scholarly journals Optimal Siting and Sizing of Distributed Generation Based on Improved Nondominated Sorting Genetic Algorithm II

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 955 ◽  
Author(s):  
Wei Liu ◽  
Fengming Luo ◽  
Yuanhong Liu ◽  
Wei Ding
Keyword(s):  
Genetic Algorithm ◽  
Distributed Generation ◽  
Distribution Network ◽  
Evaluation Criteria ◽  
Population Diversity ◽  
Nsga Ii ◽  
Voltage Deviation ◽  
Distribution Network Planning ◽  
Line Loss ◽  
Node Voltage

With the development of distributed generation technology, the problem of distributed generation (DG) planning become one of the important subjects. This paper proposes an Improved non-dominated sorting genetic algorithm-II (INSGA-II) for solving the optimal siting and sizing of DG units. Firstly, the multi-objective optimization model is established by considering the energy-saving benefit, line loss, and voltage deviation values. In addition, relay protection constraints are introduced on the basis of node voltage, branch current, and capacity constraints. Secondly, the violation constrained index and improved mutation operator are proposed to increase the population diversity of non-dominated sorting genetic algorithm-II (NSGA-II), and the uniformity of the solution set of the potential crowding distance improvement algorithm is introduced. In order to verify the performance of the proposed INSGA-II algorithm, NSGA-II and multiple objective particle swarm optimization algorithms are used to perform various examples in IEEE 33-, 69-, and 118-bus systems. The convergence metric and spacing metric are used as the performance evaluation criteria. Finally, static and dynamic distribution network planning with the integrated DG are performed separately. The results of the various experiments show the proposed algorithm is effective for the siting and sizing of DG units in a distribution network. Most importantly, it also can achieve desirable economic efficiency and safer voltage level.

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 Placement and Sizing of Distributed Generation in Smart Distribution System

2014 ◽  
Vol 513-517 ◽  
pp. 3322-3327 ◽  
Author(s):  
Shuang Zhang ◽  
Yong Mei Liu ◽  
Feng Gao ◽  
Bei Tian
Keyword(s):  
Distributed Generation ◽  
Distribution System ◽  
Stability Margin ◽  
Optimal Placement ◽  
Test Case ◽  
Planning Problem ◽  
Nsga Ii ◽  
Voltage Stability Margin ◽  
Voltage Deviation ◽  
Line Loss

This paper solves the distributed generation (DG) planning problem. Firstly, multiple-objective functions have been formed with the consideration of minimum line loss, minimum voltage deviation and maximal voltage stability margin. Secondly, the proposed improved NSGA-II algorithm has been described in detail to solve the multi-objective planning problem. An improved Non-dominated Sorting Genetic Algorithm II has been proposed for optimal planning of multiple DG units in this paper. Experiment has been made on the IEEE 33-bus test case with the consideration of multiple DG units. The computational result and comparison indicate the proposed algorithm for optimal placement and sizing of DG in distribution system is effective.

Study on System Loss in Distribution Network with DG

2014 ◽  
Vol 672-674 ◽  
pp. 1085-1089
Author(s):  
Jia Meng ◽  
Zai Lin Piao ◽  
Feng Zhou
Keyword(s):  
Theoretical Analysis ◽  
Real Number ◽  
Distribution System ◽  
Distribution Network ◽  
Output Current ◽  
Load Current ◽  
Radial Distribution System ◽  
Operation Optimization ◽  
Voltage Deviation ◽  
Node Voltage

The access of DG changes the operation and structure of traditional distribution network. This study mainly focused on controlling DG output current to reduce network loss of the system. Select a simple radial distribution system as example for theoretical analysis and derive the expressions of load current and node voltage. Assuming that there exists a real number k between DG output current and load. Then list the network loss and voltage deviation expressions. For the purpose of operation optimization, k can be determined by mathematical calculations. It proves that the method has a certain rationality to be effective in controlling network loss.

Distribution Network Planning Based on Multi-Island Group Strategy Genetic Algorithm

2014 ◽  
Vol 960-961 ◽  
pp. 964-968
Author(s):  
Si Qing Sheng ◽  
Shao Bo Yang
Keyword(s):  
Genetic Algorithm ◽  
Closed Loop ◽  
Distribution Network ◽  
Network Planning ◽  
Local Optimum ◽  
Planning Model ◽  
Slow Convergence ◽  
Group Strategy ◽  
Island Group ◽  
Distribution Network Planning

In view of faults which the traditional genetic algorithm (GA) have such as slow convergence speed and easy to fall into the local optimum. This paper put forward a genetic algorithm which is based on the multi-island group strategy, and applied it to the distribution network planning. The paper has established a planning model which takes the yearly comprehensive cost as objective function and discusses the repair methods of islands, solitary chain and closed-loop to meet with the requirements of grid radial. Finally, the proposed method is planning on a 54-node grid to prove the effectiveness of the algorithm and model.

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