCN) for anomaly detection, including bad measurements and topology change events. Compared to many existing machine learning approaches, the proposed approach embeds both spatial-temporal measurement correlations, which allows us to detect and distinguish different anomalies. Numerical results carried out on the IEEE 37-node system demonstrate that the proposed-based method can obtain high accuracy in detecting bad data and topology changes as compared to other approaches, even in the presence of high PV penetrations.

ABSTRACT. Unlike stationary wind turbines, mobile wind turbines (MWTs) can travel along the local transportation system (TS) via a truck, supplying power to microgrids (MGs), residential buildings, and critical infrastructure. This spatiotemporal flexibility can provide significant benefits, including enhancing system resilience in the aftermath of high-impact low-probability (HILP) incidents. However, the potential of such resources is currently untapped, calling for improved utilization. To address this research gap, this paper proposes an optimal scheme for strategically pre-positioning MWTs to enhance the resilience of MGs when facing extreme events. Considering that the MWTs travel time on the TS and the predicted wind energy have a significant impact on the duration and magnitude of power outages during the restoration process, a scenario-based stochastic mixed-integer linear programming (MILP) model is introduced to incorporate uncertainties related to the road status in the TS, power line faults in MGs, and wind energy forecasts. Case studies on an integrated transportation and energy network — a central Alabama interstate transportation network and two IEEE 33-node test power systems — demonstrate the effectiveness of the proposed pre-positioning scheme in boosting MGs resilience.

ABSTRACT. Studies that require the analysis of large-scale distribution system models are usually performed using phasor based simulation packages. However, phasor based simulations can only perform steady-state analysis. With the advent of distributed generators (DGs), it is necessary to evaluate the impact of smart inverter controls on the distribution network in real time. This paper evaluates the co-simulation platform between Typhoon Hardware-in-Loop (HIL) and OpenDSS by subjecting the inverter to various faults. First, the placement and sizing of DGs are determined using the Whale Optimization Algorithm (WOA). Next, the grid-tied photovoltaic (PV) inverter is simulated in Typhoon HIL and interfaced with the distribution network modeled in OpenDSS. The co-simulation platform is evaluated for its accuracy by comparing the results with those obtained from OpenDSS. Open Circuit faults are applied across the inverter terminals and their impact on the distribution network is analyzed in real-time. Additionally, the application of PV inverter control to improve the voltage profile of the distribution network is studied. Simulations carried out on the IEEE 13 bus unbalanced distribution network demonstrate the effectiveness of the co-simulation platform.

ABSTRACT. The resilience of microgrids, which is crucial for maintaining a stable and reliable power supply, has become increasingly important in the face of rising energy demands and the growing threat of extreme weather events. Incorporating wave energy systems into the grid infrastructure can diversify energy sources, improve grid stability, and ensure uninterrupted electricity supply even during disruptive events. This paper presents an innovative study on the integration of wave energy into a modified IEEE 33-node distribution network, providing a unique perspective on the impacts and opportunities of harnessing marine renewable energy in distribution microgrids. To understand the impact of wave energy on grid resilience, service restoration is compared under fault conditions with different renewable generation portfolios. This research underpins the significant potential of wave energy as a reliable and sustainable energy source for future microgrids in coastal or island areas. The results show that the use of a mixture of renewable energy sources, specifically wave and wind energy, can contribute to improved network resilience and more effective load-shedding management during faults.

