Sizing Of Distributed Generation Units Research Articles

Oppositional based artificial rabbits optimization applied for optimal allocation of nonlinear DG in distribution networks considering total harmonic distortion limit

This paper proposes a new optimization approach to identify the optimal placement and sizes of distributed generation (DG) units in a radial distribution network (RDN). Inverter-based DG also known as nonlinear DG (NLDG) units inject nonlinear current into the system, which can cause harmonic distortion. This harmonic distortion can limit the penetration of DG in the system by affecting the stability and reliability of the power system. Therefore, it is essential to consider harmonic distortion when identifying the optimal placement and sizes of DG units in RDNs to ensure DG’s safe and effective integration into the power system. The aim of the proposed work is to minimize real power loss, voltage deviation (VD), and total harmonic distortion limit (THD), and improve the voltage stability index (VSI) by integrating the search strategies of opposition-based learning and artificial rabbits optimization (ARO) to achieve better performance. The proposed algorithm, called opposition-based artificial rabbits optimization (OARO), considers different types of DG units like DG TYPE I and DG TYPE III and is suggested for two familiar RDNs: IEEE 33-bus, and 118-bus systems. The Pareto optimality concept has been introduced to solve the multi-objective problems of the systems. The simulation outcomes have been analyzed by comparing several optimization techniques in various cases. OARO has been shown to produce high-quality results with minimal iterations, reducing the time needed to solve the issue and conserving energy. Overall, the proposed approach offers a promising solution for identifying DG units’ optimal placement and sizes in RDNs while considering various operating scenarios.

Optimal DG allocation by Garra Rufa optimization for power loss reduction

<p>The rapid growth of distributed generation (DG) units has necessitated their optimization to address the increasing complexity of power grids and reduce power losses. The need for optimization of distributed generation (DG) units has been growing rapidly over the past few years. To minimize such losses, the optimal allocation of DG units needs to be correctly identified and applied. On the other hand, Garra Rufa optimization (GRO) is a mathematical optimization technique that is used to determine the high effective and efficient way to solve very complex problems to achieve optimal results. In this work, Garra Rufa optimization is used to identify the optimal placement and size of DG units in order to meet specific power loss requirements. A comparison between genetic algorithm (GA), particle swarm optimization (PSO), and GRO is done using MATLAB to validate the proposed method. The comparison shows that GRO is better than the other methods in DG allocation, especially in more than two DGs. The optimization techniques are evaluated using the IEEE standard power system case, specifically the 30-bus configuration.</p>

Optimization for position and rating of distributed generating units using bacteria foraging algorithm to reduce power losses

Distributed generation (DG) units, which include solar panels, wind turbines, and fuel cells, are gaining in popularity as a result of the fact that they are dependable and produce no harmful emissions when they generate electricity. However, the integration of these new power sources into the current power grid may result in several technical difficulties, including voltage fluctuations, power imbalances, and increased power losses. The use of optimization methods may assist to solve these difficulties by determining the best position and size of DG units. This will help to reduce the amount of power that is lost and will enhance the power system's overall performance. Therefore, the Bacteria Foraging Optimization (BFO) method was used for DG in this study. This approach helps to decrease phase jump during symmetrical and unsymmetrical faults, as well as power losses, controls voltage magnitude and angle, and regulates voltage magnitude. In addition, nonlinear restrictions that are regarded to be forward factors include voltage limitation, DG capacity limitation, and phase jump limitation. On the IEEE 69 bus radial distribution scheme, both single and double DGs are taken into consideration for the allocation process. Because of its worldwide convergence and decreased calculation liability, the BFO algorithm is used in the optimization process for both position and rating. The suggested procedure is tested on the IEEE 69 bus radial distribution system in a MATLAB environment; the results demonstrate the efficiency of the suggested approach. In this case, the proposed system was successful in achieving the triple line-to-ground fault-based phase jump of 26.3° without DG and 0.52° with DG; the line-to-line (AB) fault-based phase jump of 0.01° without DG and 0.001° with DG; the single line (A) to ground fault-based phase jump of 0.0027° without DG and 0.001° with DG; and the line to ground fault-based phase jump of 0.001° with DG.

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.

