Voltage Profile Improvement Research Articles

Design of Static Var Compensator Model for Long Transmission Line

Design of Static Var Compensator Model for Long Transmission Line

A Simple Approach to Incorporating STATCOM into a Newton-Based Power Flow and Optimal Power Flow Algorithms

Incorporating STATCOM into existing power flow (PF) or optimal power flow (OPF) algorithm usually requires the development of complex program codes to represent associated derivatives introduced by STATCOM power flow models. This procedure is time consuming as it may require various corrections of errors before having a suitable program that effectively solves the problem. To avoid this stress, an efficient way of incorporating STATCOM’s power flow models into an existing Newton-based PF and OPF algorithm is presented in this paper. These models introduce the magnitude and angle of the STATCOM’s source converter’s voltage as a state variable into the PF and OPF problem formulations. This work simply treats the STATCOM as a PV-bus with zero real power in existing PF and OPF algorithms. The proposed procedures were applied to a 5-bus test system and the results obtained were validated with similar works available in open literature. After a satisfactory performance, it was further applied to the 30-bus and 57-bus IEEE test systems. The results obtained show the effectiveness of the proposed procedures in voltage profile improvement. For example, the PF results show that the voltage magnitudes of the two buses with STATCOM in the 30-bus system were improved from 0.9881 pu and 0.9702 pu to 1.027 pu and 1.041 pu, respectively. Also, the OPF results show that the voltage magnitudes of the three buses with STATCOM in the 57-bus system were improved from 1.063 pu, 0.90 pu and 0.9683 pu to 1.039 pu, 0.9796 pu and 1.0144 pu, respectively.

Dynamic Voltage Restorer for Voltage Unbalance Mitigation and Voltage Profile Improvement in Distribution Network

Dynamic Voltage Restorer for Voltage Unbalance Mitigation and Voltage Profile Improvement in Distribution Network

Reduction of Power Losses and Voltage Profile Improvement in a Smart Grid Incorporated with Electric Vehicles

Governments worldwide have adopted energy-saving policies out of concern for the planet. System efficiency and renewable energy are needed to reduce greenhouse gas emissions, which cause climate change. Electricity generation is the biggest polluter, followed by transportation. Electric vehicles would strain electricity infrastructure without technical solutions. This study uses a hybrid genetic algorithm particle sworn optimization (HGAPSO) to find the optimal switching and feeder reconfiguration approach. Meet transmission constraints while reducing real power losses and improving system bus voltage. In the context of power system change, what are the benefits of employing the HGAPSO approach as opposed to the GA method and the PSO method, respectively. HGAPSO increases network power losses and voltage dispersion. Improved algorithms can help solve this crucial issue. It uses many heuristic optimization techniques to reconfigure transmission network connectivity and determine the best configuration. Limit bus voltage changes while maintaining the system’s radial structure and lowering power usage. MATLAB’s IEEE 33-bus communication network evaluated procedure reliability and performance. The results show that the proposed method reduces power waste during standalone runs and speeds up processing.

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

The use of FACTS devices in power systems has become increasingly popular in recent years, as they offer a number of benefits, including improved voltage profile, reduced power losses, and increased system reliability and safety. However, determining the optimal type, location, and size of FACTS devices can be a challenging optimization problem, as it involves mixed integer, nonlinear, and nonconvex constraints. To address this issue, researchers have applied various optimization techniques to determine the optimal configuration of FACTS devices in power systems. The paper provides an in-depth and comprehensive review of the various optimization techniques that have been used in published works in this field. The review classifies the optimization techniques into four main groups: classical optimization techniques, metaheuristic methods, analytic methods, and mixed or hybrid methods. Classical optimization techniques are conventional optimization approaches that are widely used in optimization problems. Metaheuristic methods are stochastic search algorithms that can be effective for nonconvex constraints. Analytic methods involve sensitivity analysis and gradient-based optimization techniques. Mixed or hybrid methods combine different optimization techniques to improve the solution quality. The paper also provides a performance comparison of these different optimization techniques, which can be useful in selecting an appropriate method for a specific problem. Finally, the paper offers some advice for future research in this field, such as developing new optimization techniques that can handle the complexity of the optimization problem and incorporating uncertainties into the optimization model. Overall, the paper provides a valuable resource for researchers and practitioners in the field of power systems optimization, as it summarizes the various optimization techniques that have been used to solve the FACTS optimization problem and provides insights into their performance and applicability.

