Electric Power Grid Research Articles

A Smart Microgrid System with Artificial Intelligence for Power-Sharing and Power Quality Improvement

The widespread popularity of renewable and sustainable sources of energy such as solar and wind calls for the integration of renewable energy sources into electrical power grids for sustainable development. Microgrids minimize power quality issues in the main grid by linking with an active filter and furnishing reactive power compensation, harmonic mitigation, and load balancing at the point of common coupling. The reliability issues faced by standalone DC microgrids can be managed by interlinking microgrids with a power grid. An artificial intelligence-based Icosϕ control algorithm for power sharing and power quality improvement in smart microgrid systems is proposed here to render grid-integrated power systems more intelligent. The proposed controller considers various uncertainties caused by load variations, state of charge of the battery of microgrids, and power tariff based on the availability of power in microgrids. This paper presents a detailed analysis of the integration of wind and solar microgrids with the grid for dynamic power flow management in order to improve the power quality and to reduce the burden, thereby strengthening the central grid. A smart grid system with multiple smart microgrids coupled with a renewable energy source with tariff control and judicious power flow management was simulated for power-sharing and power quality improvement. A hardware prototype of the artificial intelligence-based Icosϕ control algorithm with nonlinear load was also implemented successfully. Furthermore, the economic viability was investigated to ensure the feasibility of the smart microgrid system with the proposed controller design for power flow management and power quality improvement.

Complete operational voltage assessment of Shandong pilot offshore wind project

AbstractThis article presents a thorough methodology for the voltage assessment of pilot offshore wind project in Shandong province, China. The results presented in this paper can be considered as the milestone of the offshore wind research in Shandong Province since there is still no grid codes for offshore wind power plant grid connection in the local electrical power grid. It mainly consists of three parts. In the first part, a detailed model of the offshore wind farm created in the DIgSILENT PowerFacotry simulation platform is presented, including wind turbines, power converters, transformers, submarine cables and relevant control schemes. In the second part, nonlinear time‐domain simulations were performed to analyze the wind farm's active power, reactive power, and voltage conditions under different wind scenarios. Based on the simulations results, a dynamic reactive power compensation system was proposed, and the consequence of the reactive power compensation was also demonstrated using nonlinear time‐domain simulations.

Detection of Faults in Electrical Power Grids Using an Enhanced Anomaly-Based Method

Detection of Faults in Electrical Power Grids Using an Enhanced Anomaly-Based Method

Blockchain-Enabled Smart Grid Applications: Architecture, Challenges, and Solutions

The conventional electrical grid is undergoing substantial growth for reliable grid operation and for more efficient and sustainable energy use. The traditional grid is now metamorphosing into a smart grid (SG) that incorporates a diverse, heterogeneous blend of operating measures such as smart appliances, meters, and renewable energy resources. With better efficient results and dependability, the SG can be described as a modern electric power grid architecture. The SG is one of the greatest potential advances as a promising solution for the energy crisis. However, it is complex and its decentralization could be of tremendous benefit. Moreover, digitalization and integration of a large number of growing connections make it a target of cyber-attacks. In this sense, blockchain is a promising SG paradigm solution that offers several excellent features. There has been considerable effort put into using blockchains in the smart grid for its decentralization and enhanced cybersecurity; however, it has not been thoroughly studied in both application and architectural perspectives. An in-depth study was conducted on blockchain-enabled SG applications. Blockchain architectures for various applications, such as the synchrophasor applications, electric vehicles, energy management systems, etc., were proposed. The purpose of this article is to provide directions for future research efforts aimed at secure and decentralized SG applications using blockchain.

