The ongoing transition towards a more sustainable and resilient electrical system has initiated profound changes within electricity distribution networks. The substantial increase in renewable energy installations, electric vehicle adoption, and energy consumption, underscoring the urgent need for a comprehensive grid reinforcement strategy. This PhD thesis advocates a holistic perspective to facilitate the transition of distribution networks towards the smart grids. Central to this approach is the effective utilisation of network management tools, which harness the full potential of existing flexibility resources. Implementing intelligent strategies begins with the pivotal digitalisation step, incorporating real-time SCADA systems, Internet of Things devices, and distributed sensors. The thesis delves into innovative algorithms within this framework, including machine learning-based optimisation and forecasting models, transformer’s ageing prediction and energy district management, validating the tools within real case studies. The thesis further explores three key application areas, illustrating how flexibility services can integrate into distribution grids in the context of electric mobility, hydrogen infrastructure, and energy communities. The transition towards network modernisation is complex, necessitating a collaborative effort from various stakeholders, including distribution system operators, aggregators, energy retailers, end-users, energy communities, researchers, and legislators. A transversal approach and incremental steps are essential to overcome the existing barriers and provide environmentally friendly services. Ultimately, these efforts aim to foster a sustainable energy future, where distribution networks are the cornerstone of a resilient and eco-conscious electrical system.

Enhancing energy flexibility: resources integration in smart grids. A transversal approach for the ASM Terni power network

BUCARELLI, MARCO ANTONIO
2024

Abstract

The ongoing transition towards a more sustainable and resilient electrical system has initiated profound changes within electricity distribution networks. The substantial increase in renewable energy installations, electric vehicle adoption, and energy consumption, underscoring the urgent need for a comprehensive grid reinforcement strategy. This PhD thesis advocates a holistic perspective to facilitate the transition of distribution networks towards the smart grids. Central to this approach is the effective utilisation of network management tools, which harness the full potential of existing flexibility resources. Implementing intelligent strategies begins with the pivotal digitalisation step, incorporating real-time SCADA systems, Internet of Things devices, and distributed sensors. The thesis delves into innovative algorithms within this framework, including machine learning-based optimisation and forecasting models, transformer’s ageing prediction and energy district management, validating the tools within real case studies. The thesis further explores three key application areas, illustrating how flexibility services can integrate into distribution grids in the context of electric mobility, hydrogen infrastructure, and energy communities. The transition towards network modernisation is complex, necessitating a collaborative effort from various stakeholders, including distribution system operators, aggregators, energy retailers, end-users, energy communities, researchers, and legislators. A transversal approach and incremental steps are essential to overcome the existing barriers and provide environmentally friendly services. Ultimately, these efforts aim to foster a sustainable energy future, where distribution networks are the cornerstone of a resilient and eco-conscious electrical system.
6-mar-2024
Inglese
GERI, Alberto
MARTIRANO, Luigi
Università degli Studi di Roma "La Sapienza"
221
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/182532
Il codice NBN di questa tesi è URN:NBN:IT:UNIROMA1-182532