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.

这项研究的目的是开发先进的计算智能方法来监测，优化和控制大型或所谓的广域电网，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最佳使用所有这些资源。智能电网中可再生能源的行为具有不确定性和可变性，静态优化方法难以优化智能电网中不确定的非平稳分布式能源资源，使其利用率最大化。提出了一种新的ACD控制器的真实的实时动态随机优化智能电网引擎的发展。先进的智能方法，如生物启发的人工神经网络和智能设备（CNT）提供了更好的识别和控制能力，以实现最佳的电力潮流。更广泛的影响经济运行的可靠和安全的电力系统，可以适应高渗透率的可再生能源是国家利益。 能够通过未充分利用的线路路由电力将具有重大的经济和环境效益，因为避免了对新线路的需求。该项目将有几个传播渠道，包括软件、网站、现有课程的新内容、特别会议和会议辅导以及期刊出版物。