Lawrence Berkeley National Laboratory

The integration of variable Renewable Energy Sources (vRES) to alleviate greenhouse gas emissions has introduced significant challenges for power systems operations. These challenges include high levels of uncertainty due to the intermittence associated with vRES and therefore impose the need to devise a reliable and cost-effective day-ahead unit commitment and power and reserves scheduling for real-time operations. Also, this increasing penetration of vRES requires higher ramping capabilities from units originally designed for other purposes (e.g., base-load generation), which might be exacerbated during contingency states. Hence, in this work, we propose a methodology to address the day-ahead Contingency-Constrained Unit Commitment (CCUC) problem that leverages the participation of Battery Energy Storage Systems (BESSs) to address load-following and post-contingency management, therefore alleviating the ramping burden on conventional thermal generators. To do so, we formulate a three-level optimization problem that represents the decision-making process of obtaining the least-cost commitment, generation and reserves scheduling, while restricting the Conditional Value-at-Risk (CVaR) of the system imbalance at real-time operations to user-defined tolerance levels. In addition, we devise a computationally efficient solution approach for the proposed problem based on the Column-and Constraint Generation (CCG) algorithmic framework. Two numerical experiments are conducted to empirically illustrate the benefits of the proposed methodology. Key results indicate a reduction in real-time ramping needs and a better usage of the system resources, with a reduction in the overall system commitment levels and reserve scheduling costs when compared to a benchmark case in which storage is not available.