.png){kind=icon}
返回
Analytical adaptive distributed multi-objective optimization algorithm for optimal power flow problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.energy.2020.119245.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The convergence speed of analytic distributed multi-objective optimization algorithms should be higher when solving distributed multi-objective optimization algorithms. An adaptive operation is introduced into the only analytic distributed multi-objective optimization algorithm, which is an interchange objective value method. Therefore, an adaptive interchange objective value method is proposed for distributed multi-objective optimization problems. The proposed adaptive interchange objective value method updates the reward coefficients of a basic analytical distributed multi-objective optimization algorithm in the iteration process of solving distributed multi-objective optimization problems. The adaptive interchange objective value method obtains multiple satisfy optimal objectives for multiple subsidiary distributed multi-objective optimization problems security and quickly. To verify the feasibility and effectiveness of the adaptive interchange objective value method for the analytical distributed multi-objective optimization problems, the analytical distributed multi-objective optimal power flow problems under IEEE 118-bus, IEEE 30 0-bus power system and the medium part of the European system with 1472-bus test system are simulated. The numerical simulation results under these three cases show that the proposed adaptive interchange objective value method can obtain multiple distributed objectives for analytical distributed multi-objective optimal power flow problems security and quickly. (C) 2020 Elsevier Ltd. All rights reserved.
Keyword:
Adaptive interchange objective value method
Distributed multi-objective optimization problems
Optimal power flow
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.4
论文数:
4.2W
被引数:
20.2W
机构
引用论文
Equilibrium-inspired multiagent optimizer with extreme transfer learning for decentralized optimal carbon-energy combined-flow of large-scale power systems.png)
APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏Minimization of energy consumption in multi-stage evaporator system of Kraft recovery process using Interior-Point MethodENERGY
ENERGYIF9.4
PREAI
分享收藏Interchange objective value method for distributed multi-objective optimization: Theory, application, implementationAPPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏
查看更多内容