Design and analysis of smart home energy management system for energy-efficient and demand response operations

Abstract

In the movie “Iron Man”, Tony Stark, with his highly connected and smart home system, shows the audience an appealing vision of future work and domestic life. Many audiences desire such a living environment where they can not only interact with their homes but also let the homes manage their operation automatically. As technology progressively steps into such a future, realizing a responsive and autonomous smart home is not just a fantasy. To establish grid-interactive homes that help save costs for users and improve grid reliability, this study introduces an energy management framework for smart home environments. This framework provides optimal operation of multiple appliances, taking into account dynamic responses to external factors such as outside weather conditions, homeowner’s preferences, and particularly, gird conditions like time-varying pricing in demand response programs. As one of the largest energy consumers in the home, the operation of the HVAC system holds great potential for cost savings and energy flexibility—the latter being the ability to adjust its consumption based on grid signals such as time-of-use (TOU) pricing. Achieving cost savings and energy flexibility requires intelligent strategies, one of which is precooling—a control strategy where an air conditioner (AC) cools space when the electricity price is low to avoid expensive operation when the electricity price is high. In previous studies, Model Predictive Control (MPC)-based precooling strategies are typically analyzed through simulations, and field studies in residential buildings are quite limited. In this study, we developed an MPC agent and carried out extensive field tests on nine homes over a period of four months in Oklahoma and Miami. Filed test results show that the MPC agent can reduce energy cost by 28.72%–51.31% on hot summer days and by up to 60.32% on mild summer days, in addition to achieving significant energy flexibility. Moreover, the agent's performance is found to be most impacted by weather conditions, AC performance, user comfort preferences, and floor areas of the homes. In addition, to further comprehend diverse factors that may impact the results of MPC-based precooling, an EnegyPlus virtual testbed and a corresponding control framework for co-simulation are developed. The purpose of developing such a virtual testbed is to create a simulation environment that enables experiments without the limitation and variability of field tests. The virtual testbed is modified by using the Python script to mimic the on/off cycle in the majority of U.S. residential building HVAC systems. By conducting the sensitivity analysis and ablation study, the MPC-based precooling co-simulation results are evaluated. It was observed in our case study that cost savings achieved through MPC-based precooling were primarily influenced by the use of forecast weather. The accuracy of the models and the prediction horizon of the MPC models also plays a substantial but lesser extent role. With the optimal operation framework shifting from the HVAC system to multiple appliances, the proposed energy management framework has a broader scope, encompassing not only the HVAC system but also water heaters, non-thermal appliances, and the power flow between photovoltaics panel (PV), batteries, and the grid. Apart from the cost-savings and energy flexibility that can be achieved, the proposed framework also provides a more realistic simulation scenario by considering the user’s appliance time usage preference, water usage, and thermal comfort preferences. Finally, the framework also embedded multi-objective optimization to support the homeowner’s decision-making between cost saving and thermal comfort. Overall, this study aims to realize the optimal operation of various load-flexible resources under demand response programs in residential buildings. This study investigates the fundamental research for the investigation of methodologies to enhance and understand the interactions between buildings, homeowners, and the grid. Due to the flexibility of the model, this study can be adapted to other residential buildings and even in larger communities.