改造电网:人工智能在推进智能、可持续和安全能源系统中的作用

Q2 Energy
T. A. Rajaperumal, C. Christopher Columbus
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引用次数: 0

摘要

电网从早期的集中式结构到今天先进的“智能电网”的演变反映了重大的技术进步。早期的电网是为从大型发电厂到消费者的简单电力输送而设计的,在效率、可靠性和可扩展性方面面临着挑战。随着时间的推移,电网已经转变为一个分散的网络,由创新技术驱动,特别是人工智能(AI)。通过实现实时数据分析、预测性维护、需求响应优化和自动故障检测,人工智能在提高效率、安全性和弹性方面发挥了重要作用,从而提高了整体运营效率。本文考察了电网的演变,追溯了其从早期的限制到目前智能电网为应对这些挑战所采用的方法的转变。目前的智能电网利用人工智能优化能源管理,预测故障,并无缝集成电动汽车(ev),减少传输损耗并提高性能。然而,这些进步并非没有限制。目前的电网仍然容易受到网络攻击,因此有必要为未来的电网采用更强大的方法和先进的技术。展望未来,数字孪生(DT)模型、能源互联网(IoE)和分散式电网管理等新兴技术将重新定义电网架构。这些先进的技术能够实现实时仿真、自适应控制和增强的人机协作,支持动态能源分配和主动风险管理。将人工智能与先进的储能、可再生资源和自适应访问控制机制相结合,将确保未来电网具有弹性、可持续性,并能应对不断增长的能源需求。本研究强调了人工智能在应对早期电网挑战、增强当前智能电网能力以及塑造符合未来需求的安全、高效和自适应下一代电网方面的变革性作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transforming the electrical grid: the role of AI in advancing smart, sustainable, and secure energy systems

The evolution of the electrical grid from its early centralized structure to today’s advanced “smart grid” reflects significant technological progress. Early grids, designed for simple power delivery from large plants to consumers, faced challenges in efficiency, reliability, and scalability. Over time, the grid has transformed into a decentralized network driven by innovative technologies, particularly artificial intelligence (AI). AI has become instrumental in enhancing efficiency, security, and resilience by enabling real-time data analysis, predictive maintenance, demand-response optimization, and automated fault detection, thereby improving overall operational efficiency. This paper examines the evolution of the electrical grid, tracing its transition from early limitations to the methodologies adopted in present smart grids for addressing those challenges. Current smart grids leverage AI to optimize energy management, predict faults, and seamlessly integrate electric vehicles (EVs), reducing transmission losses and improving performance. However, these advancements are not without limitations. Present grids remain vulnerable to cyberattacks, necessitating the adoption of more robust methodologies and advanced technologies for future grids. Looking forward, emerging technologies such as Digital Twin (DT) models, the Internet of Energy (IoE), and decentralized grid management are set to redefine grid architectures. These advanced technologies enable real-time simulations, adaptive control, and enhanced human–machine collaboration, supporting dynamic energy distribution and proactive risk management. Integrating AI with advanced energy storage, renewable resources, and adaptive access control mechanisms will ensure future grids are resilient, sustainable, and responsive to growing energy demands. This study emphasizes AI’s transformative role in addressing the challenges of the early grid, enhancing the capabilities of the present smart grid, and shaping a secure, efficient, and adaptive next-generation grid aligned with future needs.

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来源期刊
Energy Informatics
Energy Informatics Computer Science-Computer Networks and Communications
CiteScore
5.50
自引率
0.00%
发文量
34
审稿时长
5 weeks
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