Mort Webster


Phone: (814) 863-1640

Office Address:
103 A Hosler Bldg.

Title: Professor


Research Areas:
Coal Science & Technology
Carbon Dioxide Capture, Conversion & Sequestration
Energy Economics
Petroleum & Natural Gas Engineering
Stationary Power
Sustainable Energy
Initiative for Sustainable Electric Power Systems

Education Background:
• Ph.D. (Engineering Systems), Massachusetts Institute of Technology, 2000
• M.S. (Technology and Policy), Massachusetts Institute of Technology, 1996
• B.S.E. (Computer Science and Engineering), University of Pennsylvania, 1988


Webster is a professor of energy engineering. He earned a B.S.E. in computer science and engineering from the University of Pennsylvania, an M.S. in technology and policy from the Massachusetts Institute of Technology, and a Ph.D. in engineering systems from the Massachusetts Institute of Technology. Webster’s research and teaching centers on the design, planning, and management of coupled energy and environmental systems with a particular focus on electric power systems. His research focus is on the development and application of methods for sequential decision under uncertainty, with a particular emphasis on applications to electric power systems planning and operations, energy systems design for flexibility and resilience, and environmental regulatory design. Webster received the U.S. Department of Energy Early Career Award.

Research Interests:

• Stochastic multi-stage optimization algorithms
• Electric power systems planning and operations
• Coupled energy-water-land modeling for resiliency studies

Memberships & Committees:

• Co-Director, Center for Sustainable Electric Power Systems, EMS Energy Institute, 2014-present.
• Committee Member, ICS Coordinating Committee, The Pennsylvania State U, 2014-present.
• Associate Editor, Energy Economics, 2005-2012
• U.S. EPA Science Advisory Board (nominated)
Institute for Operations Research and Management Sciences (INFORMS) • IEEE
• Association for Public Policy Analysis and Management (APPAM)
• American Geophysical Union (AGU)
• Association of Environmental and Resource Economists (AERE) • Society for Risk Analysis (SRA)
• International Association for Energy Economics (IAEE)

Honors & Awards:

• U.S. Department of Energy Early Career Award, January, 2010.
• Tanner Award for Excellence in Undergraduate Teaching, Univ. of North Carolina, Feb. 2006.
• Most Outstanding Faculty Award, Public Policy Majors Union, May, 2003.
• Martin Fellowship, Massachusetts Institute of Technology, 1996-1997
• Monbusho Scholarship, Japanese Ministry of Education, April 1989 – October 1990


• EBF 472: Probability and Statistics
• EGEE 451: Energy Systems Analysis
• EBF 483: Electricity Markets




Mort Webster's Publications
Record 1 - 10 of 46 View All
de Sisternes, F. J., M. D. Webster, and J. I. Perez-Arriaga, (2014). The Impact of Bidding Rules on Electricity Markets with Intermittent Renewables. IEEE Transactions on Power Systems.
Díaz, C. A., M. D. Webster, J. Villar, and F. A. Campos, (2014). Market Power in ERCOT System: a Fundamental CSFE with Network Constraints. IEEE Transactions on Power Systems.
Eide, J., F. de Sisternes, H. Herzog, and M. D. Webster, (2014). CO2 emissions standards and investment in carbon capture. Energy Economics 45 (2014) 53–65.
Felgenhauer, T., and M. D. Webster, (2014). Modeling Adaptation as a Flow and Stock Decision with Mitigation. Climatic Change 122: 665-679.
Jacquillat, A, A. R. Odoni, and M. D. Webster, (2014). Dynamic Control of Runway Configurations and of Arrival and Departure Service Rates at JFK Airport under Stochastic Queue Conditions. Transportation Science.
Morris, J., M. D. Webster, and J. Reilly, (2014). Hedging Strategies: Electricity Investment Decisions under Policy Uncertainty. Energy Journal.
Palmintier, B., and M. D. Webster, (2014). Heterogeneous Unit Clustering for Efficient Operational Flexibility Modeling. IEEE Transactions on Power Systems 29 (3): 1089-1098.
Parpas, P., and M. D. Webster, (2014). A stochastic multiscale model for electricity generation capacity expansion. European Journal of Operational Research 232 (2): 359-374.
Parpas, P., B. Ustun, and M. D. Webster, (2014). Importance Sampling in Stochastic Programming: A Markov Chain Monte Carlo Approach. INFORMS Journal on Computing.
Pena-Alcaraz, M., A. Ramos, and M. D. Webster, (2014). Train Timetable Design for Shared Railway Systems using a Linear Programming Approach to Approximate Dynamic Programming. Transportation Research Part B: Methodological, Urban Transportation Special Issue.