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

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.

Automated summary

Future electricity generation systems need to be optimized to provide flexibility against the variability of renewable energy sources and ensure the reliability of critical infrastructure like the electric grid. Current state-of-the-art is to optimize the design and operation of integrated energy systems (IES) using fixed parameters for electricity prices. However, recent research has shown the limitations of this approach, and this paper proposes a new optimization formulation that incorporates IES market interactions using machine learning surrogate models, resulting in more accurate predictions.