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A stochastic-interval model for optimal scheduling of PV-assisted multi-mode charging stations

Author

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  • Tostado-Véliz, Marcos
  • Kamel, Salah
  • Hasanien, Hany M.
  • Arévalo, Paul
  • Turky, Rania A.
  • Jurado, Francisco

Abstract

Nowadays, photovoltaic-assisted charging stations are becoming popular worldwide because its capacity to accommodate more clean energy, reduce carbon emissions, alleviate peak charging loads and provide wider charging infrastructures worldwide. When these infrastructures are operated locally, energy management becomes a challenge due to the large number and heterogeneity of uncertainties involved. This aspect is especially noticeable in the case of charging demand, which is difficult to predict. To address this issue, this paper develops a novel stochastic-interval model for optimal scheduling of multi-mode photovoltaic-assisted charging stations. The developed model uses interval formulation to model uncertainties from photovoltaic generation and energy price, while a comprehensive stochastic model is proposed for charging demand. The developed optimal scheduling model is solved using a developed iterative model, which avoids using interval arithmetic explicitly. This methodology encompasses two Mixed-integer linear programming problems and one Quadratic-programming problem, that can be efficiently addressed by conventional solvers, and allows adopting optimistic or pessimistic strategies. A case study is presented on a benchmark mid-size charging station to validate the developed model. As a sake of example, the system profit grows by 9% and decreases by 3% adopting optimistic and pessimistic point of view, respectively. Likewise, total PV generation increases by 150 kWh/day and reduces by 50 kWh/day. Similar conclusions are extracted for other parameters like monetary balances, PV peak power or satisfied EV demand.

Suggested Citation

  • Tostado-Véliz, Marcos & Kamel, Salah & Hasanien, Hany M. & Arévalo, Paul & Turky, Rania A. & Jurado, Francisco, 2022. "A stochastic-interval model for optimal scheduling of PV-assisted multi-mode charging stations," Energy, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:energy:v:253:y:2022:i:c:s0360544222011227
    DOI: 10.1016/j.energy.2022.124219
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    References listed on IDEAS

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    2. Tostado-Véliz, Marcos & Hasanien, Hany M. & Rezaee Jordehi, Ahmad & Turky, Rania A. & Gómez-González, Manuel & Jurado, Francisco, 2023. "An Interval-based privacy – Aware optimization framework for electricity price setting in isolated microgrid clusters," Applied Energy, Elsevier, vol. 340(C).
    3. Tostado-Véliz, Marcos & Rezaee Jordehi, Ahmad & Amir Mansouri, Seyed & Jurado, Francisco, 2022. "Day-ahead scheduling of 100% isolated communities under uncertainties through a novel stochastic-robust model," Applied Energy, Elsevier, vol. 328(C).
    4. Bogdan Gilev & Miroslav Andreev & Nikolay Hinov & George Angelov, 2022. "Modeling and Simulation of a Low-Cost Fast Charging Station Based on a Micro Gas Turbine and a Supercapacitor," Energies, MDPI, vol. 15(21), pages 1-15, October.
    5. Ahmadi Jirdehi, Mehdi & Sohrabi Tabar, Vahid, 2023. "Risk-aware energy management of a microgrid integrated with battery charging and swapping stations in the presence of renewable resources high penetration, crypto-currency miners and responsive loads," Energy, Elsevier, vol. 263(PA).
    6. Rafael Alvarenga & Hubert Herbaux & Laurent Linguet, 2023. "On the Added Value of State-of-the-Art Probabilistic Forecasting Methods Applied to the Optimal Scheduling of a PV Power Plant with Batteries," Energies, MDPI, vol. 16(18), pages 1-24, September.
    7. Tostado-Véliz, Marcos & Hasanien, Hany M. & Jordehi, Ahmad Rezaee & Turky, Rania A. & Jurado, Francisco, 2023. "Risk-averse optimal participation of a DR-intensive microgrid in competitive clusters considering response fatigue," Applied Energy, Elsevier, vol. 339(C).

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