Improved genetic algorithm applied to multiple distributed generation optimal allocation considering different load profiles

2016 ◽  
Author(s):  
Karina Yamashita ◽  
Luis Alfonso Gallego Pareja
Keyword(s):  
Genetic Algorithm ◽  
Distributed Generation ◽  
Optimal Allocation ◽  
Improved Genetic Algorithm

scholarly journals Optimal multi-objective distributed generation location and capacity by elite strategy genetic algorithm considering timing characteristics

2020 ◽  
Vol 185 ◽  
pp. 01018
Author(s):  
Xinquan Wei ◽  
Xiangjun Duan ◽  
Lei Chen ◽  
Weiyan Zheng
Keyword(s):  
Genetic Algorithm ◽  
Distributed Generation ◽  
Power Supply ◽  
Retention Mechanism ◽  
Improved Genetic Algorithm ◽  
Distributed Power ◽  
Elite Strategy ◽  
Voltage Quality ◽  
Voltage Deviation ◽  
Node Voltage

In this paper, a distributed generation location and capacity optimization model considering the probability of scenario occurrence is established. The optimization objective is to minimize the total cost of investment, annual power loss of distribution network and node voltage deviation. The improved genetic algorithm with elitist retention mechanism is used to solve the model. The IEEE33 system is used to show the location and constant capacity of the distributed power supply under different conditions. It shows that the reasonable and optimized configuration of the distributed power supply can obtain better voltage quality and minimize the cost function, which verifies the effectiveness of the proposed model.

Optimal Allocation of Distributed Generation Minimizing Loss and Voltage Sag Problem-Using Genetic Algorithm

2010 ◽  
Author(s):  
S. Biswas ◽  
S. K. Goswami ◽  
Swapan Paruya ◽  
Samarjit Kar ◽  
Suchismita Roy
Keyword(s):  
Genetic Algorithm ◽  
Distributed Generation ◽  
Optimal Allocation ◽  
Voltage Sag

The Optimal Allocation of Finishing Train in Steel Rolling Based on Improved Genetic Algorithm

2013 ◽  
Vol 433-435 ◽  
pp. 720-724
Author(s):  
Hong Xia Liu ◽  
Xin Chen
Keyword(s):  
Genetic Algorithm ◽  
Optimal Allocation ◽  
Empirical Distribution ◽  
Rolling Force ◽  
Optimization Methods ◽  
Improved Genetic Algorithm ◽  
Load Curve ◽  
Distribution Method ◽  
Steel Rolling ◽  
Mutation Operators

The central issue of finishing train is that we should distribute the thickness of each exit with reason and determine the rolling force and relative convexity. The optimization methods currently used are empirical distribution method and the load curve method, but they both have drawbacks. To solve those problems we established a mathematical model of the finishing train and introduced an improved Genetic Algorithm. In this algorithm we used real number encoding, selection operator of a roulette and elitist selection and then improved crossover and mutation operators. The results show that the model and algorithm is feasible and could ensure the optimal effect and convergence speed. The products meet the production requirements.

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