Optimal reconfiguration/distributed generation integration in distribution system using adaptive weighted improved discrete particle swarm optimization

2019 ◽  
Vol 38 (1) ◽  
pp. 247-262 ◽  
Author(s):  
Manikandan Subramaniyan ◽  
Sasitharan Subramaniyan ◽  
Moorthy Veeraswamy ◽  
Viswanatha Rao Jawalkar
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Distributed Generation ◽  
Distribution System ◽  
Power Loss ◽  
Energy Resources ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Content Type ◽  
Discrete Particle

Purpose This paper aims to address not only technical and economic challenges in electrical distribution system but also environmental impact and the depletion of conventional energy resources due to rapidly growing economic development, results rising energy consumption. Design/methodology/approach Generally, the network reconfiguration (NR) problem is designed for minimizing power loss. Particularly, it is devised for maximizing power loss reduction by simultaneous NR and distributed generation (DG) placement. A loss sensitivity factor procedure is incorporated in the problem formulation that has identified sensitivity nodes for DG optimally. An adaptive weighted improved discrete particle swarm optimization (AWIDPSO) is proposed for ascertaining a feasible solution. Findings In AWIDPSO, the adaptively varying inertia weight increases the possible solution in the global search space and it has obtained the optimum solution within lesser iteration. Moreover, it has provided a solution for integrating more amount of DG optimally in the existing distribution network (DN). Practical implications The AWIDPSO seems to be a promising optimization tool for optimal DG placement in the existing DN, DG placement after NR and simultaneous NR and DG sizing and placement. Thus, a strategic balance is derived among economic development, energy consumption, environmental impact and depletion of conventional energy resources. Originality/value In this study, a standard 33-bus distribution system has been analyzed for optimal NR in the presence of DG using the developed framework. The power loss in the DN has reduced considerably by indulging a new and innovative approaches and technologies.

scholarly journals A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode

2019 ◽  
Vol 11 (5) ◽  
pp. 1329 ◽  
Author(s):  
Wenxiang Xu ◽  
Shunsheng Guo
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Evolutionary Algorithms ◽  
Particle Swarm ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Dimensional Optimization ◽  
Matching Relation

Aimed at the problem of the green scheduling problem with automated guided vehicles (AGVs) in flexible manufacturing systems (FMS), the multi-objective and multi-dimensional optimal scheduling process is defined while considering energy consumption and multi-function of machines. The process is a complex and combinational process, considering this characteristic, a mathematical model was developed and integrated with evolutionary algorithms (EAs), which includes a sectional encoding genetic algorithm (SE-GA), sectional encoding discrete particle swarm optimization (SE-DPSO) and hybrid sectional encoding genetic algorithm and discrete particle swarm optimization (H-SE-GA-DPSO). In the model, the encoding of the algorithms was divided into three segments for different optimization dimensions with the objective of minimizing the makespan and energy consumption of machines and the number of AGVs. The sectional encoding described the sequence of operations of related jobs, the matching relation between transfer tasks and AGVs (AGV-task), and the matching relation between operations and machines (operation-machine) respectively for multi-dimensional optimization scheduling. The effectiveness of the proposed three EAs was verified by a typical experiment. Besides, in the experiment, a comparison among SE-GA, SE-DPSO, H-SE-GA-DPSO, hybrid genetic algorithm and particle swarm optimization (H-GA-PSO) and a tabu search algorithm (TSA) was performed. In H-GA-PSO and TSA, the former just takes the sequence of operations into account, and the latter takes both the sequence of operations and the AGV-task into account. According to the result of the comparison, the superiority of H-SE-GA-DPSO over the other algorithms was proved.

scholarly journals MINIMIZING POWER LOSS BY PLACING DG OPTIMALLY IN DISTRIBUTION SYSTEM BY PSO TECHNIQUE

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Prakash D B ◽  
Lakshminarayana C
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Distributed Generation ◽  
Distribution System ◽  
Power Loss ◽  
Distribution Systems ◽  
Active Power ◽  
Optimal Placement ◽  
Swarm Optimization ◽  
Intelligence Technique

— This paper presents optimal placement and sizing of Distributed Generation (DG) by using an intelligence technique called Particle Swarm Optimization (PSO). Here the objective function is considered as minimization of active power loss. The proposed methodology is applied and tested for IEEE 33 and IEEE 69 bus radial distribution systems. The result shows that the proposed algorithm is more effective.

Sign in / Sign up

Export Citation Format

Share Document