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Modelling Smart Grid Technologies in Optimisation Problems for Electricity Grids

Author

Listed:
  • Spyros Giannelos

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

  • Stefan Borozan

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

  • Marko Aunedi

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

  • Xi Zhang

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

  • Hossein Ameli

    (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)

  • Ioannis Konstantelos

    (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 decarbonisation of the electricity grid is expected to create new electricity flows. As a result, it may require that network planners make a significant amount of investments in the electricity grids over the coming decades so as to allow the accommodation of these new flows in a way that both the thermal and voltage network constraints are respected. These investments may include a portfolio of infrastructure assets consisting of traditional technologies and smart grid technologies. One associated key challenge is the presence of uncertainty around the location, the timing, and the amount of new demand or generation connections. This uncertainty unavoidably introduces risk into the investment decision-making process as it may lead to inefficient investments and inevitably give rise to excessive investment costs. Smart grid technologies have properties that enable them to be regarded as investment options, which can allow network planners to hedge against the aforementioned uncertainty. This paper focuses on key smart technologies by providing a critical literature review and presenting the latest mathematical modelling that describes their operation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5088-:d:1184502
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Adriana Grigorescu & Victor Raul Lopez Ruiz & Cristina Lincaru & Elena Condrea, 2023. "Specialization Patterns for the Development of Renewable Energy Generation Technologies across Countries," Energies, MDPI, vol. 16(20), pages 1-26, October.
    2. Masoumeh Sharifpour & Mohammad Taghi Ameli & Hossein Ameli & Goran Strbac, 2023. "A Resilience-Oriented Approach for Microgrid Energy Management with Hydrogen Integration during Extreme Events," Energies, MDPI, vol. 16(24), pages 1-18, December.
    3. Ali Reza Kheirkhah & Carlos Frederico Meschini Almeida & Nelson Kagan & Jonatas Boas Leite, 2023. "Optimal Probabilistic Allocation of Photovoltaic Distributed Generation: Proposing a Scenario-Based Stochastic Programming Model," Energies, MDPI, vol. 16(21), pages 1-18, October.
    4. Spyros Giannelos & Tai Zhang & Danny Pudjianto & Ioannis Konstantelos & Goran Strbac, 2024. "Investments in Electricity Distribution Grids: Strategic versus Incremental Planning," Energies, MDPI, vol. 17(11), pages 1-13, June.
    5. Spyros Giannelos & Xi Zhang & Tai Zhang & Goran Strbac, 2024. "Multi-Objective Optimization for Pareto Frontier Sensitivity Analysis in Power Systems," Sustainability, MDPI, vol. 16(14), pages 1-17, July.
    6. 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.
    7. Mohammad Hemmati & Navid Bayati & Thomas Ebel, 2024. "Integrated Optimal Energy Management of Multi-Microgrid Network Considering Energy Performance Index: Global Chance-Constrained Programming Framework," Energies, MDPI, vol. 17(17), pages 1-22, September.
    8. Lang Zhao & Zhidong Wang & Hao Sheng & Yizheng Li & Xueying Wang & Yao Wang & Haifeng Yu, 2024. "Optimal Configuration Model for Large Capacity Synchronous Condenser Considering Transient Voltage Stability in Multiple UHV DC Receiving End Grids," Energies, MDPI, vol. 17(21), pages 1-21, October.
    9. Lin Wang & Yugang He & Renhong Wu, 2024. "Digitization Meets Energy Transition: Shaping the Future of Environmental Sustainability," Energies, MDPI, vol. 17(4), pages 1-25, February.
    10. John O’Donnell & Wencong Su, 2023. "A Stochastic Load Forecasting Approach to Prevent Transformer Failures and Power Quality Issues Amid the Evolving Electrical Demands Facing Utilities," Energies, MDPI, vol. 16(21), pages 1-23, October.

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