Fluctuation response patterns of network dynamics – An introduction

Networked dynamical systems, i.e., systems of dynamical units coupled via nontrivial interaction topologies, constitute models of broad classes of complex systems, ranging from gene regulatory and metabolic circuits in our cells to pandemics spreading across continents. Most of such systems are driven by irregular and distributed fluctuating input signals from the environment. Yet how networked dynamical systems collectively respond to such fluctuations depends on the location and type of driving signal, the interaction topology and several other factors and remains largely unknown to date. As a key example, modern electric power grids are undergoing a rapid and systematic transformation towards more sustainable systems, signified by high penetrations of renewable energy sources. These in turn introduce significant fluctuations in power input and thereby pose immediate challenges to the stable operation of power grid systems. How power grid systems dynamically respond to fluctuating power feed-in as well as other temporal changes is critical for ensuring a reliable operation of power grids yet not well understood. In this work, we systematically introduce a linear response theory (LRT) for fluctuation-driven networked dynamical systems. The derivations presented not only provide approximate analytical descriptions of the dynamical responses of networks, but more importantly, also allow to extract key qualitative features about spatio-temporally distributed response patterns. Specifically, we provide a general formulation of a LRT for perturbed networked dynamical systems, explicate how dynamic network response patterns arise from the solution of the linearised response dynamics, and emphasise the role of LRT in predicting and comprehending power grid responses on different temporal and spatial scales and to various types of disturbances. Understanding such patterns from a general, mathematical perspective enables to estimate network responses quickly and intuitively, and to develop guiding principles for, e.g., power grid operation, control and design.

Optimization and Design of a Sustainable Industrial Grid System

Electricity is a multifaceted form of energy and is used globally, with a continuously growing demand. Electrical power grids are there for more than 150 years. The generated electrical power is delivered to different industrial, commercial, and residential sectors, thereby fulfilling the ever-growing demand. In this research paper, the design and optimization of an industrial grid for various electrical loads is discussed. The electrical grid ensures a stable power supply to the loads by providing quality power with the minimum total harmonic distortion (THD) possible. A complete study of the short circuit current has been done in two different electrical grid systems, as it is seen that the short circuit current depends on the impedance of the transformer which feeds the load. These two designs of a single diagram will be simulated by using a power system analyzer, the Electrical Transient Analyzer Program (ETAP) software. The different electrical parameters, like choosing the optimised rated generator, cables, and transformers, are done. Load flow analysis is performed on both the design to evaluate the THD, short circuit fault, as well as to choose the right protection circuit for the system.

Distributed Intelligence for Online Situational Awareness in Power Grids

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

A Multi-Filter Based Dynamic Power Sharing Control for a Hybrid Energy Storage System Integrated to a Wave Energy Converter for Output Power Smoothing

The output power fluctuations from the wave energy converters (WECs) with a high peak-to-average ratio need to be smoothed out before supplying power to electric loads or power grids. Usually, short-term energy storage is used to smooth out the output power fluctuations of WECs. In this article, a novel multi-filter-based dynamic power-sharing control is devised for a hybrid energy storage system (HESS) integrated with a grid-connected WEC. An advanced model of HESS-integrated WEC consisting of a direct-drive permanent magnet linear generator, a supercapacitor, and a battery system is developed to test the proposed multi-filter-based dynamic control strategy under realistic conditions. The distinguishing features of this study are as follows; 1) the power-sharing strategy is based on a dynamic filter that is designed to change its corner frequency depending upon the incoming ocean wave, 2) the low and the high-frequency power components are allocated based on ocean wave conditions, 3) the proposed strategy is suitable for the real-time control of the proposed HESS integrated with the WEC, and 4) a detailed model of the WEC is used instead of using a pre-saved power profile. The results suggest that with the implementation of the proposed multi-filter-based dynamic power-sharing scheme in the HESS integrated WECs, the output power can be effectively smoothed out before supplying the power to the grid.

Modified W-type configuration for a single-phase reduced parts count 81-level inverter.

