Voltage Profile Improvement Research Articles

Distribution network planning with DG units considering the network reconfiguration and reliability

The failure statistics of most utilities show that distribution networks (DN) make the most considerable individual contribution to the lack of supply to consumers. There are several advantages to optimizing distributed generation (DG) allocation in DNs, including loss reduction, voltage profile improvements, and reliability. A traditional objective function of optimal DG allocation is to minimize the total power loss and improve the voltage profile. Due to their ability to supply loads locally, DG units enhance the load-carrying capability of lines and, therefore, serve the same purpose as redundant lines. A DN’s reliability indices can also be improved by optimal DG allocation. As such, it is interesting to investigate how network reconfiguration (NR) may be further enhanced to improve the achievement of these critical objectives and ensure the system’s efficient operation in this context. The purpose of this paper is to present an intensive investigation of the reliability and performance improvement that NR can achieve when using DG units in modern DNs. An efficient metaheuristic rider optimizer algorithm (ROA) is applied to the complex combinatorial NR and DG allocation problem with stringent constraints. Analyses are conducted on benchmark IEEE 33-bus test DNs with explicit presentation of results.

Evaluation of Grid-Connected Photovoltaic Plants Based on Clustering Methods

Photovoltaic (PV) systems are electric power systems designed to supply usable solar power by means of photovoltaics, which is the conversion of light into electricity using semiconducting materials. PV systems have gained much attention and are a very attractive energy resource nowadays. The substantial advantage of PV systems is the usage of the most abundant and free energy from the sun. PV systems play an important role in reducing feeder losses, improving voltage profiles and providing ancillary services to local loads. However, large PV grid-connected systems may have a destructive impact on the stability of the electric grid. This is due to the fluctuations of the output AC power generated from the PV systems according to the variations in the solar energy levels. Thus, the electrical distribution system with high penetration of PV systems is subject to performance degradation and instabilities. For that, this project attempts to enhance the integration process of PV systems into electrical grids by analyzing the impact of installing grid-connected PV plants. To accomplish this, an indicative representation of solar irradiation datasets is used for planning and power flow studies of the electric network prior to PV systems installation. Those datasets contain lengthy historical observations of solar energy data, that requires extensive analysis and simulations. To overcome that the lengthy historical datasets are reduced and clustered while preserving the original data characteristics. The resultant clusters can be utilized in the planning stage and simulation studies. Accordingly, studies related to PV systems integration into the electric grid are conducted in an efficient manner, avoiding computing resources and processing times with easier and practical implementation.

Optimal Placement Identification of Multiple DG Types Using Optimization Technique

In this paper, a combination algorithm called GAIPSO, which combines GA and a better version of the classic particle swarm optimization process, is used. In order to calculate the data enhancement in voltage profile, this study uses the GWO algorithm. The ideal position for the proposed charging points inside the distribution system is the goal. The received comment thread solution (site & station size) is further re-optimized by PSO, improving both the functionality and outcome overall. Studies based on simulations show that the above mentioned technique outperforms GA, GWO, and PSO in respect of an improved voltage profiles as well as the quality of the solution found for the objective function. Optimum planning for the charging station’s location and size. the IEEE 33 bus system. The suggested approach takes into consideration the IEEE 33 bus service. The received thread solutions (site and station size) is further re-optimized by PSO, improving both the performance and outcome overall.

A Genetic Algorithm Approach to Optimal Sizing and Placement of Distributed Generation on Nigerian Radial Feeders

Mitigating power loss and voltage profile problems on radial distribution networks has been a major challenge to distribution system operators. While deployment of distributed generation, as compensators, has made a suitable solution option, optimum placement and sizing of the compensators has been a concern and it has thus been receiving great attention. Meta-heuristic algorithms have been found efficacious in this respect, yet the use of the algorithms in addressing problems of radial feeders is still comparatively low in Nigeria where analytical and numerical programming methods are common. Hence; the use of genetic algorithm to site and size distributed generator for real-time power loss reduction and voltage profile improvement on the Nigerian secondary distribution networks is presented. Backward-forward sweep load flow analysis, together with loss sensitivity factor, is deployed to identify the buses suitable for the installation of the distributed generation, while the algorithm is employed in estimating the optimum size. This approach is tested on the standard IEEE 15-bus system and validated using a Nigerian 11 kV feeder. The result obtained on the IEEE test system shows 183 kW loss using the compensator, as compared to 436 kW loss without the compensator; while on the Nigerian network the loss with the compensator was 4.99 kW, in comparison with no-compensation loss of 10.47kW. By the approach of this study, real power loss on the Nigerian feeder decreased by 52.3% together with energy cost reduction from N658,789.12 to N314,227.38. Likewise the minimum bus voltage magnitude and the voltage stability index of the network are improved to acceptable limits. This approach is therefore recommended as capable of strengthening the performance of the Nigerian radial distribution system.

