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

被引:10
作者
Jalving, Jordan [1 ,5 ]
Ghouse, Jaffer [2 ]
Cortes, Nicole [3 ]
Gao, Xian [3 ]
Knueven, Bernard [4 ]
Agi, Damian [3 ]
Martin, Shawn [1 ]
Chen, Xinhe [3 ]
Guittet, Darice
Tumbalam-Gooty, Radhakrishna [2 ]
Bianchi, Ludovico
Beattie, Keith [6 ]
Gunter, Daniel [6 ]
Siirola, John D. [1 ]
Miller, David C. [2 ]
Dowling, Alexander W. [3 ]
机构
[1] Sandia Natl Labs, Albuquerque, NM 87123 USA
[2] Natl Energy Technol Lab, Pittsburgh, PA 15236 USA
[3] Univ Notre Dame, Dept Chem & Biomol Engn, Notre Dame, IN 46556 USA
[4] Natl Renewable Energy Technol Lab, Golden, CO 80401 USA
[5] Pasteur Labs, Brooklyn, NY 11205 USA
[6] Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA
关键词
Integrated energy systems; Surrogate modeling; Neural networks; Electricity markets; Energy infrastructure; Computational optimization; SUPERSTRUCTURE OPTIMIZATION; FREQUENCY REGULATION; ELECTRICITY MARKETS; SUSTAINABLE DESIGN; POWER; STORAGE; MODEL; WIND; BATTERY; FLEXIBILITY;
D O I
10.1016/j.apenergy.2023.121767
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:17
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