Stability Of Electric Grid Research Articles

Deserts of Wind: Aeolian‐Pastoralism and the Limits of Climate Finance in Jordan

AbstractAround the world, a growing cadre of investors are banking on profitability of climate change through the interface of national energy transition programmes. In Jordan, national energy transition ushered in over US$1 billion in investment capital to construct 621 MW of new wind power along the arid hilltops of Tafila and Ma'an. This article situates the contested dynamics of Jordan's wind farm development within the shifting landscape of global climate finance and land futures. The transformation of southwestern Jordan's hillsides illustrates key tensions at work within the emergence of a new, renewable asset class: utility‐scale commercial wind farms, as they materialise through the unfolding project of extracting value from increasingly marginal landscapes. I argue that the transformation of Jordan's deserts reveals a key limit to the growth of climate finance, as anticipated returns on bankable projects can only be realised through contingent negotiations that entangle social, political, and electrical grid stability. At the margins of the new wind farms, local herders were able to negotiate partial usufruct rights to maintain access to grazing lands around the turbines. This arrangement reveals the subtle paradox of toiling in the margins: while hybrid land arrangements may sustain rural livelihoods for some groups, this partial amelioration permits the expansion of financial architectures that hollow out the foundations of rural‐urban life. Beyond Jordan, this article advances insights towards grounding scholarship on energy transitions, climate finance, and agrarian change through contradictory processes of de/stabilisation and ongoing negotiations over land and livelihoods at the climate finance frontier.

Challenges for Implementing Vehicle-to-Grid Services in Parking Lots: A State of the Art

Electric vehicles (EVs) play a vital role in the transition to renewable energy and decarbonisation, and there is increasing global interest in expanding their use. However, the growing number of EVs poses challenges to the electricity grid due to increasing energy demand. Vehicle-to-grid (V2G) technology can address these issues by allowing for EVs to charge and discharge energy, thus helping to balance the grid when needed. Aggregating vehicles in designated parking areas optimises energy transfer, making it crucial to identify suitable parking locations and forecast the energy available from parked vehicles. A spatial–temporal framework ensures that V2G services operate efficiently considering both the location and the timing of vehicle parking. This paper reviews studies on temporal–spatial V2G parking demand, identifying high-demand areas such as shopping centres and workplaces, where vehicles park for extended periods. Strategic locations of V2G hubs in these areas ensures seamless integration into existing mobility patterns without disrupting users’ routines. In addition, this review examines user acceptance, technical feasibility, and V2G’s role in reducing grid demand peaks. The findings indicate the potential of effectively implemented V2G services to enhance electricity grid stability and efficiency while minimising disruptions to EV users.

Efficient distribution network based on photovoltaic fed electric vehicle charging station using WSO-RBFNN approach

An integral and prevalent aspect of modern life is the electric vehicle (EV).EV charging networks struggle with power losses and high energy costs, particularly as demand rises leading to inefficiencies and potential system overloads. A hybrid WSO-RBFNN approach is proposed for the distribution network's photovoltaic (PV) fed electric vehicle charging stations. The performance of the proposed hybrid strategy is a combination of war strategy optimization (WSO) and Radial basis function neural network (RBFNN). It is hence referred to as the WSO-RBFNN technique. WSO optimizes the distribution network by minimizing power loss, improving voltage sensitivity, and reducing costs. Meanwhile, the RBFNN predicts the load demand. This innovative technique WSO-RBFNN identifies the nearest charging spots that minimize power loss and RBFNN optimizes power flow predicts charging demands and addresses both environmental and electrical grid stability concerns. The proposed technique is implemented in MATLAB. MATLAB is powerful computational software widely used in different fields like numerical computation, visualization, and algorithm development. MATLAB provides powerful tools for data visualization and plotting. The results are compared to various existing Heap-based optimizer (HBO), Wild horse optimizer (WHO), and Salp Swarm Algorithm (SSA) techniques. The proposed approach contributes only 0.59 % of a power loss it is less and the cost of energy is 0.18$ which is lesser and the voltage deviation is 6.6 pu which is less than the existing techniques.

