TY - GEN
T1 - Hybrid model predictive control for optimal energy management of a smart house
AU - Khakimova, Albina
AU - Shamshimova, Akmaral
AU - Sharipova, Dana
AU - Kusatayeva, Aliya
AU - Ten, Viktor
AU - Bemporad, Alberto
AU - Familiant, Yakov
AU - Shintemirov, Almas
AU - Rubagotti, Matteo
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/22
Y1 - 2015/7/22
N2 - This paper describes the modeling and control of heat and electricity flows in a smart house equipped with a solar heating system, PV panels, and lead-acid batteries for energy storage. The goal is to minimize electricity costs, making best use of renewable sources of heat and electricity. The system model is obtained via system identification from experimental data as a discrete-time hybrid system to capture the main thermal and electrical dynamics, the on-off activation of pumps, heating coil, the connection to the grid, and various operating constraints, including logic constraints and limits on system variables. Based on the obtained model, we derive a hybrid model predictive control (MPC) strategy. The controller is able to track the desired temperature and minimize costs for consuming electricity from the grid, while respecting all the prescribed constraints. Simulation results testify the effectiveness and feasibility of the approach.
AB - This paper describes the modeling and control of heat and electricity flows in a smart house equipped with a solar heating system, PV panels, and lead-acid batteries for energy storage. The goal is to minimize electricity costs, making best use of renewable sources of heat and electricity. The system model is obtained via system identification from experimental data as a discrete-time hybrid system to capture the main thermal and electrical dynamics, the on-off activation of pumps, heating coil, the connection to the grid, and various operating constraints, including logic constraints and limits on system variables. Based on the obtained model, we derive a hybrid model predictive control (MPC) strategy. The controller is able to track the desired temperature and minimize costs for consuming electricity from the grid, while respecting all the prescribed constraints. Simulation results testify the effectiveness and feasibility of the approach.
UR - http://www.scopus.com/inward/record.url?scp=84943150886&partnerID=8YFLogxK
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U2 - 10.1109/EEEIC.2015.7165215
DO - 10.1109/EEEIC.2015.7165215
M3 - Conference contribution
AN - SCOPUS:84943150886
T3 - 2015 IEEE 15th International Conference on Environment and Electrical Engineering, EEEIC 2015 - Conference Proceedings
SP - 513
EP - 518
BT - 2015 IEEE 15th International Conference on Environment and Electrical Engineering, EEEIC 2015 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Environment and Electrical Engineering, EEEIC 2015
Y2 - 10 June 2015 through 13 June 2015
ER -