Voltage Stability Research Articles

An artificial insurance framework for a hydrogen-based microgrid to detect the advanced cyberattack model

Microgrid systems have evolved based on renewable energies including wind, solar, and hydrogen to make the satisfaction of loads far from the main grid more flexible and controllable using both island- and grid-connected modes. Albeit microgrids can gain beneficial results in cost and energy schedules once operating in grid-connected mode, such systems are vulnerable to malicious attacks from the viewpoint of cybersecurity. With this in mind, this paper explores a novel advanced attack model named the false transferred data injection (FTDI) attack aiming to manipulatively alter the power flowing from the microgrid to the upstream grid to raise voltage usability probability. One crucial piece of information that the model uses to change the system and cause the greatest amount of damage while concealing the attacker’s view is the voltage stability index. Saying that the power transaction between the microgrid and the upstream grid is within the broad scope of bilateral exchange at any given moment is noteworthy. Put otherwise, with respect to the FTDI assault, the microgrid’s power direction is just as significant to the detection system as the transferred power value. Therefore, once the microgrid is running in the grid-connected mode, the false data detector needs to concurrently detect changes in the value and direction of power. To overcome this problem, the paper presents a learning generative network model, based on the generative adversarial network (GAN) paradigm, to recognize the change in probability values that is maliciously aimed. To this end, a studied microgrid system including the wind turbine, photovoltaic, storage, tidal turbine, and fuel cell units is performed on the tested 24-bus IEEE grid to satisfy the local load demands. Comparative analysis indicates notable gains, such as scores of 0.95%, 0.92%, 0.7%, and 10% for the Hit rate, C.R. rate, F.A. rate, and Miss rate in order to evaluate the GAN-based detection model within the microgrid.

Research on Multi-Objective Reactive Power Optimization of Distribution Grid with Photovoltaics

With the introduction of large distributed photovoltaic (PV) power and electric vehicles, the inherent volatility of their output makes it difficult for traditional power grid structures and reactive power optimization methods to meet the needs of the safe operation of distribution grids and economic benefits. Therefore, a multi-objective reactive power optimization method for a distributed grid is proposed under a distributed PV power generation scenario. Aiming at the two objectives of the network loss and voltage fluctuation rate, the improved multi-objective particle swarm optimization algorithm is used to solve the model under the condition that the output of each device does not exceed the constraint and the optimal solution that can reduce the distribution grid loss and improve the voltage stability of the distribution grid is obtained. The simulation was conducted on the IEEE 33-node and 113-node distribution networks to verify the proposed method’s feasibility.

Flexible Moisture-Driven Electricity Generators Based on Heterogeneous Gels and Carbon Nanotubes.

Recently developed asymmetric heterogeneous moisture-driven electricity generators (AHMEGs) are advantageous for harvesting energy from ubiquitous moisture due to their superior output performance and possible flexibility. However, the regeneration of AHMEG has seldom been explored. Here, we report the fabrication of flexible AHMEGs with regeneration ability simply by asymmetrically incorporating carbon nanotubes into a bilayer-structured gel with heterogeneities of both hygroscopicity and charge. The bilayered gel consists of a Na+-containing macroporous aerogel and a highly hygroscopic Cl--containing hydrogel. After assembly, the resulting flexible AHMEG unit shows excellent output performance, with a stable voltage of 1.03 V, a current density of 23.8 μA cm-2, a power density of up to 9.12 μW cm-2 and a high stability over 7 days, resulting from a synergistic effect between hygroscopicity and charge heterogeneities of the bilayered gel. The regeneration of the AHMEGs has been realized via light irradiation of the aerogel containing carbon nanotubes, and the regeneration can be repeated for at least 30 cycles, significantly expanding the lifecycle of moisture-driven electricity generators. Regeneration imposed by carbon nanotubes, combined with flexibility, simple preparation, and excellent electrical performance, enables the AHMEGs to be excellent moisture-driven electricity generators for advantageous applications.

