Impact of EU Laws on AI Adoption in Smart Grids: A Review of Regulatory Barriers, Technological Challenges, and Stakeholder Benefits
Abstrak
This scoping review examines the evolving landscape of European Union (EU) legislation, as it pertains to the implementation of artificial intelligence (AI) in smart grid systems. By outlining the current regulatory landscape, including the General Data Protection Regulation (GDPR), the EU Artificial Intelligence Act, the EU Data Act, the EU Data Governance Act, the ePrivacy framework, the Network and Information Systems (NIS2) Directive, the EU Cyber Resilience Act, the EU Network Code on Cybersecurity for the electricity sector, and the EU Cybersecurity Act, it highlights both constraints and opportunities for stakeholders, including energy utilities, technology providers, and end-users. The analysis delves into regulatory barriers such as data protection requirements, algorithmic transparency mandates, and liability concerns that can limit the scope and scale of AI deployment. Technological challenges are also addressed, ranging from the integration of distributed energy resources and real-time data processing to cybersecurity and standardization issues. Despite these challenges, this review emphasizes how compliance with EU laws may ultimately boost consumer trust, promote ethical AI usage, and streamline the roll-out of robust, scalable smart grid solutions. The paper further explores stakeholder benefits, including enhanced grid stability, cost reductions through automation, and improved sustainability targets aligned with the EU’s broader energy and climate strategies. By synthesizing these findings, the review offers insights into policy gaps, technological enablers, and collaborative frameworks critical for accelerating AI-driven innovation in the energy sector, helping stakeholders navigate a complex regulatory environment while reaping its potential rewards.
Penulis (3)
B. Jørgensen
S. Gunasekaran
Z. Ma
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Total Sitasi
- 15×
- Sumber Database
- Semantic Scholar
- DOI
- 10.3390/en18123002
- Akses
- Open Access ✓