Energy and Sustainability

In physics, when we think of addressing the grand challenge of energy & sustainability, materials and device physics immediately come to mind as obvious choices, owing to their deep contributions. Even though energy science saw its birth with Thermodynamics, which came about from an in depth study to understand the maximum efficiency of steam engines, today we find Statistical & Nonlinear Physics does not feature very much in our energy research discussions.  We however, have converged to the view that Statistical & Nonlinear Physics has much to contribute to Energy Science, as do Condensed Matter Physics, Engineering, and Policy. We are not aware of any concerted effort or a community under such a banner. So we hope to convince visitors to this page through the following, succinct argument.

All renewable energy generation mechanisms fluctuate with the natural variability everpresent in their respective energy sources, e.g. the power generated by a wind turbine fluctuates with the wind speed blowing past the turbine and stochastic cloud passage over solar photovoltaic panels cause sharp ramps in the power output. The current electricity grid was designed to distribute power generated from coal-fired or nuclear plants that generate steady power, and owing to their high inertia, ramp their production up or down slowly compared to grid response time scales. Dynamic load balancing of consumer load fluctuations occuring over shorter time scales are dealt with either through high-frequency energy trading or using low efficiency gas turbines with fast response times. If grid operators must deal with stochastic power generation from renewables over and above the consumer load fluctuations, it adds an additional cost to renewables. Furthermore, understanding the character of fluctuations in renewables is necessary to develop strategies to manage, mitigate, and to inform engineering and policy on robust design of the future smartgrid.

Statistical Physics is the natural home for the study of fluctuations. From equilibrium (ensembles, critical phenomena etc.) through linear response regime (fluctuation-dissipation theorem etc.) to stochastic processes and strongly driven phenomena, the community of Statistical & Nonlinear Physics strives to understand fluctuations in a myriad settings. If a quantity of interest fluctuates, it begs a statistical description of the underlying mechanics. Ergo, Statistical & Nonlinear Physics is ideally placed to address the pressing questions that arise in energy and sustainability.

Our efforts in the Statistical & Nonlinear Physics of Energy and Sustainability started with the desire to understand a few simple questions in wind energy:

  • Can we explain the character of wind power fluctuations from the standpoint of atmospheric turbulence for an individual turbine, a wind farm (collection of turbines), and the grid fed by distributed wind farms?
  • How do the fluctuations smooth as we go from the turbine to the grid scale?
  • How does variability in wind power at the turbine scale translate to uncertainty or error?
  • How well do grid-scale power generation forecasts based on underlying numerical weather prediction models perform against actual generation?
  • What is the character of the probability distribution of forecast error?

Buoyed by our initial success in basic theory and data analysis, we are currently engaged in a similar study of solar photovoltaic fluctuations, whose character is fundamentally different from wind power.

Furthermore, we are embarking on a series of table-top and field experiments to apply our understanding to actively manage or mitigate these fluctuations through engineering solutions. At the fundamental level, our goal is to aid in a minimal parameter description of a complex system, whereas at the applied level we strive to utilize this minimal parameter description to aid in development and better management of electrical utility assets.

Our scholarly contributions in this field to date include the following publications:

  1. AC Slim, MM Bandi, JC Miller and L Mahadevan, "Dissolution-driven convection in a Hele-Shaw cell", Phys. Fluids 25, 024101 (2013).
  2. G Bel, CP Connaughton, M Toots, and MM Bandi, "Grid-scale fluctuations and forecast error in wind power", New J. Phys. 18, 023015 (2016).
  3. Mahesh M. Bandi and Jay Apt, "Variability of the Wind Turbine Power Curve", Appl. Sci. 6, 262 (2016).
  4. MM Bandi, "Spectrum of Wind Power Fluctuations", Phys. Rev. Lett. 118, 028301 (2017).
  5. K Klima, J Apt, MM Bandi, P Happy, C Loutan, and R Young, "Geographic smoothing of solar photovoltaic electric power production in the western USA", J. Renew. and Sustain. Energy 10, 053504 (2018).
  6. Golan Bel and MM Bandi, "Geographic dependence of the Solar Radiation Spectrum at Intermediate to High Frequencies", Phys. Rev. Applied 12, 024302 (2019).