Future Smart Grids Control and Optimization: A Reinforcement Learning Tool for Optimal Operation Planning

Federico Rossi, Giancarlo Storti Gajani, Samuele Grillo and Giambattista Gruosso ()
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Federico Rossi: Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133 Milano, Italy
Giancarlo Storti Gajani: Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133 Milano, Italy
Samuele Grillo: Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133 Milano, Italy
Giambattista Gruosso: Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133 Milano, Italy

Energies, 2025, vol. 18, issue 10, 1-19

Abstract: The smart grids of the future present innovative opportunities for data exchange and real-time operations management. In this context, it is crucial to integrate technological advancements with innovative planning algorithms, particularly those based on artificial intelligence (AI). AI methods offer powerful tools for planning electrical systems, including electrical distribution networks. This study presents a methodology based on reinforcement learning (RL) for evaluating optimal power flow with respect to various cost functions. Additionally, it addresses the control of dynamic constraints, such as voltage fluctuations at network nodes. A key insight is the use of historical real-world data to train the model, enabling its application in real-time scenarios. The algorithms were validated through simulations conducted on the IEEE 118-bus system, which included five case studies. Real datasets were used for both training and testing to enhance the algorithm’s practical relevance. The developed tool is versatile and applicable to power networks of varying sizes and load characteristics. Furthermore, the potential of RL for real-time applications was assessed, demonstrating its adaptability to online grid operations. This research represents a significant advancement in leveraging machine learning to improve the efficiency and stability of modern electrical grids.

Keywords: smart grid planning; optimal power flow; reinforcement learning; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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