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The paper presents a reinforcement learning framework for designing solar PV adoption policies under uncertainty, integrating RL with a stochastic agent‑based model to simulate yearly adoption over a 16‑year horizon. Policymakers can choose annual incentives such as grants, subsidised loans, and feed‑in tariffs, and the study evaluates three RL algorithms—PPO, SAC, and TD3—within a scalarised reward framework that balances adoption gains against costs. Results show clear trade‑off patterns, with TD3 yielding the highest adoption at higher cost, PPO achieving the lowest cost with fewer adopters, and a balanced PPO policy offering a middle ground, all outperforming static baseline policies.
arXiv:2606. 00811v1 Announce Type: cross Abstract: Data centers now account for 4.
arXiv:2607. 03176v1 Announce Type: new Abstract: Understanding how households consume electricity in response to socioeconomic and climatic drivers is important for decision-makers designing energy policies in a changing climate and under geopolitical tensions.
The paper introduces a hybrid framework that uses large language models (LLMs) to assist in designing behavioural and scenario specifications for an agent‑based model of solar photovoltaic adoption by Irish dairy farms. It integrates bounded behavioural rubrics—conservative, balanced, and optimistic—with structured scenario specifications into a calibrated ABM, preserving the original techno‑economic adoption mechanism while adding controlled behavioural modulation and scenario‑driven uncertainty analysis. Experiments across various policy settings and Monte Carlo simulations show stable, economically plausible outcomes, with up to a 13% increase in behavioural adoption compared to a logistic baseline, without causing unrealistic saturation dynamics.
arXiv:2411. 12193v4 Announce Type: replace-cross Abstract: The rapid growth of distributed energy resources (DERs) presents both opportunities and operational challenges for electric grid management.
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.