• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Intelligent decision technologies >Multi-agent system based sequential energy management strategy for Micro-Grid using optimal weighted regularized extreme learning machine and decision tree
【24h】

Multi-agent system based sequential energy management strategy for Micro-Grid using optimal weighted regularized extreme learning machine and decision tree

机译：基于多功能系统的微电网顺序能量管理策略，使用最优加权正规读取机器和决策树

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Renewable energies are fundamentally changing the traditional power grid. Their integration in micro grid constitutes the best way to produce clean energy in a large scale. However, classical control methods based centralized approaches are not efficient to manage and control the different operations in micro grid. In this paper, an intelligent energy management system is presented for micro grid power control based on the distributed paradigm of Multi-Agent System. Its main objective is to find the optimal control of a MG with grid-connected mode in order to control the amount of power delivered or taken from the Distribution Network so as to minimize the cost and maximize the benefit. We present also a photovoltaic and wind power prediction method using an Optimal Weighted Regularized Extreme Learning Machine algorithm in which the Particle Swarm Optimization method is used to optimize the regularization parameter. The algorithm is tested on real weather data and has shown a good generalization performance and better results than the basic Extreme Learning Machine algorithm while keeping its extremely fast training speed ability. To establish an efficient energy management strategy, a Decision Tree is used to ensure the availability of power on demand by taking a reasonable decision about charging batteries/selling electricity and discharging batteries/buying electricity in order to reduce the balance between cost and benefit.
机译：可再生能源从根本上改变传统的电网。他们在微网格中的集成构成了大规模生产清洁能量的最佳方法。但是，基于古典控制方法的集中方法是不有效的来管理和控制微网格中的不同操作。本文基于多助理系统的分布式范式，提供了一种智能能量管理系统。其主要目的是找到具有网格连接模式的MG的最佳控制，以控制从配电网络传递或取出的功率量，以最大限度地减少成本并最大限度地提高益处。我们还使用最佳加权正规化的极端学习机算法，其中介绍了一种光伏和风电预测方法，其中粒子群优化方法用于优化正则化参数。该算法在真实的天气数据上进行了测试，并且显示出良好的泛化性能和比基本的极限学习机算法更好的结果，同时保持其极快的训练速度能力。为了建立有效的能源管理策略，通过对充电电池/销售电力和放电电池/购买电力的合理决定来确保电力的可用性按需，以减少成本和益处之间的平衡。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号