Voltage Stability Improvement Research Articles

Resilient day-ahead microgrid energy management with uncertain demand, EVs, storage, and renewables

Managing microgrid energy presents a complex challenge due to unpredictable renewable sources, fluctuating demand, and diverse equipment like batteries, distributed generators, and electric vehicles. This paper introduces a novel two-step optimization model, the Robust Day-Ahead Scheduling for Enhanced Resilience, tailored for microgrid operations. The model addresses the integration of electronic generation, uncertain demand patterns, and small-scale renewable resources. Detailed formulations optimize microgrid energy use, including strategic battery usage, efficient electric vehicle charging, balancing device utilization, and distributed generation dispatch. This multi-faceted approach aims to minimize costs over 24 h, including energy loss, power purchases, reduced power usage, generator operation, and battery/EV expenses. Employing a column-and-constraint generation (C&CG) algorithm ensures efficient problem solving. The proposed model achieved a significant reduction in operational costs, outperforming existing methods by at least 8%. Notably, it minimized energy purchases, energy losses, and load shedding while improving voltage stability, showcasing its effectiveness in enhancing microgrid performance and resilience.

Optimal Deployment of DGs, DSTATCOMs and EVCSs in Distribution System using Multi-Objective Artificial Hummingbird Optimization

Distribution systems have a lot of obstacles to deal with, like increasing load demands, environmental issues, operating limits, and infrastructure development limitations. On the other hand, the number of plug-in hybrid electric vehicles (PHEVs) has grown significantly in recent years and is likely to continue due to concerns over the environment and fossil fuel shortages. Due to the increasing use of PHEVs, distribution systems were not built to accept them, requiring planners to create parking lots that support PHEV charging. To address these issues, in this study, optimal planning of distributed generation (DG) and electric vehicle charging stations (EVCS) in radial distribution systems by the maiden application of a novel Pareto-based multi-objective artificial hummingbird optimization (MOAHO) algorithm is addressed. Three technical aspects of the distribution system are improved by optimal planning of various types of DGs and EVCSs: active power loss reduction, total voltage deviation minimization, and voltage stability improvement. The Pareto-based MOAHO is employed to generate the optimal front between the three competing objectives and later TOPSIS method is employed for selecting the most compromised solution from the optimal front. The proposed methodology is tested on IEEE-33, IEEE-69 bus radial distribution test systems. To validate the efficacy of the MOAHO algorithm, the simulation outcomes of the proposed methodology are generated using a multi-objective non-dominated sorting genetic algorithm (NSGA2), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO) and compared with the outcomes of the MOAHO algorithm.

Voltage Stability in a Photovoltaic-based DC Microgrid with GaN-Based Bidirectional Converter using Fuzzy Controller for EV Charging Applications

Electric vehicles are growing in importance owing to their desirable characteristics leading to utilization. It is a significant challenge to maintain voltage for DC microgrids when integrating with EVs. The work aims to enhance voltage stability in a DC microgrid and the electric vehicle charging using GaN devices in the converter. This is done by developing a DC microgrid with better voltage regulation, loss reduction, and increased efficiency and these are analyzed using PLECS software. Under different operating conditions, the proposed converter can respond to load fluctuations and maintain its voltage profile stable. The approach meets the increasing demand for vehicle charging by upgrading DC microgrid technology. The use of GaN-based converters improves voltage stability while allowing for efficient integration of EVs into the grid thereby giving more options for transportation.

Analysis of Grid Performance with Diversified Distributed Resources and Storage Integration: A Bilevel Approach with Network-Oriented PSO

The growing deployment of distributed resources significantly affects the distribution grid performance in most countries. The optimal sizing and placement of these resources have become increasingly crucial to mitigating grid issues and reducing costs. Particle Swarm Optimization (PSO) is widely used to address such problems but faces computational inefficiency due to its numerical convergence behavior. This limits its effectiveness, especially for power system problems, because the numerical distance between two nodes in power systems might be different from the actual electrical distance. In this paper, a scalable bilevel optimization problem with two novel algorithms enhances PSO’s computational efficiency. While the resistivity-driven algorithm strategically targets low-resistivity regions and guides PSO toward areas with lower losses, the connectivity-driven algorithm aligns solution spaces with the grid’s physical topology. It prioritizes actual physical neighbors during the search to prevent local optima traps. The tests of the algorithms on the IEEE 33-bus and the 69-bus and Norwegian networks show significant reductions in power losses (up to 74% for PV, wind, and storage) and improved voltage stability (a 21% reduction in mean voltage deviation index) with respect to the results of classical PSO. The proposed network-oriented PSO outperforms classical PSO by achieving a 2.84% reduction in the average fitness value for the IEEE 69-bus case with PV, wind, and storage deployment. The Norwegian case study affirms the effectiveness of the proposed approach in real-world applications through significant improvements in loss reduction and voltage stability.

