Electric Power Grid Research Articles

Interline hybrid continuous converter impedance control-based tuning filter: effective harmonic suppression in DG-integrated electrical power grid

Interline hybrid continuous converter impedance control-based tuning filter: effective harmonic suppression in DG-integrated electrical power grid

Assessment of Insulation Coordination and Overvoltage for Utility Girds Integrated with Solar Farms

Due to the economic and environmental concerns associated with fossil fuels, many government and private organizations are progressively shifting towards the integration of solar farms with Utility Grids. However, these systems are facing insulation failure issues due to internal and external transient overvoltage’s, in which their shape, magnitude, and duration are unpredictable, and consequently, the insulation stress also becomes unpredictable. To ensure the safety and integrity of the system against any transient overvoltage event, it is important to carry out an insulation coordination analysis. The primary goal of this research work is to achieve this optimization in an economically viable manner, ensuring both operational stability and cost-effectiveness in the design of electrical equipment like surge Arresters. The research work presented in the literature does not fully evaluate all International Electrotechnical Commission (IEC) overvoltage classes as specified in the insulation coordination standards for Utility Grids integrated with solar farms. Therefore, this research paper investigates the impact of various transient and switching overvoltage conditions, as defined in the IEC 60071.4 Insulation Coordination Standard at the Solar and Utility Grid Electrical power system using PSCAD 4.6/EMTP Software. Five distinct simulation scenarios were developed to assess the systems’ resilience against insulation stress events. The proposed system was also examined with and without the application of a lightning surge arrester.

Multivariate sensitivity-adaptive polynomial chaos expansion for high-dimensional surrogate modeling and uncertainty quantification

This work develops a novel basis-adaptive method for constructing anisotropic polynomial chaos expansions of multidimensional (vector-valued, multi-output) model responses. The adaptive basis selection is based on multivariate sensitivity analysis metrics that can be estimated by post-processing the polynomial chaos expansion and results in a common anisotropic polynomial basis for the vector-valued response. This allows the application of the method to problems with up to moderately high-dimensional model inputs (in the order of tens) and up to very high-dimensional model responses (in the order of thousands). The method is applied to different engineering test cases for surrogate modeling and uncertainty quantification, including use cases related to electric machine and power grid modeling and simulation, and is found to produce highly accurate results with comparatively low data and computational demand.

Artificial intelligence computational techniques of flywheel energy storage systems integrated with green energy: A comprehensive review

In recent years, the operation of the electric power grid has become more efficient and resilient due to the integration of renewable energy sources (RESs). Solar and wind energy are being incorporated aggressively into the main grid, while other RESs like biomass and geothermal energy are also on the rise. However, the intermittent nature of these RESs necessitates the use of energy storage devices (ESDs) as a backup for electricity generation such as batteries, supercapacitors, and flywheel energy storage systems (FESS). This paper provides a thorough review of the standardization, market applications, and grid integration of FESS. It examines the components of FESS, including the electric motor/generator set, power converters, bearings, and control techniques. The paper also highlights the application of modern artificial intelligence (AI) methodologies in optimizing FESS operations, referencing over 240 recent publications in reputable journals. Metaheuristic optimizers, machine learning techniques, and well-matures software's are the main AI aspects discussed in this paper. Additionally, it explores the use of FESS in commercial sectors such as marine, space, and transportation, and its integration with RESs for participating in green energy. Finally, the paper emphasizes the role of AI in enhancing the synergy between FESS and RESs to contribute to a more sustainable and secure energy future.

The added value of combining solar irradiance data and forecasts: A probabilistic benchmarking exercise

Despite the growing awareness in academia and industry of the importance of solar probabilistic forecasting for further enhancing the integration of variable photovoltaic power generation into electrical power grids, there is still no benchmark study comparing a wide range of solar probabilistic methods across various local climates. Having identified this research gap, experts involved in the activities of IEA PVPS T1611International Energy Agency - Photovoltaic Power Systems - Solar Resource for High Penetration and Large Scale Applications. agreed to establish a benchmarking exercise to evaluate the quality of intra-hour and intra-day probabilistic irradiance forecasts.The tested forecasting methodologies are based on different input data including ground measurements, satellite-based forecasts and Numerical Weather Predictions (NWP), and different statistical methods are employed to generate probabilistic forecasts from these. The exercise highlights different forecast quality depending on the method used, and more importantly, on the input data fed into the models.In particular, the benchmarking procedure reveals that the association of a point forecast that blends ground, satellite and NWP data with a statistical technique generates high-quality probabilistic forecasts. Therefore, in a subsequent step, an additional investigation was conducted to assess the added value of such a blended point forecast on forecast quality. Three new statistical methods were implemented using the blended point forecast as input.To ensure a fair evaluation of the different methods, we calculate a skill score that measures the performance of the proposed model relative to that of a trivial baseline model. The closer the skill score is to 100%, the more efficient the method is. Overall, skill scores of methods that use the blended point forecast ranges from 42% to 46% for the intra-hour scenario and 27% to 32% for the intra-day scenario. Conversely, methods that do not use the blended point forecast exhibit skill scores ranging from 33% to 43% for intra-hour forecasts and 8% to 16% for intra-day forecasts.These results suggest that using (a) blended point forecasts that optimally combine different sources of input data and (b) a post-processing with a statistical method to produce the quantile forecasts is an effective and consistent way to generate high-quality intra-hour or intra-day probabilistic forecasts.