The technology of modern power systems is revolutionizing as renewable energy sources are being integrated with electric power grids. In the form of inverters, power electronic converters are becoming an integral part of power systems due to their massive demand for grid integration of photovoltaic (PV) systems. Existing multilevel inverter topologies either require an output filter to get a sinusoidal voltage or generate a higher number of output voltage levels at the expense of many hardware resources. This paper presents a new single-phase 81-level inverter configuration with the name given as 'Modified W-Type Multilevel Inverter'. The proposed inverter configuration uses only eighteen power switches and four DC voltage sources to generate an 81-level output voltage approaching a sinusoidal waveform without an output filter. The general design equations are developed to calculate the number of switches, the number of voltage levels, and the number of DC sources for the proposed configuration. Loss and efficiency analysis is carried out that verifies a good practical efficiency of the proposed inverter configuration during the dynamic operation. A comparative analysis with the existing MLI topologies is also carried out that validates the effectiveness and novelty in reducing parts count and higher number of voltage levels. The proposed topology offers 1.04% total harmonic distortion of the output voltage which is within the benchmarks specified for grid integration without any filter requirements. The proposed inverter configuration is simulated in MATLAB/Simulink, and the results are validated by the design and development of a hardware prototype.

Mechanisms Regulating Energy Homeostasis in Plant Cells and Their Potential to Inspire Electrical Microgrids Models.

In this paper, the main features of systems that are required to flexibly modulate energy states of plant cells in response to environmental fluctuations are surveyed and summarized. Plant cells possess multiple sources (chloroplasts and mitochondria) to produce energy that is consumed to drive many processes, as well as mechanisms that adequately provide energy to the processes with high priority depending on the conditions. Such energy-providing systems are tightly linked to sensors that monitor the status of the environment and inside the cell. In addition, plants possess the ability to efficiently store and transport energy both at the cell level and at a higher level. Furthermore, these systems can finely tune the various mechanisms of energy homeostasis in plant cells in response to the changes in environment, also assuring the plant survival under adverse environmental conditions. Electrical power systems are prone to the effects of environmental changes as well; furthermore, they are required to be increasingly resilient to the threats of extreme natural events caused, for example, by climate changes, outages, and/or external deliberate attacks. Starting from this consideration, similarities between energy-related processes in plant cells and electrical power grids are identified, and the potential of mechanisms regulating energy homeostasis in plant cells to inspire the definition of new models of flexible and resilient electrical power grids, particularly microgrids, is delineated. The main contribution of this review is surveying energy regulatory mechanisms in detail as a reference and helping readers to find useful information for their work in this research field.

AWS IoT analytics platform for microgrid operation management

Microgrid (MG) represents a promising opportunity for integrating renewable energy systems with the electric power grid. However, numerous complexities need to be addressed in the process. The electrical grid is complex, vulnerable, and centralized. Thus, the integration is challenging owing to the stochastic nature of renewable energy generation, which affects the possibility of reliable forecasting. The wastage due to poor estimation of clean energy generation discourages new investments in this area. However, recent advancements in big data technologies enable processing a large amount of data captured from multiple sources in real-time. It opens the possibility of improving the operational optimization of MGs and the performance of forecasting models. The overall MG problem is formulated using a two-stage stochastic mixed-integer linear programming problem with recourse. Amazon Web Services (AWS) IoT analytics platform inputs data in real-time and runs a sophisticated wind generation forecast analysis. The stochastic model is solved using the Sample Average Approximation (SAA) algorithm. The innovative methodology leads to significant improvements in the total average operating cost by integrating AWS IoT Analytics compared to traditional methods that use historical data. Computations are performed for different power-grid settings, including a power-outage, and different power generators units capacities with total operation average cost savings of 7.6% and 5.9%, respectively. Sensitivity analysis showed that the SSA algorithm could solve all the instances by providing high-quality solutions. The AWS IoT strategy outperformed at 7.7 % and 3.6% for optimality gap and CPU time, respectively. We examined an actual case in Peru for an agricultural application to assess the performance of a stochastic optimization model with a real-time IoT wind generation forecast strategy. The results revealed the following capabilities of our novel framework: (1) it can realize higher cost savings from the MG operating systems; (2) improve real-time renewable energy forecasting; (3) facilitate robust decision-making under conditions of uncertainty.