A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations

In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability.

Optimal Planning of Battery Swapping Stations Incorporating Dynamic Network Reconfiguration Considering Technical Aspects of the Power Grid

In order to drive electric vehicle adoption and bolster grid stability, the incorporation of battery swapping stations (BSSs) into the power grid is imperative. Conversely, network reconfiguration plays a crucial role in optimizing energy exchange within the power network, ensuring its economical and safe operation. Therefore, this study proposes an optimal planning method for battery swapping stations that integrates dynamic power distribution network reconfiguration while addressing technical aspects of the grid. The proposed method aims to concurrently optimize the placement and capacity of battery swapping stations, along with power distribution network reconfiguration, to enhance grid reliability and efficiency. The optimization model accounts for various factors including power quality, technical considerations, grid limitations, and operational expenses. A multi-objective optimization framework is devised to simultaneously reduce system losses, improve voltage stability, and mitigate environmental impacts of the power distribution network incorporating DG units. Case studies are conducted to illustrate the efficacy of the proposed approach in enhancing overall grid performance while accommodating the integration of battery swapping stations. The findings underscore the significance of considering technical factors and grid reconfiguration in battery swapping station planning to achieve optimal system operation and maximize benefits for electric vehicle users and grid operators alike.

The Effect Pso/Abc, Abc/Bfo, Pso/Bfo and Cs/Bfo Hybrid Techniques on Power System Buses for Voltage Stability Margin Improvement

This paper presents and investigates new approaches inspired by nature to determine the optimal placement of UPFC for enhancing voltage stability margin of power system network. Voltage collapse phenomenon has remained an operational problem yet to be solved by power system engineers. To improve the voltage stability of the power system network, new optimisation techniques viz: PSO/ABC, ABC/BFO, PSO/BFO AND CS/BFO hybrids are investigated for their performance on power system buses for voltage stability improvement. The performance of the proposed hybrid methods are examined on the practical Nigerian 56 - bus network for voltage stability improvement. The simulation results has shown that ABC/BFO technique is better than BFO/PSO by 3.44 in improving voltage stability of margin of power system, gives additional improvement of 4.07, totalling 7.51% and lastly BFO/CS technique improved voltage stability margin of power system further by 5% totalling 12.51%. Also this research revealed that the smaller the gamma value the better the UPFC contribution to voltage stability enhancement. BFO/CS technique yielded the smallest gamma values of 0. 74532 and 0.830913 in both Nigerian practical network and IEEE – 14 bus network as shown in the analysis.

Evaluating Reliability and Economics of EV Charging Configurations and Deep Reinforcement Learning in Robotics and Autonomy

Growing EV popularity drives companies to focus on reliable charging station designs despite challenges in maintaining reliability. A proposed 36-ported design combines uniform and non-uniform port arrangements, tested with 50-350 kW systems. Failure rates are estimated using MILHDBK217F and MILHBK-338B standards, assessing port reliability and station success rates through binomial distribution and cost analysis. This design improves voltage stability and reduces maintenance costs through enhanced port reliability. In robotics and autonomous systems, Deep Reinforcement Learning (DRL) excels but faces challenges from unsafe policies leading to hazardous decisions. This study introduces a reliability assessment framework for DRL-controlled systems, using formal neural network analysis. A two-level verification approach evaluates safety locally using reachability tools and globally by aggregating local safety metrics across tasks. Experimental validation confirms the framework's effectiveness in enhancing RAS safety.

