Advanced Dynamic Load Modeling for Modern Smart Grids

现代智能电网的高级动态负载建模

• 批准号: RGPIN-2016-04170
• 负责人: Liang, Xiaodong
• 金额: $ 1.64万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=678943
• 关键词: Advanced; Dynamic; Load; Modeling; Modern

Power system planning, operation, and control rely heavily on simulation models. Among all component modeling, load representation remains among the least accurate due to a large number of diverse loads in the system and their time variant stochastic nature. New non-conventional power electronics loads and intermittent distributed generation add more complication and challenge to accurate load modeling. An accurate dynamic load model is very important for power system stability. Despite load modeling research efforts in past decades, a worldwide survey published in 2013 indicated the following major issues, which could hinder proper design and operation of the modern power grid. About 70% of 97 surveyed utilities and system operators around the world use only static load models for power system stability studies. Only 40% of utilities have updated their load model parameters within the last five years. About 40% of utilities do not consider distributed generation at all when modeling demand at the bulk load supply point, and further 28% of utilities simply model distributed generation as a negative load in system studies.*** The proposed research program aims to tackle these challenging issues by creating innovative new dynamic load modeling approaches in a modern smart grid environment. The long term goal of the research is to create a family of advanced and practical technologies for improved dynamic load model development and validation. To achieve this goal, the research program will be carried out in the following three interrelated areas: 1) new techniques to create accurate dynamic load models; 2) better methods to update load model parameters for off-line and real-time on-line applications; and 3) novel techniques to integrate distributed generation in load modeling. Successful development of these techniques will lead to a significantly improved approach to achieve more accurate dynamic load modeling. Aligning with the long-term goal, the short term objectives over a five year time-frame will be concentrated on the two interrelated themes: 1) dynamic load modeling using an artificial intelligent-based machine learning method and synchrophasor data; and 2) integration of renewable energy sources in dynamic load modeling. *** By investigating dynamic load modeling techniques using synchrophasor data, the most effective and best practical approaches will be obtained. An effective and intelligent load modeling tool for utilities will be developed. The research on integration of renewable energy sources in load modeling will improve planning and integration of renewable energy generation, and enhance the overall accuracy of power system simulation. The research will benefit Canadian utilities, and lead to significant advances in the load modeling field. **

电力系统的规划、运行和控制在很大程度上依赖于仿真模型。在所有的组件建模，负载表示仍然是最不准确的，由于大量的不同负载的系统和它们的时变随机性质。新型非常规电力电子负荷和间歇性分布式发电给准确的负荷建模增加了更多的复杂性和挑战。准确的动态负荷模型对电力系统的稳定性至关重要。尽管在过去几十年中进行了负荷建模研究，但2013年发布的一项全球调查表明，以下主要问题可能会阻碍现代电网的正确设计和运行。在全球97家接受调查的电力公司和系统运营商中，约有70%的公司仅使用静态负荷模型进行电力系统稳定性研究。在过去五年中，只有40%的公用事业公司更新了其负荷模型参数。大约40%的公用事业公司在对大容量负荷供应点的需求建模时根本不考虑分布式发电，另外28%的公用事业公司在系统研究中简单地将分布式发电建模为负负荷。 拟议的研究计划旨在通过在现代智能电网环境中创建创新的新动态负荷建模方法来解决这些具有挑战性的问题。该研究的长期目标是创建一个先进和实用的技术，以改进动态负荷模型的开发和验证。为了实现这一目标，研究计划将在以下三个相互关联的领域进行：1）新技术，以创建准确的动态负荷模型; 2）更好的方法，以更新负荷模型参数离线和实时在线应用;和3）新技术，以集成分布式发电负荷建模。这些技术的成功开发将导致一个显着改进的方法，以实现更准确的动态负荷建模。与长期目标一致，五年内的短期目标将集中在两个相互关联的主题上：1）使用基于人工智能的机器学习方法和同步相量数据的动态负荷建模; 2）将可再生能源集成到动态负荷建模中。*** 通过研究使用同步相量数据的动态负荷建模技术，将获得最有效和最实用的方法。一个有效的和智能的负荷建模工具，公用事业将开发。研究可再生能源在负荷建模中的集成问题，将有助于提高可再生能源发电的规划和集成，提高电力系统仿真的整体精度。这项研究将有利于加拿大的公用事业，并导致在负荷建模领域的重大进展。**