Optimal DG allocation and sizing in distribution systems with Thevenin based impedance stability index

The applications of Distributed Generation (DG) play a significant role to provide the benefit to conventional distribution systems. However, the sizing and location of these DG units should be taken into consideration to get maximum gain and benefit. If DGs are not located and sized properly, the distribution system can be adversely affected. So, optimal allocation and sizing are needed to avoid instability problems and more costs to increase the efficiency and quality of energy in distribution systems. This paper addresses the optimal sizing and allocation of DGs for power losses, voltage profile and stability improvement, introducing a proposed stability index based on Thevenin impedance in a distribution network. The stability index is calculated by the proposed approach based on Thevenin. Thevenin is obtained based on a two-bus system by reduction of more than two bus systems. The optimal sizing and location of DGs are also obtained by operating DGs under different power factors. The reactive capability of DGs is also shown by simulations with DGs operated under different power factors. The problem of optimal sizing and location of DGs, which is a nonlinear optimization problem with discrete and continuous variables, is solved based on the mixed integer Genetic Algorithm (GA). Also, the proposed approach is verified by using Grey Wolf Optimization (GWO) method. The problem contains multi-objectives with three objective functions: system power losses, voltage deviation and the Thevenin based stability index. The proposed approach has been tested on the IEEE-69 and 118-bus test system to demonstrate its effectiveness. The comparison with some other methods shows that the proposed approach gives better results than other methods while reducing the power losses and improving voltage and system stability in all suggested scenarios by finding the optimal placement and sizing of DG units. It is also shown that the reduction in power loss, voltage and stability improvements are increased more by using the reactive power capability of DGs. The results show that optimum located and sized DGs not only decreases the power loss, but also improve the voltage profile and stability of the system.

Optimal siting of solar based distributed generation (DG) in distribution system for constant power load model

Abstract In this paper optimal location and sizing of Non-dispatchable Distributed Generation (NDG) based distributed generation (DG) is investigated by considering uncertainty of NDG. In present study a solar photovoltaic (SPV) is considered as NDG because SPV based DG has uncertain nature of generated power because it is powered by solar irradiance which has uncertain characteristics. To investigate the uncertainty of intermittent nature of solar irradiance Beta Probability Density Function (BPDF) based PDF is considered for addressing the uncertainty of solar insolation. For determining the optimal size of DG unit an analytical based methodology is developed. In this study optimal capacity of NDG is estimated by deriving the expressions of DG unit at each bus. Further, a multi-objective index factor (MIF) is designed in order to find out the candidate bus for DG placement. The proposed technique is applied on IEEE 33-bus distribution network. The obtained results reveal that proposed technique provide almost similar result to that of other method which is available in literature.

Optimal Placement of Distribution Generation in Radial Distribution System Using Hybrid Genetic Dragonfly Algorithm

Optimal placement of distributed generation (DG) is an essential task for distribution companies in order to operate the distribution network in good operating conditions. Optimal placement of DG units is an optimization problem where minimization of active power losses in the network is considered as an objective function. In this paper, hybrid genetic dragonfly algorithm is used as an optimization technique to find the optimal location and size of distributed generation units. The proposed algorithm is implemented on IEEE 15 and PG & E 69 bus distribution systems in MATLAB environment. Based on the simulation results it has been observed that with proper placement and size of DG units, distribution network can be operated with less active power losses.

Multi-objective optimization of optimal placement and sizing of multiple DG placements in radial distribution system using stud krill herd algorithm

This paper presents the Stud krill herd algorithm (SKHA) implementation to find the optimal location and sizing of DG units in a radial distribution system. In recent years, distributed generation (DG) plays an important role in the electric power industry. DG units are connected to the system mainly for improving voltage stability, real power loss reduction, reliability, grid strengthening, and reduction of SO2, CO2 gas emission. The problem formulation is based on the multi-objective function. This work focuses on real power loss reduction, cost minimization, and improvement of voltage stability and formulates a single objective function with suitable weightage preference subject to various constraints like voltage limit, DG real power limit, power balance constraint, and DG location limit. The algorithm has been tested on IEEE 33 bus, 69 bus, and 94 bus radial systems at different load levels with DG placement. The obtained simulation results are compared with other methods. The comparison reveals that the proposed algorithm offers a better solution for the optimal placement of DG in the radial distribution system with faster convergence.

