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Energy storage allocation in wind integrated distribution networks: An MILP-Based approach

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  • Karimi, Ali
  • Aminifar, Farrokh
  • Fereidunian, Alireza
  • Lesani, Hamid

Abstract

Due to the unpredictable nature of wind energy and non-coincidence between wind units output power and demand peak load, wind units is deemed as an unreliable source of energy. In order to compensate for the short-term fluctuation of wind energy, deployment of energy storage (ES) units in various types have been introduced as a viable solution. This paper develops a stochastic mathematical model for the optimal allocation of ES units in active distribution networks (ADNs) in order to reduce wind power spillage and load curtailment while managing congestion and voltages deviation. Nonlinearities of the original formulation are converted to linear equivalents and the final model lies within the computationally tractable mixed-integer linear programming (MILP) fashion. The IEEE 33-bus 12.66 kV radial distribution test system is utilized to illustrate the effectiveness of the proposed methodology. It was found that the rated power and capacity of ES units are depends on wind units' location and penetration level, in such a way that ES units are allocated near wind units to absorb excessive wind energy as much as possible. Furthermore, the results indicate ES units are useful for other purposes such as voltage management issue even if the wind units are not allocated.

Suggested Citation

  • Karimi, Ali & Aminifar, Farrokh & Fereidunian, Alireza & Lesani, Hamid, 2019. "Energy storage allocation in wind integrated distribution networks: An MILP-Based approach," Renewable Energy, Elsevier, vol. 134(C), pages 1042-1055.
    • Handle: RePEc:eee:renene:v:134:y:2019:i:c:p:1042-1055
    DOI: 10.1016/j.renene.2018.11.034
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    References listed on IDEAS

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    Cited by:

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    2. Yan, Zhe & Zhang, Yongming & Liang, Runqi & Jin, Wenrui, 2020. "An allocative method of hybrid electrical and thermal energy storage capacity for load shifting based on seasonal difference in district energy planning," Energy, Elsevier, vol. 207(C).
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    5. Jieran Feng & Hao Zhou, 2022. "Bi-Level Optimal Capacity Planning of Load-Side Electric Energy Storage Using an Emission-Considered Carbon Incentive Mechanism," Energies, MDPI, vol. 15(13), pages 1-18, June.
    6. Quetzalcoatl Hernandez-Escobedo & Javier Garrido & Fernando Rueda-Martinez & Gerardo Alcalá & Alberto-Jesus Perea-Moreno, 2019. "Wind Power Cogeneration to Reduce Peak Electricity Demand in Mexican States Along the Gulf of Mexico," Energies, MDPI, vol. 12(12), pages 1-22, June.
    7. Chien-Heng Chou & Sa Ly Ngo & Phung Phi Tran, 2023. "Renewable Energy Integration for Sustainable Economic Growth: Insights and Challenges via Bibliometric Analysis," Sustainability, MDPI, vol. 15(20), pages 1-26, October.
    8. Oprea, Simona-Vasilica & Bâra, Adela & Ciurea, Cristian-Eugen, 2022. "A novel cost-revenue allocation computation for the competitiveness of balancing responsible parties, including RES. Insights from the electricity market," Renewable Energy, Elsevier, vol. 199(C), pages 881-894.

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