IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i6p1724-d520816.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/6/1724/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/6/1724/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    2. Quddus, Md Abdul & Shahvari, Omid & Marufuzzaman, Mohammad & Usher, John M. & Jaradat, Raed, 2018. "A collaborative energy sharing optimization model among electric vehicle charging stations, commercial buildings, and power grid," Applied Energy, Elsevier, vol. 229(C), pages 841-857.
    3. Li, Pei-Hao & Pye, Steve, 2018. "Assessing the benefits of demand-side flexibility in residential and transport sectors from an integrated energy systems perspective," Applied Energy, Elsevier, vol. 228(C), pages 965-979.
    4. Langenmayr, Uwe & Wang, Weimin & Jochem, Patrick, 2020. "Unit commitment of photovoltaic-battery systems: An advanced approach considering uncertainties from load, electric vehicles, and photovoltaic," Applied Energy, Elsevier, vol. 280(C).
    5. Javadi, Mohammad Sadegh & Gough, Matthew & Lotfi, Mohamed & Esmaeel Nezhad, Ali & Santos, Sérgio F. & Catalão, João P.S., 2020. "Optimal self-scheduling of home energy management system in the presence of photovoltaic power generation and batteries," Energy, Elsevier, vol. 210(C).
    6. Buonomano, A. & Calise, F. & Cappiello, F.L. & Palombo, A. & Vicidomini, M., 2019. "Dynamic analysis of the integration of electric vehicles in efficient buildings fed by renewables," Applied Energy, Elsevier, vol. 245(C), pages 31-50.
    7. Fontenot, Hannah & Dong, Bing, 2019. "Modeling and control of building-integrated microgrids for optimal energy management – A review," Applied Energy, Elsevier, vol. 254(C).
    8. Lu, Qing & Lü, Shuaikang & Leng, Yajun & Zhang, Zhixin, 2020. "Optimal household energy management based on smart residential energy hub considering uncertain behaviors," Energy, Elsevier, vol. 195(C).
    9. Jordehi, A. Rezaee, 2019. "Optimisation of demand response in electric power systems, a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 308-319.
    10. Sajjad Ali & Imran Khan & Sadaqat Jan & Ghulam Hafeez, 2021. "An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid," Energies, MDPI, vol. 14(8), pages 1-29, April.
    11. Luiz Almeida & Ana Soares & Pedro Moura, 2023. "A Systematic Review of Optimization Approaches for the Integration of Electric Vehicles in Public Buildings," Energies, MDPI, vol. 16(13), pages 1-26, June.
    12. Xiaohan Fang & Jinkuan Wang & Guanru Song & Yinghua Han & Qiang Zhao & Zhiao Cao, 2019. "Multi-Agent Reinforcement Learning Approach for Residential Microgrid Energy Scheduling," Energies, MDPI, vol. 13(1), pages 1-26, December.
    13. Elma, Onur & Selamogullari, Ugur Savas, 2015. "A new home energy management algorithm with voltage control in a smart home environment," Energy, Elsevier, vol. 91(C), pages 720-731.
    14. Àlex Alonso & Jordi de la Hoz & Helena Martín & Sergio Coronas & Pep Salas & José Matas, 2020. "A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization," Energies, MDPI, vol. 13(21), pages 1-26, October.
    15. Ahsan, Syed M. & Khan, Hassan A. & Hassan, Naveed-ul & Arif, Syed M. & Lie, Tek-Tjing, 2020. "Optimized power dispatch for solar photovoltaic-storage system with multiple buildings in bilateral contracts," Applied Energy, Elsevier, vol. 273(C).
    16. Yu Huang & Weiting Zhang & Kai Yang & Weizhen Hou & Yiran Huang, 2019. "An Optimal Scheduling Method for Multi-Energy Hub Systems Using Game Theory," Energies, MDPI, vol. 12(12), pages 1-20, June.
    17. Kuang, Yanqing & Chen, Yang & Hu, Mengqi & Yang, Dong, 2017. "Influence analysis of driver behavior and building category on economic performance of electric vehicle to grid and building integration," Applied Energy, Elsevier, vol. 207(C), pages 427-437.
    18. Thomas, Dimitrios & D’Hoop, Gaspard & Deblecker, Olivier & Genikomsakis, Konstantinos N. & Ioakimidis, Christos S., 2020. "An integrated tool for optimal energy scheduling and power quality improvement of a microgrid under multiple demand response schemes," Applied Energy, Elsevier, vol. 260(C).
    19. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Natural gas demand response strategy considering user satisfaction and load volatility under dynamic pricing," Energy, Elsevier, vol. 277(C).
    20. Colmenar-Santos, Antonio & Linares-Mena, Ana-Rosa & Borge-Diez, David & Quinto-Alemany, Carlos-Domingo, 2017. "Impact assessment of electric vehicles on islands grids: A case study for Tenerife (Spain)," Energy, Elsevier, vol. 120(C), pages 385-396.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1724-:d:520816. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.