Voltage Stability Research Articles

Coherent Strain-Inhibiting Phase Construction of Lithium-Rich Manganese-Based Oxide Toward High Mechanochemical Stability.

A layered lithium-rich manganese-based oxide cathode, containing R3̅m (LiTMO2, TM = Mn, Ni, Co) and C2/m (Li2MnO3) nanodomains, utilizes both transition metals and oxygen redox to yield substantial energy density. However, the inherent heterogeneous nature and distinct nanodomain redox chemistries of layered lithium-rich oxides will inevitably cause pernicious lattice strain and structural displacement, which can hardly be eliminated by conventional doping or coating strategies and result in accelerated performance decay. Herein, we incorporate a strain-inhibiting perovskite phase coherently grown within the layered structure to effectively restrain the displacement and lattice strain during uneven Li-ion extraction. The enhanced mechanochemical stability of the designed cathode benefits the persistent structure and reversible oxygen redox, thereby achieving high initial Coulombic efficiency and stable cycling and voltage profiles. Our approach of lattice engineering alleviates the strain and displacement caused by inhomogeneous reactivity between heterogeneous nanodomains and promotes the development of advanced cathode materials with long durability.

Optimal integration of photovoltaic sources and capacitor banks considering irradiance, temperature, and load changes in electric distribution system

This paper introduces the Efficient Metaheuristic BitTorrent (EM-BT) algorithm, aimed at optimizing the placement and sizing of photovoltaic renewable energy sources (PVRES) and capacitor banks (CBs) in electric distribution networks. The main goal is to minimize energy losses and enhance voltage stability over 24 h, taking into account varying load profiles, solar irradiance, and temperature effects. The algorithm is rigorously tested on standard distribution networks, including the IEEE 33, IEEE 69, and ZB-ALG-Hassi Sida 157-bus systems. The results reveal that EM-BT outperforms established methods like Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA), demonstrating its effectiveness in reducing energy losses and maintaining stable voltage profiles. By effectively combining PVRES and CBs, this research highlights a robust approach to enhancing both technical performance and operational reliability in distribution systems. Additionally, the consideration of temperature effects on PVRES efficiency adds depth to the study, making it a valuable contribution to the field of power system optimization.

Optimizing FACTS Device Placement Using the Fata Morgana Algorithm: A Cost and Power Loss Minimization Approach in Uncertain Load Scenario-Based Systems

Today, reliable power delivery and increasing demand are important issues in modern power systems. Flexible Alternating Current Transmission Systems (FACTS) devices are used to control transmission line parameters to increase power transfer and stability. Nevertheless, the problem of determining the optimal placement and sizing of these devices is still challenging, as the placement and sizing of the devices affects generation costs, power losses, voltage stability, and system reliability. This study proposes the Fata Morgana Algorithm (FATA), an optimization algorithm inspired by the natural process of mirage formation to optimize placement and sizing of FACTS devices in an IEEE 30 bus system with wind turbine integration. The FATA algorithm is evaluated against recently developed and improved optimization techniques, such as rime-ice formation phenomenon based Improved RIME (IRIME) Algorithm, Newton–Raphson-Based Optimization (NRBO), Resistance Capacitance Algorithm (RCA), Krill Optimization Algorithm (KOA), and Grey Wolf Optimizer (GWO), across multiple optimization objectives: reduction in generation cost, reduction in power loss and combined generation cost plus power loss, termed as Gross cost function. Results obtained show that FATA consistently outperforms the other algorithms in terms of convergence and solution quality, offering a robust approach to solving single objective optimization problems. FATA theoretically provides a good balance between exploration and exploitation, and produces better global solutions. It practically increases power system efficiency by lowering operational costs and losses and improving stability. Results indicate that the FATA algorithm produced minimum generation cost of 807.0405 $/h, which is 0.088–0.426% less than the competing algorithms. It also reduced power losses to 5.5917 MW, which is 1.095–6.781% less than other methods. For gross cost minimization, the FATA algorithm achieved a minimum gross cost of 1366.3727 $/h, which is 0.4799% better than the next best algorithm and 3.2261% better than the worst. The results also show that FATA is robust in solving complex optimization problems in power systems, and it provides significant improvements in run time and convergence efficiency. The main advantage for readers is that FATA provides a reliable and efficient way to optimize power systems. Future work could also investigate the application of FATA in real time, as well as in larger power networks with more renewable energy sources.

