Accommodating the uncertainty of 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 the uncertainty-informed VRES-quantity
adjustment is more significant under higher 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.Comment: Submitted to IEEE PES general meeting 202