Research on optimal schedule strategy for active distribution network using particle swarm optimization combined with bacterial foraging algorithm

2016 ◽  
Vol 78 ◽  
pp. 637-646 ◽  
Author(s):  
Feng Zhao ◽  
Jingjing Si ◽  
Jijuan Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Distribution Network ◽  
Optimal Schedule ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Active Distribution Network ◽  
Schedule Strategy ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm

Optimal allocation of distributed energy storage in active distribution network via hybrid teaching learning and multi-objective particle swarm optimization algorithm

2021 ◽  
pp. 002072092098369
Author(s):  
Jijun Liu ◽  
Yuxin Bai ◽  
Yingfeng He
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Storage ◽  
Optimal Allocation ◽  
Storage System ◽  
Distribution Network ◽  
Particle Swarm ◽  
Economic Cost ◽  
Energy Storage System ◽  
Swarm Optimization ◽  
Active Distribution Network

This work aims at solving complex problems of the optimal scheduling model of active distribution network, teaching strategies are proposed to improve the global search ability of particle swarm optimization. Moreover, based on the improved Euclidean distance cyclic crowding sorting strategy, the convergence ability of Li Zhiquan algorithm is improved. With the cost and voltage indexes of the energy storage system of the distribution network as the goal, different optimized configuration schemes are constructed, and the improved HTL-MOPSO algorithm is adopted to find the solution. The results show that compared with the traditional TV-MOPSO algorithm, the proposed algorithm has better convergence performance and optimization ability, and has a lower economic cost. In short, the algorithm proposed can provide a basis for improving the optimization of active distribution network scheduling strategies.

Bacterial Foraging Algorithm Based on Quantum-Behaved Particle Swarm Optimization for Global Optimization

2013 ◽  
Vol 655-657 ◽  
pp. 948-954 ◽  
Author(s):  
Ling Li ◽  
Xiong Fa Mai
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Local Minimum ◽  
Random Search ◽  
Particle Swarm ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Simulation Results

Bacterial Foraging Optimization(BFA) algorithm has recently emerged as a very powerful technique for real parameter optimization,but the E.coli algorithm depends on random search directions which may lead to delay in reaching the global solution.The quantum-behaved particle swarm optimization (QPSO) algorithm may lead to possible entrapment in local minimum solutions. In order to overcome the delay in optimization and to further enhance the performance of BFA,a bacterial foraging algorithm based on QPSO(QPSO-BFA) is presented.The new algorithm is proposed to combines both algorithms’ advantages in order to get better optimization values. Simulation results on eight benchmark functions show that the proposed algorithm is superior to the BFA,QPSO and BF-PSO.

scholarly journals Hybrid Bacteria Foraging Particle Swarm Optimization based Optimal Reactive Power Dispatch for the Alleviation of Voltage Deviations

2019 ◽  
Vol 9 (2) ◽  
pp. 5350-5354
Keyword(s):  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Bacterial Foraging ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Bacterial Foraging Algorithm ◽  
Reactive Power Dispatch

This paper presents a hybrid algorithm for optimal reactive power dispatch by combining two popular evolutionary computation algorithms; Bacterial Foraging algorithm and Particle Swarm Optimization. The Hybrid algorithm combines velocity and position updating strategy of Particle swarm optimization algorithm and reproduction and elimination dispersal of Bacterial foraging algorithm. The proposed algorithm is applied to solve optimal power flow with the objective of minimization of Sum of squares of voltage deviations of all load buses. The proposed approach has been evaluated on a standard IEEE 30 bus test system and 24 bus EHV southern region equivalent Indian power system. The results obtained by the proposed Hybrid algorithm are compared with their basic counter parts and superiority of the proposed hybrid algorithm is demonstrated

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