Resources

[1] [Patent 1]: Fan, X.; Li, X.; Barrett, E. L.; Huang, Q.; O'Brien, J. G.; Huang, R.; Hou, Z.; Diao, R. (2021). Transformative remedial action scheme tool (TRAST) ( U.S. Patent No. 16,435,352). U.S. Patent and Trademark Office. https://rb.gy/mvbzic

[2] Fan X., R. Huang, Q. Huang, X. Li, E.L. Barrett, J.G. O'Brien, and Z. Hou, et al. 2019. Adaptive RAS/SPS System Setting for Improving Grid Reliability and Asset Utilization through Predictive Simulation and Controls: A Use Case for Transformative Remedial Action Scheme Tool (TRAST): Jim Bridger RAS Evaluation and Analysis. PNNL-29522. Richland, WA: Pacific Northwest National Laboratory. Available at: https://www.pnnl.gov/main/publications/external/technical_reports/PNNL-29522.pdf

[3] X. Li; X. Fan; H. Ren, Z. Hou, Q. Huang, S. Wang, O. Ciniglio, “Data-driven Feature Analysis in Control Design for Series-Compensated Transmission Systems”, in IEEE Transactions on Power Systems, vol. 34, no. 4, pp. 3297-3299, July 2019. doi: 10.1109/TPWRS.2019.2912711

[4] Yixuan Sun, Xiaoyuan Fan, Qiuhua Huang, Xinya Li, Renke Huang, Tianzhixi Yin, and Guang Lin. 2019. "Local Feature Sufficiency Exploration for Predicting Security-constrained Generation Dispatch in Multi-Area Power Systems." In the 17th IEEE International Conference on Machine Learning and Applications, 1283-1289. doi:10.1109/ICMLA.2018.00208

[5] Xiaoyuan Fan, “Empowering Data Model Convergence in Power System Planning Study & Control Design by Machine Learning with Utility Data”, Presentation at 2019 May Western Electricity Coordinating Council Joint Synchronized Information Subcommittee meeting, Salt Lake City, Available at: https://www.wecc.org/_layouts/15/WopiFrame.aspx?sourcedoc=/Administrative/11d_20190530WECC_JSIS_XFfinal.pdf&action=default&DefaultItemOpen=1

[6] Xiaoyuan Fan, “Machine Learning Methods for RAS Design and Calculation in the Transformative RAS Tool (TRAST)”, Presentation at 2018 November Western Electricity Coordinating Council Joint Synchronized Information Subcommittee meeting, Portland, Or, Oregon. PNNL-SA-139354. Available at: https://www.wecc.biz/Administrative/Machine%20Learning%20for%20RAS-%20Fan.pdf

[7] Xiaoyuan Fan, “Data-driven Approach and Potential Cloud Application in Power System RAS Studies”, Presentation at 2018 November Western Electricity Coordinating Council Modeling and Validation Work Group (MVWG) meeting, Salt Lake City, Utah. PNNL-SA-139821. Available at: https://www.wecc.biz/Administrative/11a%202018NovWECCJSIS_RAS_XFfinal.pdf

[8] Fan X., X. Li, Z. Hou, Y. Sun, Q. Huang, H. Ren, and R. Huang, et al. 10/12/2018. "Utility Data Analysis and Exploration for Power System Control Applications." Presented by X. Fan at 2018 NSF AMPS/ATD Joint Workshop, Washington, District Of Columbia. PNNL-SA-138574.

[9] Li, Xinya; Fan, Xiaoyuan; Hou, Zhangshuan; Ren, Huiying; Huang, Qiuhua, 2018. “Adaptive RAS/SPS System Settings for Improving Grid Reliability and Asset Utilization through Predictive Simulation and Controls Task 4-Extra: Data-Driven Analysis for RAS Design and Coefficient Derivation”, PNNL-28299. Richland, WA: Pacific Northwest National Laboratory.

