Generation In Radial Distribution System Research Articles

Multi-objective Harris Hawks optimization algorithm for selecting best location and size of distributed generation in radial distribution system

Distributed Generating Units (DGUs) are widely used in backup power, renewable energy integration, voltage regulation, and energy storage applications. The DGUs are small-scale power-generating units located near the load centers of a power system, as opposed to large, centralized power plants located far away. However, the DGUs must reduce power losses and environmental impacts and improve the voltage profile. This was achieved by effective optimization of the position and rating of DGU. However, the conventional optimization methods failed to optimize the power losses, voltage-current balancing issues, and harmonic distortions. So, this article presents the selection of the best location and size of DGU in radial distribution systems using a nature-inspired multi-objective Harris Hawks optimization (HHO) algorithm while considering improvements in the voltage profile and reductions in active power losses. The HHO algorithm is inspired by the supportive actions of intelligent birds, such as Harris Hawks, while searching for prey. The distinguished hunting process of Harris Hawks using other family members makes it unique, which is useful for searching for better quality solutions while optimizing multi-objective fitness functions. Fitness function is determined by considering each bus's voltage profile and branches' active power losses. The proposed method is tested on two circle distribution systems (69 bus and 118 bus). The results are compared to those obtained using some of the more well-known optimization techniques given by scholars in recent years. The optimization of seven DGU in 118 bus radial distribution systems resulted in 405.32 kW of active power losses, a 68.78% decrease in losses, and 0.9723 Vs of minimum voltage.

A Novel Artificial Rabbits Optimization Algorithm for Optimal Location and Sizing of Multiple Distributed Generation in Radial Distribution Systems

A Novel Artificial Rabbits Optimization Algorithm for Optimal Location and Sizing of Multiple Distributed Generation in Radial Distribution Systems

Northern Goshawk Optimization for Optimal Allocation of Multiple Types of Active and Reactive Power Distribution Generation in Radial Distribution Systems for Techno-Environmental Benefits

Northern Goshawk Optimization for Optimal Allocation of Multiple Types of Active and Reactive Power Distribution Generation in Radial Distribution Systems for Techno-Environmental Benefits

Optimization Planning Method of Renewable distributed Generation in Radial Distribution Systems

Optimization Planning Method of Renewable distributed Generation in Radial Distribution Systems

A novel artificial hummingbird algorithm for integrating renewable based biomass distributed generators in radial distribution systems

Improving the performance of the electric distribution network is essential to meet the needs of the customer and guarantee the service continuity. Installing generators with small sizes known as distributed generators (DGs) can contribute to enhance the network operation by mitigating the network loss and improving the voltage profile. Integrating these generators in inappropriate places can cause serious consequences to the network operation. Therefore, this paper proposes a novel metaheuristic approach of artificial hummingbirdalgorithm (AHA) to identify the best locations and sizes of biomass-based DGs in radial distribution network. The proposed approach has enriched exploration and exploitation phases that enhancing its search capability and avoiding stuck in local optima. The network active power loss and the voltage deviation are selected as the targets to be minimized. Moreover, a new version of AHA is programmed to solve multi-objective problem with the purpose of mitigating both targets. The analysis is conducted on three radial distribution networks of IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus. Three scenarios are implemented in each network, the first one is minimizing the active power loss, the second one is mitigating the voltage deviation, and the last one is multi-objective problem. Also, biomass-based DGs with unity, fixed, and optimal power factors are analyzed. Excessive comparison to fractal search algorithm, particle swarm optimizer, genetic algorithm, the whale optimization algorithm, sperm swarm optimization, tunicate swarm algorithm, pathfinder algorithm, seagull optimization algorithm, and sine cosine algorithm, multi-objective water cycle algorithm, multi-objective grey wolf optimizer, and multi-objective sparrow search algorithm is conducted. Moreover, statistical tests of Wilcoxon, Friedman, ANOVA, and Kruskal Wallis are performed to assess the performance of the proposed approach. The gotten results confirmed the preference and competence of the proposed approach in integrating the biomass-based DGs in radial distribution networks.

A Novel Quasi-oppositional Chaotic Harris Hawk’s Optimization Algorithm for Optimal Siting and Sizing of Distributed Generation in Radial Distribution System

A Novel Quasi-oppositional Chaotic Harris Hawk’s Optimization Algorithm for Optimal Siting and Sizing of Distributed Generation in Radial Distribution System

Optimal Allocation and Sizing of Distributed Generation in Radial Distribution Systems using Geolocation-Aware Heuristic Optimization

Optimal Allocation and Sizing of Distributed Generation in Radial Distribution Systems using Geolocation-Aware Heuristic Optimization

Optimal siting and sizing of distributed generation in radial distribution system using a novel student psychology-based optimization algorithm

Optimal siting and sizing of distributed generation in radial distribution system using a novel student psychology-based optimization algorithm

Optimal Allocation of Multiple Types of Distributed Generations in Radial Distribution Systems Using a Hybrid Technique