Grid parameters analysis of 11 kV radial feeder on residential areas based on forecasted emerging load: A case study of Tarkeshwor Municipality, Nepal

The study focuses on the technical performance analysis of a radial distribution network in a rapidly urbanising area under the current, future, and optimised scenarios. The control area in this research is Tarkeshwor Municipality, Nepal, which lies in the outer stretch of Kathmandu, representing the urbanising loads. In this paper, the electric cooking load has been added to the long term forecasted load up to ten years from now, representing emerging technology and load that adds to the peak load in the daily load curve of the control area. The placement of reactive power suppliers (i.e. Capacitor placement) in optimal network buses is also proposed as an optimisation tool that exhibits improved power quality and reliability with the forecasted load. The peak load is found to be increased to 21.93 MVA at the tenth year's end including targeted emerging load due to which the voltage profile also goes out of the limits. Optimal placement of capacitor on optimal buses results in improved voltage profile and also reduces active power loss of the distribution network in the control area. The analysis and discussion in this research will be helpful to the policymakers as well as the electric utility for planning the supply and utilisation pattern of electricity in the future.

Optimal Scheduling of AC–DC Hybrid Distribution Network Considering the Control Mode of a Converter Station

Due to the difference in types of loads between regions and the increasing integration of random elements such as electric vehicles (EVs) and distributed generations (DGs), distribution station areas (DSAs) are facing challenges such as unbalanced load rates and voltage violations. An AC–DC hybrid distribution network formed by interconnecting AC-DSAs using flexible DC technology can not only address these issues, but also offer more efficient interfaces for EV charging piles and DC devices on the DC side. To fully leverage the advantages of the technology and coordinate dispatchable elements within each DSA, this paper proposes an optimal scheduling model, which balances the load rate between DSAs, improves voltage profiles, and considers the control mode of the converter station as a dispatchable element, taking into account its impact on the voltage deviation on the DC side. Simulation results demonstrate the effectiveness of the proposed model in balancing load rate and improving voltage profiles. Moreover, rational decision-making regarding the selection of the control mode for converter stations can effectively mitigate the voltage deviation on the DC side without deteriorating the voltage deviation on the AC side.

Voltage Regulation Planning Based on Optimal Grid-Connected Renewable Energy Allocation Using Nature-Inspired Algorithms to Reduce Switching Cycles of On-Load Tap Changing Transformer

Due to the non-deterministic and probabilistic qualities of renewable energy resources (RER), integrating these resources has posed a challenge for energy companies and government policy planners. Hybrid power systems require frequent voltage regulation to address the changes in renewable generation and facilitate voltage management. This leads to an increment in the operating time of the tap changer in the power transformer, thus resulting in accelerated degradation of the on-load tap changer (OLTC) device. The primary focus of this paper is to optimally manage the penetration level of renewable generation while maintaining the minimum time for proper operation of voltage regulation devices. Accordingly, the major contribution of this paper is to address the switching cycle issues of the on-load tap-changing transformer. Initially, a framework is proposed to optimize the location and size of renewable power generation using a multi-objective cuckoo search optimization algorithm. The main goal here is to reduce power losses and tap changer operations while improving the system voltage profile under different levels of renewable energy penetration. The optimization problem takes into consideration the fluctuating nature of wind speed and solar irradiance for sunny and cloudy days over 24 h. The framework has been applied to the IEEE 57-bus and 118-bus systems with different load levels. The performance of the proposed approach has been benchmarked by comparing it with the genetic optimization algorithm to identify the higher potential buses for renewable generation allocation. The cuckoo search algorithm (CSA) shows outstanding results in reducing the number of switching cycles to about (37 – 43) % whereas the genetic algorithm (GA) only (17 – 18). In this sense, the number of changes in tap position when using the CSA is 1956 for sunny days and 1763 for cloudy days compared to GA 2558 for sunny days and 2547 for cloudy days. The voltage profiles for both algorithms are maintained in the range of (1.06 and 0.94) per unit. The results affirm the effectiveness of the proposed approach in determining the optimal placement and size of RER with voltage profile improvement and reduction in the switching cycles of OLTC.

