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.
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.
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arXiv AI
Jun 26
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.
arXiv AI
Aug 14
From Caveman to Expert Analyst: Energy Consumption of Variable LLM Tasks
arXiv:2608. 12350v1 Announce Type: cross Abstract: The energy demand growth and environmental impacts of artificial intelligence (AI) have generated substantial interest in supplying sufficient low-cost electricity for AI-driven data center development.
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 18
Analysing drivers and interdependencies in European electricity markets using XAI
arXiv:2606. 19118v1 Announce Type: new Abstract: Electricity markets are inherently complex systems characterised by strong nonlinearities, high-dimensional interactions, and increasing interdependence across regions.
arXiv Machine Learning
Sep 2
Foundation models for electricity price forecasting and battery arbitrage: Can they replace market-specific forecasting models?
The study evaluates nine foundation model variants against two leading electricity price forecasting benchmarks across Germany, Poland, and Spain for 2021‑2025. Only the TabPFN models consistently outperform the benchmarks in both point and probabilistic accuracy, yet their economic advantage varies with bidding strategy and risk tolerance. The results indicate that foundation models cannot universally replace market‑specific models; their usefulness depends on the chosen architecture and the particular decision problem.
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 AI
Sep 4
Towards Affordable Energy: A Gymnasium Environment for Electric Utility Demand-Response Programs
The paper introduces DR‑Gym, an open‑source, Gymnasium‑compatible environment that simulates electric utility demand‑response programs at the market level. It uses a regime‑switching wholesale price model calibrated to real extreme events and physics‑based building demand profiles, providing a rich observational space and a configurable multi‑objective reward function for reinforcement learning. Baseline strategies and data snapshots demonstrate the simulator’s realism and learnability.
arXiv Machine Learning
Aug 24
Forecasting with an N-dimensional Langevin Equation and a Neural-Ordinary Differential Equation
arXiv:2405.07359v2 Announce Type: replace Abstract: Accurate prediction of electricity day-ahead prices is essential in competitive electricity markets. Although stationary electricity-price forecast...
arXiv Machine Learning
Jul 8
Joint Energy Management and Coordinated AIGC Workload Scheduling for Distributed Data Centers: A Diffusion-Aided Reward Shaping Approach
arXiv:2605. 02965v2 Announce Type: replace Abstract: Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content, giving rise to rapidly growing computational workloads in cloud data centers.
arXiv AI
Jul 22
Market Strategy Evaluation for Prosumers in Local Electricity Markets
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.
arXiv AI
Aug 24
AI-driven Prices for Externalities and Sustainability in Production Markets
arXiv:2106.06060v4 Announce Type: replace-cross Abstract: Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as th...