Dynamic Process Operation Under Demand Response – A Review of Methods and Tools

Esche, Erik; Repke, Jens‐Uwe

doi:10.1002/cite.202000091

Cited by 9 publications

(7 citation statements)

References 132 publications

(212 reference statements)

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“…If the variables that appear in the objective function are of varying orders of magnitude, it is necessary to normalize these deviations with the nominal value of the respective variable. Other recent advances in the area of dynamic optimization, such as nonlinear model-predictive control or the consideration of uncertainty, can be found in Esche and Repke …”

Section: Methodsmentioning

confidence: 99%

“…Other recent advances in the area of dynamic optimization, such as nonlinear model-predictive control or the consideration of uncertainty, can be found in Esche and Repke. 67 While the solution of this dynamic problem is no mathematical proof of the feasibility of the previously obtained trajectories, we still assume it to yield representative results provided that the dynamic problem is solved for a sufficiently large time horizon with multiple load increases and decreases. The dynamic optimization problem is solved subject to the dynamic process model and potential path constraints due to variable bounds and control constraints as well as ramp constraints imposed by the DR parameter v P , which depends on the active business option.…”

Section: Practical and Realizable Potentialmentioning

confidence: 99%

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Assessing the Realizable Flexibility Potential of Electrochemical Processes

Hoffmann

Hübner

Klaucke

et al. 2021

Ind. Eng. Chem. Res.

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Demand response is a viable concept to deal with and benefit from fluctuating electricity prices and is of growing interest to the electrochemical industry. To assess the flexibility potential of such processes, a generic, interdisciplinary methodology is required. We propose such a methodology, in which the electrochemical fundamentals and the theoretical potential are determined first by analyzing strengths, weaknesses, opportunities, and threats. Afterward, experiments are conducted to determine selectivity and yield under varying loads and to assess the additional long-term costs associated with flexible operation. An industrial-scale electrochemical process is assessed regarding its technical, economic, and practical potential. The required steps include a flow sheet analysis, the formulation and solution of a simplified model for operation scheduling under various business options, and a dynamic optimization based on rigorous, dynamic process models. We apply the methodology to three electrochemical processes of different technology readiness levelsthe syntheses of hydrogen peroxide, adiponitrile, and 1,2-dichloroethane via chloralkali electrolysisto illustrate the individual steps of the proposed methodology.

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Section: Methodsmentioning

confidence: 99%

Section: Practical and Realizable Potentialmentioning

confidence: 99%

Assessing the Realizable Flexibility Potential of Electrochemical Processes

Hoffmann

Hübner

Klaucke

et al. 2021

Ind. Eng. Chem. Res.

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“…However, this issue has not been widely addressed in related sources, unless only on Euclidean spaces [22,23]. In some cases, NE methods lead to a two-layer formulation [24,25]. In the present work, for a system evolving on a Lie group SO(3), the combination of NE method and NMPC is considered to estimate the optimal solution in the presence of some alteration in the initial conditions.…”

Section: Introductionmentioning

confidence: 98%

Neighboring extremal nonlinear model predictive control of a rigid body on SO(3)

2023

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The issue of implementing nonlinear model predictive control (NMPC) on mechanical systems evolving on special orthogonal group (SO(3)) is taken into consideration in the first place. Necessary conditions of optimality are extracted based on Lie group variational integrators, leading to a two-point boundary value problem (TPBVP) which is solved using sensitivity derivatives and indirect shooting methods. Fast Newton-like methods referred to as fast solvers which are commonly used to solve the TPBVP are established based on the repetition of a nonlinear process. The numerical schemes employed to alleviate the computation burden consist of eliminating some constraint-related but non-essential terms in the trend of sensitivity derivatives calculation and for solving the TPBVP equations. As another claim, assuming that a first attempt to resolve the NMPC problem is accessible, the problem subjected to some changes in its initial conditions (due to some re-planning schemes) can be resolved cost-effectively based on it. Instead of solving the whole optimization process from the scratch, the optimal control inputs and states of the system are updated based on the neighboring extremal (NE) method. For this purpose, two approaches are considered: applying NE method on the first solution that leads to a neighboring optimal solution, or assisting this latter by updating the NMPC-related optimization using exact TPBVP equations at some predefined intermediate steps. It is shown through an example that the first method is not accurate enough due to error accumulations. In contrast, the second method preserves the accuracy while reducing the computation time significantly.

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“…The need for online reoptimization of continuously operated chemical plants becomes ever more important given the increase in demand response activity of industry, increases in feed fluctuations, or changes in demand, etc. [1]. For processes with complex dynamics and slow return to steady-state, economic nonlinear model predictive control or dynamic real-time optimization has long been investigated [2,3].…”

Section: Motivation and Introductionmentioning

confidence: 99%

Adaptive Sampling of Dynamic Systems for Generation of Fast and Accurate Surrogate Models

Talis

Weigert

Esche

et al. 2021

Chemie Ingenieur Technik

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For economic nonlinear model predictive control and dynamic real-time optimization fast and accurate models are necessary. Consequently, the use of dynamic surrogate models to mimic complex rigorous models is increasingly coming into focus. For dynamic systems, the focus so far had been on identifying a system's behavior surrounding a steady-state operation point. In this contribution, we propose a novel methodology to adaptively sample rigorous dynamic process models to generate a dataset for building dynamic surrogate models. The goal of the developed algorithm is to cover an as large as possible area of the feasible region of the original model. To demonstrate the performance of the presented framework it is applied on a dynamic model of a chlor-alkali electrolysis.

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Dynamic Process Operation Under Demand Response – A Review of Methods and Tools

Cited by 9 publications

References 132 publications

Assessing the Realizable Flexibility Potential of Electrochemical Processes

Assessing the Realizable Flexibility Potential of Electrochemical Processes

Neighboring extremal nonlinear model predictive control of a rigid body on SO(3)

Adaptive Sampling of Dynamic Systems for Generation of Fast and Accurate Surrogate Models

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