Difference between revisions of "Model Predictive Control for an Uncertain Smart Thermal Grid"

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| authors = Samira S. Farahani, Zofia Lukszo, Tamas Keviczky, Bart De Schutter, Richard M. Murray
 
| authors = Samira S. Farahani, Zofia Lukszo, Tamas Keviczky, Bart De Schutter, Richard M. Murray
 
| title = Model Predictive Control for an Uncertain Smart Thermal Grid
 
| title = Model Predictive Control for an Uncertain Smart Thermal Grid
| source = Submitted, 2015 Conference on Decision and Control (CDC)
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| source = 2016 European Control Conference (ECC)
| year = 2015
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| year = 2016
 
| type = Conference Paper
 
| type = Conference Paper
 
| funding =  
 
| funding =  
| url = http://www.cds.caltech.edu/~murray/preprints/far+15-cdc_s.pdf
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| url = http://www.cds.caltech.edu/~murray/preprints/far+16-ecc.pdf
 
| abstract =  
 
| abstract =  
Smart Thermal Grids (STG) represents a new concept in the energy sector that involves the use of the smart grid concept in heat grids connecting several parties to each other via bidirectional transport of heat. The focus of this paper is on modeling and control of STGs in which the uncertainties in the demand and/or supply are included. To this end, we use Model Predictive Control (MPC), which is one of the most widely used advanced control design methods in the process industry. We solve the worst-case MPC optimization problem using mixed-integer-linear programming (MILP) techniques to provide a day-ahead prediction for the heat production in the grid. In an example, we show that this approach successfully keeps the supply-demand balance in the STG while satisfying the physical constraints of the network in the presence of uncertainties in the heat demand.
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The focus of this paper is on modeling and control of Smart Thermal Grids (STGs) in which the uncertainties in the demand and/or supply are included. We solve the corre- sponding robust model predictive control (MPC) optimization problem using mixed-integer-linear programming techniques to provide a day-ahead prediction for the heat production in the grid. In an example, we compare the robust MPC approach with the robust optimal control approach, in which the day-ahead production plan is obtained by optimizing the objective function for entire day at once. There, we show that the robust MPC approach successfully keeps the supply-demand balance in the STG while satisfying the constraints of the production units in the presence of uncertainties in the heat demand. Moreover, we see that despite the longer computation time, the performance of the robust MPC controller is considerably better than the one of the robust optimal controller.
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| flags =  
 
| flags =  
 
| filetype = PDF
 
| filetype = PDF
 
| filesize = 594K
 
| filesize = 594K
| tag = far+15-cdc
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| tag = far+16-ecc
| id = 2015d
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| id = 2015r
 
}}
 
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Revision as of 21:58, 28 May 2016


Samira S. Farahani, Zofia Lukszo, Tamas Keviczky, Bart De Schutter, Richard M. Murray
2016 European Control Conference (ECC)

The focus of this paper is on modeling and control of Smart Thermal Grids (STGs) in which the uncertainties in the demand and/or supply are included. We solve the corre- sponding robust model predictive control (MPC) optimization problem using mixed-integer-linear programming techniques to provide a day-ahead prediction for the heat production in the grid. In an example, we compare the robust MPC approach with the robust optimal control approach, in which the day-ahead production plan is obtained by optimizing the objective function for entire day at once. There, we show that the robust MPC approach successfully keeps the supply-demand balance in the STG while satisfying the constraints of the production units in the presence of uncertainties in the heat demand. Moreover, we see that despite the longer computation time, the performance of the robust MPC controller is considerably better than the one of the robust optimal controller.