A Q‐Learning and Fuzzy Logic Control of Hybrid Energy Storage System Using Two Stage Low‐Pass Filter to Smooth Power Fluctuations in Microgrid

ABSTRACTThe intermittent and fluctuating nature of wind turbine output power is increasingly being recognized as a significant issue adversely impacting the power quality and stability of electrical grids. With the increasing integration of wind power, this challenge cannot be underestimated. However, a potential solution for mitigating the adverse effects of such fluctuations lies in the Hybrid Energy Storage System (HESS), which encompasses battery energy storage systems (BESS) and supercapacitors (SC). The HESS is equipped with flexible operational modes for charging and discharging, thus enabling grid‐connected microgrids to possess the ability to counteract these oscillations. In this article, a control strategy based on the combination of Q‐learning and fuzzy logic control approaches is presented for tuning the parameters of a utilized two‐stage variable time constant low‐pass filter (LPF) in a grid‐connected microgrid. The proposed strategy adaptively tunes the time constants of LPFs to mitigate wind power fluctuations. Furthermore, practical constraints for the energy storage systems and their interfaced converters, such as preventing overcharge/discharge, ramp rate requirements, and certain maximum power conversion ranges, have been taken into account. Numerical simulation results verify the effectiveness of the proposed two‐stage variable time constant LPF for output wind power fluctuation reduction considering practical constraints of HESS.

Impact of Impedances and Solar Inverter Grid Controls in Electric Distribution Line with Grid Voltage and Frequency Instability

The penetration of solar energy into centralized electric grids has increased significantly during the last decade. Although the electricity from photovoltaics (PVs) can deliver clean and cost-effective energy, the intermittent nature of the sunlight can lead to challenges with electric grid stability. Smart inverter-based resources (IBRs) can be used to mitigate the impact of such high penetration of renewable energy, as well as to support grid reliability by improving the voltage and frequency stability with embedded control functions such as Volt-VAR, Volt–Watt, and Frequency–Watt. In this work, the results of an extensive experimental study of possible interactions between the unstable grid and two residential-scale inverters from different brands under different active and reactive power controls are presented. Two impedance circuits were installed between Power Hardware-in-the-loop (P-HIL) equipment to represent the impedance in an electric distribution line. Grid voltage and frequency were varied between extreme values outside of the normal range to test the response of the two inverters operating under different controls. The key findings highlighted that different inverters that have met the same requirements of IEEE 1547-2018 responded to grid instabilities differently. Therefore, commissioning tests to ensure inverter performance are crucial. In addition to the grid control, the residential PV installed capacity and physical distances between PV homes and the substation, which impacted the distribution wiring impedance which we characterized by the ratio of the reactive to real impedance (X/R), should be considered when assigning the grid-supporting control setpoints to smart inverters. A higher X/R of 3.5 allowed for more effective control to alleviate both voltage and frequency stability. The elimination of deadband in an aggressive Volt-VAR control also enhanced the ability to control voltage during extreme fluctuation. The analysis of sudden spikes in the grid responses to a large frequency drop showed that a shallow slope of 1.5 kW/Hz in the droop control resulted in a >65% lower sudden reactive power overshoot amplitude than a steeper slope of 2.8 kW/Hz.

Ni-In-oxalate nanostructure as electrode materials for high-performance supercapacitors

Energy storage technologies play a crucial role in addressing the intermittent characteristics of renewable energy sources, improving the stability of electrical grids, and decreasing the release of greenhouse gas emissions. In this study, we presented nickel indium oxalate (Ni1-xInxC2O4) as a promising material with potential applications in the field of electrochemical energy storage. The as-prepared Ni-In- oxalate sample was subjected to different physical characterizations, including X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). Regarding the electrochemical energy storage capability, the Ni1-xInxC2O4 electrode material exhibited a specific capacitance of 835 F g−1 (417.5 C g−1) at 1 A g−1. The incorporation of nickel (Ni) into the indium (In) oxalate nanoplates enhances their electrochemical performance. The presence of Ni in the nanoplates generated from substrate, improving the overall conductivity of the material and enhances its electrochemical reactions, thus leading to improved energy storage capabilities.

