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Artificial Intelligence for Electricity Supply Chain automation

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  • Richter, Lucas
  • Lehna, Malte
  • Marchand, Sophie
  • Scholz, Christoph
  • Dreher, Alexander
  • Klaiber, Stefan
  • Lenk, Steve

Abstract

The Electricity Supply Chain is a system of enabling procedures to optimize processes ranging from production to transportation and consumption of electricity. The proportion of distributed energy sources within the electricity system increases steadily, which necessitates an improved monitoring capability to ensure the overall reliability and quality of the Electricity Supply Chain. Automation is strongly required to process the growing amount of data. Thus, it is inevitable to handle large amounts of heterogeneous data and process the information using forecasting and optimization techniques. Artificial Intelligence techniques are crucial for extending human cognitive abilities in these tasks. In our work, we synthesize the main impacts of the Artificial Intelligence paradigm on the automation of the Electricity Supply Chain. We describe the emerging automation through Artificial Intelligence in every layer of the Smart Grid Architecture Model and highlight state-of-the-art approaches. In the review, we focus on the following Electricity Supply Chain functionalities: generation, maintenance, pre-processing, analysis, forecasting, optimization, and trading within energy systems. After investigating the individual perspectives, we examine the potential implementation of a fully automated Electricity Supply Chain. Lastly, we discuss perspectives and limitations for the transformation from conventional to automated Electricity Supply Chains, specifically in terms of human interaction, Artificial Intelligence adaptation, energy transition, and sustainability.

Suggested Citation

  • Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    • Handle: RePEc:eee:rensus:v:163:y:2022:i:c:s1364032122003653
    DOI: 10.1016/j.rser.2022.112459
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