Vehicle-to-grid as a competitive alternative to energy storage in a renewable-dominant power system: An integrated approach considering both electric vehicle drivers' willingness and effectiveness

Vehicle-to-grid (V2G) technology, which enables bidirectional power flow between electric vehicles (EVs) and power grids, is a possible solution for integrating EVs and renewable energy (RE) into the power system. While EV drivers are indispensable components for the V2G applications, the extant power system studies have underexamined the willingness of EV drivers to participate in V2G. In addition, few studies investigate whether the small-scale and time-varying V2G supply can replace utility-level energy storage. Given this background, this study combined discrete choice experiments with energy storage capacity expansion planning (ES-CEP) to simulate time-varying V2G participation and effectiveness. The choice experiments identified drivers’ preferences for V2G, which was used to simulate V2G participation under different compensation schemes. Time-varying V2G participation was integrated into ES-CEP for renewable-dominant power system to assess the potential of V2G to replace ES at the utility scale. The results showed that most drivers were unlikely to engage in V2G, although this reluctance decreased during the night. Higher guaranteed state-of-charge, accessible V2G facilities, and monetary enrollment compensation encouraged the participation. The ES-CEP results showed that, even with a limited V2G (4.5–15.3 % of EV drivers), V2G significantly reduced the optimal ES capacity (36.5–45.6 % for power and 39.6–60.6 % for energy), shaved peak netload by 18.5 %, and saved the total cost by 3.35 %, even after accounting for V2G enrollment compensation and battery degradation. EVs that charged during the daytime and participated in V2G at night were the most utilized, resulting in 0.92 % of annual battery aging, but generated a profit of 636,749 KRW (553 USD). The findings of this study underscore the importance of a suitable compensation scheme for V2G and supplementary measures for incentivizing EV charging during off-peak hours, as these elements are essential for the successful implementation of V2G.

Multi-objective optimal coordination of electric vehicle charging, power grid, energy storages and renewables

Considering that the grid connection of variable renewable energies (VREs) and the disorderly charging loads of large-scale electric vehicles (EVs) will adversely affect the power grid stability, the optimization strategy of EV charging and grid-connected scheduling are investigated, in which energy storage system is added to balance the demand and supply of the power grid. First of all, considering the profit of EV charging station, the charging cost of EV users and power loss, a multi-objective optimal scheduling model of EV charging, power grid, pumped hydro-storage (PHS) and wind farm is constructed, which is improved from the aspect of wind farm wake. Then, the multi-objective optimization algorithm based on genetic algorithm is used to solve the model to realize the optimal charging of EVs. Finally, the IEEE-13 power grid is used to verify the effectiveness of the proposed multi-objective optimal scheduling model. Combined with a specific example for simulation analysis, the results show that the model can achieve orderly charging of EVs, ensuring the safety of the power grid and promote the use of VREs. In addition, the optimization algorithm can further improve the profit, and reduce the charging cost and power loss.

Intelligent Integration of Renewable Energy Resources Review: Generation and Grid Level Opportunities and Challenges

This paper reviews renewable energy integration with the electrical power grid through the use of advanced solutions at the device and system level, using smart operation with better utilisation of design margins and power flow optimisation with machine learning. This paper first highlights the significance of credible temperature measurements for devices with advanced power flow management, particularly the use of advanced fibre optic sensing technology. The potential to expand renewable energy generation capacity, particularly of existing wind farms, by exploiting thermal design margins is then explored. Dynamic and adaptive optimal power flow models are subsequently reviewed for optimisation of resource utilisation and minimisation of operational risks. This paper suggests that system-level automation of these processes could improve power capacity exploitation and network stability economically and environmentally. Further research is needed to achieve these goals.

