Economic MPC and real-time decision making with application to large-scale HVAC energy systems

Rawlings, James B.; Patel, Nishith R.; Risbeck, Michael J.; Maravelias, Christos T.; Wenzel, Michael J.; Turney, Robert D.

doi:10.1016/j.compchemeng.2017.10.038

Cited by 101 publications

(31 citation statements)

References 38 publications

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“…Note that there are many works which have studied model predictive control with discrete control actions which precede this text [32]- [37]. As such, this feature should not be considered a primary contribution of the work.…”

Section: B Control Architecturementioning

confidence: 99%

Robust Economic Model Predictive Control of Continuous-Time Epidemic Processes

Watkins

Nowzari

Pappas

2020

IEEE Trans. Automat. Contr.

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In this paper, we develop a robust economic model predictive controller for the containment of stochastic Susceptible-Exposed-Infected-Vigilant pSEIV q epidemic processes which drives the process to extinction quickly, while minimizing the rate at which control resources are used. The work we present here is significant in that it addresses the problem of efficiently controlling general stochastic epidemic systems without relying on mean-field approximation, which is an important issue in the theory of stochastic epidemic processes. This enables us to provide rigorous convergence guarantees on the stochastic epidemic model itself, improving over the mean-field type convergence results of most prior work. There are two primary technical difficulties addressed in treating this problem: (i) constructing a means of tractably approximating the evolution of the process, so that the designed approximation is robust to the modeling error introduced by the applied moment closure, and (ii) guaranteeing that the designed controller causes the closed-loop system to drive the SEIV process to extinction quickly. As an application, we use the developed framework for optimizing the use of quarantines in containing an SEIV epidemic outbreak.

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Section: B Control Architecturementioning

confidence: 99%

Robust Economic Model Predictive Control of Continuous-Time Epidemic Processes

Watkins

Nowzari

Pappas

2020

IEEE Trans. Automat. Contr.

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“…shows the Gantt chart for dispatching the equipment (towers, pumps, chillers, etc.). 15 Rawlings et al 13 provide further details on this application. The models based on this application are available 6 for researchers to benchmark their own controller architectures.…”

Section: Large-scale Hvac Systems and Periodic Operationmentioning

confidence: 99%

Bringing new technologies and approaches to the operation and control of chemical process systems

Rawlings

Maravelias

2019

AIChE Journal

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“…In this context, heating, ventilation and air-conditioning (HVAC) systems are the largest source of energy use (nearly 50%) [2]. Central HVAC plants are sophisticated systems that connect multiple energy carriers (water, electricity, natural gas, cooling water, hot water, steam) and equipment units (pumps, heat exchangers, cooling towers, chillers, and boilers) to meet the cooling and heating loads of single buildings or collections of buildings (e.g., university campuses and urban districts) [3]. A central HVAC plant is the equivalent of a utility plant in a manufacturing facility.…”

Section: Introductionmentioning

confidence: 99%

“…Model predictive control (MPC) is becoming a established automation technology in HVAC central plants [3,[9][10][11][12][13]. MPC can anticipate and counteract disturbances and accommodate complex models, constraints, and cost functions [3,14,15]. However, existing MPC implementations for HVAC central plants use deterministic representations of the disturbances.…”

Section: Introductionmentioning

confidence: 99%

Stochastic model predictive control for central HVAC plants

Kumar

Wenzel

ElBsat

et al. 2020

Journal of Process Control

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We present a stochastic model predictive control (MPC) framework for central heating, ventilation, and air conditioning (HVAC) plants. The framework uses real data to forecast and quantify uncertainty of disturbances affecting the system over multiple timescales (electrical loads, heating/cooling loads, and energy prices). We conduct detailed closed-loop simulations and systematic benchmarks for the central HVAC plant of a typical university campus. Results demonstrate that deterministic MPC fails to properly capture disturbances and that this translates into economic penalties associated with peak demand charges and constraint violations in thermal storage capacity (overflow and/or depletion). Our results also demonstrate that stochastic MPC provides a more systematic approach to mitigate uncertainties and that this ultimately leads to cost savings of up to 7.5% and to mitigation of storage constraint violations. Benchmark results also indicate that these savings are close to ideal savings (9.6%) obtained under MPC with perfect information.

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Economic MPC and real-time decision making with application to large-scale HVAC energy systems

Cited by 101 publications

References 38 publications

Robust Economic Model Predictive Control of Continuous-Time Epidemic Processes

Robust Economic Model Predictive Control of Continuous-Time Epidemic Processes

Bringing new technologies and approaches to the operation and control of chemical process systems

Stochastic model predictive control for central HVAC plants

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