Advanced Dynamic Load Modeling for Modern Smart Grids

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。在所有部件建模中，由于系统中存在大量不同的载荷及其时变的随机性，载荷表示仍然是精度最低的。新型的非常规电力电子负载和间歇性分布式电源的出现，给精确的负荷建模带来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运行具有重要意义。尽管过去几十年来一直在进行负荷建模研究，但2013年发布的一项全球调查显示，以下主要问题可能会阻碍现代电网的正确设计和运行。在世界各地接受调查的97家公用事业公司和系统运营商中，约有70%的人只使用静态负荷模型进行电力系统稳定性研究。只有40%的公用事业公司在过去五年内更新了负荷模型参数。约40%的公用事业公司在对大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将分布式发电简单地建模为负负荷。*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动态负荷建模方法来解决这些具有挑战性的问题。这项研究的长期目标是创造一系列先进和实用的技术，以改进动态负荷模型的开发和验证。为了实现这一目标，该研究计划将在以下三个相互关联的领域开展：1)建立准确的动态负荷模型的新技术；2)更好地为离线和实时在线应用更新负荷模型参数的方法；以及3)在负荷建模中集成分布式发电的新技术。这些技术的成功开发将导致实现更准确的动态负荷建模的显著改进的方法。与长期目标相一致，五年时间框架内的短期目标将集中于两个相互关联的主题：1)使用基于人工智能的机器学习方法和同步相量数据进行动态负荷建模；以及2)将可再生能源纳入动态负荷建模。*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。将开发一种有效的、智能化的电力负荷建模工具。研究可再生能源在负荷建模中的集成问题，将有助于改进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。这项研究将使加拿大的公用事业公司受益，并导致负荷建模领域的重大进步。**

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. **