MPC-based interval number optimization for electric water heater scheduling in uncertain environments

Jidong WANG, Chenghao LI, Peng LI, Yanbo CHE, Yue ZHOU, Yinqi LI

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Front. Energy ›› 2021, Vol. 15 ›› Issue (1) : 186-200. DOI: 10.1007/s11708-019-0644-9
RESEARCH ARTICLE

MPC-based interval number optimization for electric water heater scheduling in uncertain environments

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Abstract

In this paper, interval number optimization and model predictive control are proposed to handle the uncertain-but-bounded parameters in electric water heater load scheduling. First of all, interval numbers are used to describe uncertain parameters including hot water demand, ambient temperature, and real-time price of electricity. Moreover, the traditional thermal dynamic model of electric water heater is transformed into an interval number model, based on which, the day-ahead load scheduling problem with uncertain parameters is formulated, and solved by interval number optimization. Different tolerance degrees for constraint violation and temperature preferences are also discussed for giving consumers more choices. Furthermore, the model predictive control which incorporates both forecasts and newly updated information is utilized to make and execute electric water heater load schedules on a rolling basis throughout the day. Simulation results demonstrate that interval number optimization either in day-ahead optimization or model predictive control format is robust to the uncertain hot water demand, ambient temperature, and real-time price of electricity, enabling customers to flexibly adjust electric water heater control strategy.

Keywords

electric water heater / load scheduling / interval number optimization / model predictive control / uncertainty

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Jidong WANG, Chenghao LI, Peng LI, Yanbo CHE, Yue ZHOU, Yinqi LI. MPC-based interval number optimization for electric water heater scheduling in uncertain environments. Front. Energy, 2021, 15(1): 186‒200 https://doi.org/10.1007/s11708-019-0644-9

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 51477111) and the National Key Research and Development Program of China (Grant No. 2016YFB0901102).

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