## 1 Introduction  
The urgency to mitigate fossil fuel dependency and its environmental repercussions has propelled a global shift towards renewable energy sources (RES) as a viable solution to our energy challenges. The efficiency of renewable energy sources has a significant impact on system management [1]. RES offers a compelling avenue for power generation while concurrently mitigating CO\({}_{2}\) emissions and greenhouse gas effects [2]. Among RES options, solar photovoltaic (PV), hydro, wind, geothermal, and biomass systems are noteworthy [3]. Solar PV is recognized as a leader for future energy needs due to its affordability, simple installation, and minimal maintenance. The maximization of power extraction from PV systems is a critical concern [4]. The inception of Maximum Power Point Tracking (MPPT) in PV systems dates back to 1968 when it was first incorporated into space applications. After this milestone, the widespread adoption of MPPT controllers ensued, significantly enhancing the operational efficiency of PV arrays. Over time, MPPT controllers have evolved to enhance PV array efficiency, adapting to diverse conditions. However, the interplay of irradiance levels, temperature, and other parameters can influence array performance, potentially diminishing conversion efficiency [5]. To address this, researchers have explored diverse MPPT strategies, ranging from traditional linear controllers to innovative approaches like the Jellyfish Search Optimization (JSO) method [6; 7]. Despite advances in solar cell technology, challenges persist, including series resistance losses that constrain cell performance. Equivalent circuit derivations are utilized to model and quantify the impact of these internal losses [8; 9; 10]. Additionally, the integration of thin-film solar cells such as copper indium gallium selenide (CIGS) with wind turbine blades has shown promise in boosting power generation [11]. However, this study contributes significantly by integrating a fuzzy logic-based MPPT system, an optimized PI-based voltage controller, and the Jellyfish Optimization Algorithm into a solar PV battery setup. This innovative integration tackles challenges posed by dynamic environmental factors and loads, ultimately enhancing power extraction efficiency, voltage regulation, and overall performance. The study's findings further support the use of MATLAB Simulink for modeling and the Jellyfish Optimization Algorithm for PI-controller tuning. The integrated approach's prowess becomes evident in achieving tangible enhancements through testing scenarios encompassing various irradiation levels. The study's innovation is characterized by this systematic synergy of methodologies and algorithms, addressing practical challenges and advancing the field of solar energy optimization. Also, this study uses Fuzzy Logic Control (FLC) and the Jellyfish Optimization Algorithm due to their capacity to manage dynamic solar PV system challenges. FLC's flexibility handles complex environmental interactions, while the Jellyfish Algorithm's innovative optimization with integrated system uses tuning for improved efficiency and performance. The paper introduces a Fuzzy Logic Controller (FLC) designed for general-purpose embedded processors, specifically applied to the Maximum Power Point Tracking (MPPT) of photovoltaic (PV) energy systems [12]. It utilizes linguistic rules and membership functions, showcasing adaptability to diverse applications, while Jellyfish Optimization, inspired by jellyfish behavior, is specialized for optimization tasks, potentially requiring customization for specific applications [12]. The FLC demonstrates effectiveness by exhibiting consistent results with simulations [12]. The paper explores the use of modified particle swarm optimization (MPSO) with genetic algorithms (GA) to enhance Maximum Power Point Tracking (MPPT) in partially shaded PV systems, outperforming the fuzzy logic controller (FLC) in handling multiple local peaks (LPs).