Methodology for Assessment of Congestion Problem

Abstract: Due to increasing in load demand, blackout or unexpected failure in any component of power system including generator, transformer, transmission lines, lead to congestion and over loading in power system this leads the power system to reach on its verse of emergency state ,therefore there should be a proper management or control action for that condition. Several methods are there to control congestion management including Generator Rescheduling (GR),Nodal Pricing, Particle Swarm Optimizer(PSO),Genetic Algorithm (GA) ,Grey Wolf Optimizer (GWO),This paper presents proper congestion management based on Grey Wolf Optimizer (GWO) for reducing load shedding, improve voltage profile, voltage stability, active power loss, this shows optimal load shedding is effective control action for congestion management. The algorithm applied on IEEE 30 bus system considering (n-1) contingency.

Artificial Intelligence Hybrid Algorithm for System Reconfiguration to Reduce Power Losses in the Distribution System

A number of algorithms that aim to reduce power system losses and improve voltage profiles by system reconfiguration have already been proposed, but they are still subject to several limitations. Hence, new algorithms can be developed, or existing ones can be improved so that this important issue can be addressed more appropriately and effectively. To understand smart grids, distribution network, which is a key component of Distribution System Reconfiguration (DSR), is presented in this study as an accurate and effective solution. To determine the intended optimum reconfiguration and effectively rearrange the connectivity of the distribution networks, it uses a variety of heuristic optimization techniques. In this paper, a hybrid artificial intelligent method, made up of the genetic algorithm (GA) and particle swarm optimization (PSO), is developed. Using the standard IEEE 33-bus distribution network on MATLAB, the proposed method's validity and efficacy have been evaluated. The findings demonstrate the proposed approach's robustness and demonstrate that it minimizes the average power loss of independent runs while requiring less processing time. The comparison of results shows that HGAPSO has a better solution than GA and PSO.

Integration of Distributed Generations in Smart Distribution Networks Using Multi-Criteria Based Sustainable Planning Approach

Energy planning has become more complicated in the 21st century of sustainable development due to the inclusion of numerous standards such as techno-economic, and environmental considerations. This paper proposes multi-criteria sustainable planning (MCSP) based optimization approach for identifying DGs’ optimal allocations and rating powers. The main objectives of this paper are the reduction of the network’s total power loss, voltage profile improvement, energy loss saving maximization, and curtailing environmental emissions and water consumption to achieve Sustainable Development Goals (SDGs 3, 6, 7, 13, and 15) by taking the constraints into consideration. Different alternatives are evaluated across four aspects of performance indices; technical, cost-economic, environmental, and social (TEES). In terms of TEES performance evaluations, various multi-criteria decision-making (MCDM) approaches are used to determine the optimal trade-off among the available solutions. These methods are gaining wide acceptance due to their flexibility while considering all criteria and objectives concurrently. Annual energy loss saving is increased by 97.13%, voltage profile is improved to 0.9943 (p.u), and emissions are reduced by 82.45% using the proposed technique. The numerical results of the proposed MCSP approach are compared to previously published works to validate and may be used by researchers and energy planners as a planning tool for ADN schemes.

Distribution System Reconfiguration with Soft Open Point for Power Loss Reduction in Distribution Systems Based on Hybrid Water Cycle Algorithm

