An Optimal Energy-Saving Strategy for Home Energy Management Systems with Bounded Customer Rationality

• Published in: Future internet 2019-04, Vol.11 (4), p.88
• Main Authors: Lin, Guoying, Yang, Yuyao, Pan, Feng, Zhang, Sijian, Wang, Fen, Fan, Shuai
• Format: Article
• Language: English
• Subjects: Air conditioners; Appliances; Behavior; Behavioral economics; Comfort; Computer simulation; Consumption; Customer satisfaction; Customers; Decision making; Edge computing; electric appliance utility function; Electric appliances; Electric power; Electric utilities; Electricity; Electricity distribution; Energy conservation; Energy consumption; Energy efficiency; Energy management; Energy management systems; energy saving; energy-saving cost; Field tests; Growth models; Internet; Internet of Things; Linear programming; Machine learning; Optimization; Preferences; Probability; prospect theory; Rationality; Researchers; Residential energy; Schedules; User needs; User satisfaction; Utility functions
• ISSN: 1999-5903; 1999-5903
• DOI: 10.3390/fi11040088

With the development of techniques, such as the Internet of Things (IoT) and edge computing, home energy management systems (HEMS) have been widely implemented to improve the electric energy efficiency of customers. In order to automatically optimize electric appliances’ operation schedules, this paper considers how to quantitatively evaluate a customer’s comfort satisfaction in energy-saving programs, and how to formulate the optimal energy-saving model based on this satisfaction evaluation. First, the paper categorizes the utility functions of current electric appliances into two types; time-sensitive utilities and temperature-sensitive utilities, which cover nearly all kinds of electric appliances in HEMS. Furthermore, considering the bounded rationality of customers, a novel concept called the energy-saving cost is defined by incorporating prospect theory in behavioral economics into general utility functions. The proposed energy-saving cost depicts the comfort loss risk for customers when their HEMS schedules the operation status of appliances, which is able to be set by residents as a coefficient in the automatic energy-saving program. An optimization model is formulated based on minimizing energy consumption. Because the energy-saving cost has already been evaluated in the context of the satisfaction of customers, the formulation of the optimization program is very simple and has high computational efficiency. The case study included in this paper is first performed on a general simulation system. Then, a case study is set up based on real field tests from a pilot project in Guangdong province, China, in which air-conditioners, lighting, and some other popular electric appliances were included. The total energy-saving rate reached 65.5% after the proposed energy-saving program was deployed in our project. The benchmark test shows our optimal strategy is able to considerably save electrical energy for residents while ensuring customers’ comfort satisfaction is maintained.