Scientific

Electricity markets balance an increasingly intermittent supply with price-inelastic demand, while climate change and electrification of mobility are contributing to transforming diurnal and seasonal demand patterns. Electricity systems face an increasing level of stochasticity, and market participants need to inform their dispatch decisions based on 24-hour price forecasts for participation in Day-Ahead Markets, which in turn depend on supply and demand forecasts. The arrival of grid-scale electricity storage is also creating new scope for prices forecasts, while smaller scale storage systems act as price takers. In the long term, large-scale deployment of grid-scale electricity storage has the potential of significantly reducing price variation through arbitrage, which could shift the “value added” of forecasting from short-term (intra-day) to long-term predictions, and from supporting operational (dispatch) decisions to supporting capacity (investment) decisions.

The rising U.S. offshore wind sector holds great promise, both environmentally and economically, to unlock vast supplies of clean, domestic, and renewable energy. To harness this valuable resource, Gigawatt (GW)-scale offshore wind projects are already under way at several locations off of the U.S. coastline. This promising future, however, is still clouded with uncertainties on how to optimally manage those ultra-scale offshore wind assets, which would be operating under harsh environmental and operational conditions, in relatively under-explored territories, and at unprecedented scales. I will present some of our progress in formulating tailored forecasting and optimization models aimed at minimizing some of those uncertainties. Our models and analyses are largely tailored and tested using data from the U.S. Mid-Atlantic—where several GW-scale wind projects are currently under development.

How much wind power will a turbine generate over its lifetime? To answer such questions, we can consider climate model output to generate very long-term wind power forecasts on the scale of years to decades. One major limitation of the data projected by climate models is their coarse temporal resolution that is usually not finer than three hours and can be as coarse as one month. However, wind speed distributions of low temporal resolution might not be able to account for high frequency variability which can lead to distributional shifts in the projected wind speeds. Even if these changes are small this can have a huge impact due to the highly non-linear relationship between wind and wind power and the long forecast horizons we consider. In my talk, I will discuss how the resolution of wind speed data from climate projections affects wind power forecasts.

The ability to forecast solar irradiance plays an indispensable role in solar power forecasting, which constitutes an essential step in planning and operating power systems under high penetration of solar power generation. Since solar radiation is an atmospheric process, solar irradiance forecasting, and thus solar power forecasting, can benefit from the participation of atmospheric scientists. In this talk, the two fields, namely, atmospheric science and power system engineering are jointly discussed with respect to how solar forecasting plays a part. Firstly, the state of affairs in solar forecasting is elaborated; some common misconceptions are clarified; and salient features of solar irradiance are explained. Next, five technical aspects of solar forecasting: (1) base forecasting methods, (2) post-processing, (3) irradiance-to-power conversion, (4) verification, and (5) grid-side implications, are reviewed. Following that, ten research topics moving into the future are enumerated; they are related to (1) data and tools, (2) numerical weather prediction, (3) forecast downscaling, (4) large eddy simulation, (5) dimming and brightening, (6) aerosols, (7) spatial forecast verification, (8) multivariate probabilistic forecast verification, (9) predictability, and (10) extreme weather events. Last but not least, a pathway towards ultra-high PV penetration is laid out, based on a recently proposed concept of firm generation and forecasting.