Risk-Aware Control and Optimization for High-Renewable Power Grids

Abstract

The transition of the electrical power grid from fossil fuels to renewable sources of energy raises fundamental challenges to the market-clearing algorithms that drive its operations. Indeed, the increased stochasticity in load and the volatility of renewable energy sources have led to significant increases in prediction errors, affecting the reliability and efficiency of existing deterministic optimization models. The RAMC project was initiated to investigate how to move from this deterministic setting into a risk-aware framework where uncertainty is quantified explicitly and incorporated in the market-clearing optimizations. Risk-aware market-clearing raises challenges on its own, primarily from a computational standpoint. This paper reviews how RAMC approaches riskaware market clearing and presents some of its innovations in uncertainty quantification, optimization, and machine learning. Experimental results on real networks are presented.

AIRU-WRF: A Physics-Guided Spatio-Temporal Wind Forecasting Model and its Application to the U.S. North Atlantic Offshore Wind Energy Areas

“…To that end, we propose the AI-powered Rutgers University Weather Research & Forecasting (AIRU-WRF) model, in order to make accurate OSW forecasts that are short-term (minutes to hours ahead), and of high resolution, both spatially (site-specific) and temporally (minute-level). Such hyper-detailed forecasts-practically unattainable by stand-alone physics-based models-are pivotal to several OSW operations, including power estimation [3,4], operations and maintenance [5,6], economic dispatch and reserve planning [7,8], among others.…”