Effect of NASCION‐Type ceramic dispersion in PAN/PVA‐based blend polymer electrolyte: A structural, thermal, and electrical study

AbstractSolid flexible separators having adequate ion conductivity at ambient temperature along with good thermal and voltage stability are potential candidates for separators in all solid‐state ion batteries. In the present work, zirconium and niobium‐doped Li1.3Al0.3Ti1.7 (PO4)3 ceramic particles have been incorporated into a polyvinyl alcohol (PVA) and polyacrylonitrile (PAN)‐based blend that served as a flexible matrix to create a flexible polymer–ceramic framework and have been examined for conductivity, dielectric properties, voltage stability, thermal stability, and electrochemical performance. The flexible polymer–ceramic framework shows high thermal stability, improved conductivity, and a high‐voltage window. Optimized dc conductivity of 9.8 × 10−5 S cm−1 has been observed among all investigated samples, with an improved voltage stability of 4.94 V. Further, the Trukhan model has been used to understand the effect of filler loading on ion migration properties. The optimized solid polymer electrolyte (SPE) shows improved ion mobility (μ) of 5.13 × 10−2 cm2 V−1 s, number density (n) = 2.39 × 1015 cm3, and diffusion coefficient (D) of 2.09 × 10−3 cm2 s−1. It also shows a very high specific capacitance of ~10−4 Fg−1, excellent cycling stability, and 93.75% capacity retention after 100 cycles.

Large scale penetration impact of PMS-WTGS on voltage profile and power loss for 5-bus, 330kV system of the Nigerian grid

In recent times, adverse environmental changes and the gradual reduction in fossil fuel deposits is making integration of renewable energy secure global attention. This paper therefore investigates the effect of high penetration of wind power on power loss and voltage profile of 52-bus, 330kV Nigerian grid. Modeling and simulation of the study were successfully done using the Continuation Power Flow method and Power System Analysis Toolbox in the Matrix Laboratory environment. The Ant Colony Optimization (ACO) technique codes were used to determine the best sites and sizes of Permanent Magnet Synchronous-based Wind Turbine Generators (PMS-WTGs) on the grid as nonoptimal placement and size of PMS-WTGs will result into active power losses and poor voltage profile. The ACO and power flow analysis indicated two load buses, Aladja bus and Yola bus as best sites for PMS-WTGs with optimal sizes of 100MW and 197MW respectively. The result shows a decrease in the system's active (42.1%) and reactive (43.9%) power losses while heightening voltage magnitudes of critical buses to statutory values and enhancing the grid power quality. The overall results indicate that integrating 297MW PMS-WTGs improves voltage stability and power loss reduction by maximizing system loadability and reducing line losses.

Optimal Allocation of Distributed Generation Using Improved Cuckoo Search Algorithm

This work presents an improved cuckoo search algorithm based on Success Rate (CSA-SR) for optimal allocation of Distributed Generation (DG). This is necessary in order to improve the power quality of the distribution system by minimizing power loss and improving voltage stability. Voltage Stability Index (VSI) is applied to identify restricted unhealthy buses/areas in the distribution networks. The work is implemented on standard IEEE 33 Bus test systems. Newton Raphson Power Flow (NRPF) technique is used in MATLAB R2019b environment for the power flow analysis. The overall IEEE 33 Bus system real power loss obtained from the simulation result is found to be 202.7071MW at the base case. Following the application of 3 DGs using CSA-SR, the real power loss is found to be 113.9630 MW which shows a reduction of 88.441MW which is equivalent to 43.77% as related to the base case. The results minimized the system losses while significantly enhancing the voltage profile of the bus voltages as a result of optimal siting and sizing of the DG. These show that the CSA-SR technique outperformed PSO, which was used to ascertain the effectiveness of the developed scheme.

