Risk-Aware Control and Optimization for High-Renewable Power Grids
This addresses reliability and efficiency issues in power grid operations due to renewable energy volatility, but appears incremental as it builds on existing risk-aware frameworks.
The paper tackles the challenge of integrating renewable energy into power grids by moving from deterministic to risk-aware market-clearing algorithms, presenting innovations in uncertainty quantification, optimization, and machine learning with experimental results on real networks.
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 risk-aware market clearing and presents some of its innovations in uncertainty quantification, optimization, and machine learning. Experimental results on real networks are presented.