Scenario-based model predictive control (SBMPC) is a variant of model predictive control (MPC) that explicitly accounts for uncertainty by optimizing control actions over multiple predicted scenarios. However, its computational complexity increases rapidly with the number of scenarios and prediction horizon, limiting is applicability to real-time planning and control.
arXiv:2505. 05203v3 Announce Type: replace-cross Abstract: With the increasing penetration of renewable energy and inverter-based resources, traditional physics-based power-system operation faces growing challenges in maintaining economic efficiency, security, and robustness.
By Wangkun Xu, Zhongda Chu, Fei Teng
arXiv:2605. 04568v3 Announce Type: replace-cross Abstract: State-of-the-art model-based Reinforcement Learning (RL) approaches either use gradient-free, population-based methods for planning, learned policy networks, or a combination of policy networks and planning.
By Jonathan Spieler, Sven Behnke
arXiv:2606. 08993v1 Announce Type: new Abstract: We propose LEAF, a learning-enabled ADMM framework for accelerated convex optimization.
By Binh Nguyen, Trinh Tran, Truong X. Nghiem
This study evaluates four control strategies—rule-based, model predictive control (MPC), reinforcement learning without forecasts (RL‑NF), and reinforcement learning with forecasts (RL‑F)—for a renewable‑powered hydrogen supply chain. Using a unified, physically realistic simulation that includes electrolyzer constraints, storage dynamics, and grid limits, the authors find that MPC delivers the best economic performance by leveraging short‑term forecasts, while RL‑NF performs robustly without future information. RL‑F does not consistently outperform RL‑NF, indicating that forecast uncertainty and added state complexity can hinder forecast‑augmented learning.
By Mahammad Valiyev
arXiv:2604. 21030v2 Announce Type: replace-cross Abstract: The integration of Model Predictive Control (MPC) and Reinforcement Learning (RL) has emerged as a promising paradigm for constrained decision-making and adaptive control.
By Mohsen Jalaeian Farimani, Roya Khalili Amirabadi, Davoud Nikkhouy, Malihe Abdolbaghi, Mahshad Rastegarmoghaddam, Shima Samadzadeh, Mahdi Ghane