A major obstacle to the decarbonization of the electricity production systems is the multi scale (space and time) variability of wind, solar and hydro energy sources. Much work is being done to understand the high frequency variations in these sources from the perspective of grid integration. However, as with rainfall and other natural systems, these variables can exhibit log-log fractal scaling in space and time, such that the variance of the process increases with temporal duration and with spatial scale. Focusing on high frequency variations thus grossly understates the systemic risk that is associated with these sources. Appropriate national grid design including electricity storage allocation, needs to consider both the periodic annual cycle variations and quasi-periodic inter-annual variability which have larger variance, and the phase lags in these variations across space. The proposed project would explore the development of a multi-level, hierarchical spatio-temporal model for wind or solar using data from the continental USA and its subregions to explore stochastic simulations and multi-scale predictions of the associated risk to inform system design and financial instruments development.

This is project is NOT accepting applications.

Faculty Advisor

  • Professor: upmanu lall
  • Department/School: Earth & Env. Eng, SEAS
  • Location: 842 Mudd
  • climate and water risk analysis. optimization of systems. sustainability.

Project Timeline

  • Earliest starting date: 10/15/2019
  • End date: 5/31/2020
  • Number of hours per week of research expected during Fall 2019: ~15

Candidate requirements

  • Skill sets: Bayesian Statistics, Machine Learning, Time Series Modeling
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible