scholarly journals Allocation of Distributed Generation for Maximum Reduction of Energy Losses in Distribution Systems

10.5772/62842 ◽  
2016 ◽  
Author(s):  
Juan A. Martinez-Velasco ◽  
Gerardo Guerra
Keyword(s):  
Distributed Generation ◽  
Distribution Systems ◽  
Energy Losses ◽  
Maximum Reduction

scholarly journals Optimal Sizing of Photovoltaic Generation in Radial Distribution Systems Using Lagrange Multipliers

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1728 ◽  
Author(s):  
José Adriano da Costa ◽  
David Alves Castelo Branco ◽  
Max Chianca Pimentel Filho ◽  
Manoel Firmino de Medeiros Júnior ◽  
Neilton Fidelis da Silva
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Lagrange Multipliers ◽  
Analytical Method ◽  
Distribution Systems ◽  
Jacobi Method ◽  
Energy Losses ◽  
Photovoltaic Generation ◽  
Optimal Sizing ◽  
Renewable Distributed Generation

The integration of renewable distributed generation into distribution systems has been studied comprehensively, due to the potential benefits, such as the reduction of energy losses and mitigation of the environmental impacts resulting from power generation. The problem of minimizing energy losses in distribution systems and the methods used for optimal integration of the renewable distributed generation have been the subject of recent studies. The present study proposes an analytical method which addresses the problem of sizing the nominal power of photovoltaic generation, connected to the nodes of a radial distribution feeder. The goal of this method is to minimize the total energy losses during the daily insolation period, with an optimization constraint consisting in the energy flow in the slack bus, conditioned to the energetic independence of the feeder. The sizing is achieved from the photovoltaic generation capacity and load factors, calculated in time intervals defined in the typical production curve of a photovoltaic unit connected to the distribution system. The analytical method has its foundations on Lagrange multipliers and relies on the Gauss-Jacobi method to make the resulting equation system solution feasible. This optimization method was evaluated on the IEEE 37-bus test system, from which the scenarios of generation integration were considered. The obtained results display the optimal sizing as well as the energy losses related to additional power and the location of the photovoltaic generation in distributed generation integration scenarios.

AN OPTIMIZATION APPROACH FOR MINIMIZING ENERGY LOSSES OF DISTRIBUTION SYSTEMS BASED ON DISTRIBUTED GENERATION PLACEMENT

2017 ◽  
Vol 79 (4) ◽  
Author(s):  
Umbrin Sultana ◽  
Azhar Khairuddin ◽  
A. S. Mokhtar ◽  
Sajid Hussain Qazi ◽  
Beenish Sultana
Keyword(s):  
Distributed Generation ◽  
Distribution System ◽  
Distribution Systems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Environmental Benefits ◽  
Optimal Placement ◽  
Energy Losses ◽  
Grey Wolf Optimizer ◽  
Optimization Approach

The interest of electric utilities in distributed energy resources has increased in terms of maximising the latter’s technical, economic and  environmental benefits. This paper presents a Grey Wolf Optimizer (GWO) -based approach for optimal placement and sizing of multiple Distributed Generation (DG), aimed at reducing active and reactive energy losses in the distribution system. Power system constraints, such as voltage magnitude limits and current boundaries are also considered. Recently, a swarm intelligence technique, namely, GWO was introduced, which is inspired by grey wolves strategy and utilises four categories of grey wolves (alpha, beta, delta and omega) to simulate a leadership hierarchy. The GWO technique and two other popular methods Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) – are here tested on 15- and 33-bus radial distribution systems. The numerical results obtained using these methods are compared, with the best performance recorded via the proposed GWO method in terms of not only active and reactive energy loss but also voltage profile and convergence characteristics.

scholarly journals Cost of Energy Losses for Distributed Generation Using Hybrid Evolutionary Programming-Firefly Algorithm

2021 ◽  
Vol 2107 (1) ◽  
pp. 012049
Author(s):  
Noor Najwa Husnaini Mohammad Husni ◽  
Siti Rafidah Abdul Rahim ◽  
Mohd Rafi Adzman ◽  
Muhammad Hatta Hussain ◽  
Ismail Musirin
Keyword(s):  
Distributed Generation ◽  
Distribution System ◽  
Distribution Systems ◽  
Firefly Algorithm ◽  
Evolutionary Programming ◽  
Optimization Techniques ◽  
Energy Losses ◽  
Type I ◽  
Cost Of Energy ◽  
The Cost

Abstract The cost of energy losses analysis for distributed generation (DG) is presented in this paper using a Hybrid Evolutionary Programming-Firefly Algorithm (EPFA). The proposed method was created to determine the optimal DG sizing in the distribution system while accounting for the system’s energy losses. This study presents an investigation into hybrid optimization techniques for DG capabilities and optimal operating strategies in distribution systems. The objectives of this study were to reduce the cost of energy losses while increasing the voltage profile and minimize distribution system losses. In this study, the analysis was done by consider DG type I which is DG-PV. The suggested methodology was tested using the IEEE 69-bus test system, and the simulation was written in the MATLAB programming language. Power system planners can use appropriate location and sizing from the results obtained for utility planning in terms of economic considerations. From the simulation, the result shows the proposed method can identify the suitable sizing of DG while reduce cost of energy losses and total losses in the system.

scholarly journals A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems

2021 ◽  
Vol 13 (6) ◽  
pp. 3308
Author(s):  
Chandrasekaran Venkatesan ◽  
Raju Kannadasan ◽  
Mohammed H. Alsharif ◽  
Mun-Kyeom Kim ◽  
Jamel Nebhen
Keyword(s):  
Multiobjective Optimization ◽  
Distributed Generation ◽  
Radial Distribution ◽  
Hybrid Method ◽  
Distribution Systems ◽  
Optimization Technique ◽  
Optimal Placement ◽  
Grey Wolf Optimizer ◽  
Hybrid Technique ◽  
Metaheuristic Optimization

Distributed generation (DG) and capacitor bank (CB) allocation in distribution systems (DS) has the potential to enhance the overall system performance of radial distribution systems (RDS) using a multiobjective optimization technique. The benefits of CB and DG injection in the RDS greatly depend on selecting a suitable number of CBs/DGs and their volume along with the finest location. This work proposes applying a hybrid enhanced grey wolf optimizer and particle swarm optimization (EGWO-PSO) algorithm for optimal placement and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves. On the other hand, PSO is a swarm-based metaheuristic optimization algorithm that finds the optimal solution to a problem through the movement of the particles. The advantages of both techniques are utilized to acquire mutual benefits, i.e., the exploration ability of the EGWO and the exploitation ability of the PSO. The proposed hybrid method has a high convergence speed and is not trapped in local optimal. Using this hybrid method, technical, economic, and environmental advantages are enhanced using multiobjective functions (MOF) such as minimizing active power losses, voltage deviation index (VDI), the total cost of electrical energy, and total emissions from generation sources and enhancing the voltage stability index (VSI). Six different operational cases are considered and carried out on two standard distribution systems, namely, IEEE 33- and 69-bus RDSs, to demonstrate the proposed scheme’s effectiveness extensively. The simulated results are compared with existing optimization algorithms. From the obtained results, it is observed that the proposed EGWO-PSO gives distinguished enhancements in multiobjective optimization of different conflicting objective functions and high-level performance with global optimal values.

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