Advanced Dynamic Load Modeling for Modern Smart Grids

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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.