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Stochastic day-ahead scheduling of irrigation system integrated agricultural microgrid with pumped storage and uncertain wind power

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  • Zhang, M.Y.
  • Chen, J.J.
  • Yang, Z.J.
  • Peng, K.
  • Zhao, Y.L.
  • Zhang, X.H.

Abstract

Agricultural microgrid provides a promising solution for energy supply of rural areas in a cost-effective way. In this paper, the principle of wind-pumped storage integrated agricultural microgrid to fulfill both the electric and water load demand is explored. From the perspective of risk aversion, the indexes of expected power not served (EPNS) and expected power curtailment (EPC) are derived to evaluate the uncertain wind power. Then, a stochastic day-ahead scheduling model of wind-pumped storage power system and irrigation system is proposed to minimize the total operation cost whilst achieve the peak load shaving by taking the advantage of wind-pumped storage compensation. The proposed model is a complex optimization problem that takes into account distributed generations, electrical and water demand, turbine flow rate, and irrigation time and volume. We propose a intraspecific competition (IC) based evolutionary predator and prey strategy (EPPS) to solve the model. Finally, simulation results conducted on an agricultural microgrid have verified that, compared to previous works in the literature, the proposed model can effectively improve the utilization of wind power, reduce the operation cost of microgrid as well as smooth load curve. Additionally, the proposed algorithm can get a more competitive solution than other recently developed algorithms.

Suggested Citation

  • Zhang, M.Y. & Chen, J.J. & Yang, Z.J. & Peng, K. & Zhao, Y.L. & Zhang, X.H., 2021. "Stochastic day-ahead scheduling of irrigation system integrated agricultural microgrid with pumped storage and uncertain wind power," Energy, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:energy:v:237:y:2021:i:c:s0360544221018867
    DOI: 10.1016/j.energy.2021.121638
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    References listed on IDEAS

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    Cited by:

    1. Qian Zhang & Lisheng Wei & Benben Yang, 2022. "Research on Improved BBO Algorithm and Its Application in Optimal Scheduling of Micro-Grid," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    2. Zhang, Y.Q. & Chen, J.J. & Wang, Y.X. & Feng, L., 2024. "Enhancing resilience of agricultural microgrid through electricity–heat–water based multi-energy hub considering irradiation intensity uncertainty," Renewable Energy, Elsevier, vol. 220(C).
    3. Li, Hongze & Li, Xumeng & Zhang, Yuanyuan & Zhao, Yihang & Pan, Jiaqi & Zhao, Huiru, 2024. "Declaration strategy of wind power and pumped storage participating in the power market considering multiple uncertainties," Energy, Elsevier, vol. 293(C).
    4. Mitul Ranjan Chakraborty & Subhojit Dawn & Pradip Kumar Saha & Jayanta Bhusan Basu & Taha Selim Ustun, 2022. "System Profit Improvement of a Thermal–Wind–CAES Hybrid System Considering Imbalance Cost in the Electricity Market," Energies, MDPI, vol. 15(24), pages 1-25, December.
    5. Lin, Zhiyi & Song, Chunyue & Zhao, Jun & Yin, Huan, 2022. "Improved approximate dynamic programming for real-time economic dispatch of integrated microgrids," Energy, Elsevier, vol. 255(C).
    6. Favaro, Pietro & Dolányi, Mihály & Vallée, François & Toubeau, Jean-François, 2024. "Neural network informed day-ahead scheduling of pumped hydro energy storage," Energy, Elsevier, vol. 289(C).
    7. 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).

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