How Fisheries Biologists Can Facilitate the Clean Energy Transition

Fisheries and aquatic biologists play a critical role in creating environmentally protective hydropower flow requirements that govern flow timing, frequency, magnitude, and rate of change. Hydropower's role in the U.S. electrical grid is expected to evolve in response to increased wind and solar generation as hydropower will be called upon to quickly ramp up and down in response to changes in wind and solar generation. For this reason, hydropower is expected to have increased value as fossil generation is phased out, even as rapid flow fluctuations linked with hydropower flexibility may strand fish, alter habitat, and create unsafe recreational conditions. We face a new challenge in facilitating the renewable energy transition—designing environmental flow requirements that protect against the impacts of flow fluctuations while allowing adequate hydropower flexibility to support a stable grid. In this paper, we discuss hydropower environmental flow requirements, operational flexibility, and electrical grid stability, their potential interactions, and opportunities to align environmental and power system needs to support healthy ecosystems, multiple water uses, and decarbonization of the electric grid.

Bayesian estimation for mean vector of multivariate normal distribution on the linear and nonlinear exponential balanced loss based on wavelet decomposition

This paper addresses the problem of Bayesian wavelet estimating the mean vector of multivariate normal distribution under a multivariate normal prior distribution based on linear and nonlinear exponential balanced loss functions. The covariance matrix of multivariate normal distribution is considered known. Bayes estimators of mean vector parameter of multivariate normal distribution are achieved. Then two soft shrinkage wavelet threshold estimators based on Stein’s unbiased risk estimate (SURE) and Bayes estimators are provided. Finally, the performance of the soft shrinkage wavelet estimators was checked through simulation study and Electrical Grid Stability Simulated data set. Simulation and real data results showed the better performance of SURE thresholds based on linear and nonlinear exponential balanced loss functions compared to other classical wavelet methods. Also, they showed better performance for SURE threshold based on nonlinear exponential balanced loss function in multivariate normal distribution with small dimensions.

Smart grid stability prediction model using two-way attention based hybrid deep learning and MPSO

In smart grid management, precise stability prediction is a complicated task that adds to the effective allocation of resources with grid stability. Specifically, demand-side management is considered an essential element of the overall Smart Grids system. Hence, predicting future energy demands is crucial to regulating consumption by aligning utility offerings with consumer demand. This research presents a hybrid deep learning model (Convolutional Neural Network [CNN] with Bi-LSTM) with a two-way attention method and a multi-objective particle swarm optimization method (MPSO) for short-term load prediction from a smart grid. The proposed hybrid model utilizes a two-way attention method at its encoding and decoding stages, in which an encoding attention layer helps to recognize all the essential features from an input vector, and a decoding attention layer helps to resolve the fixed context vector problem by offering better memory capacity. A CNN and Bi-LSTM are used to capture the essential features from the dataset. We also utilize a t-Nearest Neighbours algorithm to pre-process the initial dataset. An MPSO method combines the features of CNN and Bi-LSTM methods, resulting in better prediction accuracy. As far as we know, it is the first work to suggest a dynamic short-term load prediction model that considers different significant features and enables precise predicting outcomes. The performance of the proposed model and existing well-known deep learning models such as Recurrent Neural Network, Gated Recurrent Unit, Long Short-Term Memory (LSTM), Time Series Transformer, CNN-LSTM and various performance measuring parameters MAE, MSE, MAPE and RMSE are calculated on online UCI dataset (Electrical Grid Stability Simulated Dataset). The proposed hybrid model achieved a better prediction result, which proves the efficiency of the proposed model.

