Uncertainty-Informed Renewable Energy Scheduling: A Scalable Bilevel Framework
arxiv(2023)
摘要
This work proposes an uncertainty-informed bid adjustment framework for
integrating variable renewable energy sources (VRES) into electricity markets.
This framework adopts a bilevel model to compute the optimal VRES day-ahead
bids. It aims to minimize the expected system cost across day-ahead and
real-time stages and approximate the cost efficiency of the stochastic market
design. However, solving the bilevel optimization problem is computationally
challenging for large-scale systems. To overcome this challenge, we introduce a
novel technique based on strong duality and McCormick envelopes, which relaxes
the problem to a linear program, enabling large-scale applications. The
proposed bilevel framework is applied to the 1576-bus NYISO system and
benchmarked against a myopic strategy, where the VRES bid is the mean value of
the probabilistic power forecast. Results demonstrate that, under high VRES
penetration levels (e.g., 40%), our framework can significantly reduce system
costs and market-price volatility, by optimizing VRES quantities efficiently in
the day-ahead market. Furthermore, we find that when transmission capacity
increases, the proposed bilevel model will still reduce the system cost,
whereas the myopic strategy may incur a much higher cost due to over-scheduling
of VRES in the day-ahead market and the lack of flexible conventional
generators in real time.
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关键词
Bilevel optimization,electricity markets,McCormick envelope,renewable energy,scalability,uncertainty management,unit commitment
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