面向自愈控制的智能配电网快速仿真与模拟关键问题研究

项目名称： 面向自愈控制的智能配电网快速仿真与模拟关键问题研究

项目编号： No.51307116

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 电工技术

项目作者： 赵金利

作者单位： 天津大学

项目金额： 24万元

中文摘要： 项目立足于具有自愈特征的智能配电网快速仿真与模拟（DFSM）的关键科学问题，分别针对研究模式、实时模式及前瞻模式下的不同功能需求与核心特征，重点解决研究模式下面向自愈控制的DFSM在元件建模及分析计算时的基本问题，特别强调分布式电源、储能及各种控制与保护设备在仿真计算时的准确性和完整性；为了满足实时模式下自愈控制方案的快速性要求，基于锥优化方法解决实时模式下自愈控制策略的优化计算问题；针对前瞻模式下自愈控制策略的鲁棒性要求，采用鲁棒优化方法解决受分布式电源出力、负荷波动等不确定性影响较大的自愈控制策略的优化计算问题；面向复杂多源大数据环境，充分利用各种先进的计算资源与技术，基于Map-Reduce思想采用Hadoop实现大规模智能配电网的快速仿真与模拟。作为智能配电网自愈控制技术的核心，智能配电网快速仿真与模拟关键技术的研究将为自愈控制技术的发展与应用奠定坚实的理论与技术基础。

中文关键词： 智能配电网；自愈控制；快速仿真与模拟；鲁棒优化；锥规划

英文摘要： This project is established based on the key scientific issues of distribution fast simulation and modeling (DFSM) for smart distribution grid with self-healing features. Functional requirements and core characteristics are considered with respect to different modes. These modes are divided as the study mode, the real-time mode and the looking-forward mode. Basic problems of modeling, analysis and calculation in self-healing aimed DFSM under study mode are specially addressed. Model accuracy and the sufficiency of categories are emphasized for distributed generators, energy storage devices, and control & protection equipments. Under real-time mode, conic optimization method is employed for self-healing control strategy, in order to meet the requirements of simulation speed. Under looking-forward mode, robust optimization method is adopted to handle the fluctuation of distributed generators and loads, thus to satisfy the robustness requirement. By properly modeling the uncertainties within the DG and load, robust optimization of seal-healing control can be concluded to a conic optimization problem, and then to be solved. Finally, advanced computational technologies are utilized to adapt to the complex multi-source big data environment. Including that, Hadoop, based on the idea of Map-Reduce, is applied to realize

英文关键词： smart distribution system；self-healing control；fast simulation and modeling；robust optimization；conic programming