Microgrid Control and Optimization

Neueste Paper

• Neural Controlled Differential Equations for EMT-Level Surrogate Modeling of Grid-Forming Inverters

  Jiagang Qu, Yong Tao, Dan Wang, Enyi Li, Jingjing Qi, Ding Wang · 21. Juli 2026

  The application of artificial intelligence methods in power electronic converter modeling is becoming increasingly widespread, but existing applications still face many challenges, such as difficulties in multi-time-scale hybrid analysis and the lack of physics-aware evaluation criteria and constrai…

• A Quantum-Assisted Agentic Distributed Artificial Intelligence Framework for Deadline-Bounded Orchestration of Hybrid Renewable Microgrids

  Iacovos I. Ioannou, Saher Javaid, Minella Bezha, Yasuo Tan, Naoto Nagaoka, Vasos Vassiliou · 23. Juni 2026

  The real-time orchestration of microgrids that combine fluctuating renewable sources, dispatchable units, storage and curtailable consumers requires the repeated solution of combinatorial dispatch and coalition formation problems under hard control deadlines. In this paper, a quantum-assisted agenti…

• Physics-Aware Heterogeneous GNN Architecture for Real-Time BESS Optimization in Unbalanced Distribution Systems

  Aoxiang Ma, Salah Ghamizi, Jun Cao, Pedro Rodriguez · 11. Dezember 2025

  Battery energy storage systems (BESS) have become increasingly vital in three-phase unbalanced distribution grids for maintaining voltage stability and enabling optimal dispatch. However, existing deep learning approaches often lack explicit three-phase representation, making it difficult to accurat…

• Shielded Controller Units for RL with Operational Constraints Applied to Remote Microgrids

  Hadi Nekoei, Alexandre Blondin Mass\'e, Rachid Hassani, Sarath Chandar, Vincent Mai · 2. Dezember 2025

  Reinforcement learning (RL) is a powerful framework for optimizing decision-making in complex systems under uncertainty, an essential challenge in real-world settings, particularly in the context of the energy transition. A representative example is remote microgrids that supply power to communities…

• EnergyTwin: A Multi-Agent System for Simulating and Coordinating Energy Microgrids

  Jakub Muszy\'nski, Ignacy Walu\.zenicz, Patryk Zan, Zofia Wrona, Maria Ganzha, Marcin Paprzycki, Costin B\u{a}dic\u{a} · 26. November 2025

  Microgrids are deployed to reduce purchased grid energy, limit exposure to volatile tariffs, and ensure service continuity during disturbances. This requires coordinating heterogeneous distributed energy resources across multiple time scales and under variable conditions. Among existing tools, typic…

• A New Error Temporal Difference Algorithm for Deep Reinforcement Learning in Microgrid Optimization

  Fulong Yao, Wanqing Zhao, Matthew Forshaw · 25. November 2025

  Predictive control approaches based on deep reinforcement learning (DRL) have gained significant attention in microgrid energy optimization. However, existing research often overlooks the issue of uncertainty stemming from imperfect prediction models, which can lead to suboptimal control strategies.…