On the theory of economic MPC: ELOC and approximate infinite horizon EMPC

Adeodu, Oluwasanmi; Omell, Benjamin; Chmielewski, Donald J.

doi:10.1016/j.jprocont.2018.09.013

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…Thus, the constraints q min ≤ q(k|i) ≤ q max are crucial in order to generate a meaningful controller. For additional information on EMPC, please see [6,17,18].…”

Section: Economic Model Predictive Controlmentioning

confidence: 99%

“…Clearly, the problem (18) can be solved using a standard linear programming solver. It should be noted that the final cost term g N (s(i + N|i)) is not included in the problem (18). This term is essential to guarantee stability for short horizon EMPC (see [6,18]).…”

Section: Economic Model Predictive Controlmentioning

confidence: 99%

“…It should be noted that the final cost term g N (s(i + N|i)) is not included in the problem (18). This term is essential to guarantee stability for short horizon EMPC (see [6,18]). However, for the current application and with the given horizon, this term plays a less important role and is omitted due to space constraints.…”

Section: Economic Model Predictive Controlmentioning

confidence: 99%

“…Specifically, the typical quadratic objective function in MPC [7] will be replaced by an expression directly reflecting the revenue. Similar approaches have been applied to the area of Heating, Ventilation, and Air Conditioning (HVAC) control [8][9][10][11], the area of process operations scheduling [12][13][14][15][16][17][18][19][20], as well as the area of electric power system operations [21][22][23][24]. As mentioned above, the power dispatch capability of an IGCC plant can be achieved by a variety of hardware configurations.…”

Section: Introductionmentioning

confidence: 99%

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Integrated Process Design and Control for Smart Grid Coordinated IGCC Power Plants Using Economic Linear Optimal Control

2020

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The Integrated Gasification Combined Cycle (IGCC) possesses a number of advantages over traditional power generation plants, including increased efficiency, flex-fuel, and carbon capture. A lesser-known advantage of the IGCC system is the ability to coordinate with the smart grid. The idea is that process modifications can enable dispatch capabilities in the sense of shifting power production away from periods of low electricity price to periods of high price and thus generate greater revenue. The work begins with a demonstration of Economic Model Predictive Control (EMPC) as a strategy to determine the dispatch policy by directly pursuing the objective of maximizing plant revenue. However, the numeric nature of EMPC creates an inherent limitation when it comes to process design. Thus, Economic Linear Optimal Control (ELOC) is proposed as a surrogate for EMPC in the formulation of the integrated design and control problem for IGCC power plants with smart grid coordination.

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“…Thus, the constraints q min ≤ q(k|i) ≤ q max are crucial in order to generate a meaningful controller. For additional information on EMPC, please see [6,17,18].…”

Section: Economic Model Predictive Controlmentioning

confidence: 99%

Section: Economic Model Predictive Controlmentioning

confidence: 99%

Section: Economic Model Predictive Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Integrated Process Design and Control for Smart Grid Coordinated IGCC Power Plants Using Economic Linear Optimal Control

2020

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“…In the process systems engineering field, quantum computers (primarily the D-Wave quantum annealer, though with some analysis of an IBM quantum computer in ref ) have been utilized for fault detection and for optimization related to energy systems. − Applications of quantum computers for control to date have included, for example, the application of fuzzy logic control, reinforcement learning, − and model predictive control (MPC; an optimization-based and model-based control law − ) implemented on a quantum annealer . However, this is a relatively limited set of investigations that does not provide enough data for deeply understanding benefits and limitations of control implemented on quantum computers.…”

Section: Introductionmentioning

confidence: 99%

Control Implemented on Quantum Computers: Effects of Noise, Nondeterminism, and Entanglement

Nieman

Rangan

Durand

2022

Ind. Eng. Chem. Res.

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Quantum computing has advanced in recent years to the point that there are now some quantum computers and quantum simulators available to the public for use. In addition, quantum computing is beginning to receive attention within the process systems engineering community for directions such as machine learning and optimization. A logical next step for its evaluation within process systems engineering is for control, specifically for computing control actions to be applied to process systems. In this work, we provide some initial studies regarding the implementation of control on quantum computers, including the implementation of a single-input/single-output proportional control law on a quantum simulator with noise, evaluation of potential impacts of nondeterminism on theory for advanced control laws, and discussion of consequences of the way that entanglement works for next-generation manufacturing communication objectives.

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Model compendium, data, and optimization benchmarks for sector-coupled energy systems

Saß

Faulwasser

Hollermann

et al. 2020

Computers & Chemical Engineering

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On the theory of economic MPC: ELOC and approximate infinite horizon EMPC

Cited by 5 publications

References 18 publications

Integrated Process Design and Control for Smart Grid Coordinated IGCC Power Plants Using Economic Linear Optimal Control

Integrated Process Design and Control for Smart Grid Coordinated IGCC Power Plants Using Economic Linear Optimal Control

Control Implemented on Quantum Computers: Effects of Noise, Nondeterminism, and Entanglement

Model compendium, data, and optimization benchmarks for sector-coupled energy systems

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