Daniel TylavskyDaniel Tylavsky

Associate Professor
ASU Directory Profile

ERC 517
480-965-3460
tylavsky@asu.edu

Research expertise
Electric power systems, numerical methods applied to large-scale system problems, parallel numerical algorithms, new educational methods and technologies, applying social optimization to power system markets, and transformer thermal modeling

Daniel Tylavsky is internationally known for applying computation technology to the analysis and simulation of large-scale power system generation/transmission problems. He also is an avid educator who uses team/cooperative learning methods in graduate and undergraduate education and is a pioneer in the use of mediated classrooms. He has been responsible for more than $4.5 million in research funding for both technical and educational research projects. He is a member of several honor societies and has received numerous awards for his technical work, as well as for work with student research.

Professional Preparation
Ph.D., electrical engineering, The Pennsylvania State University, 1982
M.S., electrical engineering, The Pennsylvania State University, 1978
B.S., electrical engineering, The Pennsylvania State University, 1974

Recognition and awards
Senior Member of IEEE, IEEE-PES Certificate for Outstanding Student Research Supervision (three times); Six awards for outstanding research from the IEEE IAS Mining Engineering Committee; Various awards for outstanding teaching.

Selected publications

S. Rao, D. J. Tylavsky, Y. Feng, “Estimating the saddle-node bifurcation point of static power systems using the holomorphic embedding method,” International Journal of Electrical Power and Energy Systems, Vol. 84, Jan. 2017, pp. 1-12.

S. Rao, Y. Feng, D. J. Tylavsky, M. Subramanian, “The Holomorphic Embedding Method Applied to the Power-Flow Problem,” IEEE PES Trans on Power Systems, (Accepted.)

HICSS 2016 Best Paper Award for Electric Energy Systems: Mao, D. Shawhan, R. Zimmerman, J. Yan, Y. Zhu, W. Schulze, R. Schuler, D. J. Tylavsky, “The Engineering, Economic and Environmental Electricity Simulation Tool (E4ST): Description and an Illustration of its Capability and Use as a Planning/Policy Analysis Tool,” Hawaii International Conference on System Sciences, Manao, Hawaii, Jan. 2016, pgs. 8.

A, J. Lamadrid; D. L. Shawhan; C. E. Murillo-Sanchez; R. D. Zimmerman; Y. Zhu; D. J. Tylavsky; A. G. Kindle; Z. Dar, “Stochastically optimized, carbon-reducing dispatch of storage, generation, and loads,” IEEE Transactions on Power Systems, 2015;30 (2):1064-1075.

D. L Shawhan; J. T. Taber; D. Shi; R. D. Zimmerman; J. Yan; C. M. Marquet; Y. Qi; B. Mao; R. E. Schuler; W. D. Schulze; et al., “Does a detailed model of the electricity grid matter? Estimating the impacts of the Regional Greenhouse Gas Initiative,” Resource and Energy Economics, 2014;36 (1):191-207.

D. Shi, D. J. Tylavsky, N. Logic, “An Adaptive Method for Detection and Correction of Errors in PMU Measurements,” IEEE Transactions on Smart Grid, Digital Identifier: 10.1109/TSG.2012.2207468, Dec 2012, pp. 1575-1583.

  1. Li, D. Shi, D. Shawhan, D. J. Tylavsky, J. Taber, R. Zimmerman, “Optimal Generation Investment Planning: Pt 2:, Application to the ERCOT System,” North American Power Symposium 2012, Champaign Illinois, Sep. 2012, pgs. 6.