Doctorat
Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/59
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Item SoC estimation for optimal ESS’ energy management(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2025) Zermout, Abdelaziz; Belaidi, Hadjira(Directeur de thèse)Battery energy storage systems have become indispensable to modern civilization, enabling the functionality of numerous advanced technologies, including high-performance smartphones, long-range electric vehicles, and various portable electronic, tools, and backup systems. The continuous advancement of battery technology is a key driver for future innovations. A crucial component of battery systems is the Battery Management System (BMS), which monitors and optimizes various operational parameters, including the State of Charge (SoC). SoC represents the remaining useful battery capacity relative to its total capacity, however it cannot be directly measured and must be estimated through computational techniques instead. While existing estimation methods have significantly improved in terms of accuracy and reliability, they remain challenged by complexity, sensitivity to operating conditions, and dependence on dynamic load behavior. Overcoming these challenges is essential for enhancing the performance and longevity of battery systems in next-generation applications. Our contribution is a novel estimation technique that periodically stimulates the battery with a predefined current profile during charging or discharging to determine its State of Charge (SoC). Since this method is not continuous, it is combined with Coulomb counting for calibration. The results demonstrated the method's efficiency and reliability, effectively overcoming dependency on environmental conditions and dynamic load behavior. Its key advantages include independence from operating conditions and dynamic load behavior, as well as, minimal computational complexity without sacrificing accuracy achieving an error of less than 1%. This ensures high reliability and efficiency with reduced complexityItem Smart-grid supply continuity control(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2025) Kaddour, Djillali; Belaidi, Hadjira(Directeur de thèse)This thesis investigates supply continuity in smart grids, emphasizing system reliability under both gridconnected and islanded conditions. It begins with a structured review of how integrating Distributed Energy Resources (DERs) and Energy Storage Systems (ESS) affects supply reliability, assessed using standard indices: System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (SAIFI), and Customer Average Interruption Duration Index (CAIDI). Building on this review, the thesis proposes an intelligent Energy Management System (EMS) paired with a Diesel Generator–Photovoltaic (DG–PV) synchronization strategy. This system ensures continuous operation in islanded mode, overcoming the limitations of traditional grid-tied setups. The EMS, implemented in MATLAB Simulink, applies advanced methods such as peak shaving and optimized resource sizing, using estimated load data from the Institute of Electrical and Electronic Engineering (IGEE) to manage energy flow and peak demand effectively. Comparative insights from the case study demonstrate that although grid-connected systems enable bidirectional power flow and benefit from net metering, they remain vulnerable to grid disturbances. In contrast, islanded mode offers full control and supervision over local resources and improves overall system reliability. To support the preference for islanded operation, the thesis also presents a proof-ofconcept implementation of an AC stand-alone photovoltaic (PV) system, managed by a Multi-Agent System (MAS) integrated with Internet of Things (IoT) technologies. This system employs prioritybased load control and peak shaving to maintain energy stability under varying conditions