Regenerative Braking Energy Flow Control Algorithm for Power Grid Voltage Stabilization in Mobile Energy Storage Systems

Regenerative Braking Energy Flow Control Algorithm for Power Grid Voltage Stabilization in Mobile Energy Storage Systems

Optimal Allocation and Sizing of Battery Energy Storage System in Distribution Network Using Mountain Gazelle Optimization Algorithm

This paper addresses the problem of finding the optimal position and sizing of battery energy storage (BES) devices using a two-stage optimization technique. The primary stage uses mixed integer linear programming (MILP) to find the optimal positions along with their sizes. In the secondary stage, a relatively new algorithm called mountain gazelle optimizer (MGO) is implemented to find the technical feasibility of the solution, such as voltage regulation, energy loss reduction, etc., provided by the primary stage. The main objective of the proposed bi-level optimization technique is to improve the voltage profile and minimize the power loss. During the daily operation of the distribution grid, the charging and discharging behaviour is controlled by minimizing the voltage at each bus. The energy storage dispatch curve along with the locations and sizes are given as inputs to MGO to improve the voltage profile and reduce the line loss. Simulations are carried out in the MATLAB programming environment using an Australian radial distribution feeder, with results showing a reduction in system losses by 8.473%, which outperforms Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Cuckoo Search Algorithm (CSA) by 1.059%, 1.144%, and 1.056%, respectively. During the peak solar generation period, MGO manages to contain the voltages within the upper boundary, effectively reducing reverse power flow and enhancing voltage regulation. The voltage profile is also improved, with MGO achieving a 0.348% improvement in voltage during peak load periods, compared to improvements of 0.221%, 0.105%, and 0.253% by GWO, WOA, and CSA, respectively. Furthermore, MGO’s optimization achieves a reduction in the fitness value to 47.260 after 47 iterations, demonstrating faster and more consistent convergence compared to GWO (47.302 after 60 iterations), WOA (47.322 after 20 iterations), and CSA (47.352 after 79 iterations). This comparative analysis highlights the effectiveness of the proposed two-stage optimization approach in enhancing voltage stability, reducing power loss, and ensuring better performance over existing methods.

Reduction‐Induced Oxygen Loss: the Hidden Surface Reconstruction Mechanism of Layered Oxide Cathodes in Lithium‐Ion Batteries

AbstractThe surface reconstruction from the layered to rocksalt‐type phase represents a primary deterioration pathway of layered‐oxide cathodes in lithium‐ion batteries, involving irreversible oxygen loss and transition metal migration. This degradation mechanism has primarily been attributed to the oxidative instability of highly delithiated cathodes at high voltages (>4.3 V vs Li/Li+). However, the battery degradation also occurs under seemingly stable voltage ranges, the origin of which remains unclear. Herein, a hidden mechanism to induce surface reconstruction and oxygen loss is proposed, termed the “quasi‐conversion reaction”, which is revealed to occur during electrochemical reduction (discharge) processes just below 3.0 V (vs Li/Li+). Combined experiments and first‐principles calculations unveil that the oxygens at the surface can be extracted from the cathode lattice by forming lithium oxides and oxygen vacancies, at significantly higher potentials than conventional conversion reaction, due to the instability of surface oxygens coordinated with fewer cations than in the bulk. The chemical incompatibility between lithium oxides and commercial carbonate‐based electrolytes results in electrolyte decomposition, forming an organic‐rich blocking layer and gaseous byproducts, which further increases the cell impedance. This study emphasizes the necessity of a thorough understanding of surface instability upon reduction to develop long‐lasting batteries.