Localization and Sizing of Distributed Generation through a Genetic Algorithm to Improve Voltage Profile Using Ecuadorian Standards

The approach proposed for the development of this research work was based on the integration of Distributed Energy Generation (DG) into an energy distribution network, improving the voltage profile by establishing an optimal location and sizing of DG power plants, for which the use of a heuristic optimization method known as Genetic Algorithm (GA), which has several restrictions to limit its operation and thus achieve an optimal solution to the planned optimization problem, was resorted to. The development of a power flow allowed having the voltage measurement in the bars of the network before incorporating DG, allowing to verify that the voltage fluctuations present an improvement after the incorporation one by one of each DG central defined by the GA. The systems in which the voltage measurement was performed, as well as the verification of its fluctuations with and without DG once the GA was used, were the IEEE systems of 9 and 14 busbars, the latter the one used to demonstrate the scalability of the GA to locate and dimension DG without considering the number of busbars in a system.

Effective Power Loss Management in the Distribution System by the Hybrid Cuckoo Search Grey Wolf Optimizer

One of the most convoluted parts of an electrical power system is its distribution system. Compared to generation and transmission losses, distribution losses are very high in the vertically integrated power system. A modern smart grid involves bidirectional power flow due to Renewable Distributed Generations (RDGs). Integration of RDGs near the load points results in a reduction in the distribution losses. This paper proposes the new Hybrid Cuckoo Search Grey Wolf Optimizer (HCSGWO) to optimally place the RDGs such as Solar Photo Voltaic and Fuel Cells and Capacitor Banks (CBs) for the improvement of voltage profiles in each bus and minimization of losses. The hybrid algorithm utilizes the advantages of both the Cuckoo Search and Grey Wolf algorithm. The proposed approach is tested with the Standard IEEE 33-bus system and the real-time TN 84-bus distribution system at all load models such as constant current, constant power, and constant impedance. The HCSGWO minimizes the system losses by optimally placing both the RDGs and CBs when compared to other algorithms. Losses are reduced by installing CBs in the real-time system and significant improvement is achieved in the voltage levels of each bus in the system.

Power Loss Minimization through Reconfiguration Using BPSO-Based Technique

Power loss is influenced by uncertain and ever increasing loads in the system but can be minimized by network reconfiguration where the topological structure of the system is changed by altering the switching operation. In this work, a multi-objective framework through reconfiguration is formulated with loss minimization as primary objective and reliability and voltage profile improvement as secondary objectives. A binary particle swarm optimization (BPSO) base method is developed to reconfigure the network by fulfilling all distribution network constraints. The influence of the BPSO parameters to obtain the optimal switches with minimized power loss, reliability indices, and voltage deviations are studied by a faster convergence. The results have been compared for different weights of the objective to give a comprehensive understanding of the nature of the objective attempted in this work. IEEE 33 bus and radial IEEE 69 bus systems were considered for testing the proposed method.

Economic analysis of HRES-based distributed generation for mitigation of power loss and voltage profile problem

Abstract Power loss, voltage deviation and reliability are the issues that are widely encountered in the power system operation of developing countries. The Eastern region of Ethiopian Electric Power (EEP) is characterized by long transmission lines with no power generation units and large loads. Power loss and voltage profile deviations are experienced repeatedly in this region. The aim of this study is to present an extensive analysis of the role of Hybrid Renewable Energy Sources (HRES) used as Distributed Generation (DG) for power loss minimization, voltage profile and reliability improvement in EEP. The research work initially proves the superior performance of Manta Ray Foraging Optimization (MRFO) for the sizing and placement of DG. Optimal sizing and location of DG are then performed for the case study system using MRFO. The results indicate considerable power loss reduction and improvement in the voltage profile of the network. The reliability of the system is also improved by having an alternative energy source from RES. Wind and solar are considered as part of DG, the combination of which is then optimized through MRFO in the next part of the research. An economic feasibility analysis of the proposed result is also presented for this real-world problem. MATPOWER is used for executing the load flow whereas MATLAB programming is used for implementing MRFO. The result shows that the utility got economic benefits due to the introduction of DG to the network.