In recent years, the integration of distributed generators (DGs) in radial distribution systems (RDS) has received considerable attention in power system research. The major purpose of DG integration is to decrease the power losses and improve the voltage profiles that directly lead to improving the overall efficiency of the power system. Therefore, this paper proposes a hybrid optimization technique based on analytical and metaheuristic algorithms for optimal DG allocation in RDS. In the proposed technique, the loss sensitivity factor (LSF) is utilized to reduce the search space of the DG locations, while the analytical technique is used to calculate initial DG sizes based on a mathematical formulation. Then, a metaheuristic sine cosine algorithm (SCA) is applied to identify the optimal DG allocation based on the LSF and analytical techniques instead of using random initialization. To prove the superiority and high performance of the proposed hybrid technique, two standard RDSs, IEEE 33-bus and 69-bus, are considered. Additionally, a comparison between the proposed techniques, standard SCA, and other existing optimization techniques is carried out. The main findings confirmed the enhancement in the convergence of the proposed technique compared with the standard SCA and the ability to allocate multiple DGs in RDS.

Atom Search Optimization Algorithm for Allocating Distributed Generators in Radial Distribution Systems

In recent years the use of renewable energy sources (RES) by many power grid companies worldwide has increased significantly. The trend towards RES use is mainly due to environmental issues and rising fuel prices associated with conventional electricity generation. This paper introduces a hybrid approach to find the optimal location and size of distributed generations (DG) in the radial distribution system (RDS). The proposed approach is based on the atom search optimization (ASO) technique to calculate the optimal allocation of DGs and power loss sensitivity (PLS) index to obtain the best buses for DGs installation in RDS. The presented approach is applied to IEEE 33-bus RDS to increase voltage profile and minimize the power losses. The results obtained prove that the developed approach can be highly effective in integrating DG into RDS compared to many other methods in the literature.

New Enhanced Symbiotic Organisms Search for Optimal Location and Sizing of Distributed Generation in Radial Distribution System

New Enhanced Symbiotic Organisms Search for Optimal Location and Sizing of Distributed Generation in Radial Distribution System

Strategic planning of renewable distributed generation in radial distribution system using advanced MOPSO method

The integration of wind turbine, solar photovoltaic based distributed generation in low voltage distribution network has many advantages. However, the output power from wind turbine and solar PVs are intermittent in nature. The production of these intermittent power generations may introduce many challenges in distribution network such as increase in power losses, voltage deviation and instability. Therefore, this paper proposes optimal integration of these intermittent renewable DGs along with biomass and capacitor banks to optimize distribution network parameters. The real time wind speed and solar irradiance data are processed in time series mathematical model along with relevant probability distribution function (PDF) for calculating the accurate power output from these renewable resources. Later, an advanced multi-objective particle swarm optimization technique has been used to optimize the distribution network parameters such as power losses, voltage deviation and voltage stability index. The proposed model has been validates and tested on Portuguese 94 bus system. Moreover, the proposed model results revealed the power loss reduction up to 77.82%, voltage deviation 9.68% and voltage stability index 44.25%.

Modified Spider Monkey Optimization-Based Optimal Placement of Distributed Generators in Radial Distribution System for Voltage Security Improvement

Due to increase in load demand the use of distributed generation has been increased in the distribution system. Voltage profile as well as voltage security state of the distribution system can be improved by incorporating Distributed Generator (DG) of particular sizes at specific locations. In this article, modified spider monkey optimization (MSMO) technique has been proposed for finding out the optimal size and location of DGs to improve stability of the network. This method is a modification of recently developed swarm intelligent technique called spider monkey optimization (SMO) technique. Here minimization of voltage deviation is considered as objective function. The performance of MSMO technique has been tested on IEEE 33 bus, IEEE 69 bus, and Indian 85 bus practical radial distribution system. The results obtained by MSMO-based technique are compared with the results of other existing methods, which show that MSMO algorithm outperforms other standard optimization techniques in terms of voltage profile improvement of the system which proves the efficiency of the proposed method. MSMO-based DG insertion also enhances voltage security state of the system.

Optimal Allocation and Size Selection of Dispersed Generation in Radial Distribution System

Optimal Allocation and Size Selection of Dispersed Generation in Radial Distribution System - written by Aditya Prasad Padhy , Manisha Bhatt published on 2020/07/10 download full article with reference data and citations

A novel multi-objective modified symbiotic organisms search algorithm for optimal allocation of distributed generation in radial distribution system

This article presents a novel optimization technique to allocate distributed generation (DG) units optimally in radial distribution system (RDS). Three renewable type DG units (such as wind turbine, solar photovoltaic and biomass system) have been integrated in the RDS. In this regard, an optimization problem is formulated considering multiple technical and economic objectives of the DG planning. A new metaheuristic, namely multi-objective modified symbiotic organisms search (MOMSOS), is proposed to solve this optimal DG allocation problem. A chaos-based cross-over operator is introduced in the parasitism phase of the proposed MOMSOS to enhance diversity in the population. The proposed MOMSOS is equipped with hierarchical non-dominated sorting technique which is superior to the existing fast non-dominated sorting strategy in terms of computational complexity. An adaptive penalty function is utilized for constraint handling. The proposed algorithm is tested on some CEC 2009 benchmark test problems to ensure its global optimization capability. Furthermore, performance of the proposed MOMSOS is validated on 69-node RDS and the obtained results are compared with other well-established multi-objective optimization algorithms.

Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations and Distributed Generators in Radial Distribution Systems using Metaheuristic Optimization Algorithms

Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations and Distributed Generators in Radial Distribution Systems using Metaheuristic Optimization Algorithms

Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations and Distributed Generators in Radial Distribution Systems using Metaheuristic Optimization Algorithms

The acceptance rate of Electric Vehicles (EVs) in the transport industry has increased substantially due to the augmented interest towards sustainable transportation initiatives. However, their impact in terms of increased power demand on the electric power market can increase real power losses, decrease voltage profile, and consequently decrease voltage stability margins. It is necessary to install Electric Vehicle Charging Stations (EVCSs) and Distributed Generators (DGs) at optimal locations to decrease the EV load effect in the Radial Distribution System (RDS). This paper addresses a multi-objective optimization technique to obtain simultaneous EVCS & DG placement and sizing. The problem is formulated to optimize real power losses, Average Voltage Deviation Index (AVDI), and Voltage Stability Index (VSI) of the electrical distribution system. Simulation studies were performed on the standard IEEE 33-bus and 69-bus test systems. Harries Hawk Optimization (HHO) and Teaching-Learning Based Optimization (TLBO) algorithms were selected to minimize the system objectives. The simulation outcomes reveal that the proposed approach improved system performance in all aspects. Among HHO and TLBO, HHO is reasonably successful in accomplishing the desired goals.

Impact of microgrid operation on performance of radial distribution system

Voltage profile and real power loss are the criteria in determining performance of Radial Distribution System (RDS). Presence of Distributed Generators in RDS is assessed by ‘Penetration Ratio’. The use of PR fails in the analysis of the implications of the presence of a Microgrid on RDS as it is zero in islanded mode and sometimes in grid-connected mode. An attempt has been made to introduce a new term ‘Relief Factor’ to overcome this lacuna. This Factor is evaluated by developing an algorithm based on power flow analysis using Backward Forward Sweep method. The developed algorithm is tested on a 34 bus RDS by integration of a two-shift industrial Microgrid. The power flow analysis is run for 24 snapshots of the day. Relief Factor is found to be suitable to analyze the impact of a Microgrid on the performance of RDS in both the modes of Microgrid operation viz. grid-connected mode and islanded mode.

A Novel Combined Evolutionary Algorithm for Optimal Planning of Distributed Generators in Radial Distribution Systems

In this paper, a novel, combined evolutionary algorithm for solving the optimal planning of distributed generators (OPDG) problem in radial distribution systems (RDSs) is proposed. This algorithm is developed by uniquely combining the original differential evolution algorithm (DE) with the search mechanism of Lévy flights (LF). Furthermore, the quasi-opposition based learning concept (QOBL) is applied to generate the initial population of the combined DELF. As a result, the new algorithm called the quasi-oppositional differential evolution Lévy flights algorithm (QODELFA) is presented. The proposed technique is utilized to solve the OPDG problem in RDSs by taking three objective functions (OFs) under consideration. Those OFs are the active power loss minimization, the voltage profile improvement, and the voltage stability enhancement. Different combinations of those three OFs are considered while satisfying several operational constraints. The robustness of the proposed QODELFA is tested and verified on the IEEE 33-bus, 69-bus, and 118-bus systems and the results are compared to other existing methods in the literature. The conducted comparisons show that the proposed algorithm outperforms many previous available methods and it is highly recommended as a robust and efficient technique for solving the OPDG problem.

A novel stochastic fractal search algorithm for optimal allocation of distributed generators in radial distribution systems

This paper presents an application of a novel stochastic fractal search algorithm (SFSA) for solving the optimal allocation of distributed generators (OADG) problem in radial distribution systems (RDSs). The SFSA method is a newly efficient meta-heuristic algorithm inspired by the growing process of nature by using a mathematical idea known as fractal for solving optimization problems. The advantages of the SFSA method are simple implementation and few control parameters. The method is proposed to deal with the OADG problem by minimizing the active power loss, improving the voltage profile, and increasing the voltage stability while satisfying various constraints including active and reactive power balance, bus voltage limits, DG capacity limits, branch current limits, and DG penetration limit. The effectiveness of the proposed SFSA method has been verified on the IEEE 33-bus, 69-bus, and 118-bus RDSs and the obtained results have been validated via comparing to those from other methods in the literature. The result comparisons from the test systems have indicated that the proposed method can obtain higher quality solutions than many other methods for the considered scenarios from the test systems. Therefore, the proposed SFSA can be a very effective and favorable method for solving the OADG problem.