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)

BESS Revolutionizes Renewable Stability in Indonesia

This study examines the integration of Battery Energy Storage Systems (BESS) with Solar Power Plants (PLTS) to enhance electrical grid stability in Indonesia, where 90% of electricity is from fossil fuels. The intermittent nature of PLTS often destabilizes the network, causing frequency hunting or blackouts. Using Etap 12.6 software, simulations showed that adding BESS to hybrid PLTS-PLTD systems significantly mitigates these issues. For instance, a 50% drop in PLTS power without BESS required a 55% increase in PLTD power, but with BESS, this increase was only 34%. Thus, BESS is crucial for stabilizing renewable energy integration and supporting Indonesia’s Net-Zero Emissions goal. Highlights: BESS Stabilizes Power: Reduces impact of PLTS power fluctuations on PLTD. Simulation Insight: 50% PLTS drop increases PLTD by 34% with BESS. Net-Zero Goal: BESS supports Indonesia’s renewable energy and emission targets. Keywords: Renewable Energy, Solar Power, Battery Storage, Electrical Grid Stability, Indonesia

Optimized system for combined production of electricity/green hydrogen for multiple energy pathways: a case study of Egypt

Abstract By optimal sizing of a wind/photovoltaic hybrid renewable-energy (RE) system, trimming the surplus capacity to reduce the fluctuations in the electricity supplied to the grid, and using it to produce green hydrogen through electrolysis, a stable output with maximum possible capacity factor (CF) is generated to maintain the electricity grid stability. Simultaneously, the trimmed energy is used in a secondary conversion path that minimizes the weighted average cost of the energy generated from the entire plant. This surplus power-to-gas conversion allows the use of green hydrogen to produce electricity, methanol, or ammonia subject to the resource availability, site characteristics, and financial feasibility. Based on robust site selection criteria, the best performance is obtained at two sites: Ras Ghareb and Minya, achieving the lowest energy cost with some variance in their performance. For the Ras Ghareb site, the optimally sized RE plant provided the grid with a quasi-steady capacity of 423 MW with a CF of 80.04% and was capable of injecting 2965.8648 GWh throughout the year with the lowest cost of 2.4355 ¢/kWh. A surplus of 3.9% of the total energy produced from the plant was directed to produce 1922-ton H2/year, achieving the lowest cost of hydrogen production of $1.9745/kg H2. For the other selected site, Minya, the clipped energy is used to produce 3330.47-ton H2/year with an optimized lowest cost of $3.5268/kg H2. The difference in hydrogen costs was attributed to the number of full operating hours of the electrolyser in both sites. The cost is mainly affected by the electricity price and the electrolyser cost. With both tending to decrease, future forecasts show hydrogen cost reductions.

Kernel Function Selection for FISVDD Applied to Outliers Detection Application for Energy Time Series Datasets

Forecasting energy consumption is crucial for maintaining stability in the energy supply-demand balance. This process requires accurate data, necessitating deep preprocessing steps, with outlier detection being a pivotal task. While most existing methods are tailored for batch learning, smart grid data collection operates continuously, demanding online algorithms for effective data cleaning. This paper proposes an analytical study emphasizing the significance of selecting an appropriate kernel function for data description methods and its impact on forecasting efficiency. The Fast Incremental Support Vector Data Description (FISVDD) algorithm is chosen as a kernel-based approach for outlier detection in time series datasets. The evaluation involves different kernel functions on two datasets: Electrical Grid Stability Simulated Data and Individual Household Electric Power Consumption Dataset. Criteria such as the objective function and data distribution are considered to determine the most suitable kernel function. FISVDD's performance with the right kernel function is compared with other outlier detection methods, and the results are fed into a Gated Recurrent Unit model to visualize the impact on forecasting. Mean Squared Error evaluation reveals that FISVDD with the appropriate kernel function yields the best results, underscoring the strength of data description methods with the appropriate kernel function.