Evaluation of Reliability and Financial Feasibility of Various EV Charging Station Layouts

As the popularity of electric vehicles (EVs) continues to rise, companies are increasingly focusing on expanding charging infrastructure to meet growing consumer demand. Despite attempts to design charging stations that align with distribution system requirements, ensuring reliable performance for EV charging ports remains a complex challenge. To address this issue, a unique design featuring 56 ports, comprising both uniform and non-uniform arrangements, has been introduced. These configurations underwent testing across distribution systems ranging from 50 to 500 kW. Reliability assessments were carried out using established standards for failure rate estimation and Monte Carlo simulations to evaluate port probability functions in terms of failure rate and reliability. By scrutinizing the failure rates of individual ports, a systematic evaluation method was established to gauge the overall performance of the charging station. The failure rate of the proposed 56-ported charging station was further assessed using the binomial distribution method. Also, cost estimation procedures were developed, taking into account the maintenance costs associated with the failure and success rates of individual ports for the proposed design. The research findings suggest that by enhancing port arrangement reliability and improving voltage stability, it is possible to achieve lower failure rates and maintenance costs, thereby enhancing overall system performance

Voltage stability improvement of distribution networks using reactive power capability of electric vehicle charging stations

Modern power networks are affected by the increasing adoption of electric vehicles (EVs) driven by environmental concerns. Electric vehicle charging stations (EVCSs) pose serious challenges due to the fluctuating charging demand leading to voltage instabilities and affecting the secure operation of power networks. This research proposes a novel approach to address these instabilities by utilizing the capabilities of EVCSs in absorbing and injecting reactive power. An efficient management of the reactive power of EVCSs is achieved by embedding differential evolutionary algorithms in a specialized optimization framework. We employ the two-point estimation method (2PEM) to deal with the uncertainties in renewable energy, load fluctuations, and EV demand patterns. The effectiveness of 2PEM is validated through comparison with Monte Carlo simulation (MCS). Demonstrated on a 51-bus network model, the proposed framework represents its real-world applicability. The results show a significant improvement in voltage stability, along with a considerable reduction in power loss.

Voltage stability improvement and power losses reduction through multiple grid contingency supports

A stable power system is a desirable state for the optimal performance and delivery of electricity to the end-users. This study evaluates the effectiveness of grid contingency support devices in power system stability enhancement. The new voltage stability pointer (NVSP) was employed to optimally infer the flexible alternating current transmission systems devices through N − 1 contingency ranking. In this research, the static synchronous compensator (STATCOM) was considered as a grid support device. The effectiveness of this technique was validated in R2018a MATLAB with the IEEE 30-bus system using different combinable contingency cases to ascertain the critical lines and vulnerable buses to outages. Based on the results obtained through the NVSP, buses 30, 26, and 29 were cited as STATCOM candidate buses for overall optimal grid performance. The results obtained from the simulation revealed that the total real power loss was reduced by 90.21%. Similarly, the voltage magnitude of the buses was significantly improved to a minimum of 1.00 p.u. after placement of STATCOMs. Further investigation on single STATCOM placement in the IEEE 30-bus system revealed that the highest level of voltage improvement, minimum level of voltage deviation and maximum active power loss reduction was achieved when the STATCOM was placed at bus 30. The overall assessment of the proposed technique yielded better results compared with other methods on the same test system.

Application of SSSC for Voltage Stability Improvement in the Nigerian 330 kV Transmission System using Particle Swarm Optimization Technique

Load imbalance has always been a major cause of voltage instability of the power system network and increase in voltage instability affects the power system reliability. In this paper, static synchronous series capacitor (SSSC) FACTS device was implemented to improve the voltage stability in the Nigerian 330 kV transmission network using particle swarm optimization (PSO) technique. Modelled power system network in the power system analysis toolbox (PSAT) is simulated to determine the existing power flow analysis and the level of instability in the power system generation plants situated in the Northern region. The outcome of the power flow is thereafter, optimized with PSO by generating the relationship with a high prediction accuracy between voltage at each location and an inter-linked transmission line distance of the 330kV. The model was generated with least square method and the high prediction accuracy determination was carried out with R-square value of the polynomial order. The best model was optimized and the entire computations were carried out in MatLab resulting in the determination of the optimal location which was between Jebba transmission and Shiroro generation stations. SSSC FACTS controller was implemented on the optimized transmission line and the simulation results showed that the introduction of SSSC improved the voltage stability of the power system network.

Multi-Agent Graph-Attention Deep Reinforcement Learning for Post-Contingency Grid Emergency Voltage Control.

