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Quantification of the Energy Storage Contribution to Security of Supply through the F-Factor Methodology

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  • Spyros Giannelos

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

  • Predrag Djapic

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

  • Danny Pudjianto

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

  • Goran Strbac

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

Abstract

The ongoing electrification of the heat and transport sectors is expected to lead to a substantial increase in peak electricity demand over the coming decades, which may drive significant investment in network reinforcement in order to maintain a secure supply of electricity to consumers. The traditional way of security provision has been based on conventional investments such as the upgrade of the capacity of electricity transmission or distribution lines. However, energy storage can also provide security of supply. In this context, the current paper presents a methodology for the quantification of the security contribution of energy storage, based on the use of mathematical optimization for the calculation of the F-factor metric, which reflects the optimal amount of peak demand reduction that can be achieved as compared to the power capability of the corresponding energy storage asset. In this context, case studies underline that the F-factors decrease with greater storage power capability and increase with greater storage efficiency and energy capacity as well as peakiness of the load profile. Furthermore, it is shown that increased investment in energy storage per system bus does not increase the overall contribution to security of supply.

Suggested Citation

  • Spyros Giannelos & Predrag Djapic & Danny Pudjianto & Goran Strbac, 2020. "Quantification of the Energy Storage Contribution to Security of Supply through the F-Factor Methodology," Energies, MDPI, vol. 13(4), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:826-:d:320457
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    References listed on IDEAS

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    Cited by:

    1. Spyros Giannelos & Stefan Borozan & Marko Aunedi & Xi Zhang & Hossein Ameli & Danny Pudjianto & Ioannis Konstantelos & Goran Strbac, 2023. "Modelling Smart Grid Technologies in Optimisation Problems for Electricity Grids," Energies, MDPI, vol. 16(13), pages 1-15, June.
    2. Tadeusz Białoń & Roman Niestrój & Wojciech Skarka & Wojciech Korski, 2023. "HPPC Test Methodology Using LFP Battery Cell Identification Tests as an Example," Energies, MDPI, vol. 16(17), pages 1-21, August.
    3. Spyros Giannelos & Anjali Jain & Stefan Borozan & Paola Falugi & Alexandre Moreira & Rohit Bhakar & Jyotirmay Mathur & Goran Strbac, 2021. "Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty," Energies, MDPI, vol. 14(22), pages 1-27, November.
    4. Piotr Olczak & Dominika Matuszewska, 2023. "Energy Storage Potential Needed at the National Grid Scale (Poland) in Order to Stabilize Daily Electricity Production from Fossil Fuels and Nuclear Power," Energies, MDPI, vol. 16(16), pages 1-11, August.
    5. Spyros Giannelos & Alexandre Moreira & Dimitrios Papadaskalopoulos & Stefan Borozan & Danny Pudjianto & Ioannis Konstantelos & Mingyang Sun & Goran Strbac, 2023. "A Machine Learning Approach for Generating and Evaluating Forecasts on the Environmental Impact of the Buildings Sector," Energies, MDPI, vol. 16(6), pages 1-37, March.

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