Multi-objective Dispatch of Microgrid Based on Dynamic Fuzzy Chaotic Particle Swarm Algorithm

Tang Jun-jie; Chen Jing-hua; Qiu Ming-jin

doi:10.12052/gdutxb.170130

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Multi-objective Dispatch of Microgrid Based on Dynamic Fuzzy Chaotic Particle Swarm Algorithm

更新时间：2026-05-27

- Multi-objective Dispatch of Microgrid Based on Dynamic Fuzzy Chaotic Particle Swarm Algorithm
- Vol. 35, Issue 3, Pages: 100-106(2018)
- 作者机构：
  
  广东工业大学　自动化学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.170130
  CLC： TM734
- Online First：09 May 2018，
  
  Published：2018
- 稿件说明：
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唐俊杰, 陈璟华, 邱明晋. 基于动态模糊混沌粒子群算法的含电动汽车微电网多目标优化调度研究[J]. 广东工业大学学报, 2018, 35(3): 100-106.

Tang Jun-jie, Chen Jing-hua, Qiu Ming-jin. Multi-objective Dispatch of Microgrid Based on Dynamic Fuzzy Chaotic Particle Swarm Algorithm[J]. 2018, 35(3): 100-106.
唐俊杰, 陈璟华, 邱明晋. 基于动态模糊混沌粒子群算法的含电动汽车微电网多目标优化调度研究[J]. 广东工业大学学报, 2018, 35(3): 100-106. DOI： 10.12052/gdutxb.170130.

Tang Jun-jie, Chen Jing-hua, Qiu Ming-jin. Multi-objective Dispatch of Microgrid Based on Dynamic Fuzzy Chaotic Particle Swarm Algorithm[J]. 2018, 35(3): 100-106. DOI： 10.12052/gdutxb.170130.

摘要

提出一种基于动态模糊混沌粒子群算法的微电网多目标优化调度方法. 利用动态目标函数与模糊理论解决多目标主观权值的缺陷

建立以运行维护成本、环境污染物成本为目标的微电网多目标调度模型. 微电网多目标优化调度属于多变量、强非线性优化问题

针对传统粒子群算法求解时容易陷入局部最优等问题

在粒子初始化时

引入一种结合Chebyshev映射和Logistic映射的组合混沌映射

在粒子更新过程中

引入Logistic映射

从而增加粒子寻优的遍历性

加强算法全局优化能力. 针对惯性权重在粒子群更新过程中的取值问题

采用迭代次数梯度改变惯性权重的策略. 仿真结果证明了算法具有更高的收敛效率和更好的收敛效果.

Abstract

A method to deal with dispatch of Microgrid is proposed based on the dynamic fuzzy chaotic particle swarm algorithm. Using dynamic objective function and fuzzy theory to solve the defects of the subjective weight

a microgrid scheduling model is found which aims for lower maintenance cost and environmental pollution. Multi-objective dispatch of microgrid belongs to the multivariable and strongly nonlinear optimization problem. Since the traditional particle swarm algorithm tends to trap in the local superior

in the particle initialization

a chaotic mapping combining the Chebyshev maps and the Logistic map is introduced

and in the process of particle update

the Logistic map is introduced which increases the ergodicity of particles and strengthens global optimization ability of algorithm. According to the value of inertia weight in particle swarm updating

the strategy of changing the inertia weight with the gradient of iteration number adopted. The simulation experiment of multi-objective dispatch of microgrid shows that the algorithm has higher convergence speed and better convergence effect.

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references

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