In this paper, the role of soft open point (SOP) is investigated with and without system re-configuration (SR) in reducing overall system power losses and improving voltage profile, as well as the effect of increasing the number of SOPs connected to distribution systems under different scenarios using a proposed hybrid water cycle algorithm (HWCA). The HWCA is formulated to enhance the water cycle algorithm (WCA) search performance based on the genetic algorithm (GA) for a complex nonlinear problem with discrete and continuous variables represented in this paper by SOP installation and SR. The WCA is one of the most effective optimization algorithms, however, it may have difficulty striking a balance between exploration and exploitation due to the nature of the proposed nonlinear optimization problem, which mostly causes slow convergence and poor robustness. Consequently, the HWCA proposed in this paper is an efficient solution to improve the balance between exploration and exploitation, which in turn leads to improving the WCA’s overall performance without the possibility of getting trapped in local minima. Several cases are studied and conducted on an IEEE 33-node and the IEEE 69-node to investigate the real benefit gained from using SOPs alone or simultaneously with the SR. Based on the obtained results, the proposed HWCA succeeds in enhancing the performance of the proposed test systems considerably in terms of loss reduction (e.g., 31.1–63.3% for IEEE 33-node and 55.7–82.1% for IEEE 69-node compared to the base case) and voltage profile when compared to the base case while maintaining acceptable voltage magnitudes in most cases. Furthermore, the superiority of the proposed method based on the HWCA is validated when compared with the GA and WCA separately for both test systems. The obtained results show the outperformance of the proposed HWCA in attaining the best optimal solution with the least number of iterations.

Nodal Electricity Price Based Optimal Size and Location of DGs in Electrical Distribution Networks Using ANT LION Optimization Algorithm

Distribution system has been the weakest link in the entire power system supply chain. It is also one of the most vital parts of the power system. However, a lot of methods have been developed to improve the condition of the distribution system. The use of distributed generations (DGs) is one such method where the generated power is closer to the load center, and the DG is also providing ancillary services to the grid. The nodal electricity price for DGs location is determined based on the Locational Marginal Price (LMP). LMP implies the price to buy and sell power at each node within electrical distribution markets. In the nodal electricity market (EM), the cost of energy is determined by the location of DG to which it is provided. This paper presents a novel approach that utilizes nodal electricity price for optimal sizing and location (OSL) of DGs. A multi-objective ANTLION optimization (MOALO) has been utilized as an optimization approach to compute the OSL of DGs units. ANTLION optimization (ALO) is based on the unique hunting behaviour of antlions. Optimization has been done for social welfare maximization, loss minimization, and voltage profile improvement in distribution networks (DNs). The results of the proposed technique have been evaluated for IEEE 33 bus DNs.

Voltage profile and power quality improvement using multicell dynamic voltage restorer

Multi-level converter topologies are increasingly being used in various applications due to their high power, high voltage, and low harmonic levels in the output waveforms. These converter topologies produce different output voltage levels and have a highly modular structure. This paper proposes the design of a dynamic voltage restorer (DVR) based on multilevel topology to enhance the voltage profile and improve the power quality in the network. The DVR is an effective, fast-acting device which detects voltage sags and swells in a transmission line and inject a compensating voltage through a boost transformer. A simulation study is carried out under MATLAB/Simulate to demonstrate the performance of the proposed DVR circuit. The simulation results show improved transient response and enhanced power quality in the transmission network.

Hybrid optimization for optimal positioning and sizing of distributed generators in unbalanced distribution networks

Abstract The goal of this work is to reduce power loss and improve voltage profile by formulating the optimal DG placement problem as a restricted nonlinear optimisation problem. As a novelty, the proposed hybrid algorithm, referred to as Multifactor Update-based Hybrid Model (MUHM) is constructed by merging the concepts of Lion Algorithm (LA) & Sea Lion Algorithm (Sea Lion Optimization Algorithm (SLnO). The Forward-Backward Sweep (FBSM) Model is used to calculate the power loss. Three test cases are examined for the voltage profile & loss minimization in the feeder team with DGs: “case 1(DG supplying real power alone (P), case 2 (DG supplying reactive power alone (Q) and Case 3 (DG supplying both real and reactive power)”. Application of the suggested method to various IEEE test systems, including IEEE 33, IEEE 123, and IEEE 69, respectively, is used to assess its efficacy. According, the results show that the presented work at loading percentage = 0 is 12, 15, 135, 4.65, and 8 superior to SFF, BBO, BAT, LA and SLnO, respectively.

Fuzzy-Based Simultaneous Optimal Placement of Electric Vehicle Charging Stations, Distributed Generators, and DSTATCOM in a Distribution System

