Optimal Home Energy Management Considering Uncertainties in Occupancy, Consumption, and Electricity Price

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  •   Amin Dindar

  •   Salman Mohagheghi

Abstract

Power utilities issue demand response (DR) during the hours of peak load in order to reduce the demand on the network and provide congestion relief to overloaded circuits. While traditional residential DR programs are mainly one-way in the form of remote on/off control of air conditioning (A/C) units, residential customers can adopt a more proactive role through utilizing the capabilities of smart meters and home energy management systems (HEMS). HEMS can monitor energy rates and DR incentives, and accordingly change the temperature setpoint of the A/C unit and/or shift appliance loads from peak to off-peak hours in order to maximize financial benefits. All this can be achieved in an automated human-out-of-the-loop fashion. From the HEMS’ standpoint, the task can be viewed as solving an optimization problem with the goal of reducing power consumption while maximizing financial gains. However, another equally important goal would be to ensure that the comfort level of residents, if present in the building, is not compromised. This is especially crucial during periods of extreme temperatures where maintaining an acceptable indoor temperature has a direct impact on the residents’ health, especially children and the elderly. What makes this multi-objective optimization problem more challenging is the uncertain nature of some model parameters, e.g., electricity rates, building occupancy levels, and demand. This paper presents a novel solution for energy management of a smart home using DR by considering the above factors. To ensure that the solution found is feasible against all possible uncertainties, a robust model is developed and solved for a given time horizon. As shown through simulation results, considering uncertainties are necessary, since they can change the solution in a nonnegligible way.


Keywords: Demand response, demand side management, home energy management, smart home

References

Mohagheghi S, Stoupis J, Wang Z, Li Z, Kazemzadeh H. Demand Response Architecture – Integration into the Distribution Management System. In Proc. SmartGridComm, 2010: 501–506.

Pedrasa MAA, Spooner TD, MacGill IF. Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services. IEEE Trans. Smart Grid. 2010; 1(2): 134-143.

Rajalingam S, Malathi V. HEM Algorithm Based Smart Controller for Home Power Managmenet System. Energy and Buildings. 2016; 131: 184-192.

Byun J, Hong I, Park S. Intelligent Cloud Home Energy Management System Using Household Appliance Priority Based Scheduling Based on Prediction of Renewable Energy Capability. IEEE Trans. Consumer Electronics. 2012; 58(4): 1194-1201.

Teng R, Yamazaki T. Load Profile-Based Coordination of Appliances in a Smart Home. IEEE Trans. Consumer Electronics. 2019; 65(1): 38-46.

Luo F, Ranzi G, Wan C, Xu Z, Dong Z. Y. A Multistage Home Energy Management System with Residential Photovoltaic Penetration. IEEE Trans. Industrial Informatics. 2019; 15(1): 116-126.

Wang F, Zhou L, Ren H, Liu X, Talari S, Shafie-Khah M, Catalão JPS. Multi-Objective Optimization Model of Source-Load-Storage Synergetic Dispatch for a Building Energy Management System Based on TOU Price Demand Response. IEEE Trans. Industry Applications. 2018; 54(2): 1017-1028.

Molina D, Lu C, Sherman V, Harley R. G. Model Predictive and Genetic Algorithm-Based Optimization of Residential Temperature Control in the Presence of Time-Varying Electricity Prices. IEEE Trans. Industry Applications. 2013; 49(3): 1137-1145.

Jo H-C, Kim S, Joo S-K. Smart Heating and Air Conditioning Scheduling Method Incorporating Customer Convenience for Home Energy Management System. IEEE Trans. Consumer Electronics. 2013; 59(2): 316-322.

Zhang D, Li S, Sun M, O’Neill Z. An Optimal and Learning-Based Demand Response and Home Energy Management System. IEEE Trans. on Smart Grid. 2016; 7(4): 1790-1801.

Anvari-Moghaddam A, Monsef H, Rahimi-Kian A. Optimal Smart Home Energy Management Considering Energy Saving and a Comfortable Lifestyle. IEEE Trans. Smart Grid. 2015; 6(1): 324-332.

Wang N, Zhang J, Xia X. Energy Consumption of Air Conditioners at Differenct Temperature Set Points. Energy and Buildings. 2013; 65: 412-418.

Ben-Tal A, Ghaoui L, Nemirovski A. S. Robust Optimization. Princeton, NJ, USA: Princeton Univ. Press, 2009.

Jones D, Tamiz M. Practical Goal Programming, New York, NY: Springer, 2010.

Rardin R. L. Optimization in Operations Research, Upper Saddle River, NJ: Pearson, 1997.

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How to Cite
Dindar, A., & Mohagheghi, S. (2022). Optimal Home Energy Management Considering Uncertainties in Occupancy, Consumption, and Electricity Price. European Journal of Energy Research, 2(3), 1–8. https://doi.org/10.24018/ejenergy.2022.2.3.62