Modeling and Optimal Management of Energy Storage Technologies in Smart Energy Systems

The environmental crisis of CO2 emissions is rising concern and driving research and applications towards non-emitting technologies. The energy sector is one of the largest contributors to global carbon emissions and worldwide energy demand is expected to continue growing. Renewable Energy Sources (RESs) offer enormous potential to decarbonise the environment as they do not contribute to Green-House Gases (GHGs) or other polluting emissions. However, RESs rely on natural resources, such as sunlight, wind, water, geothermal, which are generally unpredictable and dependent on weather, season, and year. As the world strives for sustainable and efficient energy solutions, the growing adoption of RESs raises concerns about the inherent variability in electricity generation, given that RESs are not easily controllable. This means that the power system is becoming increasingly complex and requires innovative solutions to enhance flexibility and enable the effective utilization of RESs. In this context, Energy Storage Systems (ESSs) are emerging as an effective solution for managing the unpredictability of RESs. Indeed, using an ESS, renewable energy can be stored using a variety of techniques and then used in a consistent and controlled manner when needed. This doctoral thesis presents an in-depth investigation into the modelling and optimal management of energy storage technologies within the framework of Smart Energy Systems (SESs) where ESSs are exploited to efficiently use RESs and/or provide support to the control of the power system. The thesis begins with comprehensive review on existing literature on energy storage technologies, assessing their potentials and critical aspects to build appropriate models. The thesis then delves into the optimization and management of energy storage technologies within SESs. A range of control strategies and optimization algorithms are developed to maximize the benefits derived from energy storage resources. Factors such as electricity prices, renewable energy generation profiles, demand patterns, and grid constraints are taken into account to deliver optimal dispatch and scheduling strategies. These strategies aim to achieve objectives such as optimal economic operation, renewable energy integration, and grid stability enhancement. Moreover, the thesis explores the integration of Energy Storage Systems (ESSs) with other components of SESs, such as RESs and different energy storage technologies, in a multi-vector energy framework. The synergistic effects of these integrated systems are analyzed to highlight the potential for improved operational efficiency and enhanced reliability of the overall energy system. To validate the proposed models and management strategies, extensive case studies and simulations are conducted using real-world data and scenarios. These investigations provide quantitative assessments of the performance and economic viability of energy storage technologies in SESs. The findings of this doctoral thesis hopefully contribute to the advancement of knowledge in the field of energy storage technologies and their optimal management within SESs. The research outcomes may have practical implications for policy makers, system operators, and researchers working towards the development of sustainable and resilient energy infrastructures. Ultimately, this work aims to contribute to pave the way for a greener and more efficient future by harnessing the potential of energy storage technologies within SESs. In Chapter 1, the general features of ESSs and SESs are introduced. The chapter provides a general overview of existing energy storage technologies and their adoption in the context of SESs, focusing on methodologies and tools to optimally manage these technologies. In Chapter 2, an extensive review of ESSs modeling is given, focusing on electrochemical energy storage technologies, such as secondary batteries and redox flow batteries, and on Power-to-Hydrogen. Innovative strategy to address the Remaining Useful Life (RUL) of Lithium-ion Batteries (LiBs) developed by the author during his Ph.D., in the context of the “Analysis and Test of Models for Battery Degradation Estimation” project is presented. In Chapter 3, the concept of Renewable Energy Communities (RECs) in the European and Italian framework is addressed. Different solutions that have been developed by the author during his Ph.D. are presented. In Chapter 4, the matter of multi-energy hub is addressed. In particular, the aspects of future Italian seaports are investigated and dedicated smart solutions designed by the author during his Ph.D. in the context of the “Ship2Grid” project are presented. In Chapter 5, the idea of Community Batteries (CBs) is explored, and appropriate case studies based on real data from Australia are developed to investigate the goodness of this concept. To complete the thesis, the Conclusions are presented together with some final remarks. Lastly, the lists of author’s publications, of projects in which the author was involved, of collaborations and attended courses by the author, and of references end the dissertation. This thesis represents the completion of the doctoral research carried out under the careful supervision of Professor Stefano Massucco of the University of Genova, Professor Francesco Conte of the Campus Bio-Medico University of Rome, and Professor Pierluigi Mancarella of the University of Melbourne, to whom I express my deepest gratitude.