Plug-in charging or electric roads? Powering U.S. long-haul heavy-duty trucks in 2050

Abstract Pervasive plug-in fast chargers and/or electrified roadways (eRoads) might address the limited range, long recharging times, and reliance on greenhouse gas (GHG)-intensive, costly, and heavy batteries associated with electrifying long-haul heavy-duty trucks (HDTs). While these large-scale interventions shift environmental and cost burdens onto infrastructure, there is a lack of studies investigating how eRoads affect system-level GHG emissions, costs, material use, and peak electric grid power demands. We compare these aspects for the case of electrifying U.S long-haul HDTs (Class 8) in 2050 powered by combinations of plug-in fast chargers and eRoads. Our model accounts for battery downsizing, energy consumption, and truck operation patterns in quantifying life cycle impacts of batteries, plug-in chargers, eRoads, and hourly truck electricity demand. We find that plug-in fast chargers and eRoads reduce annualized 2050 HDT life cycle GHG emissions by 8% to 14% compared to using long-range batteries, which in turn have at least 50% lower emissions than diesel trucks. Conductive rails, overhead lines, and wireless eRoads (amortized across light- and heavy-duty vehicles) have lower system-wide costs than long-range batteries, plug-in fast chargers, or diesel trucks. Cost savings from smaller batteries, lower energy use and avoided recharging time offset high eRoads capital costs. While eRoads can reduce both system-level GHG and costs compared to diesel trucks, these reductions are sensitive to eRoads capital costs and losses from wireless power transfer and air resistance. eRoads require less lithium (87%) and copper (67%) than long-range batteries but increase regional peak power demands by up to 32%. Efficient wireless power transfer and aerodynamic pantographs enhance eRoads’ GHG and cost advantages, which may diminish if future batteries are more energy-dense, cheaper, or less GHG intensive. If successfully deployed, eRoads present opportunities for tighter integration between the transportation and electricity infrastructure systems, enabling optimized charging strategies to lower GHG emissions and costs.

Stabilizing Large-Scale Electric Power Grids with Adaptive Inertia

The stability of ac power grids relies on ancillary services that mitigate frequency fluctuations. The electromechanical inertia of large synchronous generators is currently the only resource to absorb frequency disturbances on subsecond time scales. Replacing standard thermal power plants with inertialess new renewable sources of energy (NREs) therefore jeopardizes grid stability against, e.g., sudden power-generation losses. To guarantee system stability and compensate the lack of electromechanical inertia in grids with large penetrations of NREs, virtual synchronous generators, which emulate conventional generators, have been proposed. Here, we propose a novel control scheme for virtual synchronous generators, where the provided inertia is large at short times—thereby absorbing faults as efficiently as conventional generators—but decreases over a tunable time scale to prevent coherent frequency oscillations from setting in. We evaluate the performance of this adaptive-inertia scheme under sudden power losses in large-scale transmission grids. We find that it systematically outperforms conventional electromechanical inertia and that it is more stable than previously suggested schemes. Numerical simulations show how a quasioptimal geographical distribution of adaptive-inertia devices not only absorbs local faults efficiently but also significantly increases the damping of interarea oscillations. Our results show that the proposed adaptive-inertia control scheme is an excellent solution to strengthen grid stability in future low-inertia power grids with large penetrations of NREs. Published by the American Physical Society 2024

Focus on the disruption of networks and system dynamics.

Networks are designed to ensure proper functioning and sustained operability of the underlying systems. However, disruptions are generally unavoidable. Internal interactions and external environmental effects can lead to the removal of nodes or edges, resulting in unexpected collective behavior. For instance, a single failing node or removed edge may trigger a cascading failure in an electric power grid. This Focus Issue delves into recent advances in understanding the impacts of disruptions on networks and their system dynamics. The central theme is the disruption of networks and their dynamics from the perspectives of both data-driven analysis as well as modeling. Topics covered include disruptions in the dynamics of empirical systems such as nuclear reaction networks, infrastructure networks, social networks, epidemics, brain dynamics, and physiology. Emphasis is placed on various phenomena in collective behavior, including critical phase transitions, irregular collective dynamics, complex patterns of synchrony and asynchrony, chimera states, and anomalous oscillations. The tools used for these studies include control theory, diffusion processes, stochastic processes, and network theory. This collection offers an exciting addition to the evolving landscape of network disruption research.

