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

Author

Listed:
  • Guoying Lin

    (Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510080, China)

  • Yuyao Yang

    (Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510080, China)

  • Feng Pan

    (Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510080, China)

  • Sijian Zhang

    (Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510080, China)

  • Fen Wang

    (Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Department of Electrical Engineering, Shanghai Jiao-Tong University, Shanghai 200240, China)

  • Shuai Fan

    (Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Department of Electrical Engineering, Shanghai Jiao-Tong University, Shanghai 200240, China)

Abstract

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.

Suggested Citation

  • Guoying Lin & Yuyao Yang & Feng Pan & Sijian Zhang & Fen Wang & Shuai Fan, 2019. "An Optimal Energy-Saving Strategy for Home Energy Management Systems with Bounded Customer Rationality," Future Internet, MDPI, vol. 11(4), pages 1-16, April.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:4:p:88-:d:219268
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    References listed on IDEAS

    as
    1. Mayank Singh & Rakesh Chandra Jha, 2019. "Object-Oriented Usability Indices for Multi-Objective Demand Side Management Using Teaching-Learning Based Optimization," Energies, MDPI, vol. 12(3), pages 1-25, January.
    2. Jingsha He & Qi Xiao & Peng He & Muhammad Salman Pathan, 2017. "An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning," Future Internet, MDPI, vol. 9(1), pages 1-15, March.
    3. Serafín Alonso & Antonio Morán & Miguel Ángel Prada & Perfecto Reguera & Juan José Fuertes & Manuel Domínguez, 2019. "A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants: A Hospital Case Study," Energies, MDPI, vol. 12(5), pages 1-28, March.
    4. Heiko Dunkelberg & Maximilian Sondermann & Henning Meschede & Jens Hesselbach, 2019. "Assessment of Flexibilisation Potential by Changing Energy Sources Using Monte Carlo Simulation," Energies, MDPI, vol. 12(4), pages 1-24, February.
    5. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    6. Chang-Ming Lin & Chun-Yin Wu & Ko-Ying Tseng & Chih-Chiang Ku & Sheng-Fuu Lin, 2019. "Applying Two-Stage Differential Evolution for Energy Saving in Optimal Chiller Loading," Energies, MDPI, vol. 12(4), pages 1-12, February.
    7. Luis Gomes & Carlos Ramos & Aria Jozi & Bruno Serra & Lucas Paiva & Zita Vale, 2019. "IoH: A Platform for the Intelligence of Home with a Context Awareness and Ambient Intelligence Approach," Future Internet, MDPI, vol. 11(3), pages 1-21, March.
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    Cited by:

    1. Sudesh Sheoran & Sanket Vij, 2023. "A Consumer-Centric Paradigm Shift in Business Environment with the Evolution of the Internet of Things: A Literature Review," Vision, , vol. 27(4), pages 431-442, August.

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