An MILP framework for optimizing demand response operation of air separation units

Kelley, Morgan T.; Pattison, Richard C.; Baldick, Ross; Bâldea, Michael

doi:10.1016/j.apenergy.2017.12.127

Cited by 85 publications

(62 citation statements)

References 40 publications

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“…The problem will then become more challenging since further relaxation has to be performed to improve computational performance. Finally, while pressure changing equipment, such as pumps or compressors, are usually considered as fast‐dynamics processes, 6 in most applications, however, these equipment will not be used separately. For example, in an energy‐intensive process flow sheet, the pump network might be connected to other slow dynamics processes such as distillation columns.…”

Section: Discussionmentioning

confidence: 99%

“…Tong et al 16 introduced a decomposition scheme to perform process scheduling while optimizing the controller parameters that can manage the scheduling sequence under time‐varying electricity prices. Kelley et al 6 proposed an MILP scheduling model to account for the process dynamics using a low‐order model. In addition to the studies focusing on operational strategies, several other studies have focused on electricity contract procurement or price bidding strategies.…”

Section: Introductionmentioning

confidence: 99%

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A flexible design framework for process systems under demand‐side management

et al. 2020

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Demand response (DR) has been an appealing strategy for both residential and commercial/industrial customers of electricity due to the increasing penetration of renewable energy resources into the power grid and the deregulation of the energy markets. For industrial DR participants, the management of energy consumption along with the satisfaction of production expectations is generally referred to as demand‐side management. Recent research in this area has focused mostly on process scheduling and capacity planning, especially for energy‐intensive processes. This article presents a new direction through an optimization‐based process design paradigm that allows for the potential reconfiguration of the process flow sheet in real time. Based on a case study of pump network design, a network superstructure is first defined, followed by a two‐stage stochastic optimization model. The method of progressive hedging is used to solve the resulting mixed‐integer stochastic programming problem. The results demonstrate that, under various scenarios, the reconfigurable design offers significant benefits compared to a fixed network structure in terms of total design cost and expected operating cost.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A flexible design framework for process systems under demand‐side management

et al. 2020

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“…This work focuses on a process for producing highly pure gaseous nitrogen for an on‐site customer, such as a chemical plant (Figure ). Similar process designs are well investigated in the literature …”

Section: Air Separation Unitsmentioning

confidence: 99%

“…The vast majority of literature concerning the operation of ASUs subject to fluctuating electricity prices accounts for dynamic responses only indirectly by restricting the transitions between quasi‐stationary operating points . Recent work aims at directly accounting for dynamics when making scheduling decisions through the use of low‐order dynamic models, which is also extended to a closed‐loop scheduling formulation . This framework furthermore allows for explicit consideration of the tracking behavior of a subordinated model predictive controller (MPC) in scheduling of ASUs .…”

Section: Introductionmentioning

confidence: 99%

Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks

et al. 2019

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The availability of reduced‐dimensional, accurate dynamic models is crucial for the optimal operation of chemical processes in fast‐changing environments. Herein, we present a reduced modeling approach for rectification columns. The model combines compartmentalization to reduce the number of differential equations with artificial neural networks to express the nonlinear input–output relations within compartments. We apply the model to the optimal control of an air separation unit. We reduce the size of the differential equation system by 90% while limiting the additional error in product purities to below 1 ppm compared to a full‐order stage‐by‐stage model. We demonstrate that the proposed model enables savings in computational times for optimal control problems by ~95% compared to a full order and ~99% to a standard compartment model. The presented model enables a trade‐off between accuracy and computational efficiency, which is superior to what has recently been reported for similar applications using collocation‐based reduction approaches.

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“…Thus, authors proposed piecewise linearization approaches for capturing the nonlinearities by introducing logical disjunctions represented by binary variables, still leading to MILP formulations. 8,9 At the same time, potentials from using more sophisticated surrogate models for representing highly nonlinear relations in chemical engineering 10,11 are increasingly recognized. Consequently, the utilization of such mostly nonlinear surrogate models to accurately capture the (nonlinear) process characteristics when making scheduling decisions seems promising.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear scheduling with time‐variable electricity prices using sensitivity‐based truncations of wavelet transforms

2020

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We propose an algorithm for scheduling subject to time‐variable electricity prices using nonlinear process models that enables long planning horizons with fine discretizations. The algorithm relies on a reduced‐space formulation and enhances our previous work (Schäfer et al., Comput Chem Eng, 2020;132:106598) by a sensitivity‐based refinement procedure. We therein expose the coefficients of the wavelet transform of the time series of independent process variables to the optimizer. The problem size is reduced by truncating the transform and iteratively adjusted using Lagrangian multipliers. We apply the algorithm to the scheduling of a multi‐product air separation unit. The nonlinear power consumption characteristic is replaced by an artificial neural network trained on data from a rigorous model. We demonstrate that the proposed algorithm reduces the number of optimization variables by more than one order of magnitude, whilst furnishing feasible schedules with insignificant losses in objective values compared to solutions considering the full dimensionality.

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An MILP framework for optimizing demand response operation of air separation units

Cited by 85 publications

References 40 publications

A flexible design framework for process systems under demand‐side management

A flexible design framework for process systems under demand‐side management

Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks

Nonlinear scheduling with time‐variable electricity prices using sensitivity‐based truncations of wavelet transforms

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