Voltage Stability Enhancement Research Articles

Enhancing voltage stability through wavelet-fuzzy control of hydrogen flow in OC-PEM fuel cell

Open cathode proton exchange membrane fuel cells (OC-PEM fuel cells) serve as electricity generators, utilizing hydrogen as an input source. While effective for fixed loads like residential applications, challenges arise in dealing with output voltage fluctuations caused by rapid load changes. These fluctuations not only impact fuel cell performance but also introduce instability in the supplied power. To solve this issue, the study proposes an innovative hydrogen flow control system employing a feedforward wavelet- fuzzy method. The primary goal of this control system is to enhance fuzzy control performance using wavelets, mitigating signal fluctuations and achieving optimal stability in fuel cell output voltage under constant load conditions. Wavelet functions act as filters on the fuzzy control input, minimizing fluctuations and refining the entire process. Additionally, a feedforward system is incorporated to maintain hydrogen flow at the set point value. The proposed control system is implemented on a validated model using experimental data. Performance analysis reveals that the proposed method effectively stabilizes voltage by accelerating the recovery time from disturbances.

A novel hybrid algorithm based on optimal size and location of photovoltaic with battery energy storage systems for voltage stability enhancement

A novel hybrid algorithm based on optimal size and location of photovoltaic with battery energy storage systems for voltage stability enhancement

Optimizing network reconfiguration to reduce power loss and improve the voltage profile in the distribution system: A practical case study

The electricity provider incurs a high costs and investments to transfer the electrical power to customers in the distribution network. Hence, the reliability of the distribution network (DN) is particularly important in the lives of customers. A distribution system faces many issues due to its direct impact of the voltage profile (VP) and power losses (PL) problems. The aim of this study is to provide an effective scheme to address the of VP and PL problems. In this research, a Selective Particle Swarm Optimization (SPSO) algorithm was used to find combination of switches to enhance VP and reduce PL. The proposed method has been tested in the distribution network system, 125-buses Sigeri-Makassar Indonesia. There are three kinds of considered load scenarios, namely: nominal load; active power load increase; active and reactive power load increase. The research results indicated that the VP of the system can increased from 0.918 pu to be 0.922 pu and decreased PL from 0.6482 MW to be 0.6052 MW; the VP can increased from 0.895 pu to be 0.901 pu and reduced PL from 1.0469 MW to be 0.97532 MW, the VP can increased from 0.895 pu to be 0.900 pu and reduced PL from 1.1929 MW to be 1.0758 MW, for scenarios 1, 2 and 3, respectively. The simulation results showed that the proposed method provides optimal solution, which can enhance the VP and reduce PL. This is indicated that the proposed method is an effective strategy for enhancing voltage stability and solve PL problem.

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.

Reinforcement learning-driven proximal policy optimization-based voltage control for PV and WT integrated power system

The depletion of conventional fuel reserves and the high carbon emissions have alarmed the energy sector drivers. The integration of renewable energy resources (RERs) towards power system transformation is an obvious approach for a new-zero future. However, this integration also brings challenges in terms of voltage stability and power losses due to the variable output of RERs. To address these challenges, this work proposes a novel approach utilizing photovoltaic (PV) inverters and static var compensators (SVCs) for reactive power control in power distribution networks (PDNs). It enhances voltage stability and minimizes power losses. The proposed study deploys a proximal policy optimization (PPO) algorithm for real-time communication and control between reactive power devices. Performance evaluation was made on an IEEE-33 Bus system to demonstrate the effectiveness of the proposed scheme in integrating RER-based distributed generators (DGs). The proposed system achieved 68 % voltage control, keeping the voltage within a certain range of ±5 % while minimizing power losses. The proposed system also reduced the voltage out-of-control ratio to 0.044, which indicates minimum voltage deviation from the standard value. The proposed study provides a promising solution for controlling the voltage of DGs integrated PDN, which can potentially enhance the efficiency and reliability of the power system.

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.

ANFIS controller for photovoltaic inverter transient and voltage stability enhancement

Grid operations are evolving as a result of the extension of existing huge scope inverter-based power stations and sun oriented (PV) power plants. In light of these alterations, the grid code has been updated to mandate the use of inverter based. In the event of grid failure, power plants should remain online and offer dynamic backup. The term “momentary cessation of operation” is commonly used to describe this characteristic. Very little is known about how PV power plants' temporary shutdowns contribute to the larger issue of unstable power supplies, and very few studies have been published on the topic. While trying to settle this issue, this study offers an ANFIS regulator engineering for PV inverters to work on that ephemeral solidness about coordinated dynamo associated through this network. This examination shows how each direct electric current connect condensers on that solar cell inverter might be utilized to assimilate a piece of the dynamic energy put away in the coordinated machine during the brief delays by utilizing the ANFIS regulator framework introduced. Besides, the recommended approach further develops voltage solidness by infusing responsive power. The viability of the recommended control system is affirmed by the introduced exploratory and reproduced discoveries.

