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Optimal Placement of Energy Storage and Wind Power under Uncertainty

Author

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  • Pilar Meneses de Quevedo

    (Power and Energy Analysis and Research Laboratory (PEARL), Universidad de Castilla-La Mancha, Ciudad Real 13071, Spain)

  • Javier Contreras

    (Power and Energy Analysis and Research Laboratory (PEARL), Universidad de Castilla-La Mancha, Ciudad Real 13071, Spain)

Abstract

Due to the rapid growth in the amount of wind energy connected to distribution grids, they are exposed to higher network constraints, which poses additional challenges to system operation. Based on regulation, the system operator has the right to curtail wind energy in order to avoid any violation of system constraints. Energy storage systems (ESS) are considered to be a viable solution to solve this problem. The aim of this paper is to provide the best locations of both ESS and wind power by optimizing distribution system costs taking into account network constraints and the uncertainty associated to the nature of wind, load and price. To do that, we use a mixed integer linear programming (MILP) approach consisting of loss reduction, voltage improvement and minimization of generation costs. An alternative current (AC) linear optimal power flow (OPF), which employs binary variables to define the location of the generation, is implemented. The proposed stochastic MILP approach has been applied to the IEEE 69-bus distribution network and the results show the performance of the model under different values of installed capacities of ESS and wind power.

Suggested Citation

  • Pilar Meneses de Quevedo & Javier Contreras, 2016. "Optimal Placement of Energy Storage and Wind Power under Uncertainty," Energies, MDPI, vol. 9(7), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:7:p:528-:d:73694
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    Citations

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    Cited by:

    1. Corentin Jankowiak & Aggelos Zacharopoulos & Caterina Brandoni & Patrick Keatley & Paul MacArtain & Neil Hewitt, 2019. "The Role of Domestic Integrated Battery Energy Storage Systems for Electricity Network Performance Enhancement," Energies, MDPI, vol. 12(20), pages 1-27, October.
    2. Mengying Chen & Yifeng Wang & Liang Yang & Fuqiang Han & Yuqi Hou & Haiyun Yan, 2018. "A Variable-Structure Multi-Resonant DC–DC Converter with Smooth Switching," Energies, MDPI, vol. 11(9), pages 1-21, August.
    3. Miquel Escoto & Mario Montagud & Noemi González & Alejandro Belinchón & Adriana Valentina Trujillo & Julián Romero & Julio César Díaz-Cabrera & Marta Pellicer García & Alfredo Quijano López, 2020. "Optimal Scheduling for Energy Storage Systems in Distribution Networks," Energies, MDPI, vol. 13(15), pages 1-12, July.
    4. Märkle-Huß, Joscha & Feuerriegel, Stefan & Neumann, Dirk, 2020. "Cost minimization of large-scale infrastructure for electricity generation and transmission," Omega, Elsevier, vol. 96(C).
    5. Hameed, Zeenat & Hashemi, Seyedmostafa & Ipsen, Hans Henrik & Træholt, Chresten, 2021. "A business-oriented approach for battery energy storage placement in power systems," Applied Energy, Elsevier, vol. 298(C).
    6. Ying-Yi Hong & Yong-Zhen Lai & Yung-Ruei Chang & Yih-Der Lee & Chia-Hui Lin, 2018. "Optimizing Energy Storage Capacity in Islanded Microgrids Using Immunity-Based Multiobjective Planning," Energies, MDPI, vol. 11(3), pages 1-15, March.
    7. Stefano Bracco, 2020. "A Study for the Optimal Exploitation of Solar, Wind and Hydro Resources and Electrical Storage Systems in the Bormida Valley in the North of Italy," Energies, MDPI, vol. 13(20), pages 1-26, October.
    8. Fabio Massimo Gatta & Alberto Geri & Regina Lamedica & Stefano Lauria & Marco Maccioni & Francesco Palone & Massimo Rebolini & Alessandro Ruvio, 2016. "Application of a LiFePO 4 Battery Energy Storage System to Primary Frequency Control: Simulations and Experimental Results," Energies, MDPI, vol. 9(11), pages 1-16, October.
    9. Zora Luburić & Hrvoje Pandžić & Tomislav Plavšić, 2017. "Assessment of Energy Storage Operation in Vertically Integrated Utility and Electricity Market," Energies, MDPI, vol. 10(5), pages 1-16, May.
    10. Ceyhun Yıldız & Mustafa Tekin & Ahmet Gani & Ö. Fatih Keçecioğlu & Hakan Açıkgöz & Mustafa Şekkeli, 2017. "A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool," Sustainability, MDPI, vol. 9(5), pages 1-15, May.

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