System Voltage Stability Research Articles

A comprehensive simultaneous allocation algorithm of charging stations and vehicle to grid operation in radial networks

The widespread adoption of electric vehicles (EVs) helps improve air quality by minimizing pollutants and promoting sustainable transportation practices. The integration of a substantial fleet of EVs leads to an increase in the installation of charging stations. The unplanned allocation of these loads impacts the distribution systems, such as increasing power loss. This paper introduces an algorithm that proposes an optimal allocation strategy for charging stations (CSs) in a distribution system. The allocation process aims to minimize the total apparent energy losses and ensure that the system voltage profile remains within limits. Load profile, charging and discharging profiles of CSs are considered. Vehicle-to-Grid (V2G) mode is one of the merits of integrating EVs in the grid, so this feature has been used to maintain system voltage stability and minimize energy loss. Power rating and locations of V2G mode are optimally determined to guarantee power quality indices. A hybrid algorithm of genetic algorithm (GA) and Self-Adaptive Multi-Population Elitist JAYA (SAMPE-JAYA) is developed to simultaneously allocate CSs and V2G in the systems. The proposed algorithm is verified on standard systems, IEEE 33, and 69-bus systems. The proposed algorithm is verified on standard IEEE 33-bus and 69-bus systems. V2G integration with CSs results in a reduction of energy losses by 6.33% and 22.25%, respectively, and voltage deviation improvements to 7.61% and 7.88% for the two systems.

Comparison of Advanced Flexible Alternating Current Transmission System (FACTS) Devices with Conventional Technologies for Power System Stability Enhancement: An Updated Review

The continuously growing penetration of renewable energy sources (RESs) in electrical networks provides increasing challenges and critical situations to be managed by worldwide system operators. Due to their features and variability, non-programmable RES power plants, whose increasing penetration reduces the inertia level of the power system, may determine the instability effects on the grids, especially from the frequency and voltage regulation standpoints. The present study focuses on the support that advanced FACTS (Flexible Alternating Current Transmission System) devices, such as STATCOMs (Static Synchronous Compensators), can provide to the power system operation in terms of system inertia improvement, frequency stability, and voltage stability. In particular, a review of the scientific literature and practice is performed, with the aim of benchmarking the ongoing evolution of these technologies, also comparing them with different options based on synchronous condensers, synchronous condensers integrated with flywheels, and STATCOMs with supercapacitors. The outcome of the analysis consists of an updated evaluation of the state-of-the-art technological development in the field and of a comparison between different FACTSs with the purpose of identifying the most suitable solutions for different practical situations, also taking account of synergies across various options. This study includes an updated overview regarding the status of STATCOM installation in the Italian power grid.

A new combined analytical–numerical probabilistic method for assessing the impact of DERs on the voltage stability of bulk power systems

The integration of distributed energy resources (DERs) and unpredictable loads has increased uncertainty in power systems. Traditional methods struggle to assess performance under these uncertainties, and existing probabilistic methods face challenges with complexity and accuracy. This paper introduces a new combined analytical–numerical probabilistic method to assess the impact of DERs on voltage stability. Using Bayesian Parameter Estimation (BPE), the method derives the analytical properties of random variables (RVs) associated with DERs and loads, obtaining posterior distributions. The Metropolis–Hastings sampling technique then estimates these posteriors numerically, enabling accurate predictions of DERs and load outputs. Voltage stability analysis was performed using the continuation power flow method and validated on the IEEE 59-bus test system in MATLAB/Simulink. The results show that integrating DERs significantly improves voltage stability. The proposed method outperforms the Monte Carlo simulation (MCS)-based method in accuracy and computational speed, increasing DERs penetration and voltage stability limits by 3%. It closely matches MCS voltage estimates but requires fewer iterations (500 per loading increment) compared to MCS’s 1000, leading to faster computation times (a few hours to one day versus up to three days for MCS). This method provides an efficient solution for managing uncertainties in power systems.

