Collaborative Research: Computational Intelligence Methods for Dynamic Stochastic Optimization of Smart Grid Operation with High Penetration of Renewable Energy

合作研究：可再生能源高渗透智能电网运行动态随机优化的计算智能方法

• 批准号: 1232070
• 负责人: Ganesh Venayagamoorthy
• 金额: $ 18.03万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1232070&HistoricalAwards=false
• 关键词: Collaborative; Research; Computational; Intelligence; Methods

The objective of this research is to develop advanced computational intelligence methods to monitor, optimize and control large or so-called wide areas of a power network that will include solar farms (SFs) and wind farms (WFs), and controllable network transformers (CNTs), in order to ensure optimum usage of all these resources both during slow changing semi-steady state conditions, as well as during transient conditions. The research will be carried out in off-line simulations and then implemented on a real-time simulator.Intellectual meritThe behavior of renewable energy sources is uncertain and variable, and it is difficult for static optimization methods to optimize uncertain non-stationary distributed energy resources in a smart grid for maximum utilization. A novel ACD controller is proposed for the development of a real- time dynamic stochastic optimization smart grid engine. Advanced intelligent methods such as the biologically inspired artificial neural network and smart devices (CNTs) provide better identification and control capabilities for implementation of optimal power flows.Broader ImpactsEconomically operated reliable and secure power systems that can accommodate high penetration of renewable energy are of national interest. Being able to route power through underutilized lines will have major economic and environmental benefits due to avoiding the need for new lines. This project will have several dissemination channels, including software, websites, new contents added to existing courses, special sessions and tutorials at conferences, and journal publications.

这项研究的目标是开发先进的计算智能方法来监视、优化和控制电力网络的大范围或所谓的广域区域，其中包括太阳能发电场（SF）和风力发电场（WF）以及可控网络变压器（CNT） ），以确保在缓慢变化的半稳态条件下以及瞬态条件下所有这些资源的最佳使用。研究将在离线模拟中进行，然后在实时模拟器上实施。智力价值可再生能源的行为是不确定的、可变的，静态优化方法很难优化不确定的非平稳分布式能源在智能电网中实现最大利用率。提出了一种新型 ACD 控制器，用于开发实时动态随机优化智能电网引擎。先进的智能方法，如受生物启发的人工神经网络和智能设备（CNT），为实现最佳潮流提供了更好的识别和控制能力。更广泛的影响经济运行、可适应可再生能源高渗透率的可靠、安全的电力系统符合国家利益。 由于避免了对新线路的需求，能够通过未充分利用的线路输送电力将带来重大的经济和环境效益。该项目将有多种传播渠道，包括软件、网站、现有课程中添加的新内容、会议上的特别会议和教程以及期刊出版物。