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A Prosumer Model Based on Smart Home Energy Management and Forecasting Techniques

Author

Listed:
  • Nikolaos Koltsaklis

    (Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece)

  • Ioannis P. Panapakidis

    (Department of Electrical and Computer Engineering, University of Thessaly, 38221 Volos, Greece)

  • David Pozo

    (Center for Energy Science and Technology, Skolkovo Institute of Science and Technology (Skoltech), 121205 Moscow, Russia)

  • Georgios C. Christoforidis

    (Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece)

Abstract

This work presents an optimization framework based on mixed-integer programming techniques for a smart home’s optimal energy management. In particular, through a cost-minimization objective function, the developed approach determines the optimal day-ahead energy scheduling of all load types that can be either inelastic or can take part in demand response programs and the charging/discharging programs of an electric vehicle and energy storage. The underlying energy system can also interact with the power grid, exchanging electricity through sales and purchases. The smart home’s energy system also incorporates renewable energy sources in the form of wind and solar power, which generate electrical energy that can be either directly consumed for the home’s requirements, directed to the batteries for charging needs (storage, electric vehicles), or sold back to the power grid for acquiring revenues. Three short-term forecasting processes are implemented for real-time prices, photovoltaics, and wind generation. The forecasting model is built on the hybrid combination of the K-medoids algorithm and Elman neural network. K-medoids performs clustering of the training set and is used for input selection. The forecasting is held via the neural network. The results indicate that different renewables’ availability highly influences the optimal demand allocation, renewables-based energy allocation, and the charging–discharging cycle of the energy storage and electric vehicle.

Suggested Citation

  • Nikolaos Koltsaklis & Ioannis P. Panapakidis & David Pozo & Georgios C. Christoforidis, 2021. "A Prosumer Model Based on Smart Home Energy Management and Forecasting Techniques," Energies, MDPI, vol. 14(6), pages 1-32, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1724-:d:520816
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    References listed on IDEAS

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    1. Erdinc, Ozan, 2014. "Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households," Applied Energy, Elsevier, vol. 126(C), pages 142-150.
    2. Nan, Sibo & Zhou, Ming & Li, Gengyin, 2018. "Optimal residential community demand response scheduling in smart grid," Applied Energy, Elsevier, vol. 210(C), pages 1280-1289.
    3. Thomas, Dimitrios & Deblecker, Olivier & Ioakimidis, Christos S., 2018. "Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule," Applied Energy, Elsevier, vol. 210(C), pages 1188-1206.
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    Citations

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    Cited by:

    1. Agnieszka Izabela Baruk, 2021. "Prosumers’ Needs Satisfied Due to Cooperation with Offerors in the Context of Attitudes toward Such Cooperation," Energies, MDPI, vol. 14(22), pages 1-16, November.
    2. Jonas Sievers & Thomas Blank, 2023. "A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems," Energies, MDPI, vol. 16(4), pages 1-21, February.
    3. Isaías Gomes & Karol Bot & Maria Graça Ruano & António Ruano, 2022. "Recent Techniques Used in Home Energy Management Systems: A Review," Energies, MDPI, vol. 15(8), pages 1-41, April.
    4. Joanna Henzel & Łukasz Wróbel & Marcin Fice & Marek Sikora, 2022. "Energy Consumption Forecasting for the Digital-Twin Model of the Building," Energies, MDPI, vol. 15(12), pages 1-21, June.
    5. Hannie Zang & JongWon Kim, 2021. "Reinforcement Learning Based Peer-to-Peer Energy Trade Management Using Community Energy Storage in Local Energy Market," Energies, MDPI, vol. 14(14), pages 1-18, July.
    6. Klaus Rheinberger & Peter Kepplinger & Markus Preißinger, 2021. "Flexibility Control in Autonomous Demand Response by Optimal Power Tracking," Energies, MDPI, vol. 14(12), pages 1-14, June.
    7. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
    8. Li, Wenda & Yigitcanlar, Tan & Liu, Aaron & Erol, Isil, 2022. "Mapping two decades of smart home research: A systematic scientometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    9. Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.
    10. Nemanja Mišljenović & Matej Žnidarec & Goran Knežević & Damir Šljivac & Andreas Sumper, 2023. "A Review of Energy Management Systems and Organizational Structures of Prosumers," Energies, MDPI, vol. 16(7), pages 1-32, March.

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