Optimal configuration of dynamic VAR compensators considering uncertainty and correlation

AbstractThe transient voltage stability of modern power systems is challenged by the increasing uncertainty on source‐load dual sides. To cope with it, a robust optimal configuration method for dynamic VAR compensators (DVCs) is proposed. First, a series of power‐flow scenarios are generated with uncertainty and correlation considered, where nonparametric kernel density estimation is used to predict the marginal distribution of wind speeds and combined Copula function is employed to characterize correlations between nearby wind farms. Then, the variation‐of‐information indicator is introduced to assess the similarity among power‐flow (PF) scenarios, and representative PF (RPF) scenarios are screened with the help of Spectral clustering algorithm. Finally, locating and sizing of DVCs for RPF scenarios are achieved. By synthesizing the compensation schemes for RPF scenarios, the robust configuration scheme is suggested. Simulation studies in the modified New England 10‐machine 39‐bus system show the proposed method can ensure transient voltage security of the studied power system under source‐load uncertainty.

Application of cascaded neural network for prediction of voltage stability margin in a solar and wind integrated power system

Voltage stability is a paramount concern in the management of renewable-rich power systems. As the diffusion of renewable energy sources continues to increase, accurately estimating voltage stability margins becomes essential. This paper addresses the critical need for effective voltage stability margin estimation and presents a novel approach to predict the same on the solar and wind integrated power system. A new machine learning methodology is developed based on the cascaded neural network (CNN). The proposed CNN methodology effectively estimates the loadability margin in a renewable-rich power system under normal operating conditions, varied loading directions, and various contingency situations. The results of CNN are compared with different machine learning techniques and tested in the IEEE 30 bus system and IEEE 118 bus system under various operational circumstances. The results illustrate the efficiency of the suggested method for online assessment of a power system's Voltage Stability Margin. The proposed approach gives real-time insights into voltage stability margins by leveraging the power of data and powerful algorithms, allowing grid operators to proactively manage and optimize renewable energy integration while ensuring grid dependability and stability.

Multi-Period Optimal Transmission Switching with Voltage Stability and Security Constraints by the Minimum Number of Actions

Due to the ever-growing load demand and the deregulation of the electricity market, power systems often run near the stability boundaries, which deteriorates system voltage stability and raises voltage issues for the stable operations of power systems. Transmission switching (TS) has been applied to improve economic benefits and security operations for many applications. In this paper, a multi-period voltage stability-constrained problem (MP-VSTS) is established, intending to improve voltage security and the stability of a power system. Considering the online application of transmission switching, the minimum number of switching actions is taken as the objective function of the proposed MP-VSTS problem, which extends the TS application for real industries. The proposed model provides the switching lines for the upcoming period and the state of power systems for several successive periods. To overcome the solving difficulties of the proposed model, a two-stage approach is presented, which balances speed and accuracy. Numerical studies on the IEEE 118- and 662-bus power systems have demonstrated the proposed approach’s performance.

Power and efficiency enhancement of solar photovoltaic power plants through grouped string voltage balancing approach

Solar photovoltaic (PV) power plants’ performance is severely impacted by multi-level irradiances or partial shading, leading to power losses and voltage instability. Also, partial shading adds further complexity to the maximum power point tracking algorithms by introducing numerous peaks in the power curves, resulting in additional losses. Numerous solutions are presented to deal with shading losses, and dynamic reconfiguration is the most effective; however, higher switch count and complex architecture make it impractical in real-world implementation. Hence, this study proposes a low-complexity architecture based on the grouped string voltage balancing approach. This approach utilizes a voltage balancing converter connected to groups of strings to enhance the power output of the array of PV plants, maintain overall system voltage stability, and eliminate the possibility of multiple peaks formation in the power curves. The effectiveness of the proposed approach is tested under numerous static and dynamic partial shadings and analyzed using power curves, power output, losses, efficiencies, and voltage stability. The validation is done by comparing the proposed approach with conventional and advanced architectures for a 32.5 kW system. The results show that the proposed method requires a 50 % reduced switch count than existing techniques, achieves 99.54 % efficiency, and maintains an average voltage stability of 0.01.