A Nondominated Sorting Stochastic Fractal Search Algorithm for Multiobjective Distribution Network Reconfiguration with Distributed Generations

This paper aims to propose a new multiobjective algorithm for multiobjective distributed network reconfiguration (DNR) with the placements of distributed generation (DG) in radial distribution networks (RDNs). The new proposed algorithm, called the nondominated sorting stochastic fractal search (NSSFS), is a new multiobjective version of the original SFS algorithm. NSSFS incorporated fast nondominated sorting strategies, crowding distance computation, and selection mechanism into SFS to find and maintain the best nondominated solutions. The proposed NSSFS algorithm was tested with eight multiobjective benchmark test functions to validate its performance. The NSSFS was then implemented to define the optimal network configuration, positions, and sizes of DG units in the RDNs, where real power loss, voltage profile, and voltage stability index were optimized simultaneously. The implementation of multiobjective DNR-DG (MODNR-DG) significantly enhanced the performance of the system. Based on the comparison outcomes, the NSSFS algorithm obtained better solution quality than other multiobjective techniques, proving the effectiveness of NSSFS in dealing with the MODNR-DG problem.

Optimal placement of distributed generation using firefly algorithm

Optimal placement of Distributed Generation (DG) is an important activity for distribution network operator to operate the network with more optimal in terms of real power losses (RPL). In this paper, a meta-heuristic algorithm called firefly algorithm has been used find optimal location and size of DG units based on RPL. The proposed approach is implemented on IEEE 15 bus and PG & E 69 bus distribution systems in MATLAB environment. Based on simulation results, it has been observed that firefly algorithm is performing well to identify optimal location and size with minimum RPL.

Optimization of DG Units in Distribution Systems for Voltage Sag Minimization Considering Various Load Types

The optimum installation of distributed generation (DG) units in distribution systems has always been challenging due to the continually increase of electrical energy demands. However, the improper installation of DG units may have negative impact on the power quality of the distribution system. Voltage sag is one of the most important power quality problems, which has the bad effects on the performances of different loads, especially on sensitive and uninterruptible loads. To mitigate the voltage sag problem in distribution systems, a new formulation is proposed for optimal location and sizing of DG units. Particle swarm optimization algorithm is employed to solve the problem. The minimization of the total power losses, total cost of DG units, improvement of the voltage profile, and the reduction in the voltage sag effect are considered as the main objectives. To involve the voltage sag occurrence in distribution system, a penalty is considered for interruption of each type of available load. Different types of load models such as industrial, residential and commercial loads are considered in the system. For the demonstration of the effectiveness of the proposed algorithm, 34-bus test system is considered as the case study. The proposed method results are shown that there is about 72% reduction in total power losses in the optimal configuration of DG units in the distribution system. It is observed that the total number of disturbed buses decreases to 4, which is equal to 12%, as the number of DG units increases. It can be seen that the total cost of the system is reduced from 36.250 to 31.824 M$, when the optimum number of DG units is increased from 1 to 5. The simulation results show the effectiveness of the proposed algorithm.

Optimal location and capacity of DG systems in distribution network using genetic algorithm

The assimilation of distributed generators (DG) does play a critical role in modern distribution networks. Due to increasing demand for electrical energy, the DG sources are becoming more significant in distribution systems. The position and size of DG units will have an impact on losses and voltage profile of the distribution system. This work proposes implementing the Genetic Algorithm approach to determine the optimal site as well as the size of DG units in the distribution network to mitigate actual power losses and enhance the voltage profile. The optimal position and optimal capacity of DG unit is computed by GA algorithm and by using three indices namely PLRI, VDI and MORI we determine three solutions one for exclusively reduction in system loss, the second one for improvement of voltage profile and third one for combined benefit in minimization of losses and improved performance of the bus voltages, the proposed method is applied to the IEEE-33 bus test system. The programming is executed in the software MATLAB 2018α.

Optimal Siting and Sizing of Multiple Distributed Generation Units in Radial Distribution System Using Ant Lion Optimization Algorithm

The installation of distributed generation (DG) units with optimal capacities at optimal locations is becoming an essential issue in a distribution power network to reduce total power loss and to enhance voltage profile. In this paper, a recently included optimization technique called ant lion optimizer (ALO) has been presented for assessing the optimal size of multiple DG units in a balanced radial distribution system. An integrated methodology of loss sensitivity factor and voltage sensitivity factor is utilized for finding the optimal bus locations for the multiple DG unit installation. The optimal size of DG units at the identified bus locations are computed using ALO algorithm by minimizing the total real power loss of distribution network. The minimization of total real power loss will lead to considerable enhancement in voltage profile. The performance of the proposed algorithm has been evaluated against IEEE 33, 69 and 119 bus balanced radial distribution systems. Furthermore, to outline the superiority of proposed ALO algorithm, the attained results are related with the results of hybrid optimization techniques such as GA-PSO, ABC-CSO and ABC-BAT in terms of total real power reduction and voltage profile improvement.