Enhancing Voltage and Power Stability in Distribution System with Photovoltaic from the Benefits of Battery Energy Storage

This paper presents a method of enhancing voltage and power stability in a distribution system with the photovoltaic benefits of battery energy storage. The objective is to use photovoltaic-distributed generation and a battery energy storage system in order to reduce power loss to a minimum and generate a voltage up to or above 0.95 p.u, which is the voltage standard in Thailand. This paper used MATLAB (Version R2024b-acdemic use) to conduct test experiments, and the system for the case studies is an IEEE 33-bus radial distribution system. There are five study cases in this paper. Case 1 is before the installation of the photovoltaic and battery energy storage system. Case 2 is the installation of the photovoltaic-distributed generator in four buses. Case 3 is the installation of the photovoltaic-distributed generator in only one bus. Case 4 is the installation of the photovoltaic distributed generator in two buses that have the most voltage drop. Case 5 is the installation of the photovoltaic-distributing generator in four buses and the battery energy storage system in two buses. The results show that Case 5 is the best because the voltage drop is never below 0.95 per unit or below the lowest power loss.

Graph-Based Topological Embedding and Deep Reinforcement Learning for Autonomous Voltage Control in Power System

With increasing power system complexity and distributed energy penetration, traditional voltage control methods struggle with dynamic changes and complex conditions. While existing deep reinforcement learning (DRL) methods have advanced grid control, challenges persist in leveraging topological features and ensuring computational efficiency. To address these issues, this paper proposes a DRL method combining Graph Convolutional Networks (GCNs) and soft actor-critic (SAC) for voltage control through load shedding. The method uses GCNs to extract higher-order topological features of the power grid, enhancing the state representation capability, while the SAC optimizes the load shedding strategy in continuous action space, dynamically adjusting the control scheme to balance load shedding costs and voltage stability. Results from the simulation of the IEEE 39-bus system indicate that the proposed method significantly reduces the amount of load shedding, improves voltage recovery levels, and demonstrates strong control performance and robustness when dealing with complex disturbances and topological changes. This study provides an innovative solution to voltage control problems in smart grids.

Deep Reinforcement Learning Based Optimal Operation of Low-Carbon Island Microgrid with High Renewables and Hybrid Hydrogen–Energy Storage System

Hybrid hydrogen–energy storage systems play a significant role in the operation of islands microgrid with high renewable energy penetration: maintaining balance between the power supply and load demand. However, improper operation leads to undesirable costs and increases risks to voltage stability. Here, multi-time-scale scheduling is developed to reduce power costs and improve the operation performance of an island microgrid by integrating deep reinforcement learning with discrete wavelet transform to decompose and mitigate power fluctuations. Specifically, in the day-ahead stage, hydrogen production and the hydrogen blending ratio in gas turbines are optimized to minimize operational costs while satisfying the load demands of the island. In the first intraday stage, rolling adjustments are implemented to smooth renewable energy fluctuations and increase system stability by adjusting lithium battery and hydrogen production equipment operations. In the second intraday stage, real-time adjustments are applied to refine the first-stage plan and to compensate for real-time power imbalances. To verify the proposed multi-stage scheduling framework, real-world island data from Shanghai, China, are utilized in the case studies. The numerical simulation results demonstrate that the proposed innovative optimal operation strategy can simultaneously reduce both the costs and emissions of island microgrids.

Volt/VAr Regulation of the West Mediterranean Regional Electrical Grids Using SVC/STATCOM Devices With Neural Network Algorithms

ABSTRACTReactive power compensation is now a challenging issue in maintaining adequate power quality and improving the performance of the transmission system. Many researchers have focused on the behavior of voltage in the unbalanced state, and the majority of the studies have been done with STATCOM. In this study, the increasing power demand in power grids and the challenges associated with maintaining power quality and stability are discussed. The power system in the western Mediterranean region of Turkey is modeled with the DigSILENT Power Factory program using real data. It highlights the importance of reactive power compensation and introduces FACTS devices as a solution to control power factors and reactive power in the grid. In the realistic model, voltage and reactive power regulation is achieved by using SVC and STATCOM devices to ensure voltage stability. A load flow and short circuit analysis is performed on the designed transmission system for a number of critical scenarios. The modeled power system is optimized for the size and location of the FACTS devices by applying genetic algorithms (GAs) and particle swarm optimization (PSO) algorithms to the selected busbars of the FACTS devices, a strategy designed to significantly reduce system losses. The load values used in the heuristics differ from those used in other studies in that they use a realistic load profile that varies randomly and instantaneously over the long term, as opposed to a fixed load profile. All the comparative results show that the STATCOM controller can maintain the most significant reduction in technical losses and excellent performance in controlling the reactive power response under various outages. The effectiveness of the proposed methodology has been validated in various outage scenarios, and it has been shown that it will reduce the total cost of electricity generation in the western Mediterranean region on an annual basis. Considering the results of the Turkish National Transmission System, increasing voltage sensitivity and reactive power control can be beneficial in reducing power transmission costs and maintaining the balance between generation and consumption.

