ABSTRACT The rapid growth of LLM‐driven artificial intelligence (AI) and data center deployment is imposing a new paradigm of massive and highly dynamic power demand on the grid. Volatility in both computing and renewable energy resources introduces severe challenges to power system operation. Although typical computing workloads are technically flexible, their responsive capacity remains largely untapped in real‐world scenarios. This paper argues that the main barriers to scalable computing–electricity coordination are not solely technical, but arise from weak electricity cost pass‐through, limited cost visibility, and misaligned compute‐side incentives. In leasing and cloud‐service modes, electricity costs of data centres are often bundled into broader service charges, reducing their influence on workload scheduling. Meanwhile, compute‐side actors prioritise GPU utilisation, return on investment, latency, throughput, and service quality over flexible load profiles supporting power‐system operation. Therefore, green operational constraints and new pricing mechanisms that expose carbon or electricity signals to compute‐side decision‐makers are critical to making computing–electricity coordination scalable in practice. Flexible computing loads can support power systems only when technical flexibility is matched with actionable economic, contractual, and operational incentives.
Luo et al. (Tue,) studied this question.