arXiv:2608. 16238v1 Announce Type: new Abstract: The increasing share of renewable energy in power systems creates a need for fast-response and flexible resources to maintain system stability.
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:2606. 16051v1 Announce Type: cross Abstract: Residential battery energy storage systems (BESS) are increasingly deployed alongside photovoltaic (PV) generation to reduce household energy costs under volatile time-of-use (TOU) tariffs.
This study evaluates how flexible electric vehicle charging can improve energy efficiency and grid stability. Using real‑world data from public charging stations in Prague, the authors analyze individual and aggregated charging sessions to show that optimizing charging times reduces energy waste and grid imbalances. By aligning EV charging with periods of lower demand and higher renewable generation, they demonstrate a significant improvement in energy efficiency and a reduced need for costly system support.
arXiv:2606. 26400v1 Announce Type: new Abstract: Agentic systems are changing how complex operational tasks are coordinated, introducing a new paradigm for connecting heterogeneous data sources and automating processes.
arXiv:2608. 15396v1 Announce Type: new Abstract: Battery energy storage systems (BESS) are increasingly used in distribution networks for voltage regulation and demand response, which increases the volume and complexity of operational telemetry available to grid operators.
arXiv:2606. 13407v1 Announce Type: new Abstract: Renewable energy is essential for meeting future energy demands; however, solar energy generation, which occurs only during daylight hours often does not align with household consumption patterns.
CityLearn v3 is a configurable simulation and evaluation framework designed for realistic control studies of renewable energy communities (RECs). It models dynamic participation, equipment availability, service deadlines, and data quality, allowing for flexible-load deadlines, demand-response requests, local energy sharing, and failure scenarios within a single environment. The framework records controller inputs, distinguishes requested actions from applied ones, and provides reference controllers, performance indicators, and trajectory exports for comprehensive comparisons across communities.
By focusing on electrolytes, MIT scientists are making sodium-metal batteries a more practical energy storage option.
The paper presents a two-part workflow for analyzing smart‑meter data to uncover patterns of residential co‑adoption of photovoltaic (PV) systems and electric vehicles (EVs). First, dynamic time warping k‑means clustering identifies distinct daily import/export archetypes for PV‑only, EV‑only, co‑adopters, and neither groups, revealing a midday‑centered export pattern for many co‑adopters. Second, a bidirectional LSTM model trained on 21‑day windows achieves high detection performance (AUROC 0.991, macro‑F1 0.906) for PV/EV activity, outperforming tabular baselines and remaining robust across labeling rules and temporal splits.
The paper compares rule‑based and reinforcement‑learning (RL) pricing mechanisms for peer‑to‑peer electricity trading in residential photovoltaic communities. Rule‑based benchmarks—bill‑sharing, mid‑market rate, and supply‑demand‑ratio pricing—outperform the best RL policy in a PV‑only setup, while RL policies achieve higher community savings when battery storage is added. Across both configurations, SDR‑shaped pricing outperforms multiplier‑based parameterization, but benefit distribution remains heterogeneous among households.
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.