Tag
Delhi reduced electricity loss from over 50% in 2002 to 5-6% in 2026 through grid modernization and utility reforms, leading to improved reliability and quality of life.
This paper proposes an LLM-enhanced multi-agent reinforcement learning framework to simultaneously optimize electric vehicle charging scheduling, station profitability, and grid stability, using LLMs for feature selection and adaptive weighting, outperforming state-of-the-art methods with reduced training time.
This paper evaluates AlphaZero-inspired reinforcement learning for topological control in power networks, achieving 98.43% survivability and emphasizing the effectiveness of minimalist integration with domain heuristics.
This paper extends AssetOpsBench with a Smart Grid Transformer asset class and introduces ScenarioGeneratorAgent, a pipeline for synthetic industrial-agent scenario generation that achieves an 8x runtime improvement while maintaining scenario quality.
This paper presents a comprehensive benchmark for electrical load forecasting across grid levels, evaluating ten methods and finding that Transformer-based approaches consistently outperform established methods, reducing forecast error by 6.6–10.7%. The standard Transformer achieves superior performance over a novel flexible architecture, and the foundation model Chronos-2 shows competitive zero-shot performance on some datasets.
GM announces that 12 of its EV models now support bidirectional charging, enabling owners to power their homes and sell excess energy back to the grid, though adoption requires costly hardware and utility partnerships.
Nvidia is backing Span's initiative to deploy residential mini-data centers that leverage underutilized home electricity to run distributed AI workloads. The concept aims to bypass grid constraints by placing GPU nodes beside houses, though it remains largely unproven in real-world deployments.