Reinforcement learning has shown strong potential for sequential control problems, providing an effective framework for adaptive policy derivation in complex and dynamic environments. However, standalone RL controllers may behave unstably or too aggressively, particularly in safety-critical applications where constrain...
P. Michailidis, Iakovos T. Michailidis, F. Minelli et al.· Engineer· 0 citations
Decarbonization, electrification, and digitalization are reshaping European electricity systems by increasing the role of distributed energy resources in flexibility provision, local resilience, and market participation. However, the value of distributed resources is not defined by technology characteristics alone. It...
Shoaib Ahmed, Jamshed Ahmed Ansari, Mahwish Memon et al.· Frontiers in Energy Research· 1 citation
A comprehensive review of RL-based control applications across RES-integrated energy domains, including power grids, microgrids, and building energy systems, identifies emerging trends and highlights dominant design patterns across power grid, microgrid, and building-level applications.
P. Michailidis, F. Minelli, H. Coban et al.· Infrastructures· 0 citations
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