Optimal location and sizing of distributed generation for distribution systems: An improved analytical technique

ABSTRACT Distributed generation (DG) significantly improves power system reliability and voltage profile, and reduces power losses. These benefits can be maximized by optimizing the DG utilization, which greatly depends on adequate number, size, and placement of DG. This study proposes a high-convergence optimization technique for optimal DG placement in distribution networks, aiming to reduce power losses and improve voltage profile and voltage stability index. The technique is tested on a 7-bus system before being verified on the IEEE 33-bus system. After validation, it is tested on a 71-bus distribution network (DN). The estimated ideal size for a single DG is used as a limit for determining the optimal placement and size of multiple DG units. The results show that there is a loss reduction of 70.09%, 61.43%, and 71.15% for the 7-bus, IEEE 33-bus, and 71-bus systems, respectively, from their base case loss. The average absolute error of voltage of the aforementioned buses also shows similar enhancement, decreasing from 0.0144, 0.0561, and 0.0527, to 0.0058, 0.0209, and 0.0184, respectively. The voltage stability index of each DN has increased significantly in comparison to the base case. The comparison demonstrates that the proposed technique can provide better results for any size of distribution network.

Voltage profile improvement and power loss minimization of new haven injection substation for optimal placement of DG using harmony search algorithm optimization technique

Voltage profile improvement and power loss minimization of new haven injection substation for optimal placement of DG using harmony search algorithm optimization technique

Voltage profile improvement and power loss minimization of new haven injection substation for optimal placement of DG using harmony search algorithm optimization technique

The main importance of a power grid is to transfer electrical energy from the production to the consumption, while maintaining an acceptable reliability and voltage quality for all customers. The role of electrical power distributions to have centralized plants distributing electricity through Distributed Generation (DG) which reduces the Power Loss on transmission and distribution station and also improve voltage profile by optimization. This research work analyzes New Haven injection substation for optimal placement of DG generation based on wind power renewable energy source to the Distribution network and voltage stability using Harmony Search Algorithm. The model developed was simulated using MATLAB/SIMULINK software. The work applied new improved Harmony Search Algorithm for integration of renewable energy generation in the distribution network with positive impact on the operation of the voltage stability between 0.95 to 1.05p.u and reactive power distribution improvement through minimization of line losses. The total power system Loss without and with DG are 338.712KW and 307.9KW. Also, the voltage profiles of the sixteen faulty buses are stabilized for efficient performance of substation. Hence, the percentage of power system improvement is 27.23%. Therefore from the results, there is reduction of power Loss when DG is applied and optimised in the power system.

Utilization of adaptive swarm intelligent metaheuristic in designing an efficient photovoltaic interfaced Static Synchronous Series Compensator

To cope with the growing load demand of power system due to world’s rapid socio-economic growth, the system compels to run with increased load. Hence, the system must sustain at maximum loading level, termed as the Maximum Loadability Limit (MLL) of the system, without violating its security limits. To increase the system-MLL further the Static Synchronous Series Compensator (SSSC) among existing Flexible AC Transmission Systems (FACTS) devices has been preferred here for its capability of compensating the real and reactive power losses simultaneously. Initially, the SSSC parameters are optimized which are further validated through the dynamic model of the Photovoltaic (PV) interfaced SSSC (PV-SSSC) in MATLAB/SIMULINK. To make the PV-SSSC more efficient, firstly, the maximum feasible power can be acquired from PV system using a robust Maximum Power Point Tracking (MPPT) technique. Secondly, Firing Angle Optimization (FAO) can improve the output power quality of the inverter in SSSC. Thus, this research handles three optimization problems separately i.e., the SSSC parameter optimization, the FAO (minimization), and the MPPT (maximization) problems. For solving such complex, nonlinear, and nonconvex engineering optimization problems, a recently developed swarm-based metaheuristic namely Levy Flight motivated Adaptive Particle Swarm Optimization (APSOLF) technique is employed. Moreover, the superiority of APSOLF is justified over contemporary state-of-the-art techniques using statistical analyses. The APSOLF-based-MPPT can achieve tracking-efficiency above 99.8% and the lowest settling-time. The APSOLF-based-FAO attains 6.7148% THD satisfying the IEEE-519 standard without any filter circuit The MLL point enhancement and bus voltage profile improvement are also the notable observations.

