scholarly journals Multi-objective for optimal placement and sizing DG units in reducing loss of power and enhancing voltage profile using BPSO-SLFA

2020 ◽  
Vol 6 ◽  
pp. 1581-1589 ◽  
Author(s):  
Abdurrahman Shuaibu Hassan ◽  
Yanxia Sun ◽  
Zenghui Wang
Keyword(s):  
Optimal Placement ◽  
Voltage Profile ◽  
Multi Objective

scholarly journals Multi-objective optimal placement of distributed generations for dynamic loads

2019 ◽  
Vol 9 (4) ◽  
pp. 2303
Author(s):  
Shah Mohazzem Hossain ◽  
Abdul Hasib Chowdhury
Keyword(s):  
Power System ◽  
Active Power ◽  
Optimal Placement ◽  
Voltage Profile ◽  
Power Losses ◽  
Long Distance ◽  
Utilization Factor ◽  
Distributed Generations ◽  
Multi Objective ◽  
Active Power Losses

<span lang="EN-US">Large amount of active power losses and low voltage profile are the two major issues concerning the integration of distributed generations with existing power system networks. High </span><em><span lang="EN-US">R</span></em><span lang="EN-US">/</span><em><span lang="EN-US">X</span></em><span lang="EN-US"> ratio and long distance of radial network further aggravates the issues. Optimal placement of distributed generators can address these issues significantly by alleviating active power losses and ameliorating voltage profile in a cost effective manner. In this research, multi-objective optimal placement problem is decomposed into minimization of total active power losses, maximization of bus voltage profile enhancement and minimization of total generation cost of a power system network for static and dynamic load characteristics. Optimum utilization factor for installed generators and available loads is scaled by the analysis of yearly load-demand curve of a network. The developed algorithm of N-bus system is implemented in IEEE-14 bus standard test system to demonstrate the efficacy of the proposed method in different loading conditions.</span>

Metaheuristic optimization based placement of SVCs with multiple objectives

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arun Nambi Pandian ◽  
Aravindhababu Palanivelu
Keyword(s):  
Harmony Search ◽  
Optimal Placement ◽  
Voltage Profile ◽  
Mathematical Methods ◽  
Content Type ◽  
Multi Objective ◽  
Search Optimization ◽  
Improved Harmony Search ◽  
The Given ◽  
Self Adaptive

Purpose Optimal placement of static VAR compensator (SVC) devices not only improves the voltage profile (VP) but also reduces the active power loss (APL) and enhances the voltage stability (VS) through injecting appropriate VARs at optimal buses. The traditional mathematical methods may not provide global best solution and pose difficulties in handling multi-objective SVC placement (SVCP) problem with complex constraints and forcefully place all the given number of SVCs in the system without assessing their real requirements in enhancing the chosen performances. The purpose of this paper is to formulate the SVCP as a multi-objective optimization problem and solve it using a metaheuristic algorithm for global best solution. Design/methodology/approach The proposed SVCP method uses improved harmony search optimization (IHSO) with dissonance-avoiding mechanism for obtaining the global best solution through driving away the solution from the sub-optimal traps. In addition, the method uses a self-adaptive technique for optimally tuning the IHSO parameters and places only the required number of SVCs from the given number of SVCs. Findings This paper presents the results of the proposed method for 14, 30 and 57 bus systems and exhibits that the proposed method outperforms the existing SVCP methods in achieving the desired performances. Originality/value This paper proposes a new self-adaptive IHSO based SVCP method for optimally placing only the required number of SVCs with a goal of attaining the global best performances.

scholarly journals PSO Based Multi-Objective Optimization For Distribution Planning With Distributed Generation

KURVATEK ◽  
2018 ◽  
Vol 3 (2) ◽  
pp. 111-117
Author(s):  
Sugiarto Sugiarto
Keyword(s):  
Distributed Generation ◽  
Distribution Systems ◽  
Test System ◽  
Optimal Placement ◽  
Multi Objective Optimization ◽  
Voltage Profile ◽  
Power Losses ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Weight Method

This paper presents a multi-objective function for optimal placement of distributed generation (DG) resources in distribution systems in order to minimize the power losses and improve voltage profile. Particle swarm optimization (PSO) and weight method are applied to the proposed technique to obtain the best compromise between these costs. Simulation results on IEEE 30-bus test system are presented to demonstrate the usefulness of the proposed procedure.