Lagrange Theory-Based Optimization Strategy to Minimizing Power Loss of an Electric Power Grid: Theoretical and Experimental Study

Lagrange Theory-Based Optimization Strategy to Minimizing Power Loss of an Electric Power Grid: Theoretical and Experimental Study

Optimal allocation of distributed energy resources to cater the stochastic E-vehicle loading and natural disruption in low voltage distribution grid

The everyday extreme uncertainties become the new normal for our world. Critical infrastructures like electrical power grid and transportation systems are in dire need of adaptability to dynamic changes. Moreover, stringent policies and strategies towards zero carbon emission require the heavy influx of renewable energy sources (RES) and adoption of electric transportation systems. In addition, the world has seen an increased frequency of extreme natural disasters. These events adversely impact the electrical grid, specifically the less hardened distribution grid. Hence, a resilient electrical network is the demand of the future to fulfill critical loads and charging of emergency electrical vehicles (EV). Therefore, this paper proposes a two-dimensional methodology in planning and operational phase for a resilient electric distribution grid. Initially stochastic modelling of EV load has been performed duly considering the geographical feature and commute pattern to form probability distribution functions. Thenceforth, the impact assessment of extreme natural events like earthquakes using damage state classification has been done to model the impact on distribution grid. The efficacy of the proposed methodology has been tested by simulating an urban Indian distribution grid with mapped EV on DigSILENT PowerFactory integrated with supervised learning tools on Python. Subsequently 24-h load profile before event and after event have been compared to analyze the impact.

Optimal active and reactive energy management for a smart microgrid system under the Moroccan grid pricing code

This article presents an innovative active and reactive energy management system (AR-EMS) specifically designed for residential buildings in Morocco, seamlessly integrated with a Smart Microgrid (SMG) and the Electrical Power Grid (EPG) supplier. The considered SMG incorporates a Photovoltaic system (PV) and an Energy Storage System (ESS). In Morocco, the EPG utilizes a Range Tariff System (RTS), where the tariff structure depends on the total energy consumed at the end of each month. Hence, the proposed AR-EMS is designed to computes optimal active and reactive power setpoints of each source at 15-minute intervals for the next 30 days, based on forecasts of energy consumption and PV power production. The AR-EMS consists of two distinct layers: the Long-Term Optimization Layer (LTOL) and the Short-Term Optimization Layer (STOL). The LTOL focuses on optimizing active and reactive power flow over an extended time horizon of 30 days, relying on forecasted data. In contrast, the STOL involves short-term forecasting and energy optimization over the next 24-hour horizon, addressing the LTOL forecasting uncertainties. The proposed strategy emphasizes a multi-objective problem aiming at minimizing both active and reactive energy bill, ESS degradation cost, Peak-to-Average Ratio (PAR) and carbon emissions. The considered problem is solved using a metaheuristic algorithm, specifically the Lexicographic approach within the Particle Swarm Optimization (PSO) method. To validate the effectiveness of the proposed approach, two comparative analyses are conducted. The first involves the impact of time horizons on the energy cost and the management performances within the Moroccan context. The second comparison, highlights the benefits and shortfalls of using simultaneous active and reactive power management, comparing it with a system exclusively managing active power (A-EMS). Both compared strategies are evaluated using the Moroccan tariff RTS and real world data. The comparative analysis highlights the advantages of the proposed AR-EMS, demonstrating benefits in terms of active and reactive energy bill reduction by 58.9% and 83.2%, respectively, and showcases a 49.3% reduction in carbon emissions while improving the EPG power factor by 6% compared to A-EMS, In conclusion, the optimization results are encouraging the Moroccan government to further develop the installation of SMGs.

Copper-Graphene Composite (CGC) Conductors: Synthesis, Microstructure, and Electrical Performance.

Improving the electrical performance of copper, the most widely used electrical conductor in the world is of vital importance to the progress of key technologies, including electric vehicles, portable devices, renewable energy, and power grids. Copper-graphene composite (CGC) stands out as the most promising candidate for high-performance electrical conductor applications. This can be attributed to the superior properties of graphene fillers embedded in CGC, including excellent electrical and thermal conductivity, corrosion resistance, and high mechanical strength. This review highlights the recent progress of CGC conductors, including their fabrication processes, electrical performances, mechanisms of copper-graphene interplay, and potential applications.