Optimal Reactive Power Planning Under Wind Power Uncertainties with Techno-Commercial Assessment

Power systems have numerous issues arising from increased demand and advancements in technology. Among these challenges, the most major ones are the planning of reactive power and the enhancement of voltage stability. Previous studies focused on improving the voltage stability and lesser power losses with installation of renewable sources. Cost effective planning lacks the consideration of service provider’s benefits of buying the renewable power. This work is focused to reduce the cost on consumers with multiple reactive planning objectives like voltage stability and power losses. FACT device and Weibull modeled wind power plant are installed and sensitive installation location is identified by PTDF. Thus, formulated nonlinear multi-objective techno-economic problem is solved by a novel enhanced pareto front optimization algorithm which is uses the strength of a hypergraph network. The hypergraph network is modeled to solve the complex connections and locate the best solution in the search space. The results demonstrate that the proposed novel multi-objective hypergraph particle swarm optimization (MOHGPSO) effectively improves the voltage stability index by up to 30.10% when compared to MOPSO. The power loss and total cost decreased by 31.70% and 7.43%, respectively.

Assessing Reliability and Economic Viability of Different EV Charging Station Configurations

With the increasing popularity of electric vehicles (EVs) as a mode of transportation, companies are prioritizing the development of charging infrastructure to cater to customer needs. Despite efforts to align charging station designs with distribution system requirements, maintaining reliability for EV charging ports remains challenging. To enhance reliability, a novel 56-ported design incorporating both uniform and non-uniform port arrangements has been proposed. These configurations have been tested with distribution systems ranging from 50 to 500 kW. Reliability assessments were conducted using standards outlined failure rate estimation and monte-carlo functions for evaluating port probability functions in terms of failure rate and reliability. By analyzing the failure rates of individual ports, an evaluation process was introduced to determine the overall success rate of the charging station. The failure rate of the proposed 56-ported charging station was further evaluated using the binomial distribution method. Additionally, cost estimation procedures were implemented considering the failure and success rates of individual port maintenance for the proposed configuration. The findings indicate that achieving lower failure rates and maintenance costs is possible through improved port arrangement reliability and enhanced voltage stability.

Deep-Learning-Aided Voltage-Stability-Enhancing Stochastic Distribution Network Reconfiguration

Power distribution networks are approaching their voltage stability boundaries due to the severe voltage violations and the inadequate reactive power reserves caused by the increasing renewable generations and dynamic loads. In the broad endeavor to resolve this concern, we focus on enhancing voltage stability through stochastic distribution network reconfiguration (SDNR), which optimizes the (radial) topology of a distribution network under uncertain generations and loads. We propose a deep learning method to solve this computationally challenging problem. Specifically, we build a convolutional neural network model to predict the relevant voltage stability index from the SDNR decisions. Then we integrate this prediction model into successive branch reduction algorithms to reconfigure a radial network with optimized performance in terms of power loss reduction and voltage stability enhancement. Numerical results on two IEEE network models verify the significance of enhancing voltage stability through SDNR and the computational efficiency of the proposed method.

Review on various voltage stability issues in AC distribution network

With the rapidly depleting natural energy resources and increasing population worldwide, the calls for more energy demand have risen globally. In this vein, the world is transitioning from traditional to renewable energy sources (RES) to meet the required demands. However, the (RES), due to its variable nature, has voltage stability and power quality issues that need to get minimized to enhance the reliability of the (RES) system. This paper reviews the voltage stability and quality issues in the AC distribution system based on renewable energy sources (RES). Initially the renewable energy sources and problems associated with renewable energy sources are discussed. This article gives an overview of voltage control strategies based on local as well as communication-based phenomenon for distribution networks. In this review a hybrid solar photovoltaic and wind system is used to generate the power of 3-5 kW. A voltage controller topology is suggested using MATLAB/Simulink for enhancing voltage stability of the system. It is suggested that a voltage stability controller can be designed using recommended topology by using Arduino with pulse width modulation technique. Thus, by measuring and correcting real-time voltage instability values of the system, the system can be made more reliably secure, efficient, and stable than the previously proposed renewable AC distribution systems.