A Robust Linear Active Disturbance Rejection Control for Renewable Energy Grid-Connected System Based on APF

With the energy transition and changes in electrical load structures, harmonic distortion in power systems is increasingly threatening grid stability, especially during the integration of renewable energy sources. Active power filters (APFs) are commonly used to improve power quality, but current fluctuations and voltage instability caused by DC-side capacitor charging and discharging hinder effective harmonic compensation. To address this, this paper presents a method combining a variable-step-size adaptive linear predictor with linear active disturbance rejection control (VALP-LADRC). By integrating an adaptive linear predictor (ALP) with LADRC, the proposed method effectively reduces the impact of disturbances on control, improving voltage regulation and harmonic compensation, and enhancing renewable energy grid integration. To address delays during the adjustment of initial parameters in the adaptive predictor, we combine it with a variable-step-size algorithm, significantly improving disturbance rejection and tracking accuracy, while solving voltage drop issues. The simulation results in a photovoltaic grid-connected system show that VALP-LADRC effectively mitigates disturbances, ensures voltage stability, and enhances power quality. This approach offers a promising solution for supporting sustainable development through better renewable energy integration and improved power quality.

Improving Power Quality and Fault Ride-Through (FRT) in Grid-Connected Hybrid Energy Systems: Design and Optimization of Dynamic Voltage Restorers

The integration of renewable energy sources (RESs) into grid-connected systems has become pivotal in addressing climate change and building a sustainable, carbon-neutral society. However, challenges like power quality issues, grid stability, and fault ride-through (FRT) requirements remain significant. This study explores strategies to enhance FRT capabilities and improve power quality in hybrid energy systems. It emphasizes the role of advanced devices such as Dynamic Voltage Restorers (DVRs), EV charging stations, and energy storage solutions. The effectiveness of these technologies is analyzed under various fault scenarios, highlighting improvements in transient response, voltage stability, and harmonic reduction. Additionally, hybrid energy systems, combining multiple RESs and backup sources, are proposed as viable solutions to address intermittency and system reliability. MATLAB/SIMULINK simulations validate the proposed approaches, showcasing their effectiveness in mitigating grid disturbances and maintaining operational efficiency. The findings underline the importance of innovative control strategies and hybrid configurations in ensuring a resilient and sustainable energy infrastructure.

Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning

Volt–VAR control (VVC) is essential in maintaining voltage stability and operational efficiency in distribution networks, particularly with the increasing integration of distributed energy resources. Traditional methods often struggle to manage real-time fluctuations in demand and generation. First, various resources such as static VAR compensators, photovoltaic systems, and demand response strategies are incorporated into the VVC scheme to enhance voltage regulation. Then, the VVC scheme is formulated as a constrained Markov decision process. Next, a safe deep reinforcement learning (SDRL) algorithm is proposed, incorporating a novel Lagrange multiplier update mechanism to ensure that the control policies adhere to safety constraints during the learning process. Finally, extensive simulations with the IEEE-33 test feeder demonstrate that the proposed SDRL-based VVC approach effectively improves voltage regulation and reduces power losses.

Methodology for Determining the Optimal Location and Capacity of STATCOM Based on Grid Clustering for Grid Voltage Stability

Methodology for Determining the Optimal Location and Capacity of STATCOM Based on Grid Clustering for Grid Voltage Stability

ENHANCING VOLTAGE STABILITY AND ENERGY OUTPUT OF PLTM SALIDO IN THE PLN DISTRIBUTION NETWORK

Distributed generation (DG) integration in PLN’s extended distribution network often encounters voltage instability issues due to high voltage drops. These challenges restrict power delivery and can lead to disconnection, particularly during peak load conditions. This study investigates methods to enhance voltage stability and optimize the energy output of PLTM Salido. Using ETAP Power Station software, various operational schemes were simulated to address the problem of low terminal voltage. The study focused on the impact of reactive power injection and governor settings optimization to maintain generator performance under high demand. Results indicate that reactive power injection significantly improves terminal voltage, ensuring continuous power delivery. Adding a third generator unit further increased energy supply capacity, leveraging the full potential of available hydropower. The optimized operation of PLTM Salido with two generators increased daily energy output by 32.2%, while introducing a third generator boosted energy delivery by 107%. This research highlights the critical role of reactive power management and system configuration in enhancing the performance of small-scale hydropower plants in rural distribution networks.

