With the widespread use of smart appliances, the optimization of electricity consumption behavior has become an important research content for residential smart electricity consumption. Considering the economy and comfort of electricity consumption, we propose an optimization model of users' electricity consumption behavior under the intelligent electricity consumption environment. Firstly, we model the operation characteristics of household load equipment, establish different comfort evaluation indexes for different household loads, and add the influence of the number of interruptions on users' comfort to the comfort evaluation indexes of interruptible loads; then, based on the time-sharing tariff, we propose an optimized operation model of residential smart electricity equipment with the goals of economy and residents' comfort; finally, through Matlab simulation experimental cases. It is verified that arranging a reasonable number of interruptible load interruptions achieves the purpose of reducing residential electricity consumption costs while safeguarding customer comfort.

Xiecheng Yao, Lei Ding, Jiazhen Wang. Research on Optimal Control of Home Energy Management System Based on Real-time Tariffs. International Journal of Frontiers in Engineering Technology (2022), Vol. 4, Issue 3: 66-72. https://doi.org/10.25236/IJFET.2022.040309.

[1] Shafie-Khah M, Siano P. A Stochastic Home Energy Management System Considering Satisfaction Cost and Response Fatigue[J]. IEEE Transactions on Industrial Informatics, 2018, 14(2): 629–638.

[2] Chavali P, Yang P, Nehorai A. A Distributed Algorithm of Appliance Scheduling for Home Energy Management System[J]. IEEE Transactions on Smart Grid, 2014, 5(1): 282–290.

[3] Mohsenian-Rad A-H, Leon-Garcia A. Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments[J]. IEEE Transactions on Smart Grid, 2010, 1(2): 120–133.

[4] Yousefi M, Hajizadeh A, Soltani M N, et al. Predictive Home Energy Management System With Photovoltaic Array, Heat Pump, and Plug-In Electric Vehicle[J]. IEEE Transactions on Industrial Informatics, 2021, 17(1): 430–440.

[5] Nguyen D T, Le L B. Joint Optimization of Electric Vehicle and Home Energy Scheduling Considering User Comfort Preference[J]. IEEE Transactions on Smart Grid, 2014, 5(1): 188–199.

[6] Gao L , Wang Z , Zhou J , et al. Design of Smart Home System Based on ZigBee Technology and R&D for Application[J]. Energy and Power Engineering, 2016, 08(1):13-22.

[7] Roh H T , Lee J W . Residential Demand Response Scheduling With Multiclass Appliances in the Smart Grid[J]. IEEE Transactions on Smart Grid, 2016, 7(1):94-104.

[8] Lu, Ning. An Evaluation of the HVAC Load Potential for Providing Load Balancing Service[J]. IEEE Transactions on Smart Grid, 2012, 3(3):1263-1270.