Novel Enriched Indus River Flow Dynamics Optimization Algorithm to solve the Electrical Energy -Active Power Loss Reduction and Voltage Stability Enhancement

Novel Enriched Indus River Flow Dynamics Optimization Algorithm to solve the Electrical Energy -Active Power Loss Reduction and Voltage Stability Enhancement

Optimal allocation of distributed energy storage systems to enhance voltage stability and minimize total cost.

The enhancement of energy efficiency in a distribution network can be attained through the adding of energy storage systems (ESSs). The strategic placement and appropriate sizing of these systems have the potential to significantly enhance the overall performance of the network. An appropriately dimensioned and strategically located energy storage system has the potential to effectively address peak energy demand, optimize the addition of renewable and distributed energy sources, assist in managing the power quality and reduce the expenses associated with expanding distribution networks. This study proposes an efficient approach utilizing the Dandelion Optimizer (DO) to find the optimal placement and sizing of ESSs in a distribution network. The goal is to reduce the overall annual cost of the system, which includes expenses related to power losses, voltage deviation, and peak load damand. The methods outlined in this study is implemented on the IEEE 33 bus distribution system. The outcomes obtained from the proposed DO are contrasted with those of the original system so as to illustrate the impact of ESSs location on both the overall cost and voltage profile. Furthermore, a comparison is made between the outcomes of the Ant Lion Optimizer (ALO) and the intended Design of Experiment DO, revealing that the DO has obtained greater savings in comparison to the ALO. The recommended methodology's simplicity and efficacy in resolving the researched optimization issue make the acquired locations and sizes of ESSs favorable for implementation within the system.

A New Framework for Active Loss Reduction and Voltage Profile Enhancement in a Distributed Generation-Dominated Radial Distribution Network

In recent times, a significant amount of power loss and system instability due to high voltage deviation experienced by modern power systems, in addition to the pressing issues challenging the power industry such as pollution—especially the emission of greenhouse gases—and aging infrastructures, have posed a serious threat to system operations. Distributed generation has been identified as one main solution capable of reducing pollution when solar and wind power are used and, hence, rejuvenating dilapidated infrastructures and redeeming climatic changes. This paper presents a novel two-stage approach for the identification of suitable locations for DG placement and the sizing of DG for loss reduction and voltage stability enhancement. The first stage explored the use of a network structure to develop a coupling factor (CF) approach that was non-iterative in nature to determine suitable DG locations. In the second stage, the size of the DG was determined using the particle swarm optimization (PSO) algorithm. The main objective was to obtain an optimal voltage profile of the system under consideration while lowering the power loss in the system and ensuring network stability amidst DG incorporation. The model design, optimization and simulation were carried out using the MATLAB 2016a environment and the IEEE 33-bus test system, in which DG was integrated. The influence of increasing the level of DG placement in the system was then investigated. The forward/backward sweep method was applied to monitor the optimization process. The voltage profiles for both the base case when no DG was integrated and the case of incremental DG integration were considered. The results obtained for both single and multiple DG integration are compared with those obtained using the existing methods. The results show the efficiency and applicability of the new non-iterative scheme in the quick identification of DG locations for voltage profile enhancement and network real power loss reduction in radial distribution networks.

A novel optimal allocation of STATCOM to enhance voltage stability in power networks

Utilizing a static synchronous compensator (STATCOM) in the electrical power grid greatly improves the grid's voltage profile by enhancing voltage stability. This article proposes a novel approach based on Mixed Integer Distributed Ant Colony Optimization (MIDACO) to determine the optimal STATCOM installation in the electrical power grid. This approach has two control variables to optimize: the STATCOM size and location. This optimization aims to enhance voltage stability with minimum cost by minimizing two objectives: the voltage deviation index and the STATCOM cost. Also, this article presents a sensitivity analysis to show the stochastic nature of MIDACO and to explain the effect of MIDACO parameters on the optimization approach and the process of reaching the optimal solution. The proposed method has been evaluated on three standard test systems: IEEE 14-bus, IEEE 57-bus, and IEEE 118-bus. In addition, the MIDACO results are compared to those of the artificial bee colony algorithm, the genetic algorithm, and particle swarm optimization.

Optimal power flow analysis with circulatory system-based optimization algorithm

Optimal power flow (OPF) is a challenging optimization problem with a large number of variables and constraints. To overcome the OPF issue, high-performance optimization algorithms are needed. In this direction, this paper has been centered on the optimization of the OPF with the circulatory system-based optimization (CSBO) algorithm. The performance of the algorithm was evaluated on the IEEE 57- and 118-bus power networks for the optimization of non-convex OPF objectives, i.e., fuel cost, power loss, voltage deviation, and enhancement of voltage stability. The solution quality of CSBO is compared with state-of-the-art metaheuristic algorithms such as Artificial Rabbits Optimization (ARO), African Vultures Optimization Algorithm (AVOA), and Chaos Game Optimization (CGO). Based on the OPF results, it is seen that the best fuel cost and voltage deviation results are calculated to be 41666.2344 $/h and 0.5871 p.u with the CSBO algorithm for the IEEE 57-bus power system. The CSBO algorithm obtained the best objective function results for the IEEE 118-bus power network with a fuel cost of 134934.3140 $/h and a power loss of 16.4688 MW. In conclusion, the present paper reports that the CSBO is a powerful and efficient metaheuristic algorithm to solve the OPF problem.