A Novel Pelican Bird Optimization Algorithm based Optimal Allocation of Combined DGs and EVCSs for Improving Voltage Stability of RDS

Objectives: The main objective of the proposed investigation is to enhance the voltage stability of Radial Distribution System (RDS). It is achieved by proper allocation between combined Electric Vehicle Charging Stations (EVCSs) and Distributed Generators (DGs) in RDS. Here, three objective functions such as Real Power loss, Average Voltage Devotion Index (AVDI) and Voltage Stability Index (VSI) are considered. Methods: The multi-objective optimization problem is implemented using a novel and an effective Pelican Optimization Algorithm (POA) to attain the best compromised solutions. POA is a nature inspired parameter less optimization tool and it is well suited for solution of complex, non-linear voltage stability problem. It determines the most excellent site and size of the EVCSs and DG units. The control parameter of POA considered are population size as 40, the number of variables as 8 and the maximum number of iterations as 100. MATLAB 14.0 platform is utilized for the projected problem. Findings: The bench mark test system of IEEE-33 node system is considered to authenticate the presentation of planned POA methodology. The outcomes such as minimized power loss and AVDI, improved voltage at each bus and VSI are presented. The percentage of loss reduction 39.30% is enhanced, when compared with the base case approach and 5.42 % more improved than the other existing methods like TLBO and HHO approaches. Similarly, the VSI 24.96 % is improved, when compared with the base case method and 1.58 % better than the other obtainable methods. Novelty: The POA is a newly urbanized and well-designed optimization algorithm. It efficiently reaches the global optimal solutions with less time duration. The comparative study with the existing algorithm of TLBO and HHO is considered to prove the performance of Projected POA approach. Keywords: Radial distribution system, Distributed Generators, Electric Vehicles, Charging Stations, Voltage stability, Network loss reduction, Pelican Optimization Algorithm

A Predictive Model Using Long Short-Time Memory (LSTM) Technique for Power System Voltage Stability

The stability of the operation of the power system is essential to ensure a continuous supply of electricity to meet the load of the system. In the operational process, voltage stability (VS) should be recognized and predicted as a basic requirement. In electrical systems, deep learning and machine learning algorithms have found widespread applications. These algorithms can learn from previous data to detect and predict future scenarios of potential instability. This study introduces long short-term memory (LSTM) technology to predict the stability of the nominal voltage of the power system. Based on the results, the recommended LSTM technology achieved the highest accuracy target of 99.5%. In addition, the LSTM model outperforms other machine learning (ML) and deep learning techniques, i.e., support vector machines (SVMs), Naive Bayes (NB), and convolutional neural networks (CNNs), when comparing the accuracy of the VS forecast. The results show that the LSTM method is useful to predict the voltage of an electrical system. The IEEE 33-bus system indicates that the recommended approach can rapidly and precisely verify the system stability category. Furthermore, the proposed method outperforms conventional assessment methods that rely on shallow learning.

Stability analysis of power system under n-1 contingency condition

Several voltage stability indices (VSIs) have been developed to assess the potential for voltage collapse. However, certain indexes are computationally costly. Meanwhile, some have been noted to underperform across various conditions. This work proposes a novel line index called the super voltage stability index (SVSI) to calculate the system's voltage stability margin (VSM). The suggested approach is based on the transmission system's two bus systems. The reactive power loss and N-1 contingency conditions to voltage sensitivity is a unique calculation approach used in this study to identify voltage instability. Day to day, the demand for electric power is being increased due to incessant increments in technology and population growth. Therefore, the power system networks are under pressure. The operational conditions of transmission system networks are affected at this point, which may result in voltage collapse. Regular monitoring of power supply is essential to avert voltage collapse. The effectiveness of the suggested index has been assessed using the IEEE 5 and 30-bus systems across diverse operating scenarios, including variations in active and reactive power loading as well as single line losses. The findings indicate that SVSI provides a more reliable indication of the proximity to voltage collapse when compared to conventional line VSIs.

An enhanced jellyfish search optimizer for stochastic energy management of multi-microgrids with wind turbines, biomass and PV generation systems considering uncertainty

The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively.

SE-CNN based emergency control coordination strategy against voltage instability in multi-infeed hybrid AC/DC systems

For receiving-end power systems with multi-infeed HVDCs and large-scale renewable energy generation, the challenges of weak reactive power support and limited anti-interference capacity pose a threat to the power system voltage stability. To enhance the voltage stability under large disturbances, this paper proposes a data-driven method for coordinated emergency control strategy against voltage instability in multi-infeed hybrid AC/DC systems based on the squeeze and excitation (SE) module and convolutional neural network (CNN). Firstly, the SE module and CNN are integrated to intuitively capture correlations among various feature channels and extract key features related to voltage stability levels. Secondly, a voltage stability evaluation method is proposed based on the SE-CNN model, utilizing a dual channel structure to unveil the quantitative relationship between key response characteristics and voltage stability margin. Finally, regarding the control measures containing load shedding and DC modulation, the emergency control sensitivity index is proposed to assess the stability margin improvement effect of various control objects, and the optimal emergency control strategy is formulated to coordinate multiple voltage stability emergency control measures. Case studies were conducted on a typical China local region receiving-end power grid test system with voltage instability problems, and the simulation results verified the effectiveness of the proposed method. © 2017 Elsevier Inc. All rights reserved.

