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Energy Flexibility Management Based on Predictive Dispatch Model of Domestic Energy Management System

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  • Amin Shokri Gazafroudi

    (BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain)

  • Francisco Prieto-Castrillo

    (BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain
    MediaLab, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA
    Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA)

  • Tiago Pinto

    (BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain)

  • Javier Prieto

    (BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain
    StageMotion, R&D Department, C/Orfebres 10, 34005 Palencia, Spain)

  • Juan Manuel Corchado

    (BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain
    Osaka Institute of Technology, Asahi-ku Ohmiya, Osaka 535-8585, Japan)

  • Javier Bajo

    (BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain)

Abstract

This paper proposes a predictive dispatch model to manage energy flexibility in the domestic energy system. Electric Vehicles (EV), batteries and shiftable loads are devices that provide energy flexibility in the proposed system. The proposed energy management problem consists of two stages: day-ahead and real time. A hybrid method is defined for the first time in this paper to model the uncertainty of the PV power generation based on its power prediction. In the day-ahead stage, the uncertainty is modeled by interval bands. On the other hand, the uncertainty of PV power generation is modeled through a stochastic scenario-based method in the real-time stage. The performance of the proposed hybrid Interval-Stochastic (InterStoch) method is compared with the Modified Stochastic Predicted Band (MSPB) method. Moreover, the impacts of energy flexibility and the demand response program on the expected profit and transacted electrical energy of the system are assessed in the case study presented in this paper.

Suggested Citation

  • Amin Shokri Gazafroudi & Francisco Prieto-Castrillo & Tiago Pinto & Javier Prieto & Juan Manuel Corchado & Javier Bajo, 2017. "Energy Flexibility Management Based on Predictive Dispatch Model of Domestic Energy Management System," Energies, MDPI, vol. 10(9), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1397-:d:111755
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    References listed on IDEAS

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    1. Vale, Zita & Morais, Hugo & Faria, Pedro & Ramos, Carlos, 2013. "Distribution system operation supported by contextual energy resource management based on intelligent SCADA," Renewable Energy, Elsevier, vol. 52(C), pages 143-153.
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    Cited by:

    1. Pedro Faria, 2019. "Distributed Energy Resources Management," Energies, MDPI, vol. 12(3), pages 1-3, February.
    2. Francisco Prieto-Castrillo & Amin Shokri Gazafroudi & Javier Prieto & Juan Manuel Corchado, 2018. "An Ising Spin-Based Model to Explore Efficient Flexibility in Distributed Power Systems," Complexity, Hindawi, vol. 2018, pages 1-16, May.
    3. Markos A. Kousounadis-Knousen & Ioannis K. Bazionis & Athina P. Georgilaki & Francky Catthoor & Pavlos S. Georgilakis, 2023. "A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models," Energies, MDPI, vol. 16(15), pages 1-29, July.

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