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Dynamic Residential Energy Management for Real-Time Pricing

Author

Listed:
  • Leehter Yao

    (Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Fazida Hanim Hashim

    (Faculty of Engineering and Built Environment, National University of Malaysia, Selangor 43600, Malaysia)

  • Chien-Chi Lai

    (Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

Abstract

A home energy management system (HEMS) was designed in this paper for a smart home that uses integrated energy resources such as power from the grid, solar power generated from photovoltaic (PV) panels, and power from an energy storage system (ESS). A fuzzy controller is proposed for the HEMS to optimally manage the integrated power of the smart home. The fuzzy controller is designed to control the power rectifier for regulating the AC power in response to the variations in the residential electric load, solar power from PV panels, power of the ESS, and the real-time electricity prices. A self-learning scheme is designed for the proposed fuzzy controller to adapt with short-term and seasonal climatic changes and residential load variations. A parsimonious parameterization scheme for both the antecedent and consequent parts of the fuzzy rule base is utilized so that the self-learning scheme of the fuzzy controller is computationally efficient.

Suggested Citation

  • Leehter Yao & Fazida Hanim Hashim & Chien-Chi Lai, 2020. "Dynamic Residential Energy Management for Real-Time Pricing," Energies, MDPI, vol. 13(10), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2562-:d:359807
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    References listed on IDEAS

    as
    1. Marin Cerjan & Marin Matijaš & Marko Delimar, 2014. "Dynamic Hybrid Model for Short-Term Electricity Price Forecasting," Energies, MDPI, vol. 7(5), pages 1-15, May.
    2. Rastegar, Mohammad & Fotuhi-Firuzabad, Mahmud & Aminifar, Farrokh, 2012. "Load commitment in a smart home," Applied Energy, Elsevier, vol. 96(C), pages 45-54.
    3. Papadimitriou, Theophilos & Gogas, Periklis & Stathakis, Efthimios, 2014. "Forecasting energy markets using support vector machines," Energy Economics, Elsevier, vol. 44(C), pages 135-142.
    4. Liu, Heping & Shi, Jing, 2013. "Applying ARMA–GARCH approaches to forecasting short-term electricity prices," Energy Economics, Elsevier, vol. 37(C), pages 152-166.
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    Cited by:

    1. Francesco Mancini & Jacopo Cimaglia & Gianluigi Lo Basso & Sabrina Romano, 2021. "Implementation and Simulation of Real Load Shifting Scenarios Based on a Flexibility Price Market Strategy—The Italian Residential Sector as a Case Study," Energies, MDPI, vol. 14(11), pages 1-21, May.
    2. Qingle Pang & Lin Ye & Houlei Gao & Xinian Li & Yang Zheng & Chenbin He, 2021. "Penalty Electricity Price-Based Optimal Control for Distribution Networks," Energies, MDPI, vol. 14(7), pages 1-16, March.

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