Low-Dimensional Space- and Time-Coupled Power System Control Policies Driven by High-Dimensional Ensemble Weather Forecasts

Many predictive control problems can be solved at lower cost if the practitioner is able to make use of a high-dimensional forecast of exogenous uncertain quantities. For example, power system operators must accommodate significant short-term uncertainty in renewable energy infeeds. These are predicted using sophisticated numerical weather models, which produce an ensemble of scenarios for the evolution of atmospheric conditions. We describe a means of incorporating such forecasts into a multistage optimization framework able to make use of spatial and temporal correlation information. We derive an optimal procedure for reducing the size of the look-ahead problem by generating a low-dimensional representation of the uncertainty, while still retaining as much information as possible from the raw forecast data. We then demonstrate application of this technique to a model of the Great Britain grid in 2030, driven by the raw output of a real-world highdimensional weather forecast from the U.K. Met Office. We also discuss applications of the approach beyond power systems.

I. Introduction

Many systems are operated in the presence of high-dimensional disturbance forecasts. Examples include transportation networks with uncertain usage patterns, air traffic control carried out under complex atmospheric conditions, and power systems with estimated short-term renewable power infeeds. This letter focuses on power system dispatch in the presence of uncertain wind power injections, but we note from the outset that the approach we present is general, and could be applied to other uncertainty sources or other applications.