ABSTRACT. Microgrid systems have emerged as a viable solution to address the challenges associated with conventional power grids, such as reliability, resiliency, and sustainability. The protection of microgrids plays a crucial role in ensuring their safe and efficient operation. This paper presents a novel approach to enhance microgrid protection by applying wavelet response analysis for current measurements. The proposed technique utilizes a differential technique for fault identification in both islanded and grid-connected modes. The proposed enhanced microgrid protection scheme provides an innovative and robust solution for ensuring the reliable fault detection of microgrids in both islanded and grid-connected modes of operation. Simulation results highlight the application of wavelet response analysis offering a comprehensive and efficient approach to detect and mitigate power system abnormalities, contributing to microgrid systems' overall stability and resilience. The proposed technique can effectively identify abnormal conditions by implementing wavelet transform to analyze current waveforms through differential relaying techniques distinguishing between short circuit faults, external disturbances, and tap loads. Simulation studies were conducted on a representative 4-Bus benchmark microgrid model to evaluate the performance of the protection scheme. Results demonstrate the effectiveness and superiority of the proposed scheme in accurately identifying symmetrical and asymmetrical faults, effectively segregating tap loads, and contributing to the reliability and resilience of microgrid systems.

ABSTRACT. Effective power flow modeling critically affects the ability to efficiently solve large-scale grid optimization problems, especially those with topology-related decision variables. In this work, we put forth a generative modeling approach to obtain a piecewise linear (PWL) approximation of AC power flow by training a simple neural network model from actual data samples. By using the ReLU activation, the NN models can produce a PWL mapping from the input voltage magnitudes and angles to the output power flow and injection. Our proposed generative PWL model uniquely accounts for the nonlinear and topology-related couplings of power flow models, and thus it can greatly improve the accuracy and consistency of output power variables. Most importantly, it enables to reformulate the nonlinear power flow and line status-related constraints into mixed-integer linear ones, such that one can efficiently solve grid topology optimization tasks like the AC optimal transmission switching (OTS) problem. Numerical tests using the IEEE 14- and 118-bus test systems have demonstrated the modeling accuracy of the proposed PWL approximation using a generative approach, as well as its ability in enabling competitive OTS solutions at very low computation order.

ABSTRACT. The stochastic nature of solar energy generation poses a challenge for grid operators, especially with higher penetration of solar-based renewables in the grid. This paper proposes an attention-based temporal fusion transformer (TFT) model for short-term (an hour ahead) photovoltaic (PV) power forecasting using available geographic data such as solar irradiation, temperature, and statistical features extracted from historical PV data. TFT utilizes a self-attention layer for long-term dependencies where recurrent networks are used for local processing. The model selects relevant features through a series of gating layers to achieve high performance for multi-horizon forecasting. The temporal fusion transformer model also provides interpretable insights into the temporal dynamics of different features. A real-world PV dataset has been utilized to compare the model performance with some other state-of-the-art forecasting models.

ABSTRACT. Battery energy storage systems (BESSs) are gaining attention due to reduced costs and high flexibility, but developing accurate models for operation presents challenges. This paper introduces a model for the charging and discharging processes via a single current decision variable, approximates the relation between the open circuit voltage and the state of charge with linear functions, and presents an optimization model with bilinear constraints for identifying optimal BESS operational strategies. A transformation technique is introduced to manage the bilinear constraints, transforming the model into an exponential optimization problem with linear constraints. A new sequential linear and quadratic programming approach is developed, with proven convergence. Preliminary experiments demonstrate the efficacy and efficiency of this approach.

ABSTRACT. In this paper, we address the problem of frequency and voltage control in microgrids in which generators and loads are interfaced via grid-forming (GFM) inverters. In our setting, the output voltage and frequency of the inverters is determined by a primary control scheme realized through a control strategy referred to as dispatchable virtual oscillator control (dVOC). This type of GFM primary control is known to stabilize system frequency and voltage magnitudes, but it is not capable of regulating them to their nominal values. To address this issue, we propose secondary frequency and voltage control schemes, both of which rely on integral control. The secondary frequency control scheme is centralized and similar in nature to that utilized in bulk power systems, whereas the secondary voltage control is completely decentralized. The operation of the proposed secondary frequency and voltage controls is illustrated via numerical simulations.

ABSTRACT. This paper reviews traveling wave based fault localization methods, including the traditional single-ended and the double-ended methods, and a most recent one. The first method relies on local measurements only while the second one relies on measurements from both ends. Both methods require the information of line length and traveling wave propagation speed. To determine the speed, an additional experiment is required to generate traveling waves. The third method is based on the fault location computing equations from the first two methods. Further manipulation leads to a new computing equation without the input of propagation speed. The paper also presents how to extract traveling waves from instantaneous measurements and how to compute fault location using computer simulation. A 300- km transmission line energized by two 500 kV, 60 Hz sources is simulated in OPAL RT-LAB real-time digital simulator. Traveling waves are extracted from the phase currents by use of a high- pass filter with kilo-Hz bandwidth. The three methods are tested and demonstrated for various fault types at different locations. In addition, characteristics of traveling waves of different fault types have been closely examined.

ABSTRACT. This paper proposes an advanced technique for open switch fault of a dual T-type multilevel converters. The technique could detect and isolate the fault of the three phases switches, 24 switches here, using a single voltage sensor. Additionally, this technique is one that can be implemented at low cost with contemporary DSP applicable technology and is able to detect and isolate a single switch fault, when one occurs, using samples of the waveform alone. The technique shows that the crest factor is 26dB if we oversample by a factor of $200 the PWM generated waveform (with respect to the Nyquist Rate for a 60Hz Source). The results show that the technique is fact enough to detect and isolate the fault in a single voltage cycle.

ABSTRACT. Identifying and classifying faults in photovoltaic (PV) arrays is critical for enhancing system performance and working lifespan. Supervised machine learning (ML) strategies are appealing for detecting faults in PV arrays. Obtaining labeled data to train these algorithms is challenging task. This research work uses extracted solar cell parameters for sensor less classification of faults. Solar cell parameters start varying even before fault occurrence. Partial shadow (PS), inter and intra string line-to-line (LL), line-to-ground (LG), and open circuit (OC) faults are classified using different variants of K-nearest neighbor (KNN), ensembles, support vector machines (SVM), and neural networks (NN). Moreover, sensor less classification elevates the resource-intensive process, which contributes in overcoming traditional ML-based classification using external parameters. The merits of proposed technique are corroborated through MATLAB simulations. Bootstrap aggregating results in near-perfect fault classification accuracy, followed by KNN, SVMs, and NN, with an average accuracy of 98.7%, 98.1%, and 71%, respectively.

ABSTRACT. This paper proposes a methodology for detecting fire ignition electrical faults and enhancing power line situational awareness to prevent electrical wildfires from occurring. Not all vegetation high impedance faults (HIF) necessarily cause ignition. Therefore, this paper aims to distinguish fire-ignition vegetation HIF from non-ignition ones using a classification method. To this end, a multi-head CNN-LSTM method is proposed. Individual heads for each feature in the time-series obtain the spatial features independently using a convolutional neural network (CNN). The features are then concatenated and fed to a Long-Short Term Memory (LSTM) to extract the temporal features. The results show that the model can successfully detect the occurrence of a vegetation HIF and accurately classify between fire-ignition and non-ignition scenarios. The detection time of ignition HIF in the proposed approach is 1.2 seconds, which is fast enough to de-energize the line in a timely manner and prevent a wildfire from occurring.

ABSTRACT. Protection coordination is a critical aspect of reliable and safe power system operation. As the traditional radial distribution system undergoes a transformation into a more complex mesh system and network of microgrids, it becomes crucial to establish efficient coordination among protection devices. This coordination aims to minimize the impact of faults, uphold power system reliability, and safeguard both equipment and personnel. This paper addresses the integrity of protection coordination; it aims to investigate the issue of miscoordination in overcurrent relays, that can occur in mesh networks with Distributed Generators (DG). Traditionally, protection coordination challenges are addressed under the assumption of a static network. However, during fault isolation, there are often transient changes in the network topology that can affect the coordination of overcurrent protection relays. This study identifies and presents the issue of miscoordination in such scenarios, and proposes a nonlinear optimization formulation to effectively prevent this possible miscoordination.