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Demand-side management strategy in stand-alone hybrid photovoltaic systems with real-time simulation of stochastic electricity consumption behavior

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  • Thiaux, Yaël
  • Dang, Thu Thuy
  • Schmerber, Louis
  • Multon, Bernard
  • Ben Ahmed, Hamid
  • Bacha, Seddik
  • Tran, Quoc Tuan

Abstract

Demand-side management (DSM) represents a potential way to improve the profitability of renewable energy systems. In this paper, power management including a new DSM strategy in a stand-alone hybrid Photovoltaic (PV) Diesel/Battery system with multiple customers has been studied. A new probabilistic model of the consumer behavior based on Bayesian network and Monte Carlo simulation has been carried out so as to capture the real-time and stochastic aspect of the demand. The analysis has been made by means of a one-year period simulation of the whole system. Statistical data on consumers and meteorological observation data have been used to set the simulation’s parameters. Numerical results showed that with the implementation of DSM, energy costs are reduced by 11.3% for equal total consumption, and the use of solar energy resources rose to 54%. This provides insight on the significant performance enhancement offered by a DSM scheme in such a system.

Suggested Citation

  • Thiaux, Yaël & Dang, Thu Thuy & Schmerber, Louis & Multon, Bernard & Ben Ahmed, Hamid & Bacha, Seddik & Tran, Quoc Tuan, 2019. "Demand-side management strategy in stand-alone hybrid photovoltaic systems with real-time simulation of stochastic electricity consumption behavior," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:253:y:2019:i:c:61
    DOI: 10.1016/j.apenergy.2019.113530
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    3. Aatabe, Mohamed & El Guezar, Fatima & Vargas, Alessandro N. & Bouzahir, Hassane, 2021. "A novel stochastic maximum power point tracking control for off-grid standalone photovoltaic systems with unpredictable load demand," Energy, Elsevier, vol. 235(C).
    4. Yang, Wangwang & Shi, Jing & Li, Shujian & Song, Zhaofang & Zhang, Zitong & Chen, Zexu, 2022. "A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior," Applied Energy, Elsevier, vol. 307(C).
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    6. Piotr F. Borowski, 2020. "Zonal and Nodal Models of Energy Market in European Union," Energies, MDPI, vol. 13(16), pages 1-21, August.
    7. Georgios T. Tzanes & Dimitrios P. Zafirakis & John K. Kaldellis, 2024. "Practice of a Load Shifting Algorithm for Enhancing Community-Scale RES Utilization," Sustainability, MDPI, vol. 16(13), pages 1-16, July.
    8. Zeljković, Čedomir & Mršić, Predrag & Erceg, Bojan & Lekić, Đorđe & Kitić, Nemanja & Matić, Petar, 2022. "Optimal sizing of photovoltaic-wind-diesel-battery power supply for mobile telephony base stations," Energy, Elsevier, vol. 242(C).
    9. Satoshi Nakano & Ayu Washizu, 2020. "On the Acceptability of Electricity Demand Side Management by Time of Day," Energies, MDPI, vol. 13(14), pages 1-21, July.

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