arXiv AI
Jun 2
Certificates without Electrons? Theory and Evidence on Impacts from AI-Driven Power Demand
arXiv:2606. 00811v1 Announce Type: cross Abstract: Data centers now account for 4.
Power Couple? AI Growth and Renewable Energy Investment
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.
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arXiv AI
Aug 14
EU-ETS under attack? The impact of carbon price suppression on the decarbonization of the power sector
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 Machine Learning
Sep 15
A Game-Theoretic Framework for Incentive-Compatible AI training Under Renewable-Energy Constraints
arXiv:2609.15389v1 Announce Type: cross Abstract: As artificial intelligence systems increasingly rely on distributed and collaborative training, the energy footprint of these processes becomes a sha...
arXiv AI
Jun 9
Powering the Future of AI: Navigating the Trade-offs for Europe's Energy Transition and Net-Zero Goals
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.
arXiv AI
Jun 16
Green AI Carbon Optimizer: Carbon-Efficient Training Location Recommendation and Global AI Energy Demand Forecasting
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.
arXiv AI
Sep 4
Artificial Intelligence for Energy Optimization in Data Centers
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 AI
Jul 14
Comparing Socially-Equitable Renewable Energy Budget Allocation MDP Policies in Mature and Emerging Economies
arXiv:2607. 10201v1 Announce Type: cross Abstract: Equitable renewable-energy planning is a sequential decision problem, but the decision variables available to a public planner differ sharply between mature and emerging economies.
arXiv AI
Sep 2
A Checklist to assess the energy and carbon impacts of ML/AI applications in Earth System Modeling
arXiv:2609.00847v1 Announce Type: cross Abstract: As machine learning and artificial intelligence find their way into nearly every aspect of climate, weather, and Earth system modeling, it is worth p...
arXiv Machine Learning
Sep 22
Cost-Aware Reinforcement Learning with Action Masking and Projection for Battery Energy Storage Dispatch under Suppressed-Spread Market Shifts
arXiv:2609.23590v1 Announce Type: new Abstract: Battery energy storage system (BESS) dispatch must preserve operational feasibility while declining price spreads reduce the margin available to pay fo...
arXiv AI
Sep 10
AI for AI: Optimizing Additional Infrastructure Build-out to Power Artificial Intelligence Data Centers
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 AI
Jul 22
CWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farms
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 Computation and Language
Sep 3
Grounded, Compute-Efficient LLM Policy Agents for Energy-Poverty Equity in Physically-Constrained Peer-to-Peer Energy Markets
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."