arXiv:2606. 00811v1 Announce Type: cross Abstract: Data centers now account for 4.
arXiv:2603. 26678v2 Announce Type: replace-cross Abstract: AI and renewable energy are increasingly framed as a "power couple," on the premise that surging AI demand will accelerate clean-energy investment, yet concerns persist that AI will entrench fossil-fuel carbon lock-in.
arXiv:2606. 14707v1 Announce Type: cross Abstract: AI training and deployment consume substantial electricity, but carbon outcomes remain weakly integrated into routine model development decisions.
The paper presents a framework that links data‑center electricity demand growth, available generation capacity, and market‑clearing prices to explain rising electricity costs. It first uses a deterministic model to show how varying demand and supply growth estimates influence prices, then extends to stochastic processes that generate probabilistic distributions for supply, demand, and prices. Finally, it formulates generation expansion as a stochastic control problem, illustrating how uncertainties in load forecasts, development risks, and potential overbuilding can dampen investment incentives needed to stabilize prices.
arXiv:2605. 23348v2 Announce Type: replace-cross Abstract: AI power demand is growing at an unprecedented rate while power grids are often ailing and struggle to keep up.
arXiv:2608. 12363v1 Announce Type: cross Abstract: European countries are debating policies to mitigate the increased energy costs caused by renewed geopolitical tensions, while pursuing decarbonization and electrification.
arXiv:2608. 12915v1 Announce Type: cross Abstract: The rapid growth of LLM inference is shifting sustainability concerns from one-time training to continuous serving, where infrastructure decisions shape energy use, carbon emissions, water consumption, and service quality.
The paper reviews 194 papers on using artificial intelligence to optimize data center energy use, coding 63 of them. It finds that most control studies validate only in simulation, none consider water withdrawal or embodied carbon, and savings estimates overlap across methods, preventing ranking. The authors propose CLEAR‑DC, a framework that links control and workload demand through elasticity, reports net benefits, and records energy, carbon, water, embodied share, and validation venue.
arXiv:2608.23796v1 Announce Type: cross Abstract: Problem definition: Solar electricity generation is a strategic component of energy portfolios designed to meet growing demand and reduce carbon emis...
arXiv:2606. 09617v1 Announce Type: cross Abstract: The rapid expansion of AI globally has led to the proliferation of energy-intensive hyperscale data centres (DCs), making them as a structurally challenging component in power system planning and operation.
The paper introduces EqGrid, a closed‑loop simulation that uses a low‑frequency, open‑weight LLM policy agent to set price, carbon limits, and subsidies for a community of empirically‑grounded household personas, while high‑frequency multi‑agent RL traders clear a continuous double auction on a physically constrained IEEE‑33‑bus grid. It demonstrates that the LLM can reduce energy‑poverty inequality—lowering the Gini of energy burden from 0.351 to 0.305 and mean burden by 28%—without increasing net grid cost, and that a compressed sub‑1B model retains 92–95% of this benefit at dramatically lower inference energy. The study also establishes a compute‑efficiency frontier and a decoupled‑safety design that eliminates grid violations. whyItMatters":"By showing that a lightweight LLM can effectively manage energy markets to reduce poverty and inequality while staying energy‑efficient, the work offers a practical, low‑carbon AI solution for humanitarian energy‑poverty interventions."
arXiv:2606. 10660v1 Announce Type: cross Abstract: AI inference services -- API subscriptions, enterprise chat tools, and SaaS products with embedded AI features -- fall unambiguously within Scope 3 Category 1 under the Corporate Sustainability Reporting Directive (CSRD), which requires disclosure for fiscal years starting January 2024.
arXiv:2607. 18272v1 Announce Type: cross Abstract: Prosumers equipped with distributed generation and flexible loads form autonomous cyber-physical energy systems that control local resources and participate in local energy markets with minimal human intervention.