Voltage Stability Enhancement with Optimal Placement of IPFC

2019 ◽  
Vol 8 (4) ◽  
pp. 9465-9471
Keyword(s):  
Transmission Line ◽  
Objective Function ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Test System ◽  
Optimal Location ◽  
Optimal Placement ◽  
Utilization Factor ◽  
Location Selection ◽  
Multi Objective

This paper presents a novel technique based on Cuckoo Search Algorithm (CSA) for enhancing the performance of multiline transmission network to reduce congestion in transmission line to huge level. Optimal location selection of IPFC is done using subtracting line utilization factor (SLUF) and CSA-based optimal tuning. The multi objective function consists of real power loss, security margin, bus voltage limit violation and capacity of installed IPFC. The multi objective function is tuned by CSA and the optimal location for minimizing transmission line congestion is obtained. The simulation is performed using MATLAB for IEEE 30-bus test system. The performance of CSA has been considered for various loading conditions. Results shows that the proposed CSA technique performs better by optimal location of IPFC while maintaining power system performance

scholarly journals Capacity-Based Optimization Using Whale Optimization Technique of a Power Distribution Network

2020 ◽  
Author(s):  
Bawoke Simachew ◽  
baseem khan ◽  
Josep M Guerrero ◽  
Sanjeevikumar *Padmanaban ◽  
Om Prakash Mahela ◽  
...  
Keyword(s):  
Distributed Generation ◽  
Power Distribution ◽  
Optimization Technique ◽  
Distribution Network ◽  
Optimal Size ◽  
Voltage Profile ◽  
Power Distribution Network ◽  
Multi Objective ◽  
Whale Optimization ◽  
Profile Management

In the power distribution network, real power loss and voltage profile management are critical issues. By providing active and reactive power support, both of these issues can be managed. This paper utilized the Meta heuristic-based method for the optimal size and placement of distributed generation (DG) and capacitor (QG) sources for loss reduction by incorporating network current carrying capacity constraint in the optimization problem. The overall problem is optimized using an upgraded method of the fitness assignment and solution chasing based on the aggregate approach called Multi-objective Whale Optimization Algorithm (MWOA). Wind and solar photovoltaic sources are utilized as the distributed generation with their probabilistic outputs. The developed method is tested using two feeders of practical Bahir Dar Distribution Network, Ethiopia. The results of loss minimization and voltage profile management with MWOA are compared with multi-objective particle swam optimization (MPSO) with an equal number of iteration to show the superiority of the developed method.

scholarly journals Investigation of Optimal PV Allocation to Minimize System Losses and Improve Voltage Stability for Distribution and Transmission Networks Using MATLAB and DigSilent

2021 ◽  
Vol 9 ◽  
Author(s):  
Mohamed Abdul Rasheed ◽  
Renuga Verayiah
Keyword(s):  
Objective Function ◽  
Radial Distribution ◽  
Power Factor ◽  
Analytical Method ◽  
Distribution Network ◽  
Computation Time ◽  
Transmission Network ◽  
Voltage Profile ◽  
Multi Objective ◽  
Test Systems

Electricity generation from renewable energy sources such as solar energy is an emerging sustainable solution. In the last decade, this sustainable source was not only being used as a source of power generation but also as distributed generation (DG). Many literatures have been published in this field with the objective to minimize losses by optimizing the DG size and location. System losses and voltage profile go hand-in-hand; as a result, when system losses are minimized, eventually the voltage profile improves. With improvement in inverter technologies, PV-DG units do not have to operate at a unity power factor. The majority of proposed algorithms and methods do not consider power factor optimization as a necessary optimization. This article aims to optimize the size, location, and power factor of PV-DG units. The simulations are performed on the IEEE 33 bus radial distribution network and IEEE 14 bus transmission network. The methodologies developed in this article are divided into two sections. The first section aims to optimize the PV-DG size and location. A multi-objective function is developed by using system losses and a voltage deviation index. Genetic algorithm (GA) is used to optimize the multi-objective function. Next, analytical processes are developed for verification. The second section aims to further enhance PV-DG by optimizing the power factor of PV-DG. The simulation is performed for static load in both systems, which are the IEEE 33 bus radial distribution network and IEEE 14 bus transmission network. A mathematical analytical method was developed, and it was found to be sufficient to optimize the power factor of the PV-DG unit. The results obtained show that voltage stability indices help minimize the computation time by determining the optimal locations for DG placement in both networks. In addition, the GA method attained faster convergence than the analytical method and hence is the best optimal sizing for both test systems with minimum computation time. Additionally, the optimization of the power factor for both test systems has demonstrated further improvement in the voltage profile and loss minimization. In conclusion, the proposed methodology has shown promising results for both transmission and distribution networks.

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