Impact of Solar Inverter Dynamics during Grid Restoration Period on Protection Schemes Based on Negative-Sequence Components

The growing penetration of renewable resources such as wind and solar into the electric power grid through power electronic inverters is challenging grid protection. Due to the advanced inverter control algorithms, the inverter-based resources present fault responses different from conventional generators, which can fundamentally affect the way that the power grid is protected. This paper studied solar inverter dynamics focused on negative-sequence quantities during the restoration period following a grid disturbance by using a real-time digital simulator. It was found that solar inverters can act as negative-sequence sources to inject negative-sequence currents into the grid during the restoration period. The negative-sequence current can be affected by different operating conditions such as the number of inverters in service, grid strength, and grid fault types. Such negative-sequence responses can adversely impact the performance of protection schemes based on negative-sequence components and potentially cause relay maloperations during the grid restoration period, thus making system protection less secure and reliable.

Coevolution and Evaluation of Electric Vehicles and Power Grids Based on Complex Networks

The connection of electric vehicles to the power grid has a significant impact on the structure and stability of the power grid. To evaluate the performance index of a power grid, this paper introduces a collaborative evolution model of a power grid containing electric vehicles based on a complex network. The location of the electric vehicle network node is obtained by combining the evolution mode of the complex network and the restrictive conditions of the electric vehicle access network. Due to the dual attributes of electric vehicles, such as load and battery, electric vehicle access points will be used as special nodes in the network to calculate their effect on the power grid. In this paper, the Monte Carlo method is used to evaluate the effect of electric vehicle nodes on the whole network in the case of each probability failure in the power grid. The proposed method is numerically illustrated in the test case of the rbt-bus-f4 feeder system.

Autonomous vehicle-to-grid design for provision of frequency control ancillary service and distribution voltage regulation

We develop a system-level design for the provision of Ancillary Service (AS) for control of electric power grids by in-vehicle batteries, suitably applied to Electric Vehicles (EVs) operated in a sharing service. An architecture for cooperation between transportation and energy management systems is introduced that enables us to design an autonomous Vehicle-to-Grid (V2G) for the provision of multi-objective AS: primary frequency control in a transmission grid and voltage amplitude regulation in a distribution grid connected to EVs. The design is based on the ordinary differential equation model of distribution voltage, which has been recently introduced as a new physics-based model, and is utilized in this paper for assessing and regulating the impact of spatiotemporal charging/charging of a large population of EVs to a distribution grid. Effectiveness of the autonomous V2G design is evaluated with numerical simulations of realistic models for transmission and distribution grids with synthetic operation data on EVs in a sharing service. In addition, we present a hardware-in-the-loop test for evaluating its feasibility in a situation where inevitable latency is involved due to power, control, and communication equipments.

Head-Cover vibration investigation of a Prototype Reversible Pump-Turbine Unit during Start-up in Pump mode. Part I: Fluid dynamic analysis

Reversible pump-turbine units of pumped storage power stations can generate power in turbine mode and store electric energy in pump mode for load balancing of the electric power grid. During the start-up transient process in pump mode of a reversible pump-turbine, the flow pattern and the pressure distribution in the flow channel change dramatically with the increasing opening angle of guide vanes. The unsteady pressure of the flow passing channel can cause severe vibration on the head-cover, bottom ring, and other stationary structures around the impeller. In this paper, a coupled 1D/3D co-simulation approach is developed to investigate the pressure distribution characteristics of the flow field during the start-up transient process in pump mode for a large prototype reversible pump-turbine unit. The entire model of flow channel from spiral casing to draft tube is constructed and the flow characteristics of key fluid fields such as spiral casing, stay vanes, guide vanes, impeller, and draft tube are analyzed. The simulation results show that under the condition of minimum guide vane opening the flow is insufficient and vortices occur on the impeller blades. The pressure distribution results of the flow channel from 3D fluid dynamic analysis during the start-up transient process in pump mode are exported as boundary conditions for the subsequent fluid-structure coupling analysis of the head-cover vibration investigation.

