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Sizing and Coordination Strategies of Battery Energy Storage System Co-Located with Wind Farm: The UK Perspective

Author

Listed:
  • Fulin Fan

    (Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK)

  • Giorgio Zorzi

    (Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK)

  • David Campos-Gaona

    (Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK)

  • Graeme Burt

    (Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK)

  • Olimpo Anaya-Lara

    (Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK)

  • John Nwobu

    (Offshore Renewable Energy Catapult, Glasgow G1 1RD, UK)

  • Ander Madariaga

    (Offshore Renewable Energy Catapult, Glasgow G1 1RD, UK)

Abstract

The rapid development and growth of battery storage have heightened an interest in the co-location of battery energy storage systems (BESS) with renewable energy projects which enables the stacking of multiple revenue streams while reducing connection charges of BESS. To help wind energy industries better understand the coordinated operation of BESS and wind farms and its associated profits, this paper develops a simulation model to implement a number of coordination strategies where the BESS supplies enhanced frequency response (EFR) service and enables the time shift of wind generation based on the UK perspective. The proposed model also simulates the degradation of Lithium-Ion battery and incorporates a state of charge (SOC) dependent limit on the charge rate derived from a constant current-constant voltage charging profile. In addition, a particle swarm optimisation-based battery sizing algorithm is developed here on the basis of the simulation model to determine the optimal size of the co-located BESS along with SOC-related strategy variables that maximise the net present value of the wind + BESS system at the end of the EFR contract.

Suggested Citation

  • Fulin Fan & Giorgio Zorzi & David Campos-Gaona & Graeme Burt & Olimpo Anaya-Lara & John Nwobu & Ander Madariaga, 2021. "Sizing and Coordination Strategies of Battery Energy Storage System Co-Located with Wind Farm: The UK Perspective," Energies, MDPI, vol. 14(5), pages 1-21, March.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1439-:d:511703
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    References listed on IDEAS

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    1. Zhongbao Wei & Feng Leng & Zhongjie He & Wenyu Zhang & Kaiyuan Li, 2018. "Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method," Energies, MDPI, vol. 11(7), pages 1-16, July.
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    4. Johnston, Lewis & Díaz-González, Francisco & Gomis-Bellmunt, Oriol & Corchero-García, Cristina & Cruz-Zambrano, Miguel, 2015. "Methodology for the economic optimisation of energy storage systems for frequency support in wind power plants," Applied Energy, Elsevier, vol. 137(C), pages 660-669.
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    Cited by:

    1. Yuan, Meng & Sorknæs, Peter & Lund, Henrik & Liang, Yongtu, 2022. "The bidding strategies of large-scale battery storage in 100% renewable smart energy systems," Applied Energy, Elsevier, vol. 326(C).
    2. Asmamaw Sewnet & Baseem Khan & Issaias Gidey & Om Prakash Mahela & Adel El-Shahat & Almoataz Y. Abdelaziz, 2022. "Mitigating Generation Schedule Deviation of Wind Farm Using Battery Energy Storage System," Energies, MDPI, vol. 15(5), pages 1-26, February.
    3. Alba Leduchowicz-Municio & Miguel Edgar Morales Udaeta & André Luiz Veiga Gimenes & Tuo Ji & Victor Baiochi Riboldi, 2022. "Socio-Environmental Evaluation of MV Commercial Time-Shift Application Based on Battery Energy Storage Systems," Energies, MDPI, vol. 15(14), pages 1-21, July.
    4. Pablo Fernández-Bustamante & Oscar Barambones & Isidro Calvo & Cristian Napole & Mohamed Derbeli, 2021. "Provision of Frequency Response from Wind Farms: A Review," Energies, MDPI, vol. 14(20), pages 1-24, October.

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