Performance enhancement of radial distribution system with optimal DG allocation

ABSTRACT During the modernization era, the power generation has been shifted to decentralized manner with more renewable distributed generation (DG) units integration at the distribution level in disperse manner. These DG units have to be properly placed for performance enhancement of distribution system. In this paper, the performance of radial distribution system has been enhanced by optimally allocating biomass type DG in the network. Here, a combination of sensitivity index method and a recently developed nature inspired algorithm known as Artificial Gorilla Troops Optimizer (AGTO) algorithm has been used for optimal siting and sizing of DG units in the 33- and 69-bus radial distribution network by taking real and reactive power loss minimization, voltage profile improvement and voltage stability enhancement as the primary objectives along with the system operational constraints. The composite type of load model has been considered in this paper. Out of all the load buses, some severe buses are selected by index vector sensitivity method. AGTO has been applied on these selected buses for appropriate allocation of DG. The effectiveness of this algorithm has been compared with Grey Wolf Optimization (GWO) algorithm and other reported results. The concept of load growth has also been studied in this work.

Hybrid GWO-PSO based optimal placement and sizing of multiple PV-DG units for power loss reduction and voltage profile improvement

Distributed generation (DG) is integrated in a passive distribution system to reduce power loss, improve voltage profile, and increase power output. To reap the most benefits of the distribution system, the best location and appropriate DG size must be determined. This paper presents a hybrid Grey wolf Optimizer (GWO) and Particle swarm optimization (PSO) approach for determining the best placement and DG size while considering a multi-objective function that includes active and reactive power loss minimization as well as voltage profile enhancement. Dilla distribution system was used as a case study and the weighted technique was used to convert to a single objective function while taking into account multiple constraints such as bus voltage limit, DG output limit, and branch current limit. DG penetration is limited to up 60% of the total active load on the feeder and a forward–backward sweep load flow algorithm was used to generate the load flow solutions. The findings of the study show that combining three PV-DGs (Case 3) is the best way to improve voltage profile and minimize losses. In addition, the proposed hybrid GWO-PSO algorithm performed better compared to the other four algorithms (Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Particle swarm optimization (PSO), and sine cosine algorithm (SCA)) in terms of achieving the best multi-objective function (MOF) outcome.

Capacity assessment and scheduling of battery storage systems for performance and reliability improvement of solar energy enhanced distribution systems

The intuition of the utilities in the power distribution system is to provide premium quality electricity to the consumers uninterruptedly and environment friendly. The inclusion of renewable energy sources (RES) in association with conventional power generation has undoubtedly made the distribution system clean and green to meet the dynamic load demand but invited significant instability and complexity to the system. The traditional power supply and stochastic photo voltaic (PV) generation can be managed in an organized way to meet the wide variability in power demand by using battery storage systems (BSSs). This paper presents a novel strategy to optimally determine the size and charging discharging scheduling of BSSs in a distribution system (DS) to enhance its performance and reliability. The DS technical performance and reliability improvement are collectively investigated using the weight-aggregated particle swarm optimization (WAPSO) technique. The proposed strategy has been implemented in 28-bus and 69-bus radial DS over the various seasons. The DS minimum voltage profile and reliability improvement have been performed by minimizing the SADPL, SADD, SAENS through optimal BSSs size and optimized charging discharging scheduling. Through the proposed strategy the percentage reductions in SADPL, SADD, SAENS in the 28-bus DS are found to be 0.18 %, 13 %, and 16 % respectively which expresses the efficacy of the present work. The comparative analysis of the outcomes, optimal power injection by BSSs with optimized scheduling, and sensitivity analysis has been performed.

Coordinated voltage control of three-phase step voltage regulators and smart inverters to improve voltage profile and energy efficiency in unbalanced distribution networks

This paper proposes a coordinated voltage control by three-phase step voltage regulators (3ϕSVRs) and photovoltaic (PV) units with smart inverters. An optimization problem is formulated to improve the voltage profile of distribution networks and reduce the active power curtailment of PVs. The tap positions of 3ϕSVRs and the active and reactive power output of PVs are coordinated by whale optimization algorithm. The effectiveness of the proposed approach is verified by case studies on the IEEE 123 node test feeder. The results show that the proposed approach achieves lower voltage unbalance while avoiding excessive PV curtailment, improving utility, consumer, and environmental benefits.