An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings

Bianchini, Gianni; Casini, Marco; Pepe, Daniele; Vicino, Antonio; Zanvettor, Giovanni Gino

doi:10.1016/j.apenergy.2019.01.187

Cited by 89 publications

(29 citation statements)

References 35 publications

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“…Due to switching behavior of components such as heat pumps, combined heat and power plants etc., application of MPC for energy systems often requires the solution of mixed-integer optimization problems on real time suitable time scales. Therefore, according optimization problems are often formulated as either Mixed-Integer Linear Programs (MILPs) as in Bianchini et al (2019) and Lv et al (2019), or Mixed-Integer Quadratic Programs (MIQPs) as in Vasallo and Bravo (2016) and Killian et al (2018), using linear(ized) models for system modeling.…”

Section: Introductionmentioning

confidence: 99%

A whole-year simulation study on nonlinear mixed-integer model predictive control for a thermal energy supply system with multi-use components

et al. 2020

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This work presents a whole-year simulation study on nonlinear mixed-integer Model Predictive Control (MPC) for a complex thermal energy supply system which consists of a heat pump, stratified water storages, free cooling facilities, and a large underground thermal storage. For solution of the arising Mixed-Integer Non-Linear Programs (MINLPs) we apply an existing general and optimalcontrol-suitable decomposition approach. To compensate deviation of forecast inputs from measured disturbances, we introduce a moving horizon estimation step within the MPC strategy. The MPC performance for this study, which consists of more than 50,000 real time suitable MINLP solutions, is compared to an elaborate conventional control strategy for the system. It is shown that MPC can significantly reduce the yearly energy consumption while providing a similar degree of constraint satisfaction, and autonomously identify previously unknown, beneficial operation modes.

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Section: Introductionmentioning

confidence: 99%

A whole-year simulation study on nonlinear mixed-integer model predictive control for a thermal energy supply system with multi-use components

et al. 2020

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“…For the cases that use BCVTB as the software environment to perform the co-simulation between EnergyPlus and MATLAB ® , the optimizations carried out are focused on Model Predictive Control (MPC) optimizations [13][14][15][16][17][18]; on building energy consumption improvements [19,20]; on thermal comfort using the Human and Building Interaction Toolkit [21]; on considering the condensation risk of a thermally activated building (TAB) in the cooling operation [22]; on how to integrate the ability to simulate double-skin facades into EnergyPlus [23]; on thermal bridges using MATLAB ® [24]; and so on.…”

Section: • Co-simulation Between Energyplus and Matlab ®mentioning

confidence: 99%

EplusLauncher: An API to Perform Complex EnergyPlus Simulations in MATLAB® and C#

Gordillo¹,

Ruiz

Stauffer

et al. 2020

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There is a growing concern about how to mitigate climate change, in which the production and use of energy has a great impact as one of the largest sources of global greenhouse gases (GHG). Buildings are responsible for a large percentage of these emissions. Therefore, there has been an increase in research in this area, in order to reduce their consumption and increase their efficiency. One of the major simulation programs used in optimization research is EnergyPlus. The purpose of this software is the complete energy simulation of a building, although it lacks tools to analyze its results and, above all, to manage and edit its simulations. For this reason, we developed an application programming interface (API) that serves to merge two areas which are highly demanded by researchers: energy building simulation (using EnergyPlus) and tools for the management and design of research experiments (in this case, MATLAB®). The developed API allows the user to perform complex simulations using EnergyPlus in a simple way, as it allows the editing of each simulation and the analysis of the simulation results through MATLAB®. In addition, it enables the user to simultaneously run multiple simulations, using either all computer core processors or a selection of them (i.e., allowing parallel computing), reducing the simulation time. The API was developed in the C# language, such that it can be used with any software that can import . N E T libraries.

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“…Only the first control action is implemented, and the procedure is repeated in the next discrete time step. MPC smart home appliance scheduling is mainly concerned with thermostatically regulated loads, such as heating, ventilation and air-conditioning (HVAC) units [27]- [29] and refrigerators [30]. Nonetheless, it has also been recently applied for load coordination of multiple customer microgrids [31], [32].…”

Section: Introductionmentioning

confidence: 99%

A Compressive Receding Horizon Approach for Smart Home Energy Management

et al. 2021

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An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings

Cited by 89 publications

References 35 publications

A whole-year simulation study on nonlinear mixed-integer model predictive control for a thermal energy supply system with multi-use components

A whole-year simulation study on nonlinear mixed-integer model predictive control for a thermal energy supply system with multi-use components

EplusLauncher: An API to Perform Complex EnergyPlus Simulations in MATLAB® and C#

A Compressive Receding Horizon Approach for Smart Home Energy Management

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