Reduced Switch Multilevel Inverter Based Shunt Active Power Filter With ANN Controller for Power Quality Improvement

In order to improve power quality, this research investigates the use of SAPF with Seventeen Level Reduced Switch Multilevel Inverter. The electricity grid's stability is significantly influenced by various non-linear loads. To address this, SAPF is proposed and designed to monitor, manage the current movement between the power source and the load. The variance between the intended and real currents drawn from utility grid is assessed using p-q theory based hysteresis current controller. The calculated error current guides the controllers in forecasting the best-suited switching signals and modulation index for the seventeen level RSMLI-SAPF system. In this paper the traditional PI controller, Artificial Neural Network based controller are compared and the results are provided to validate the performance of SAPF. The proposed system is modelled and analysed using MATLAB/Simulink. The simulation results are provided for various modulation index using Hybrid Pulse Width Modulation in which Triangle saturated Phase disposition PWM using Unipolar Third harmonic injection as Reference is used and are subjected to non-linear loads.

COMPARATIVE ANALYSIS OF FAULT DIAGNOSIS IN POWER SYSTEM USING EVOLUTIONARY COMPUTATION AND MACHINE LEARNING

This paper provides a comparative analysis of fault diagnosis in power systems using evolutionary computation and machine learning techniques. Power system faults, including line-to-ground, line-to-line, and three-phase faults, pose significant challenges to the stability and reliability of electrical grids. Efficient and accurate fault diagnosis is crucial for minimizing downtime and maintaining optimal power delivery. This research evaluates the performance of various evolutionary computation algorithms such as genetic algorithms, particle swarm optimization and differential evolution alongside machine learning methods including decision trees, support vector machines, and k-nearest neighbor, in diagnosing power system faults. The study compares the accuracy, speed, and reliability of these methods in detecting and classifying faults under different system conditions. The findings highlight the strengths and limitations of EC and ML-based fault diagnosis techniques in power systems, providing valuable insights for researchers and practitioners in the field. Key Words: Fault diagnosis, Power system, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution(DE) , Support Vector Machine (SVM) , Decision Tree (DT), K-Nearest Neighbors (KNN).

Energy Policies and Interconnection Standards for Energy generation Distributed: Global Status, Development Trends and Perspectives in Peru

Energy policies and interconnection standards play a key role in promoting and regulating distributed energy generation globally. The work should examine the current state of global interconnection policies and standards, emerging trends and their impact on clean energy adoption. Globally, energy policies for distributed generation typically focus on marketing renewable energy sources and improving energy efficiency. Many countries implement tax incentives, subsidies and support programs to stimulate the adoption of distributed generation technologies, such as solar panels and small wind systems. The rules of Interconnections often focus on establishing technical standards to ensure the security, stability and reliability of electrical grids, as well as facilitating the connection and seamless integration of distributed generation systems

Cost-benefit analysis and comparison of grid-stabilizing energy flexibility options and their applications in relation to the German energy system

With an increasing share of renewable energies, flexibility is becoming an important factor to ensure the German electricity grid stability. Therefore, decision-makers have to deal with the implementation of the necessary transformations and adaptions. To provide an indication, a cost–benefit analysis and comparison of grid-stabilizing energy flexibility options and their applications for a current and an outlook scenario is conducted. In the results, due to its low costs, the flexibilization of existing CHP (combined heat and power generation) plants stands out as a particularly attractive option. Similarly, the flexibilization of existing wind turbines and photovoltaic systems, as well as the flexibilization of small heat pumps, appear to be highly attractive in the outlook (integration is enabled by pooling). Furthermore, in the future, large-scale batteries and PEM (Polymer electrolyte membrane) electrolysis as well as controlled charging (pooling) will become increasingly beneficial for the energy system. The overall worst performing flexibility option is power-to-methane, which requires additional effort from the system in both scenarios. In general, the research showed that even for similar flexibility options, the resulting systemic benefit highly depends on the specific flexibility application. Nonetheless, the results give an indication towards important sources of flexibility and further research priorities.