Power Generation Systems Assessment: A New Approach

Power generation systems have become useful for grid base and off grid electric power generation. Hence, its performance has become critical for sustainable growth and development. Performance evaluation of power generation systems has always been carried out using the independent assessment approach (IAA) whose models are: the reliability, availability, emission characteristics, energy and exergy efficiency. The IAA approach only assesses the system performance in part without recur to other indexes, hence, limiting a holistic view of the true plant state. In this paper, the development of a new performance index using the combined assessment approach (CAA) is explored. This approach (CAA) seeks to combine two relatable traditional measures for the assessment of power generation system. The model combines exergy efficiency and reliability measures for analysis. First Independent Power Limited (FIPL) gas turbine power plant was used to test the model. The plant reliability and availability were evaluated along with its thermal efficiency using the exergy model. The analysis of plant thermal efficiency was carried out using the steady state model. Results of the traditional indexes of the plant were compared with the proposed (Bassy-II index) index. It was seen that the new index provided a new assessment criteria. The exergy efficiency, reliability and availability measures indicated a fairly rated plant state. However, the new index defined a new plant state which is unique and represents the true status of the system in whole. Hence, the proposed index (Bassy-II index) is recommended for use in the holistic assessment of power generation systems.

Using a Differential Magnetometer Technique to Measure Geomagnetically Induced Currents: An Augmented Approach

AbstractGeoelectric fields produced by time‐varying magnetic fields during geomagnetic storms can result in potentially damaging geomagnetically induced currents (GICs) in long conductors at the Earth's surface. GICs can pose a significant risk to the integrity of grounded electrical infrastructure, particularly high‐voltage transformers. In this study, an inferred GIC is calculated using an augmented differential magnetometer measurement (DMM) technique on a 500 kV transmission line in central Alberta and is validated using a proximal transformer neutral‐to‐ground (TNG) current measurement by AltaLink L.P. using a Hall probe at a transformer substation. This research outlines a custom‐built and innovative DMM design by which both DMM sensors deployed around a power line measure the background geomagnetic disturbance (GMD) field and the magnetic field generated locally by the GIC. We show how this modified approach provides two independent estimates for GIC derived using only ΔBy or ΔBz, the magnetic field components perpendicular to the line carrying GIC. Results for a geomagnetic storm on 12 Oct 2021 show contemporaneous peaks in the TNG current and the DMM‐inferred GIC. The two data sets have similar waveforms and are within the same order of magnitude. The background GMD is reconstructed using DMM and shows excellent correlation to the measured GMD at the permanent Canadian Array for Real‐time Investigations of Magnetic Activity magnetic station at Ministik Lake, approximately 48.5 km away. Based on the results presented here, we verify the added utility value of DMM for temporary deployments for assessing GIC risk in electrical power grids.

Erratum: “Dynamic stability of electric power grids: Tracking the interplay of the network structure, transmission losses, and voltage dynamics” [Chaos 32, 053117 (2022)

Erratum: “Dynamic stability of electric power grids: Tracking the interplay of the network structure, transmission losses, and voltage dynamics” [Chaos 32, 053117 (2022)

MATHEMATICAL MODEL AND ALGORITHM FOR THE DETERMINATION OF THE ORIGIN OF ELECTRICITY FROM RENEWABLE ENERGY SOURCES IN THE ELECTRIC POWER SYSTEM

The article deals with the development of a method and algorithm for calculation of individual compo-nents of electricity flows in the branches of an electric power grid circuit caused by generation and con-sumption in its nodes. In particular, it is about estimating the share of renewable energy sources (RES) in the electricity consumption of a given consumer. The method is based on methods and algorithms for the calculation of steady-state modes of power grids, integrating a mathematical model for determining the components of current flows in the branches of the power system circuit. The model employs distribution coefficients of currents in the circuit branches from RES nodes and node voltages to form a matrix of cur-rent distribution coefficients for RES across the power grid branches. Since RES in electric power systems (EPS) use general power grids to transmit the electricity they generate, accurate determination of their power share within overall flows enables the impact of RES on the EPS mode parameters to be considered. The peculiarity of the algorithm for estimating the share of RES in the electricity consumption of a given consumer is its consideration of physical processes in the power system. The input data for the calculation are the power system parameters, the node loads (specified by capacity or schedules), as well as arrays of RES generation and centralized power supply nodes and consumers with a controlled value of the Guar-anteed Origin of electricity from RES. The voltages during the formation of the matrix of power distribu-tion coefficients at nodes along the branches of the power grid are determined by the calculation results of the power grid steady-state modes or by experimental measurement data. The method's efficacy and the accuracy of estimating the RES share in the power consumption of a given consumer are verified and demonstrated through a practical example. Determination of the share of the consumer's electricity con-sumption, which origin from renewable sources can be guaranteed, creates an enabling environment that would foster producers and consumers to increase the use of renewable energy sources and promotes the development of the green energy market. Ref. 18. Fig.3.

Energy Justice and Equity: Applying a Critical Perspective to the Electrical Power Grid for a More Just Transition in the United States

Energy Justice and Equity: Applying a Critical Perspective to the Electrical Power Grid for a More Just Transition in the United States