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Digitalization opportunities to enable local power system transition to net-zero

  • Elnaz Azizi*
  • , Weiqi Hua
  • , Bruce Stephen
  • , David C.H. Wallom
  • , Malcolm McCulloch
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

The energy sector is pivotal in driving the transition towards a sustainable net-zero future, with the adoption of digital technologies playing a key role in this evolution. While much of the existing research has centered on sustainability through centralized energy generation and large-scale transmission systems, this paper addresses a notable gap by focusing on how data-driven decisions in local power systems, supported by digitalization, can enhance efficiency, reliability, and sustainability. The framework of this paper is derived from a survey from eight organizations of power sector, covering transmission and distribution, aiming to answer the question: “Which decisions within local power systems need to be informed by data?”. Through inductive coding of survey responses, we identified key themes fall into two main categories: requirements of local energy systems and opportunities provided by digitalization to meet these requirements. Mapping responses to each of these categories underscore the equal significance stakeholders place on the role of digitalization in both planning and operation. Also, with this mapping, we uncovered four key opportunities of digitalization, with predictability and interoperability being the most crucial, followed by responsiveness and automation. Aligned with the identified codes, academic research, industrial implementations and ongoing projects are reviewed to showcase the current practical applications of digitalization. Finally, we identify potential research areas that could further benefit local energy systems through effective use of digitalization. This paper provides insights into the digitalization opportunities in local power systems, offering a comprehensive overview for the data science and power engineering communities.
Original languageEnglish
Article number101596
Number of pages12
JournalEnergy for Sustainable Development
Volume84
Early online date29 Nov 2024
DOIs
Publication statusPublished - Feb 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Data analytics
  • Distribution network
  • Machine learning
  • Operation
  • Planning
  • Power system reliability
  • Smart grid
  • Sustainability transition

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