@inproceedings{Sha2025,
Author = {Sharaf Alsharif},
Title = {Aggregation and Disaggregation of Power Flexibility to Support Reinforcement Learning in Large-Scale Grid Management},

Year = {2025},
Month = {09},
Series = {the 14th DACH+ Conference on Energy Informatics 2025},
Booktitle = {ACM SIGEnergy Energy Informatics Review, September Issue 3 2025},
Url = {https://energy.acm.org/eir/category/2025/eir-sep-3-2025/},
type = {inproceedings},
Abstract = {Efficient coordination of power flexibility is vital for the secure operationof modern power systems, especially in Active Distribution Grids (ADGs). As the number of Flexibility-Providing Units (FPUs) grows, managing flex-ibility becomes increasingly complex, particularly under time-dependentconstraints like battery state of charge. Reinforcement Learning (RL) offersa model-free solution for real-time grid control, but faces challenges such asactuator conflicts and performance degradation due to changing marginaldistributions. This work proposes a data-driven approach for aggregatingflexibility into non-conflicting interfaces suitable for RL decision-making. Se-lected set points are then disaggregated to minimize their long-term impact,enhancing the stability and effectiveness of RL-based control in large-scaleADGs.}
}
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