Placement of Distributed Generation and Shunt Capacitor in Distribution Network using Cuckoo Search Algorithm

This work aims at reduction in active and reactive power loss reduction in distribution networks as well as to improve the voltage stability of the networks. Optimum Distributed Generation (DG) placement and sizing is carried out in conjunction with shunt capacitor placement and sizing to determine the appropriate sizes of DG units and Capacitor banks to be placed in the networks so as not to violate certain constraints. The optimal sizes of the DG units and capacitor banks were obtained on application of a Cuckoo Search Optimization Algorithm while computations for Voltage stability was performed using the Voltage Stability Index (VSI). The obtained optimal sizes of DG units and Capacitors were individually and simultaneously placed on the distribution networks to ascertain the behaviour of the networks prior to and after their placements. The performance factors considered are power loss and voltage stability. A comparison of these performance factors under separate and simultaneous placement method was demonstrated using IEEE 33 and 69 test buses. Result show that power loss (active and reactive) reduced by 63.29% and 59.38% respectively for 33 bus system, with a 74.29% and 79.19% reduction on 69 bus system. Voltage stability also increased by 7.89% and 3.79% respectively for 33 and 69 bus system relative to values obtained for base case and separate DG and shunt capacitor placement.
 Keywords: Distributed generation, shunt capacitor, Cuckoo Search Algorithm (CSA), power loss and voltage stability.

Multi-objective distributed generation penetration planning with load model using particle SWARM optimization

The paper presents an approach for simultaneous optimization of Distributed Generation (DG) penetration level and network performance index to obtain the optimal numbers, sites, and sizes of DG units. Two objective functions are formulated. These are: (II) DG penetration level, (II) network performance index. The minimization of the first objective reduces the capital investment cost of a distribution network owner (DNO) to integrate DG. The minimization of the second objective helps in reduction of network losses and improvement in node voltage profile and line loading. The solution approach provides a set of non-dominated solutions with different values of DG penetration level and network performance index. Thus, it offers more flexibility to a DNO to choose a final solution from the set of solutions according to its strategic decisions, regulatory directives, and budget restrictions. The solution approach used is multi-objective particle swarm optimization. The approach is validated on a 38-node distribution system. The results are compared with some existing approaches

A hybrid algorithm based optimal placement of DG units for loss reduction in the distribution system

Distributed generation (DG) has been utilized in some electric power networks. Power loss reduction, environmental friendliness, voltage improvement, postponement of system upgrading, and increasing reliability are some advantages of DG-unit application This paper uses a hybrid technique to optimize the position and size of DG units to reduce losses in the distribution system. The hybrid technique is the joined execution of both the Grasshopper Optimization Algorithm (GOA) and Cuckoo Search (CS) technique. Here, the GOA optimization behavior is upgraded by utilizing the CS technique. Here, the perfect position of the DG unit is settled with respect to the power loss, line power flow and voltage profile using the proposed system. For improving the dynamic execution, the limit of DG is directed by the proposed technique with respect to the cost work. The motivation behind the proposed system is to produce optimal capacity to lessen the aggregate power loss and enhance the voltage profiles of power distribution networks. The proposed hybrid technique is executed in MATLAB/Simulink working platform and the dynamic dependability execution is tested and considered with IEEE 33-bus distribution networks and IEEE 69-bus system. The stability by diminishing loss of the distribution system is investigated by executed different load state of the system. The execution of the proposed system is analyzed and compared with different existing techniques.