Electric vehicles (EVs) are becoming increasingly popular due to their inexpensive maintenance, performance improvements, and zero carbon footprint. The electric vehicle’s load impacts the distribution system’s performance as the electric vehicle’s adoption rises. As a result, the distribution system’s dependability depends on the precise location of the electric vehicle charging station (EVCS). The main challenge is the deteriorating impact of the distribution system caused by the incorrect placement of the charging station. The distribution system is integrated with the charging station in conjunction with the distribution static compensator (DSTATCOM) and distributed generation (DG) to reduce the impact of the EVCS. This paper presents a fuzzy classified method for optimal sizings and placements of EVCSs, DGs, and DSTATCOMs for 69-bus radial distribution systems using the RAO-3 algorithm. The characteristic curves of Li-ion batteries were utilized for the load flow analysis to develop models for EV battery charging loads. The prime objective of the proposed method is to (1) Reduce real power loss; (2) Enhance the substation (SS) power factor (pf); (3) Enhance the distribution network’s voltage profile; and (4) Allocate the optimum number of vehicles at the charging stations. The proposed fuzzified RAO-3 algorithm improves the substation pf in the distribution system. The fuzzy multi-objective function is utilized for the two stages and simultaneous placements of the EVCS, DG, and DSTATCOM. The simulation results reveal that the simultaneous placement method performs better, due to the significant reduction in real power loss, improved voltage profile, and the optimum number of EVs. Moreover, the existing system performances for increased EV and distribution system loads are presented.

Implementation of genetic and particle swarm optimization algorithm for voltage profile improvement and loss reduction using capacitors in 132 kV Manipur transmission system

Complex power plays a key role in maintaining and sustaining the fields i.e. magnetic and electric fields. The efficiency of the power system depends entirely on the loss and voltage profile level of the system. The lower the level of loss and the higher the voltage profile, the higher the system efficiency. Installation of the capacitors on a system greatly helps in controlling and reducing levels of the reactive energy of the system. The location and size of the capacitors that need to be installed are of great importance in the installation of the bank of capacitors. The idea of a Genetic Algorithm and also a Particle Swarm Algorithm are deployed in this paper for calculating the optimal location and size of the capacitor. In this paper, the system of the 132 KV Manipur Transmission System is designed in ETAP considering a radial distribution part and the optimization method of a genetic method, and also the particle swarm algorithm is utilized for finding the optimal location and size of the capacitor. The system is further optimized by reducing the loss and improving the voltage profile of the system. And the comparison of both the optimization techniques is presented. The results show that PSO provides optimal solutions while GA is simpler in the optimization process.

Robust control of adaptive power quality compensator in Multi-Microgrids for power quality enhancement using puzzle optimization algorithm

The primary purpose of this paper is to design a novel multi-microgrid connection that uses power compensation methods to improve voltage profiles and reduce harmonic distortions using specific compensation devices in low inertia systems. The utilized compensation device is an adaptive power quality compensator (APQC) which includes series and shunt compensators. Thyristor controlled series capacitor (TCSC) is used as a series compensator to alleviate the dynamic voltage profile as well as diminish transient voltage. A shunt active power filter (SAPF) is utilized as a shunt compensator to improve the power factor and lessen voltage and current harmonics. The TCSC has a power level of error of the PID controller, while the SAPF has a dynamic multi-level of errors of the PID controller. Further, the recent swarm intelligence-based puzzle optimization algorithm is employed to get the best self-tuning of the PID controller gains to reveal the act of the APQC typology. The efficacy of the presented APQC with the multi-level mechanism approach, assessed using Matlab/Simulink time-domain simulations, has been compared with the recently reported literature. Two operation modes are addressed, one for the island mode and the other for the grid-connected mode with significant renewable generation penetration and nonlinear loads. APQC successfully enhances the power quality of multi-microgrids (MMGs), according to theoretical research and simulation statistics. Finally, three comparative study assessments of numerous optimization approaches, with a unified power flow conditioner (UPFC), and with other series-shunt compensators like distributed power condition controller (DPCC) are proposed to attain efficacy and strength of the compensator.

Voltage Profile Improvement by Integrating Renewable Resources with Utility Grid

There are three main parts of an electric power system—power generation, transmission, and distribution. For electric companies, it is a tough challenge to reduce losses of the power system and deliver lossless and reliable power from the generating station to the consumer end. Nowadays, modern power systems are more complex due to gradually increasing loads. In the electrical power system, especially in transmission and distribution networks, there are power losses due to many reasons such as overloading of the line, long distribution lines, low power factors, corona losses, and unsuitable conductor size. The main performance factor of the power system is reliability. Reliability means continuity of the power supply without any interruptions from the generating station to the demand side. Thus, due to these power losses, there are voltage stability problems and economic losses in the electrical system. The voltage stability of the power system can be increased by improving the voltage profile. In this paper, different techniques are analyzed that include the integration of wind power, the integration of photovoltaic power, and reactive power injection by integrating FACTS devices. These techniques are applied to the IEEE 57 bus system with standard data using simulation models developed in MATLAB. Thus, the results of the analysis of these techniques have been compared with each other.

