Energy Data Analytics for Reliable and Efficient Electric Grid Operations

点击翻译按钮获取中文摘要

Across North America, phasor measurement units (PMUs) and smart meters have been extensively deployedacross the electric grid. The volume and variety of the data collected from these devices have the potential toenable a broad array of power system monitoring and operations tasks. Specifically, this project will leveragemeasurement data collected from PMUs and smart meters to improve [T1] real-time transient stabilityassessment, and [T2] online learning for demand response.Transient stability will be an important concern in the future power system, primarily due to rapidly varyingrenewable generation, which is expected to gradually but widely displace fossil-fuel-based technologies.Data-centric approaches based on machine learning have been shown to predict whether or not the system willbe stable with high accuracy. In [T1], along with our industry partner, we will extend existing tools to alsoidentify the root cause of instability, so that electric utilities can repair damages and restore normal systemoperations in a timely manner.Demand response programs help to improve power system reliability and market efficiency, which can beachieved by incentivizing customers via real-time pricing to shift their electricity usage away from periods ofpeak demand. Here, a major challenge is in predicting human behaviours, which can be solved via onlinelearning algorithms, but they generally neglect electrical network effects. In [T2], we will investigate theimpact of the electric network, which imposes nontrivial physical and operational constraints, on real-timeimplementations of demand response strategies.In close collaboration with our industry partner, the proposed tools will be prototyped and implemented intotheir commercial-grade software to extend its applicability to energy data analytics. By leveraging datacollected across the electric grid, the project outcomes help to ensure power availability and quality in the faceof growing uncertainty arising from high levels of renewable penetration and customer participation.