An easy method for simultaneously enhancing power system voltage and angle stability using STATCOM

The global electricity demand is growing rapidly and is expected to double by 2050. This growth requires significantly expanding the current high-voltage transmission lines, which is a long-term and costly solution. As an alternative, utilities use advanced technologies like Flexible Alternating Current Transmission System (FACTS) devices as a temporary measure, postponing the expansion. However, it is necessary to locate these devices in the network properly to avoid negative outcomes. Traditional methods for FACTS placement focus on voltage profile improvement and power loss reduction, neglecting rotor angle stability. This can lead to oscillatory instability, especially in weak power grids. To address this issue, the paper proposes a simple approach to optimize the placement of shunt FACTS devices in power systems. The method improves both voltage and rotor angle stability without compromising power loss. The proposed approach considers rotor angle stability constraints, which is essential for system stability during transient events. The study was demonstrated on Nigeria’s 50-bus 330 kV power system, with results indicating a 31% improvement in angle stability with an enhanced voltage profile. The method is straightforward, non-iterative, and provides a cost-effective solution for FACTS device placement in power systems.

Hybrid model-data-driven dynamic VAR planning for wind-penetrated power system using spectral surrogate techniques

The increasing wind power penetration and proliferation of induction motor loads, of which dynamic impact cannot be revealed through steady-state analysis, bring challenges to the short-term voltage stability of modern power systems. This study proposes a hybrid model-data-driven approach for dynamic VAR source planning to enhance the short-term voltage stability of wind-penetrated power systems to reduce the computation burden of electro-mechanical transient models. Firstly, the theoretical background of Stochastic Spectral Embedding (SSE) is introduced. Then, a surrogate model for the electromechanical transient model is established using SSE following efficient expansion coefficient calculation and a designed partition strategy for the studied problem. Furthermore, a hybrid model-data-driven VAR deployment optimization model is established with 3 objectives, which is solved by a dual-population-based evolutionary algorithm (DPEA). The accuracy and effectiveness of the model are verified on a modified New England 39-bus system. Simulation results prove that the computational cost is reduced significantly in comparison with conventional model-based method without a compromise in accuracy and the proposed method is also more accurate than methods based on other surrogate models. The proposed SSE-based model can be applied to other power system analysis with electro-mechanical transient models to alleviate the computational cost.

The Impact of Hybrid Power Generations on a Power System's Voltage Stability

The Impact of Hybrid Power Generations on a Power System's Voltage Stability

Correction Control Model of L-Index Based on VSC-OPF and BLS Method

With the advancement of artificial intelligence (AI) technology, the real-time measurement and control technology of power systems has also progressed. This paper proposes a correction control model for L-indexes based on voltage stability constrained optimal power flow (VSC-OPF) and a broad learning system (BLS) (BLS-VSC-OPF). This model aims to quickly assess the system’s voltage stability and accurately correct the operation mode when the voltage stability indexes are out of the security range. Firstly, the BLS is used to predict the L-index and to analyze the voltage stability of the power system. Secondly, the approximate first-order sensitivity of the L-index is calculated by the combination of the BLS and the perturbation method. This method solves the problem of the complex sensitivity derivation process in the modeling process of the VSC-OPF model. Meanwhile, when the L-index exceeds the threshold, the BLS and VSC-OPF models are combined to correct this operation mode. The feasibility of the proposed method is verified by the simulation of IEEE-30, IEEE-118, and 1047 bus systems. Finally, the BLS-VSC-OPF model is compared with the linear programming correction model based on BLS (BLS-LPC). The results show that the BLS-VSC-OPF model provides a better correction and control performance.

