A Scalable Bilevel Framework for Renewable Energy Scheduling

TL;DR

This paper introduces a scalable bilevel optimization framework for renewable energy scheduling that improves market efficiency and reduces system costs, especially at high VRES penetration levels, by using a novel linear relaxation technique.

Contribution

It proposes a linear programming relaxation based on strong duality and McCormick envelopes to efficiently solve large-scale bilevel problems in renewable energy scheduling.

Findings

Achieves a 0.7% system cost gap compared to stochastic benchmarks.

Solves large-scale NYISO system in minutes.

More effective at higher VRES penetration levels, e.g., 70%.

Abstract

Accommodating the uncertain and variable renewable energy sources (VRES) in electricity markets requires sophisticated and scalable tools to achieve market efficiency. To account for the uncertain imbalance costs in the real-time market while remaining compatible with the existing sequential market-clearing structure, our work adopts an uncertainty-informed adjustment toward the VRES contract quantity scheduled in the day-ahead market. This mechanism requires solving a bilevel problem, which is computationally challenging for practical large-scale systems. To improve the scalability, we propose a technique based on strong duality and McCormick envelopes, which relaxes the original problem to linear programming. We conduct numerical studies on both IEEE 118-bus and 1814-bus NYISO systems. Results show that the proposed relaxation can achieve good performance in accuracy (0.7%-gap in the system cost wrt. the least-cost stochastic clearing benchmark) and scalability (solving the NYISO system in minutes). Furthermore, the benefit of this bilevel VRES-quantity adjustment is more significant under higher penetration levels of VRES (e.g., 70%), under which the system cost can be reduced substantially compared to a myopic day-ahead offer strategy of VRES.