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

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

doi:10.1016/j.apenergy.2023.121767

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

Journal Article · 01 September 2023 · Applied Energy

DOI: https://doi.org/10.1016/j.apenergy.2023.121767 · OSTI ID:2331410

^[1]; Ghouse, Jaffer ^[2]; Cortes, Nicole ^[3];

^[3]; Knueven, Bernard ^[4];

^[3]; Martin, Shawn ^[5];

^[3];

^[4];

^[2]; Bianchi, Ludovico ^[6];

^[6]; Gunter, Daniel ^[6]; Siirola, John D. ^[5];

^[7];

^[3]

Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Pasteur Labs, Brooklyn, NY (United States)
National Energy Technology Laboratory (NETL), Pittsburgh, PA (United States); National Energy Technology Laboratory (NETL), Pittsburgh, PA (United States). Support Contractor
University of Notre Dame, IN (United States)
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
National Energy Technology Laboratory (NETL), Pittsburgh, PA (United States)

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, here 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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Fossil Energy and Carbon Management (FECM)

Grant/Contract Number:: AC02-05CH11231; AC36-08GO28308; NA0003525

OSTI ID:: 2331410

Journal Information:: Applied Energy, Journal Name: Applied Energy Journal Issue: N/A Vol. 351; ISSN 0306-2619

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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