A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for optimal allocation of DG in radial distribution networks

This paper aims to apply an improved meta-heuristic method to optimize the allocation of distributed generation (DG) units in radial distribution networks (RDNs). The proposed method, namely the Quasi-Oppositional Chaotic Symbiotic Organisms Search (QOCSOS) algorithm, is the improved version of the original SOS algorithm. QOCSOS incorporates the Quasi-Opposition-Based Learning (QOBL) and Chaotic Local Search (CLS) strategies into SOS to improve the global search capacity. In this study, the objective of the optimal DG allocation (OGDA) problem is to optimally reduce the real power loss, improve the voltage profile, and increase the voltage stability in RDNs. The proposed QOCSOS algorithm was applied to find the optimal locations and sizes of DG units with different DG power factors (unity and non-unity) in the RDNs including 33, 69, and 118-bus. It was found that the operation of DG units with optimal power factor significantly improved the performance of RDNs in terms of voltage deviation minimization, and voltage stability maximization, especially for power loss reduction. After the DG integration, for the case of DG units operating with unity power factor, the power loss reduction was reduced by 65.50%, 69.14%, and 60.23% for the 33, 69, and 118-bus RDNs, respectively. In addition, it should be emphasized that for the cases of DG units operating with optimal power factor, the power loss reduction was reduced up to 94.44%, 98.10%, and 90.28% for these RDNs, respectively. The obtained results from QOCSOS were evaluated by comparing to those from SOS and other optimization methods in the literature. The results showed that the proposed QOCSOS method performed greater than SOS, and offered better quality solutions than many other compared methods, suggesting the feasibility of QOCSOS in solving the ODGA problem, especially for a complex and large-scale system.

Optimal Siting and Sizing of Distributed Generation Based on Improved Nondominated Sorting Genetic Algorithm II

With the development of distributed generation technology, the problem of distributed generation (DG) planning become one of the important subjects. This paper proposes an Improved non-dominated sorting genetic algorithm-II (INSGA-II) for solving the optimal siting and sizing of DG units. Firstly, the multi-objective optimization model is established by considering the energy-saving benefit, line loss, and voltage deviation values. In addition, relay protection constraints are introduced on the basis of node voltage, branch current, and capacity constraints. Secondly, the violation constrained index and improved mutation operator are proposed to increase the population diversity of non-dominated sorting genetic algorithm-II (NSGA-II), and the uniformity of the solution set of the potential crowding distance improvement algorithm is introduced. In order to verify the performance of the proposed INSGA-II algorithm, NSGA-II and multiple objective particle swarm optimization algorithms are used to perform various examples in IEEE 33-, 69-, and 118-bus systems. The convergence metric and spacing metric are used as the performance evaluation criteria. Finally, static and dynamic distribution network planning with the integrated DG are performed separately. The results of the various experiments show the proposed algorithm is effective for the siting and sizing of DG units in a distribution network. Most importantly, it also can achieve desirable economic efficiency and safer voltage level.

Integration of distributed generation in electrical grid: Optimal placement and sizing under different load conditions

This paper proposes a novel approach for the placement and sizing of distributed generation (DG) ensuring voltage stability and reducing total power losses in electrical grid. The main concern of the proposed method is to guarantee voltage stability along with load fluctuations, while selecting optimal DG sites and power capacities. First, vulnerable buses are determined using Voltage Stability Margin Index (VSMI). Optimum size of DG unit is computed using Matlab curve-fitting approximation. For each load level, optimal DG size and its corresponding site are recomputed. General expressions for DG power capacity are established. For different load scenarios, the determined DG locations and size are always valid. The proposed method is implemented on the 33-bus and the 69-bus distribution systems using PSAT MATLAB toolbox and results are compared to other techniques published in the literature. The obtained results prove the effectiveness, the robustness and the good performances of the proposed method.

A Novel Method Based on PPSO for Optimal Placement and Sizing of Distributed Generation

Energy loss minimization, voltage profile improvement, and increasing reliability of the power system are the prominent advantages of distributed generation (DG) unit's integration in distribution systems. Therefore, optimal placement and sizing of DG become a critical issue. This article proposes a recently developed adaptive particle swarm optimization (PSO) algorithm known as phasor particle swarm optimization (PPSO) to solve the problem of optimal placement and sizing of DG units in radial distribution networks. The PPSO algorithm is based on modeling the particle control parameters with a phase angle (θ) transforming standard PSO into a self‐adaptive and parametric independent metaheuristic optimization algorithm. The main objective is to minimize the energy loss in the network considering various technical constraints and load variations. In particular, the proposed approach is implemented on a practical radial distribution feeder located at the Haripur area in Pakistan taking into account the hourly actual load values. Comparison of the devised PPSO algorithm with other techniques in the literature was done via an IEEE 33 bus test system. The results obtained by the proposed PPSO algorithm show that it enables a significant reduction in power loss for all the analyzed cases, and outperforms many of other solution techniques applied for the same problem. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.