Allocation and Sizing of DSTATCOM with Renewable Energy Systems and Load Uncertainty Using Enhanced Gray Wolf Optimization

Over the last decade, flexible alternating current transmission systems (FACTS) have been crucial in ensuring optimal power distribution within modern power systems. A vital component of FACTS devices is the distribution static compensator (DSTATCOM), which is essential for maintaining a reliable power supply. It is commonly used for reactive power compensation, voltage regulation, and harmonic reduction. Determining the appropriate size and placement of DSTATCOMs is vital to ensuring their efficiency. This study introduces the improved gray wolf optimizer (I-GWO), a refined version of the classical gray wolf optimization (GWO) method. The I-GWO incorporates a dimension learning-based hunting (DLH) strategy to preserve population diversity, balance exploration and exploitation, and prevent the premature convergence of classical GWO. In this research, the I-GWO was applied to determine the optimum allocation and sizing of the DSTATCOMs, considering system constraints, including those presented by the intermittent and stochastic nature of the load and renewable energy resources, specifically wind and solar energy. The suggested approach was successfully tested on 33-, 69-, and 85-bus distribution systems and then compared with existing studies. The results demonstrated the I-GWO-based approach’s superiority in terms of reducing power losses, improving voltage profiles, and enhancing voltage stability.

Scheduling Versus Multicasting Capability on Enhancing Voltage Stability of Interconnected DERs

Scheduling Versus Multicasting Capability on Enhancing Voltage Stability of Interconnected DERs

Frequency and voltage stability of multi microgrid system using 2-DOF TIDF FUZZY controller

ABSTRACT Two parameters, voltage and frequency, are required for smooth delivery of quality power to consumer. Therefore, in this paper enhancement of frequency and voltage stability of multi microgrid system with a proposed hybrid fuzzy logic controller with two-degree of freedom tilt-integral derivative with filter (2DOFTIDFFUZZY) are analyzed. A novel algorithm known as artificial electric field algorithm (AEFA) technique is employed to obtain optimal controller parameters. Firstly, a two-area system widely used in literature is considered for proper comparison. Efficacy of 2DOFTIDFFUZZY controller is confirmed by comparing it with different controllers for the same system. Further, a multi microgrid system is considered for unified voltage-frequency control. Automatic Voltage Regulator (AVR) effect states a substantial improvement in system dynamics with the proposed controller. Under different unfavorable conditions like source and load variation, dynamic system performances are studied to enlighten robustness of proposed controller. Simulation results elucidate that proposed 2DOFTIDFFUZZY controller optimized with AEFA technique can achieve better system stability in case of any vulnerability.

Achieving energy independence in urban microgrids: Strategies for domestic resource utilization and environmental sustainability

The quest for energy independence within urban microgrids (MGs) has become increasingly crucial for ensuring domestic resource utilization and environmental sustainability. One of the pivotal challenges lies in the clustering of MGs, a complex task aimed at enhancing their robustness and economic performance during events. However, limited research has been conducted on this intricate clustering problem. This study addresses this gap by proposing an artificial intelligence-based method tailored for MG clustering. In this paper, we introduce a novel multi-objective optimization approach to determine the optimal clustering of standalone MGs. Our methodology considers a range of critical parameters, including distributed generation (DG) droop settings, DG locations and capacities, renewable energy resources from solar units and wind turbines, capacitors, and power transmission lines. By minimizing power losses and enhancing voltage stability, we identify a virtual cut-set line, often referred to as the "virtual line," characterized by minimal power exchange between clustered MGs. To achieve optimal and cost-effective performance, we develop an enhanced differential genetic evolution algorithm designed to address multi-objective problems within stringent constraints. Additionally, we employ Bayesian theory within artificial intelligence to estimate the optimal output power of renewable energy sources, specifically wind turbines and solar panels. To validate the effectiveness of our approach, we apply it to a 69-bus MG across various scenarios. Our findings demonstrate the proficiency of our method in achieving energy independence, domestic resource utilization, and environmental sustainability within urban microgrids.