A data-driven multi-objective optimization framework for optimal integration planning of solid-state transformer fed energy hub in a distribution network

This study proposes an optimal planning technique for a solid-state transformer (SST) fed hybrid energy hub consisting of a wind turbine distributed generator (WTDG) and a battery energy storage system (BESS) in a distribution network (DN). The energy exchange within the energy hub and with the utility grid is regulated by an efficient energy management plan (EMP). The simultaneous planning of a WTDG-BESS energy hub in a DN fed through an SST is a challenging combinatorial optimization problem. It is not only about capacity allocation and placement, but also about charging and discharging dispatches of BESSs under dynamically altering system parameters with the intermittent nature of WTDG outputs and the operational limits of the system itself. As a result, simultaneous allocation becomes a mixed integer nonlinear programming (MINLP) problem, with the operational aspect acting as a limitation for the planning aspect and adding to the complexity of the problem. While the power flow takes into consideration the operational losses of the traditional distribution transformers (DT), the optimization problem reduces voltage deviations and energy losses of the DN. A 33-bus radial DN was used to test the proposed technique, and the outcomes of various case studies are included. The MINLP problem was addressed using the non-dominated sorted genetic algorithm (NSGA-II) in the MATLAB R2020a platform. The outcomes of the evaluation show an energy loss reduction (ELR) of 62%, while a voltage profile improvement (VPI) of 69% is obtained.

Distribution Networks Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Hybrid TLBO-BH Algorithm

In this paper, a new method based on the combination of the Teaching-learning-based-optimization (TLBO) and Black-hole (BH) algorithm has been proposed for the reconfiguration of distribution networks in order to reduce active power losses and improve voltage profile in the presence of distributed generation sources. The proposed method is applied to the IEEE 33-bus radial distribution system. The results show that the proposed method can be a very promising potential method for solving the reconfiguration problem in distribution systems and has a significant effect on loss reduction and voltage profile improvement.

Optimal Placement of Micro Distribution Generator in Micro-Grid for Loss Minimization Using PSO

The major aim of DG optimal placement is obtaining the best DGs units sizes and locations so as to have optimum operation and planning of the distribution network system while considering DG capacity constraint. This paper addresses the issues related to the improvement of voltage profile and power loss reduction by integrating DG units. The technique was applied to optimally placed optimum DG unit size in distribution systems for the improvement of candidate bus voltage and reduction of power loss in the system. The technique proposed was simulated on IEEE-10 bus and IEEE-13 bus standard test system, and the obtained results show that the proposed method is strong and effective for optimal placement of DG units.

A hybrid multi-objective Evolutionary Programming-Firefly Algorithm for different type of Distributed Generation in distribution system

With the rise in electricity demand, various additional sources of generation, known as Distributed Generation (DG), have been introduced to boost the performance of power systems. A hybrid multi-objective Evolutionary Programming-Firefly Algorithm (MOEPFA) technique is presented in this study for solving multi-objective power system problems which are minimizing total active and reactive power losses and improving voltage profile while considering the cost of energy losses. This MOEPFA is developed by embedding Firefly Algorithm (FA) features into the conventional EP method. The analysis in this study considered DG with 4 different scenarios. Scenario 1 is the base case or without DG, scenario 2 is for DG with injected active power, scenario 3 is for DG injected with reactive power only and scenario 4 is for DG injected with both active and reactive power. The IEEE 69-bus test system is applied to validate the suggested technique.

Improving of electric network feeding nuclear facility based on multiple types DGs placement

Abstract Nuclear Facility (NF), during shutdown and startup, are in the essential need for reliable electric power that should be delivered by electric power grid to NF. Safe operation of NF needs a limited variation in both frequency and voltage.The reduction of power losses, improving voltage profile, and frequency in electric grid connected with NF can be achieved by optimally distributed generators (DGs) placement. This paper presents a mathematical model for multible types of DGs placement in electric grid feeding NF. Also, it proposes artificial intelligence solution methodology for active and reactive power DGs placement problem. The trained Adaptive Neuro-Fuzzy Inference System (ANFIS) with Cat Swarm Optimization algorithm (CSO) is used for optimal solution. The optimization technique is tested and validated by using different sizes of electric grid. Test results showed a more reliable and efficient approach compared with other approachs.