Energy Management of Hybrid Electrochemical Storage Systems for Decentralized and Sustainable Energy Systems

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  • Author
    Dr. Adriano Valle
  • Level
    Advanced
  • Study time
    ~ 22 minutes
  • Videos
    1
  • Contact
    adriano.pozzessere@uniroma1.it

Module Description

This module explores the crucial role of energy storage in the energy transition. The lesson analyzes the need to decouple renewable energy production from demand, ensure grid stability, and decarbonize the transport sector. It details various storage technologies (battery systems, hydrogen, and supercapacitors), highlighting their specific limitations and strengths regarding power dynamics and energy density. The core of the module focuses on Energy Management Systems (EMS) for Hybrid Energy Storage Systems (HESS), critically comparing Rule-Based (RB), Optimization-Based (OB), and Learning-Based (LB) strategies. Finally, theoretical concepts are applied to real-world case studies—including advanced energy management for the conversion of a hybrid train and day-ahead strategies to mitigate grid imbalance risks—demonstrating how tailored control logic is essential to maximize efficiency, reduce component degradation, and lower operational and capital costs.

 Learning Outcomes

  • Understand the necessity of storage for the energy transition, microgrid stabilization, and the decarbonization of the transport sector. 
  • Identify the specific strengths and limitations of batteries, hydrogen fuel cells, and supercapacitors based on their energy density and dynamic response capabilities. 
  • Distinguish the characteristics, advantages, and trade-offs of different management algorithms, evaluating their performance, computational cost, flexibility, and ease of implementation. 
  • Understand the technical alignment required between final application constraints, the physical characteristics of the storage system, and the chosen control logic.
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Dr. Adriano Valle

Adriano Valle is a PostDoctoral Researcher at Sapienza University of Rome, specializing in the modeling, control, and optimization of sustainable energy systems. His research focuses on developing advanced Energy Management Systems (EMS) for Hybrid Electrochemical Storage architectures, with a specific emphasis on the technical and economic integration of Hydrogen fuel cells and Battery systems.
Throughout his academic career, including a PhD in Energy and Environment, he has gained extensive experience in simulating complex power profiles for Heavy Duty transport, such as the hydrogen conversion of non-electrified trainlines, and optimizing stationary Microgrid operations. During his international research tenure at INESC TEC in Porto, he further specialized in data-driven strategies, applying Reinforcement Learning frameworks to mitigate grid imbalances and enhance the flexibility of renewable-integrated systems.
His work aims to bridge the gap between storage physics and control engineering, providing the methodological tools necessary to design efficient, resilient, and sustainable energy solutions for the global transition.