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Multi-objective stochastic programming energy management for integrated INVELOX turbines in microgrids: A new type of turbines

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  • Shaterabadi, Mohammad
  • Jirdehi, Mehdi Ahmadi

Abstract

In this paper, a new type of wind turbine that is called INVELOX has been used. INVELOX has many advantages such as six times more power generation than previous types, work at low speed, inconsiderable maintenance and investment costs, and reduce the environmental effects of previous wind turbines. Moreover, other renewable and nonrenewable generators are used in the energy management and scheduling of the microgrid. The test case is a microgrid with selling and buying energy capability in which the cost and pollution are considered as the objective functions. In the following, Uncertainties of wind speed, solar radiation and electrical-thermal loads are investigated and a multi-objective stochastic mixed integer linear programming is solved in the first scenario. Then, in the second scenario, the effects of fuel cost uncertainty on generation units and objective functions have been studied. The Epsilon constraints method and fuzzy satisfying are utilized to solve the problem and choose the best solution, respectively. By using of INVELOX turbines, total cost and pollution of the microgrid in both deterministic and stochastic planning are reduced from 192.68 $ to 97.23 $ and 249.28 $ to 126.38 $, as well 3334.76 Kg to 3302.7 and 3925.63 to 3910.2 Kg respectively.

Suggested Citation

  • Shaterabadi, Mohammad & Jirdehi, Mehdi Ahmadi, 2020. "Multi-objective stochastic programming energy management for integrated INVELOX turbines in microgrids: A new type of turbines," Renewable Energy, Elsevier, vol. 145(C), pages 2754-2769.
    • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:2754-2769
    DOI: 10.1016/j.renene.2019.08.002
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    Citations

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    Cited by:

    1. Torkan, Ramin & Ilinca, Adrian & Ghorbanzadeh, Milad, 2022. "A genetic algorithm optimization approach for smart energy management of microgrids," Renewable Energy, Elsevier, vol. 197(C), pages 852-863.
    2. Hemmati, Reza & Mehrjerdi, Hasan & Bornapour, Mosayeb, 2020. "Hybrid hydrogen-battery storage to smooth solar energy volatility and energy arbitrage considering uncertain electrical-thermal loads," Renewable Energy, Elsevier, vol. 154(C), pages 1180-1187.
    3. Shaterabadi, Mohammad & Jirdehi, Mehdi Ahmadi & Amiri, Nima & Omidi, Sina, 2020. "Enhancement the economical and environmental aspects of plus-zero energy buildings integrated with INVELOX turbines," Renewable Energy, Elsevier, vol. 153(C), pages 1355-1367.
    4. Chang, Weiguang & Dong, Wei & Wang, Yubin & Yang, Qiang, 2022. "Two-stage coordinated operation framework for virtual power plant with aggregated multi-stakeholder microgrids in a deregulated electricity market," Renewable Energy, Elsevier, vol. 199(C), pages 943-956.
    5. Hlalele, Thabo G. & Naidoo, Raj M. & Bansal, Ramesh C. & Zhang, Jiangfeng, 2020. "Multi-objective stochastic economic dispatch with maximal renewable penetration under renewable obligation," Applied Energy, Elsevier, vol. 270(C).
    6. Zandrazavi, Seyed Farhad & Guzman, Cindy Paola & Pozos, Alejandra Tabares & Quiros-Tortos, Jairo & Franco, John Fredy, 2022. "Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles," Energy, Elsevier, vol. 241(C).
    7. Mehdi Ahmadi Jirdehi & Mohammad Shaterabadi, 2021. "A low‐carbon strategy using INVELOX turbines in the presence of real‐time energy price uncertainty," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 11(3), pages 461-482, June.
    8. Chang, Weiguang & Dong, Wei & Yang, Qiang, 2023. "Day-ahead bidding strategy of cloud energy storage serving multiple heterogeneous microgrids in the electricity market," Applied Energy, Elsevier, vol. 336(C).
    9. Rodriguez, Mauricio & Arcos–Aviles, Diego & Martinez, Wilmar, 2023. "Fuzzy logic-based energy management for isolated microgrid using meta-heuristic optimization algorithms," Applied Energy, Elsevier, vol. 335(C).

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