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。在所有组件建模中，由于系统中有大量不同的负载及其时变随机特性，负载表示仍然是最不准确的。新型的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至关重要。尽管在过去几十年里对负荷建模进行了大量研究，但2013年发布的一项全球调查表明，存在以下主要问题，这些问题可能会阻碍现代电网的合理设计和运行。在全球97个被调查的公用事业和系统运营商中，约有70%只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他们的负荷模型参数。约40%的公用事业公司在对大负荷供电点的需求建模时根本不考虑分布式发电，另有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。

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.