[10] Fan, Xiaoyuan; Li, Xinya; Barrett, Emily L; Huang, Qiuhua; O'Brien, James G; Huang, Renke; Hou, Zhangshuan; Ren, Huiying; Kincic, Slaven; Zhang, Hongming, 2018. “Adaptive RAS/SPS System Settings for Improving Grid Reliability and Asset Utilization through Predictive Simulation and Controls Task 4 Report – Prototype Design for Transformative Remedial Action Scheme Tool (TRAST)”, PNNL-28295. Richland, WA: Pacific Northwest National Laboratory.

[11] Fan X., E.L. Barrett, J.G. O'Brien, R. Huang, and Q. Huang. 2018. Adaptive RAS/SPS System Settings for Improving Grid Reliability and Asset Utilization through Predictive Simulation and Controls Task 2 Report – Jim Bridger RAS and its operation logics for use case development. PNNL-28209. Richland, WA: Pacific Northwest National Laboratory.

[12] O'Brien J.G., E.L. Barrett, X. Fan, R. Diao, R. Huang, and Q. Huang. 2017. Adaptive RAS/SPS System Settings for Improving Grid Reliability and Asset Utilization through Predictive Simulation and Control, Task 1 Report – Survey on RAS/SPS modeling practice. PNNL-27032. Richland, WA: Pacific Northwest National Laboratory. Available at: https://www.pnnl.gov/main/publications/external/technical_reports/PNNL-27032.pdf

 Industry Collaborators in Western Interconnection:

  1. PacifiCorp (https://www.pacificorp.com/index.html )

PacifiCorp is one of the Western United States’ leading utilities, serving more than 1.8 million customers in six states. The company was formed in 1984, when its electric utility, natural resource development, and telecommunications businesses grew into full-fledged enterprises. In 1989, it merged with Utah Power & Light and continued doing business as Pacific Power and Utah Power. PacifiCorp was acquired by MidAmerican Energy Holdings Company in 2006. In 2014, MidAmerican changed its name to Berkshire Hathaway Energy. Today, PacifiCorp consists of two business units: Pacific Power, which delivers electricity to customers in Oregon, Washington, and California; and Rocky Mountain Power, which delivers electricity to customers in Utah, Wyoming, and Idaho. PacifiCorp owns, or has interests in, 72 thermal, hydroelectric, wind-powered, and geothermal generating facilities, with a net owned capacity of 10,894 megawatts. PacifiCorp has approximately 63,000 miles of distribution line and 16,500 miles of transmission line – more than any other single entity in the West. The company continues to invest to meet customers’ needs, making only the critical investments now to ensure future reliability, security, and safety. PacifiCorp's Energy Gateway transmission expansion program represents plans to build approximately 2000 miles of new high-voltage transmission lines, with an estimated cost exceeding $6 billion.

  1. Idaho Power Company (https://www.idahopower.com/ )

Idaho Power Company is an electric utility engaged in the generation, transmission, distribution, sale, and purchase of electric energy. It is regulated by the Federal Energy Regulatory Commission, as well as the state regulatory commissions of Idaho and Oregon. Idaho Power’s unique service area spans some of the most rugged and remote landscape across southern Idaho and eastern Oregon, covering 24,000 (estimate) square miles of service area and with 550,000 total customers, as of August 2018. The total annual sales of 2017 are 16,706,603 megawatt-hours. Idaho Power relies on 17 hydroelectric generating plants on the Snake River and its tributaries, natural gas-fired plants, and shares of three jointly-owned coal-fired plants. With the 17 low-cost hydroelectric projects as the core of its generation portfolio, its residential, business, and agricultural customers pay some of the nation’s lowest prices for electricity.

  1. Peak Reliability (former WECC Reliability Coordinator)

Peak Reliability performs the function of Reliability Coordinator (RC) for the Western Interconnection, excluding the Alberta Electric System Operator (AESO). Peak Reliability (Peak) was formed as a result of the bifurcation of the Western Electricity Coordinating Council into a Regional Entity (WECC) and a Reliability Coordinator (Peak). The bifurcation of WECC received final approval from the Federal Energy Regulatory Commission (FERC) on February 12, 2014. Peak, a company wholly independent of WECC, performs the Reliability Coordinator function in its RC Area in the Western Interconnection.