Polysaccharides: The Sustainable Foreground in Energy Storage Systems

Polysaccharides offer a perfect option as raw materials for the development of a new generation of sustainable batteries and supercapacitors. This is due to their abundance and inherent structural characteristics. Polysaccharides can be chemically functionalized and engineered, offering a wide range of possibilities as electrode materials (as precursors of porous nanocarbons), binders and separators. Their hierarchical morphology also enables their exploitation as aerogel and hydrogel structures for quasi-solid and solid polymer electrolytes with high conductivity and wide voltage stability windows. In this review, we discuss how different polysaccharides, such as lignocellulosic biomass, starch, chitosan, natural gums, sugars and marine polysaccharides, can be applied in different components of energy storage systems (ESSs). An overview of the recent research work adhering to each functionality of different polysaccharides in various storage systems is provided.

Improving the Thermal Stability of Indium Oxide n-Type Field-Effect Transistors by Enhancing Crystallinity through Ultrahigh-Temperature Rapid Thermal Annealing.

Ultrathin indium oxide films show great potential as channel materials of complementary metal oxide semiconductor back-end-of-line transistors due to their high carrier mobility, smooth surface, and low leakage current. However, it has severe thermal stability problems (unstable and negative threshold voltage shifts at high temperatures). In this paper, we clarified how the improved crystallinity of indium oxide by using ultrahigh-temperature rapid thermal O2 annealing could reduce donor-like defects and suppress thermal-induced defects, drastically enhancing thermal stability. Not only does more crystalline indium oxide depict the high stability of threshold voltage in stringent high-temperature test environments and under positive bias, but it also shows much less degradation under forming gas annealing than as-deposited transistors. Furthermore, we also successfully solved the channel length-dependent threshold voltage problem, which is often observed in oxide transistors, by suppressing defects induced by the metal deposition process and metal doping.

EV and PV penetration impact on grid with conservative voltage regulation and reactive voltage compensation.

As global interest grows in renewable energy sources, the impact of combined Electric Vehicle (EV) and PhotoVoltaic (PV) penetration on the power grid stability requires renewed attention, to incorporate new technologies to maintain the power quality under operational constraints. Energy-saving techniques such as Conservation Voltage Reduction (CVR) allow the power utilities to transmit voltage at a lower operation limit, increasing the generation margin to absorb the peak load demands. Increased reverse PV penetration results in grid overvoltage while EV charging absorbs the reactive power causing grid instability. Both overvoltage and loss of reactive power in the grid can be reduced by using CVR and reactive power injection techniques. A power electronic secondary var controller (SVC) can dynamically inject reactive power into selected grid buses. This work compares the voltage stability of an IEEE 33 bus system operating with and without CVR. The simulation studies analyzed the effects of EV penetration level, and PV hosting capacity with SVC compensation paired with and without conservation voltage reduction technique. The analysis results demonstrate that tandem usage of CVR and SVC maintains the grid voltage under operational limits, meets load and EV demand, and increases power efficiency and PV penetration.

Improved threshold voltage stability for GaN p-channel field effect transistors by hydrogen plasma treatment

Improved threshold voltage stability for GaN p-channel field effect transistors by hydrogen plasma treatment

Two-stage multi-objective framework for optimal operation of modern distribution network considering demand response program.

To improve the inadequate reliability of the grid that has led to a worsening energy crisis and environmental issues, comprehensive research on new clean renewable energy and efficient, cost-effective, and eco-friendly energy management technologies is essential. This requires the creation of advanced energy management systems to enhance system reliability and optimize efficiency. Demand-side energy management systems are a superior solution for multiple reasons. Firstly, they empower consumers to actively oversee and regulate their energy consumption, resulting in substantial cost savings by minimizing usage during peak hours and enhancing overall efficiency. The Demand Response Program (DRP) and optimal power sharing have gained significant attention to provide technical and economic benefits, while they require an efficient operation framework. Therefore, a two-stage framework is proposed for multi-objective operation of a distribution network with several generation resources. The first stage applies DRP to maximize the distribution network operator's (DNO) profit by optimizing common incentive rate for all consumers participate in DRP and an individual curtailed power for each consumer. In addition to an individual incentive rate for each consumer participates in DRP which is a new solution in the field of demand side management. The second stage achieves optimal power sharing among generation resources, while considering multiple objectives and incorporating the modified load of the first stage. The multi-objective problem is formulated to reduce energy losses, voltage deviation, total operational cost, gas emissions, and maximize the voltage stability index. The problem is optimized using a combination of the technique for order of preference by similarity to ideal solution (TOPSIS) and the elephant herding optimization (EHO) technique. The framework is validated using a modified IEEE 33-bus that incorporates photovoltaic system, diesel generators, and wind generation system. The proposed framework based on an individual incentive rate DRP provides superior response compared to common incentive rate DRP which reduces the total energy losses by 38.13%, reduces the total generation cost by 9.468%, and reduces the emission by 5.9%.

