LoaDiff is a diffusion-based generative model that produces year-long, sub-hourly smart‑meter electricity consumption time series. It can be conditioned on static household attributes like appliance ownership and dynamic factors such as calendar dates and outdoor temperature. Evaluations on three residential datasets show that LoaDiff generates realistic, diverse load profiles, limits memorization, retains useful information for downstream tasks, and responds coherently to conditioning changes.
By Mariia Baranova, Adrien Petralia, Etienne Le Naour, Nathan Etourneau, Guillaume Hofmann, Themis Palpanas
arXiv:2606. 02049v1 Announce Type: new Abstract: The increasing integration of renewable energy sources into power systems, particularly in buildings equipped with photovoltaic (PV) panels and energy storage systems, introduces significant complexity in energy systems.
By Hallah Shahid Butt, Qiong Huang, G\"okhan Demirel, Kevin F\"orderer, Erfan Tajalli-Ardekani, Simnon Waczowicz, Luigi Spatafora, Veit Hagenmeyer, Benjamin Sch\"afer
The paper proposes a neural‑network algorithm for contextual electricity pricing, framing the problem as a Stackelberg game and using a mean‑field solution to learn constrained mappings from contextual features (e.g., weather, sunrise/sunset, day‑of‑week) to feasible price signals. The method is validated through simulations of the energy grid in several U.S. cities, demonstrating that incorporating rich contextual information can significantly enhance the value of demand‑response programs.
By Jing Shang, Mohammad Mehrabi, Xinyang Zhou, Mahmoud Saleh, Andrey Bernstein, Stefan Wager
OpenHail is an open-source Gymnasium environment designed for controlling electric ride‑hailing fleets. It offers a fixed‑size observation–action interface that handles request assignment, repositioning, and charging, while its event‑driven simulator models pickup deadlines, vehicle job queues, battery dynamics, and finite‑capacity charging facilities with FIFO queues. The environment supports various decision‑epoch mechanisms—event‑driven, periodic, hybrid, and policy‑requested—allowing flexible policy interactions within a unified operational model, and includes tools for evaluation, metrics, and baseline policies.
By Tommaso Schettini, Nicholas D. Kullman, Jorge E. Mendoza
PowerZooJax is a JAX-based benchmark suite designed for reinforcement learning in power system operation. It offers five constrained Markov decision process tasks covering generation, transmission, distribution, distributed energy resources, and data center microgrids. By implementing power flow, economic dispatch, market clearing, and device dynamics as JAX computation graphs, the entire training and evaluation loop runs on the GPU, yielding significant speedups over CPU-based simulations and enabling standardized evaluation of policy returns, safety violations, and out-of-distribution stress conditions.
By Zhanhua Pan, Xiao Liu, Zhilong Cao, Jianhong Wang, Dawei Qiu
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.
By Yang Fu, Peng Qin, Liming Chen, Zihao Zhang, Hao Yu, Yifei Wang
The paper introduces a Large Language Model–enhanced Multi-Agent Reinforcement Learning framework for optimizing electric vehicle charging, station profitability, and grid stability in public charging systems. By using an LLM to select interpretable features from IoT data and dynamically balance conflicting objectives, the approach unifies grid, EV, and station optimization in a single loop. Experiments show the method outperforms existing baselines, improving market efficiency and cutting training time by more than 70%.
By Yang Zhang, Lindong Xie, Chongyu Wang, Gaojunjie Li, Siqi Bu, Edward Chung
arXiv:2607. 03140v1 Announce Type: new Abstract: Industrial automation increasingly demands control strategies that balance operational performance with strict energy efficiency requirements.
By Georg Sch\"afer, Jakob Rehrl, Stefan Huber, Simon Hirlaender
arXiv:2511. 05420v2 Announce Type: replace-cross Abstract: As smart grids evolve to meet growing energy demands and modern operational challenges, the ability to accurately predict faults becomes increasingly critical.
By Emad Efatinasab, Nahal Azadi, Davide Dalle Pezze, Gian Antonio Susto, Chuadhry Mujeeb Ahmed, Mirco Rampazzo
arXiv:2606. 30316v1 Announce Type: new Abstract: This paper studies Reinforcement Learning as an online controller for curtailment-aware workload shifting in wind-turbine-integrated high-performance computing (HPC) data centers.
By Jan Stenner, Alexander Kilian, Sebastian Peitz, Hermann de Meer
arXiv:2606. 31347v1 Announce Type: new Abstract: The electrification of transportation through electric vehicles introduces new challenges for power grid management, such as increased peak demand, voltage fluctuations, line overloads, and the integration of variable renewable energy sources.
By Xavier Rate, Eloann Le Guern, Rapha\"el F\'eraud, Fatma Salem, Melissa Chiknoun, Eymeric Giabicani, Mehdi Feki, Patrick Maill\'e, Guy Camilleri, Anne Blavette, Hamid Benhamed
arXiv:2608.21858v1 Announce Type: cross
Abstract: The rapid evolution of energy structures has positioned microgrids as pivotal components of next-generation power systems, offering enhanced resilien...
By Xu Yang, Chenhui Lin, Haotian Liu, Kaihang Deng, Yunhe Li, Wenchuan Wu