Dynamic optimization approach to coordinate industrial production and cogeneration operation under electricity price fluctuations

Pablos, Cristian; Merino, Alejandro; Acebes, Luis Felipe; Pitarch, José Luis; Biegler, Lorenz T.

doi:10.1016/j.compchemeng.2021.107292

Cited by 9 publications

(6 citation statements)

References 35 publications

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“…While the turnpike property has not received much attention in process system engineering, it has been studied in various chemical and biochemical processes. For example, it has been investigated in the optimal control of a large-scale chemical plant, anaerobic digestion, microbial metabolite production, , sugar production with cogeneration, and the classical Van de Vusse reactor . Additionally, some studies have explored the relationship between the turnpike property, dissipativity, and singular control, as well as its application to economic model predictive control .…”

Section: Numerical Studiesmentioning

confidence: 99%

Optimal Operation of a Reaction-Absorption Process for Ammonia Production at Low Pressure

Andrés-Martínez,

Zhang,

Daoutidis

2023

Ind. Eng. Chem. Res.

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Section: Numerical Studiesmentioning

confidence: 99%

Optimal Operation of a Reaction-Absorption Process for Ammonia Production at Low Pressure

Andrés-Martínez,

Zhang,

Daoutidis

2023

Ind. Eng. Chem. Res.

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“…Moreover, the objective of the process to “balance” the demand of others (when it itself imposes the main load) is questionable. Given this, recent studies have sought to coordinate demand‐side management from multiple facilities, 42,43 including with co‐generation 44 . Some studies also propose avoiding the price‐taker assumption via surrogate modeling, 45 or to couple the scheduling problem between power generation and demand using an iterative cutting‐plane strategy 46 …”

Section: Introductionmentioning

confidence: 99%

“…Given this, recent studies have sought to coordinate demand-side management from multiple facilities, 42,43 including with co-generation. 44 Some studies also propose avoiding the price-taker assumption via surrogate modeling, 45 or to couple the scheduling problem between power generation and demand using an iterative cutting-plane strategy. 46 Motivated by the above, this article presents a computational study of the design and operational optimization of a large-scale, electrolysis-based hydrogen production facility, considering that a process with electricity demand of its scale is likely to affect how the electricity grid expands and sets prices.…”

Section: Introductionmentioning

confidence: 99%

Integrating process and power grid models for optimal design and demand response operation of giga‐scale green hydrogen

Tsay,

Qvist

2023

AIChE Journal

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Electrolysis‐based hydrogen production can play a significant role in industrial decarbonization, and its economic competitiveness can be promoted by designing demand response operating schemes. Nevertheless, the scale of industrial supply plants may be significantly large (on the order of gigawatts), meaning that electricity prices cannot be treated as an input for scheduling problems, that is, the “price taker” approach. This article presents a framework for the optimization of a large‐scale, electricity‐powered hydrogen production facility considering its integration with the power grid. Using a computational case study, we present an iterative scheme for integrating the process model with a model for power grid optimization and capacity expansion, taking the popular GenX model as an example.

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“…The assignment of production system to the upper level and energy system to the lower level is chosen, since we assume that the production system announces its schedule first, and subsequently, the energy system fulfills the energy demand resulting from the production schedule. This is the same order of decision making as in the sequential optimization, which is the most used in industry (Pablos et al 2021). Compared to the sequential optimization, the production system considers the sequential decision making in the bilevel optimization and benefits from this knowledge.…”

Section: Introductionmentioning

confidence: 99%

Bilevel optimization for joint scheduling of production and energy systems

et al. 2022

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Energy-intensive production sites are often supplied with energy by on-site energy systems. Commonly, the scheduling of the systems is performed sequentially, starting with the scheduling of the production system. Often, the on-site energy system is operated by a different company than the production system. In consequence, the production and the energy system schedule their operation towards misaligned objectives leading in general to suboptimal schedules for both systems. To reflect the independent optimization with misaligned objectives, the scheduling problem of the production system can be formulated as a bilevel problem. We formulate the bilevel problem with mixed-integer decision variables in the upper and the lower level, and propose an algorithm to solve this bilevel problem based on the deterministic and global algorithm by Djelassi, Glass and Mitsos (J Glob Optim 75:341–392, 2019. https://doi.org/10.1007/s10898-019-00764-3) for bilevel problems with coupling equality constraints. The algorithm works by discretizing the independent lower-level variables. In the scheduling problem considered herein, the only coupling equality constraints are energy balances in the lower level. Since an intuitive distinction is missing between dependent and independent variables, we specialize the algorithm and add a procedure to identify independent variables to be discretized. Thereby, we preserve convergence guarantees. The performance of the algorithm is demonstrated in two case studies. In the case studies, the production system favors different technologies for the energy supply than the energy system. By solving the bilevel problem, the production system identifies an energy demand, which leads to minimal cost. Additionally, we demonstrate the benefits of solving the bilevel problem instead of solving the common integrated or sequential problem.

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Dynamic optimization approach to coordinate industrial production and cogeneration operation under electricity price fluctuations

Cited by 9 publications

References 35 publications

Optimal Operation of a Reaction-Absorption Process for Ammonia Production at Low Pressure

Optimal Operation of a Reaction-Absorption Process for Ammonia Production at Low Pressure

Integrating process and power grid models for optimal design and demand response operation of giga‐scale green hydrogen

Bilevel optimization for joint scheduling of production and energy systems

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