AI-Enabled Smart Grid Optimization for Low-Carbon Energy Systems
Keywords:
Smart Grid Optimization, Artificial Intelligence, Low-Carbon Energy Systems, Socio-Technical Infrastructure, Energy Governance, Sustainability, Grid Resilience.Abstract
The global transition toward low-carbon energy systems necessitates a fundamental paradigm shift in power grid management, moving from centralized, fossil-fuel-dependent infrastructures to decentralized, renewable-rich networks. At the heart of this transformation is the integration of Artificial Intelligence (AI) as a systemic optimizer for smart grid operations. This paper provides an interdisciplinary analysis of the socio-technical complexities involved in deploying AI-enabled smart grids. We investigate the structural trade-offs between system efficiency, operational robustness, and carbon mitigation goals. The research explores the architectural requirements for multi-agent AI systems to manage volatile renewable energy loads and decentralized storage resources while maintaining grid stability. Beyond technical metrics, the discussion encompasses the governance of energy data, the ethical implications of automated demand-side management, and the policy frameworks required to ensure fairness and equity in the energy transition. By synthesizing principles from systems engineering, machine learning, and environmental sociology, this work elucidates how AI can facilitate a resilient and inclusive low-carbon future. We analyze the tensions between algorithmic autonomy and human oversight, proposing a governance-by-design framework that prioritizes transparency and social license. This paper concludes that the successful optimization of low-carbon grids depends not only on the sophistication of the AI models but on the holistic integration of these digital infrastructures within the broader socio-political and ecological landscape.
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