Optimizing Reactive Compensation for Enhanced Voltage Stability in Renewable-Integrated Stochastic Distribution Networks

The rapid expansion of renewable energy sources and the increasing electrical load demand are complicating the operational dynamics of power grids, leading to significant voltage fluctuations and elevated line losses. To address these challenges, we propose an information gap decision-theory-based robust optimization method for the siting and operation of reactive compensation equipment, utilizing static var generators (SVGs) to mitigate voltage fluctuations and reduce losses. Our approach begins by projecting the scale of renewable energy integration and load growth, establishing scenarios with varying renewable-to-load growth ratios. We then develop a multi-objective optimization model that incorporates voltage–loss sensitivity, accounting for the uncertainties in renewable energy production. A case study demonstrates that our method reduces grid voltage fluctuations and losses by 29.53% and 7.75%, respectively, compared to non-intervention scenarios, highlighting its effectiveness in stabilizing distribution networks.

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.

Modeling and Simulation on the Hybrid Solution of Static Var Generator and Synchronous Condenser for Unlocking Power Output Limitation of Wind Farms Integrated into Weak Grid

The issues of low inertia, overvoltage, and wide-frequency oscillations in high-proportion renewable energy systems have become prominent, posing major challenges to renewable energy integration and threatening grid stability. Currently, many wind-rich areas ensure grid safety and stability by reducing wind farm output. To enhance the active power delivery capability of wind farms, this paper proposes a hybrid solution of a small synchronous condenser (SC) and static var generator (SVG) within wind farm stations to optimize reactive power and voltage at the point of grid connection. First, it was analyzed that the low short-circuit ratio (SCR) is a key factor affecting the stable operation of wind farms, and the sub-transient reactance of the SC can increase the SCR. Based on this, a method for configuring the capacity of the SC was developed. Next, simulation models for both the SC and the SVG were established, and their reactive power compensation capabilities were verified. The hybrid control approach combined the advantages of both devices, providing comprehensive voltage support across sub-transient, transient, and steady-state conditions for renewable energy stations. Furthermore, based on a practical 50.5 MW wind farm, which has been operating with a power delivery consistently limited to 60% of its capacity, a simulation model and scenarios were set up. A comparison of the simulation results shows that, with only the SVG in operation, the wind farm is prone to oscillations after a grid fault. However, after adopting the hybrid control of the SC and SVG, the wind farm operates stably. Therefore, installing a small SC within wind farms can effectively address the limitations of voltage stability and a low short-circuit ratio, thereby supporting higher levels of renewable energy integration.

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.

Design and Implementation of a Smart Solar Tracker using Arduino for Enhanced Energy Efficiency

In the universe of solar energy systems, the quest for enhanced energy efficiency has led to the design and implementation of a Smart Solar Tracker using Arduino. This innovative solution leverages advanced control mechanisms to optimize solar panel orientation dynamically, addressing the inherent challenges posed by varying climatic conditions. Traditionally, solar trackers have faced limitations in adapting to changing weather patterns, impacting energy capture efficiency. The Smart Solar Tracker, outlined in this study, overcomes these challenges by integrating Arduino-based technology, demonstrating a robust and strong approach to solar tracking. The main result presented in this work showcases the superior performance of the Smart Solar Tracker, consistently delivering high and stable voltage outputs even in adverse weather conditions. Shows that the implementation of Arduino-based control systems in solar tracking significantly enhances energy efficiency, ensuring consistent power generation across diverse climatic scenarios. This advancement not only underscores the critical role of technology in optimizing renewable energy systems but also positions the Smart Solar Tracker as a promising solution for reliable and resilient solar energy harvesting. In terms of costing smart solar tracker is 3 times higher compared to traditional solar tracker whereas in terms of efficiency smart solar tracker using Arduino is 10 times which is 100 percentage more efficient compared to traditional solar tracker. The smart solar tracker using Arduino efficiency output was 68% which is the highest compared to both solar tracker which is 5% and 27% over 5 days of reading the Smart Solar Tracker. Comparison shows that the smart solar tracker using Arduino is the most efficient. The findings presented specifically lead the way for the broader integration of advanced control mechanisms in renewable energy infrastructure, fostering sustainability in the face of environmental variability.

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