Multi-Agent Reinforcement Learning-based Distributed Economic Dispatch Considering Network attacks and Uncertain Costs

Dong, Mei; Ding, Lifu; Zhang, Chen; Rao, Hanyu; Yan, Gangfeng

doi:10.1109/iciea51954.2021.9516143

Cited by 5 publications

(1 citation statement)

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“…Numerous research studies have been conducted in recent years on the integration of AI and IoT in the power generation and distribution sector [7]. These studies sought to address the major challenges and opportunities in the field of artificial intelligence and cybersecurity in the power sector.…”

Section: ░ 3 Literature Reviewmentioning

confidence: 99%

Staying Ahead of Threats: A Review of AI and Cyber Security in Power Generation and Distribution

Mohamed¹,

Oubelaid

Almazrouei³

2023

IJEER

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The integration of artificial intelligence (AI) and the Internet of Things (IoT) in the power generation and distribution industry presents opportunities and challenges, particularly in the area of cybersecurity. Previous studies have explored the potential of AI to enhance cybersecurity in power systems, but limitations in terms of sample size and scope have hindered a comprehensive understanding of the current state of the field. To address this gap, this paper presents a systematic literature review of 30 papers that analyzes and categorizes relevant research based on their focus on threats, solutions, and future trends. The results indicate that 30 articles provide evidence supporting the use of AI and machine learning techniques to significantly enhance cybersecurity in the power sector. However, the study also highlights the need for continuous monitoring, threat intelligence, and risk management to stay ahead of evolving threats. Notably, this paper provides novel insights into the use of cybersecurity measures, blockchain technology, and awareness of the impact of AI in the power sector, with 90% of organizations using cybersecurity measures, 50% employing blockchain technology, 20% experiencing a cyberattack, and 60% being aware of the impact of AI. The study's limitations include a lack of detailed information on the organizations studied, such as their size and location, and the absence of a standardized approach to data collection across the selected papers. Nonetheless, this paper offers a valuable contribution to the field of AI and cybersecurity in the power industry by providing a comprehensive overview of the current state of research and identifying key areas for further investigation.

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Section: ░ 3 Literature Reviewmentioning

confidence: 99%