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Investments in Electricity Distribution Grids: Strategic versus Incremental Planning

Author

Listed:
  • Spyros Giannelos

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

  • Tai Zhang

    (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 ongoing electrification of the transport sector is expected to cause an increase in electricity demand and, therefore, trigger significant network investments to accommodate it. This paper focuses on investment decision-making for electricity distribution grids and specifically on the strategic and incremental investment network planning approaches. In particular, the former involves network planning with the consideration of a long-term multi-stage study horizon, as opposed to a shorter–term view of the future that applies to the latter case. An investment analysis that is carried out underlines the economic savings generated from adopting a strategic investment perspective over an incremental one. These economic savings are achieved from the fact that the associated fixed investment costs are incurred only once in the horizon under strategic planning. On the other hand, incremental planning involves a series of network reinforcement decisions, thereby incurring the fixed cost multiple times. In addition, sensitivity analyses that are carried out capture the effect of key parameters, such as investment cost, discount rate and investment delay, on the generated economic savings.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2724-:d:1408145
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    References listed on IDEAS

    as
    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. Dixit, Avinash K, 1989. "Entry and Exit Decisions under Uncertainty," Journal of Political Economy, University of Chicago Press, vol. 97(3), pages 620-638, June.
    3. Pindyck, Robert S, 1988. "Irreversible Investment, Capacity Choice, and the Value of the Firm," American Economic Review, American Economic Association, vol. 78(5), pages 969-985, December.
    4. 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.
    5. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, May.
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    Cited by:

    1. Spyros Giannelos, 2025. "Reinforcement Learning in Energy Finance: A Comprehensive Review," Energies, MDPI, vol. 18(11), pages 1-41, May.
    2. Gómez, Stefanía & Olmos, Luis & Ramos, Andrés & Rivier, Michel, 2025. "A bi-level framework to analyse the alternative use of FTRs as a long-term risk hedging instrument. Application to a European case study," Applied Energy, Elsevier, vol. 380(C).
    3. Huiqun Yu & Haoyi Sun & Yueze Li & Chunmei Xu & Chenkun Du, 2024. "Enhanced Short-Term Load Forecasting: Error-Weighted and Hybrid Model Approach," Energies, MDPI, vol. 17(21), pages 1-22, October.
    4. Giannelos, Spyros & Pudjianto, Danny & Strbac, Goran, 2025. "Smart home economic operation under uncertainty: comparing monte carlo and stochastic optimization using gaussian and KDE-based data," Operations Research Perspectives, Elsevier, vol. 15(C).
    5. Tai Zhang & Goran Strbac, 2025. "Novel Artificial Intelligence Applications in Energy: A Systematic Review," Energies, MDPI, vol. 18(14), pages 1-51, July.
    6. Pedro Torres-Bermeo & Kevin López-Eugenio & Carolina Del-Valle-Soto & Guillermo Palacios-Navarro & José Varela-Aldás, 2025. "Sizing and Characterization of Load Curves of Distribution Transformers Using Clustering and Predictive Machine Learning Models," Energies, MDPI, vol. 18(7), pages 1-24, April.
    7. Tai Zhang & Goran Strbac, 2025. "Artificial Intelligence Applications for Energy Storage: A Comprehensive Review," Energies, MDPI, vol. 18(17), pages 1-44, September.

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