The consequences and barriers of power system voltage stability in the integration of solar and wind energy: An in-depth analysis

Wind and solar energy production is intermittent and variable, causing voltage fluctuations in the power system. These fluctuations can affect supply and demand balance, leading to voltage instability. In some cases, integrating solar and wind energy can result in overvoltage or undervoltage conditions, which can negatively impact the power grid's operation. The research examines the effects and challenges of integrating wind and solar energy into power grids, concentrating in particular on issues with voltage stability. The investigation explores fundamental issues like voltage stability-based distributed power generation (DG) unit sizing and optimal locations, stability of voltage assessment and methods for enhancement. Experiments are conducted to examine the effects of energy storage devices, adaptive alternate current (AC) transmission lines, static caps and other electric components of the system on the stability of the voltage of the distribution and transmission networks. It provides a comprehensive examination of the technical difficulties, such as the stability issues associated with incorporating massive solar systems into the electrical grid. The research obtained results on technical methods to address the power instability challenges with the integration of solar power on a massive scale into the transmission system and its subtransmissions, known as the medium voltage distribution system.

Suitability of capacitor allocation methods in different kinds of distribution networks

In the context of power systems with “a high percentage of renewable energy” and “a high percentage of power electronic equipment”, reactive power capacitors are demanded to support the voltage and keep the economic operation of the distribution network. Reasonable configuration of reactive power compensation including capacitor location and compensation capacity can keep the system voltage at a reasonable level, improve the system voltage stability, and reduce the network loss. In this paper, a reactive power optimization model based on three capacitor allocation methods, namely the reactive power sensitivity method, the active power loss method, and the voltage stability method, was established. Considering the stability and economy of distribution network operation, the suitability of the different methods in different kinds of distribution networks was evaluated in terms of the indicators of minimizing the network loss, minimizing the voltage offset, and minimizing the system operation cost. The simulation results show that the active power loss method is the most suitable for reducing voltage fluctuations and network losses.

Multistrategy Fusion Particle Swarm for Dynamic Economic Dispatch Optimization of Renewable Energy Sources

This paper presents a multistrategy fusion particle swarm optimization model for dynamic economic dispatching of renewable energy in distribution networks. The objective is to minimize active network losses and system voltage deviation while considering the integration of distributed energy sources and static reactive power compensators. The algorithm incorporates specific strategies, including a particle position change strategy based on the midpipeline convergence approach, a strategy for generating exploding particles near the optimal particles, and a particle velocity update strategy relying on the global optimal particle position. The inertia weights and particle position update methods of the simplified particle swarm optimization algorithm are also utilized. Simulation experiments are conducted on an IEEE 33 bus radial distribution system, demonstrating the effective optimization of system losses while ensuring system voltage stability. This research contributes to the scientific understanding of renewable energy integration in distribution networks and its economic dispatching.

Optimizing smart microgrid performance: Integrating solar generation and static VAR compensator for EV charging impact, emphasizing SCOPE index

The rapid global increase in electric vehicle (EV) usage, driven by its low CO2 emissions, uncomplicated maintenance, and minimal operating costs, has prompted extensive research in the field of electric vehicle charging station (EVCS). The integration of EVCS into the current distribution grid poses challenges due to potential power losses and voltage variations beyond acceptable limits. This complexity is heightened by the growing penetration of randomly dispersed solar-based distributed generation (SDG) and battery energy storage system (BESS). To address these challenges, distribution static VAR compensator (DSVC) systems have been introduced, offering benefits such as enhanced power transfer capacity, improved voltage regulation, and increased system security without requiring extensive infrastructure upgrades. This study offers SCOPE, a novel multi-objective framework that unifies the optimization goals of minimizing real power loss, lowering bus voltage variation, maximizing system voltage stability, minimizing system operating costs, and mitigating CO2 emissions. The EVCS problem is optimized within this multi-objective framework utilizing an improved bald eagle search algorithm (IBESA). The proposed model accounts for vehicle to grid (V2G) capabilities and the actual driving patterns of users over a 24-h horizon. The formulation of a smart microgrid (SMG) structure is based on modifying the standard IEEE 33-bus test radial distribution network (RDN), comprising three interconnected SMGs serving residential, commercial, and industrial users. The optimization approach based on IBESA is utilized to optimize both the siting and capacity of EVCS as well as renewable energy sources (RESs). The findings show that SDG and DSVC are effective at lowering the SCOPE index, highlighting the advantages of the suggested approach.