Beyond price taker: Conceptual design and optimization of integrated energy systems using machine learning market surrogates

Jordan Jalving, Jaffer Ghouse, Nicole Cortes, Xian Gao, Bernard Knueven, Damian Agi, Shawn Martin, Xinhe Chen, Darice Guittet, Radhakrishna Tumbalam-Gooty, Ludovico Bianchi, Keith Beattie, Daniel Gunter, John D. Siirola, David C. Miller and Alexander W. Dowling

Applied Energy, 2023, vol. 351, issue C, No S0306261923011315

Abstract: Future electricity generation systems must be optimized to provide flexibility that counteracts the variability of non-dispatchable renewable energy sources and ensures the reliability and safety of critical infrastructure, including the electric grid. The current state-of-the-art is to co-optimize the design and operation of integrated energy systems (IES) treating historical or predicted time-series electricity prices as fixed parameters. Recent literature has shown the limitations of this price taker assumption, which neglects how IES optimization decisions influence market outcomes. As such, this paper proposes a new optimization formulation that uses machine learning surrogate models, trained from a library of annual market operation simulations, to embed IES market interactions into the co-optimization problem directly. Using a thermal generator example built in the open-source IDAES computational environment, we show that the price taker approach routinely over-predicts annual revenues by 8% or more compared to a validation simulation, where the proposed approach has a typical relative error of 1% or less.

Keywords: Integrated energy systems; Surrogate modeling; Neural networks; Electricity markets; Energy infrastructure; Computational optimization (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.apenergy.2023.121767

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