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.
By Pablo Benalcazar, Maciej Kalka, Wilian Guam\'an, Jacek Kami\'nski
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.
By Chunyang Zhao, Stoyan Trenchev, Shi You, Chresten Tr{\ae}holt
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.
By J\^onatas Augusto Manzolli, Ali Eslami, Luis Miranda-Moreno, Jiangbo Yu
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."
By Kunal Jadhav, Siddhesh More
The increasing share of renewable energy in power systems creates a need for fast-response and flexible resources to maintain system stability. With the expansion of electricity markets and ancillary...
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 Tiago Fonseca, Luis Lino Ferreira, Armando Sousa, Ava Mohammadi, Zoltan Nagy