MPC-based Appliance Scheduling for Residential Building Energy Management Controller. Chen, C., Wang, J., Heo, Y., & Kishore, S. IEEE Transactions on Smart Grid, 4(3):1401–1410, September, 2013. doi abstract bibtex This paper proposes an appliance scheduling scheme for residential building energy management controllers, by taking advantage of the time-varying retail pricing enabled by the two-way communication infrastructure of the smart grid. Finite-horizon scheduling optimization problems are formulated to exploit operational flexibilities of thermal and non-thermal appliances using a model predictive control (MPC) method which incorporates both forecasts and newly updated information. For thermal appliance scheduling, the thermal mass of the building, which serves as thermal storage, is integrated into the optimization problem by modeling the thermodynamics of rooms in a building as constraints. Within the comfort range modeled by the predicted mean vote (PMV) index, thermal appliances are scheduled smartly together with thermal mass storage to hedge against high prices and make use of low-price time periods. For non-thermal appliance scheduling, in which delay and/or power consumption flexibilities are available, operation dependence of inter-appliance and intra-appliance is modeled to further exploit the price variation. Simulation results show that customers have notable energy cost savings on their electricity bills with time-varying pricing. The impact of customers' preferences of appliances usage on energy cost savings is also evaluated.
@article{6575202,
title = {{{MPC-based}} Appliance Scheduling for Residential Building Energy Management Controller},
author = {Chen, C. and Wang, J. and Heo, Y. and Kishore, S.},
year = {2013},
month = sep,
journal = {IEEE Transactions on Smart Grid},
volume = {4},
number = {3},
pages = {1401--1410},
issn = {1949-3053},
doi = {10.1109/TSG.2013.2265239},
abstract = {This paper proposes an appliance scheduling scheme for residential building energy management controllers, by taking advantage of the time-varying retail pricing enabled by the two-way communication infrastructure of the smart grid. Finite-horizon scheduling optimization problems are formulated to exploit operational flexibilities of thermal and non-thermal appliances using a model predictive control (MPC) method which incorporates both forecasts and newly updated information. For thermal appliance scheduling, the thermal mass of the building, which serves as thermal storage, is integrated into the optimization problem by modeling the thermodynamics of rooms in a building as constraints. Within the comfort range modeled by the predicted mean vote (PMV) index, thermal appliances are scheduled smartly together with thermal mass storage to hedge against high prices and make use of low-price time periods. For non-thermal appliance scheduling, in which delay and/or power consumption flexibilities are available, operation dependence of inter-appliance and intra-appliance is modeled to further exploit the price variation. Simulation results show that customers have notable energy cost savings on their electricity bills with time-varying pricing. The impact of customers' preferences of appliances usage on energy cost savings is also evaluated.},
keywords = {appliance scheduling scheme,Building,building management systems,Buildings,Delays,Electricity,energy cost savings,energy management controller,energy management systems,finite-horizon scheduling optimization problems,Home appliances,low-price time periods,model predictive control,MPC,MPC method,nonthermal appliance scheduling,operation dependence,optimisation,optimization,Optimization,PMV index,Power demand,predicted mean vote,predictive control,price variation,pricing,residential building energy management controllers,scheduling,smart grid,smart power grids,thermal mass storage,Thermodynamics,time-varying retail pricing,time-varying systems,two-way communication infrastructure}
}
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Finite-horizon scheduling optimization problems are formulated to exploit operational flexibilities of thermal and non-thermal appliances using a model predictive control (MPC) method which incorporates both forecasts and newly updated information. For thermal appliance scheduling, the thermal mass of the building, which serves as thermal storage, is integrated into the optimization problem by modeling the thermodynamics of rooms in a building as constraints. Within the comfort range modeled by the predicted mean vote (PMV) index, thermal appliances are scheduled smartly together with thermal mass storage to hedge against high prices and make use of low-price time periods. For non-thermal appliance scheduling, in which delay and/or power consumption flexibilities are available, operation dependence of inter-appliance and intra-appliance is modeled to further exploit the price variation. 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Finite-horizon scheduling optimization problems are formulated to exploit operational flexibilities of thermal and non-thermal appliances using a model predictive control (MPC) method which incorporates both forecasts and newly updated information. For thermal appliance scheduling, the thermal mass of the building, which serves as thermal storage, is integrated into the optimization problem by modeling the thermodynamics of rooms in a building as constraints. Within the comfort range modeled by the predicted mean vote (PMV) index, thermal appliances are scheduled smartly together with thermal mass storage to hedge against high prices and make use of low-price time periods. For non-thermal appliance scheduling, in which delay and/or power consumption flexibilities are available, operation dependence of inter-appliance and intra-appliance is modeled to further exploit the price variation. 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