Large Geomagnetically Induced Currents at Equator Caused by an Interplanetary Magnetic Cloud

AbstractHere, we report a rare observation of an extremely large and rapid change of geomagnetic field (dB/dt), a proxy for the geomagnetically induced currents (GICs), at the equator caused by a sudden drop in solar wind density at the front boundary of a magnetic cloud (MC) during the great 31 March 2001 storm. The horizontal component at the Indian equatorial station, Tirunelveli, recorded a sharp decline of ∼350 nT in just 5 min with a peak dB/dt exhibiting a concerning value of 136 nT/min, a possible GIC risk to the electric power systems. The responsible physical mechanisms were examined through magneto‐hydrodynamic model simulations and found that a prompt penetration of strong westward‐overshielding electric fields and ionospheric currents at the equator play a dominant role. This study provides some new insights into the extent of extreme geomagnetic field changes that can occur at the equatorial region due to solar wind density reduction at MC, which can have potential impacts on the electric power grid systems.

Physics-Driven Page Fault Handling for Customized Deception against CPS Malware

Malware crafted to attack cyber-physical systems such as the electrical power grid have a physics-centric nucleus. Cyber-physical systems malware understand physics and hence use their knowledge to guide how they initiate physical damage on a compromised industrial computer. We develop a physics-driven page fault handler in the seL4 microkernel, which, in addition to reducing the page fault rate, differentiates active physics in main memory from passive physics in the backing store. We aid the identification of active physics via a CPU scheduler that tracks the evolution of active physics over time. We exploit the concept of active physics to develop deception that is customized to attack the physics-centric nucleus of malware. We evaluated this research against a variety of malware samples and techniques, including both numerous samples from publicly available repositories and custom-made academic code, and present our findings in the article. The physics data of reference pertain to an electrical substation, with a higher focus on a power transformer and related industrial computer algorithms.

Battery storage systems in electric power grid: A review

Renewable energy within the utility grid has increased during the previous decade. Severe issues have been raised about the dependability, effectiveness, and efficiency of energy infrastructure. Therefore, energy storage devices have considerably enhanced the network’s accuracy and reliability. Battery energy storage systems (BESS) are among the greatest widely used storage solutions because they have several advantages over traditional power sources, including fast and accurate response, flexibility, and predictability. They are regarded as a reasonable alternative to the threat of climate change. This paper provides a comprehensive evaluation of the BESS’s optimum size targets, limitations, methodology, benefits and disadvantages. Furthermore, energy storage technologies and improved application targets have been presented and discussed for greater clarity. This study focuses primarily on BESS deployments, methodologies, and environmental impact. BEES innovations and achievements for electrical networks are also compared to other energy storage technologies. Concerns and obstacles are discussed to offer researchers in BESS a comprehensive picture. Finally, this study provides several essential ideas that economists and practitioners can use to design a fruitful, robust, efficient, and resilient BESS for a brighter and more optimal deployment.

Powering Through Wildfires: An Integrated Solution for Enhanced Safety and Resilience in Power Grids

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

Dynamic stability of electric power grids: Tracking the interplay of the network structure, transmission losses, and voltage dynamics.

Dynamic stability is imperative for the operation of the electric power system. This article provides analytical results and effective stability criteria focusing on the interplay of network structures and the local dynamics of synchronous machines. The results are based on an extensive linear stability analysis of the third-order model for synchronous machines, comprising the classical power-swing equations and the voltage dynamics. The article addresses the impact of Ohmic losses, which are important in distribution and microgrids but often neglected in analytical studies. We compute the shift of the stability boundaries to leading order, and thus provide a detailed qualitative picture of the impact of Ohmic losses. A subsequent numerical study of the criteria is presented, without and with resistive terms, to test how tight the derived analytical results are.