Evaluating hydraulic dissipation in a reversible mixed-flow pump for micro-pumped hydro storage based on entropy production theory

The promotion of renewables improves energy security but degrades the electricity quality and grid stability. This paradox can be resolved by enlarging the capacity of pumped hydro storage. Although high-head hydropower resources have been exhausted, untapped low-head resources are ideal alternatives and are utilized for micro-pumped hydro storage. Instead of the miniaturized pump-turbine or pump as turbine, we apply a low-head reversible mixed-flow pump (RMFP), which can efficiently fulfil both storage and generation requirements in microgrids without complicated guide vanes. To enhance the efficiency of the RMFP in two reverse directions simultaneously, high-hydraulic dissipation zones associated with deformed flows and unreasonable structural parameters must be investigated. This study introduces entropy production theory into computational fluid dynamics numerical simulation to evaluate hydraulic dissipation under the rated discharge in both pump and turbine modes of the RMFP. Refined mesh and validation from the energy characteristics experiment demonstrate the accuracy of the numerical scheme. The head loss assessed by the entropy Production method is compared with the predictions of the pressure drop method, and its precision exceeds 7%. In pump mode, entropy production in the volute constitutes the largest proportion (43%), followed by that in the runner (35%). However, in turbine mode, the largest proportion of the total entropy is generated in the runner (45%), followed by that in the draft tube (33%). The dissipation in the runner is bound up with the secondary flows over the blades. Backflow occurs on the suction surface near the leading edge; however, across different spans, flow separation occurs on the suction surface near the trailing edge. The requests for blade modification are opposite in pump and turbine modes. Significantly, hydraulic dissipation of the draft tube concentrates within its 0.5D2 segment upstream. The dissipation there is bound up with the transport of two types of vortex ropes. The originality lies in evaluating hydraulic dissipation and explaining the high-dissipation zone with the specific unsteady secondary flows in pump and turbine modes of an RMFP.

Energy analysis and environmental impact assessment for self-sufficient non-interconnected islands: The case of Nisyros island

The European continent is surrounded by islands, which even though geographically and socially diverse, share common energy burdens regarding heavy fossil fuels reliance. More specifically, the non-interconnected islands (NII) encounter unique challenges in the decarbonization processes, rendering them particularly vulnerable to the impacts of such transitions. Numerous studies have been conducted to address such emerging issues. However, a research gap exists, as most of the proposed RES-related grid interventions neglect actual historical data in favor of a more theoretical, but possibly misleading, approach. In addition, potential grid stability concerns arising from high levels of RES penetration remain unaddressed. To this end, the objective of the present study focuses on addressing mainly electrical grid stability and resiliency considerations (e.g. imbalances, frequency fluctuations, voltage unbalance rate, for the case of energy islands, as in the case of the NII Greek island of Nisyros). Two sustainable scenarios including novel technologies (e.g. flywheel instead of conventional BESS, a hybrid transformer, vehicle-to-grid chargers) are examined from an energetic and environmental perspective, for short- (2030) and mid-term (2050) implementation horizons, reaching electrical and energy self-sufficiency targets up to 50% and 95%, respectively. For the energy analysis, capacity-investment optimization problems, power flow simulations and specialized transient simulations were conducted, while for the environmental impact assessment a novel in-house developed tool was utilized. As a result, the proposed interventions can result in significant fossil fuel cost savings of about 88%, reduced CO2 emissions up to 38% and overall remarkable financial and environmental benefits. In conclusion, this study demonstrates that a NII can realistically abolish thermal energy to a bare minimum and enhance its self-sufficiency by harnessing local renewable energy potential, while at the same time ensuring grid stability.

Increase of the Annual Energy Output in Hydraulic Powerplants through Active Flow Control

Traditionally, hydraulic powerplants have been strongly contributing to energy generation. At present, with the expanding participation of other non-storable renewable energy sources in the electrical market composition, water turbines can importantly contribute to the electrical grid stability and to energy storage. With the increasing demand for such services, new procedures have to be developed, in order to allow the hydraulic turbines to operate in critical load conditions, as part load, for example. The technique proposed here, based on pitching guide vanes, was numerically tested with instationary CFD models and delivered possibly promising results, concerning the machine performance and stability, at part load conditions. The extension of the operating range of hydraulic powerplants, through the turbine active flow control, yields even greater flexibility to them in providing regulation services to the electrical grid. This technique has also the potential to extend the operational life of main turbine components and may lead to the increase of the annual energy output in some cases.