Grid emergency voltage control (GEVC) is paramount in electric power systems to improve voltage stability and prevent cascading outages and blackouts in case of contingencies. While most deep reinforcement learning (DRL)-based paradigms perform single agents in a static environment, real-world agents for GEVC are expected to cooperate in a dynamically shifting grid. Moreover, due to high uncertainties from combinatory natures of various contingencies and load consumption, along with the complexity of dynamic grid operation, the data efficiency and control performance of the existing DRL-based methods are challenged. To address these limitations, we propose a multi-agent graph-attention (GATT)-based DRL algorithm for GEVC in multi-area power systems. We develop graph convolutional network (GCN)-based agents for feature representation of the graph-structured voltages to improve the decision accuracy in a data-efficient manner. Furthermore, a cutting-edge attention mechanism concentrates on effective information sharing among multiple agents, synergizing different-sized subnetworks in the grid for cooperative learning. We address several key challenges in the existing DRL-based GEVC approaches, including low scalability and poor stability against high uncertainties. Test results in the IEEE benchmark system verify the advantages of the proposed method over several recent multi-agent DRL-based algorithms.

Improving voltage collapse point under transmission line outage by optimal placement and sizing of SVC using genetic algorithm

In many power systems, voltage instability can increase the risk of voltage collapse and, as a result, turn the power system toward a blackout. Therefore, increasing the voltage collapse point is required. A transmission line outage is an emergency condition in power systems that can lead to voltage instability and voltage collapse. Thus, it is expected to employ shunt-connected flexible AC transmission systems (FACTS) such as the static var compensator (SVC) to increase the voltage collapse point when lines outage. This paper presents the genetic algorithm (GA) application to optimal placement and sizing of an SVC for increasing voltage collapse points following lines outage. The continuation power flow (CPF) technique has been used to determine the maximum loading point (MLP) corresponding to the point of voltage collapse. Also, to reduce the number of scenarios when line outages occur, a list in ascending order is established based on the line outage priority (LOP). The IEEE 14-bus test system is chosen to carry out simulations, and an SVC will be installed in the system based on the GA results. Simulation results confirm the effectiveness of an SVC for improving voltage stability as well as increasing voltage profile.

Optimal allocation of Combined DG and DSTATCOM for Improving Voltage Stability and Economical Benefits of Distribution System

In the vertically integrated power sector, the distribution system serves as the link between customers and the transmission network. Different load models, including industrial, residential, and commercial, are taken into account within the distribution systems. The loading pattern of these load models varies with a large range according to the peak hours. During peak load conditions, the voltage magnitude of some nodes exceeds the permissible limit of voltage value. It affects the voltage stability of the distribution system, leading to decreased reliability, heightened power losses, diminished voltage stability, and other safety concerns. Optimal placement of capacitors, applying DGs, using FACTS devices, and Distribution Network reconfigurations are possible to reduce the power loss. This paper focuses on reducing power loss and enhancing the voltage profile and stability of the system. It addresses the technical, economic, and environmental benefits of the distribution system through appropriate placement and sizing of DG and DSTATCOM units in a coordinated manner. The issue is approached as a Multi-Objective Optimization Problem (MOP) and is tackled through the application of the Group Teaching Optimization (GTO) algorithm, an innovative metaheuristic method. A numerical illustration utilizing an IEEE 69-node system is examined, and the simulated outcomes are presented in tabular form. These results are then juxtaposed with findings from comparable methodologies outlined in existing literature.

Improving voltage stability in subtransmission through pv curves and critical voltage balance for photovoltaic compensation

Improving voltage stability in subtransmission through pv curves and critical voltage balance for photovoltaic compensation

Realization of Small Scale Models of FCATS Devices and its Optimization in Power Systems using Stability Indices and Particle Swarm Optimization

This article focuses on the physical realization of SVC, TCSC, combined SVC and TCSC scale down models, which have been realized in the laboratory, developed and tested and presented the experimental results. These practical test results have been proven the effectiveness of these devices with closed loop control systems developed with microcontrollers. The voltage stability can be assessed accurately using stability indices. These indices can either reveal the critical bus or line of a power system. The said indices plays vital role in identifying the most critical n-1 and n-2 contingencies. This article further focuses on optimization of these devices in IEEE 9 bus, 14 bus and 30 bus test systems using stability indices and Particle Swarm Optimization (PSO). The simulation results for all these systems have been presented and which proves the tremendous improvement in voltage stability of power systems.