Optimal Siting, Sizing, and Energy Management of Distributed Renewable Generation and Storage Under Atmospheric Conditions

Integrating new generation and storage resources within power systems is challenging because of the stochastic nature of renewable generation, voltage regulation, and the use of microgrids. Classical optimization methods struggle with these nonlinear, multifaceted issues. This paper presents a novel optimization framework for integrating, sizing, and siting distributed renewable generation and energy storage systems in power distribution networks. To accurately reflect load variability, the framework considers four distinct load models—constant impedance, current, power, and ZIP (constant impedance, constant current, constant power). Our approach utilized three metaheuristic approaches to enhance the efficiency of power system management. The validation results on the IEEE 33 Bus System conclude that the Elephant Herding Optimization (EHO) emerged as the best performer regarding voltage stability and real power loss reduction with a voltage stability index of 0.0031346. Modified Ant Lion Optimization (ALO) achieved a best voltage stability index of 0.0024115 and power losses of 7.5092 MVA. The Red Colobus Monkey Optimization (RMO) algorithm realized a voltage stability index of 0.0052053 and real power losses of 20.7564 MVA. Overall, the results conclude that ALO is the most effective approach for optimizing distributed renewable energy systems under different climatic conditions. According to the analysis, the algorithm works best in ideal circumstances when the percentages of wind and irradiance are 60% or greater.

Optimal voltage and frequency control strategy for renewable-dominated deregulated power network

Maintaining stable voltage and frequency regulation is critical for modern power systems, particularly with the integration of renewable energy sources. This study proposes a coordinated control strategy for voltage and frequency in a deregulated power system comprising six Generation Companies (GENCOs) and six Distribution Companies (DISCOs). The system integrates thermal, diesel, wind, solar photovoltaic (PV), and hydroelectric sources. Two stochastic modeling techniques are used to characterize wind and solar generation, accounting for their variability within the control loops. A novel Leader Harris Hawks Optimization-based Model Predictive Controller (MPC-LHHO) is implemented, achieving a reduction in frequency deviation undershoot by 67.45% and voltage settling time by 91.11% compared to conventional controllers under poolco and bilateral transactions. Auxiliary devices, including the Unified Power Flow Controller (UPFC) and grid-connected electric vehicles (EVs), further enhance performance, reducing frequency deviations by 52.18% under stochastic scenarios. Rigorous evaluation under contract violations, random load variations, and renewable intermittency demonstrates the strategy’s robustness and efficacy.

Voltage support strength analysis and stability control strategy for grid‐forming wind power transmission system

AbstractIncreasing the short‐circuit ratio (SCR) of the power transmission system is crucial to ensuring voltage stability when the system has a high‐penetration of wind energy resources. This paper first analyses the weak nodes in the power system and quantifies the SCR requirements of these nodes under voltage safety constraints. Based on this analysis, a third‐order dynamic model of a virtual synchronous generator is established, and a design method for virtual transient reactance is proposed to meet the system's SCR requirements. Finally, a power system simulation with high‐penetration of wind energy is constructed, validating that under the proposed voltage stability support control strategy, grid‐forming wind turbines can meet the system's SCR requirements by optimizing the design of transient reactance, thereby improving the voltage stability of the system.

Impacts of grid-connected wind power generation on the voltage stability

Impacts of grid-connected wind power generation on the voltage stability