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Potential of Model-Free Control for Demand-Side Management Considering Real-Time Pricing

Author

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  • Suyang Zhou

    (School of Electrical Engineering, Southeast University, 2 Sipailou Xuanwu Qu, Nanjing 210096, China)

  • Fenghua Zou

    (School of Electrical Engineering, Southeast University, 2 Sipailou Xuanwu Qu, Nanjing 210096, China)

  • Zhi Wu

    (School of Electrical Engineering, Southeast University, 2 Sipailou Xuanwu Qu, Nanjing 210096, China
    Jiangsu Key Laboratory of Smart Grid Technology and Equipment, 2 Sipailou, Nanjing 210096, China)

  • Wei Gu

    (School of Electrical Engineering, Southeast University, 2 Sipailou Xuanwu Qu, Nanjing 210096, China)

Abstract

This paper presents a detailed description of data predictive control (DPC) applied to a demand-side energy management system. Different from traditional model-based predictive control (MPC) algorithms, this approach introduces two model-free algorithms of artificial neural network (ANN) and random forest (RF) to make control strategy predictions on system operation, while avoiding the huge cost and effort associated with learning a grey/white box model of the physical system. The operating characteristics of electrical appliances, system energy consumption, and users’ comfort zones are also considered in the selected energy management system based on a real-time electricity pricing system. Case studies consisting of two scenarios (0% and 15% electricity price fluctuation) are delivered to demonstrate the effectiveness of the proposed approach. Simulation results demonstrate that the DPC controller based on ANN pays only 0.18% additional bill cost to maintain users’ comfort zones and system economy standardization while using only 0.096% optimization time cost compared with the MPC controller.

Suggested Citation

  • Suyang Zhou & Fenghua Zou & Zhi Wu & Wei Gu, 2019. "Potential of Model-Free Control for Demand-Side Management Considering Real-Time Pricing," Energies, MDPI, vol. 12(13), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2587-:d:245756
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    References listed on IDEAS

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    1. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    2. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
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    Cited by:

    1. Suyang Zhou & Di He & Zhiyang Zhang & Zhi Wu & Wei Gu & Junjie Li & Zhe Li & Gaoxiang Wu, 2019. "A Data-Driven Scheduling Approach for Hydrogen Penetrated Energy System Using LSTM Network," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
    2. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).

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