Control and management of a hybrid PV-Fuel Cell system using intelligent techniques

Abstract

This thesis emphasizes designing, improving, and validating new enhanced control and power management approaches for a hybrid standalone photovoltaic-fuel cell (PV-FC) system, especially under challenging circumstances involving multiple energy sources. The primary aim is to develop effective control strategies to mitigate the challenges presented by varying weather conditions, nonlinear output characteristics, and fluctuating load demands that impact system efficiency, stability, and dependability. The most significant contribution of this thesis is the enhanced pufferfish optimization algorithm (EPFOA), which improves MPPT in solar energy systems. This strategy was evaluated against four established metaheuristic methods: PSO, GWA, AOA, and SOA, in the context of rapidly fluctuating irradiance and intricate shading circumstances. The EPFOA exhibited the ability to consistently identify the global maximum power point using just two tuning parameters, successfully overcoming local maxima and ensuring enhanced output power without oscillations around the MPP. Experiments conducted under intricate real-world conditions demonstrated significantly enhanced tracking speed, accuracy, and robustness. The proposed approaches demonstrated substantial enhancements in resilience, efficiency, and dynamic response relative to current techniques, validated by comprehensive simulations and real-time hardware-in-the-loop experiments. The EPFOA was recognized for its stability under real operating situations, confirming its practical application. On the other hand, an improved FSSO was proposed for the control of polymer electrolyte membrane fuel cells (PEMFC) under fluctuating pressure and temperature circumstances. The FSSO was evaluated against the traditional control method (CSO) and proved effective in achieving reduced settling times and improved stability while accurately tracking the MPP despite changes in the environment. Ultimately, an efficient EHOA energy control approach has been developed to regulate the operation of several energy sources and ensure the continuous availability of power to fulfill load demand. This approach efficiently allocates energy, emphasizes renewable sources, and prevents the system from surpassing the operational thresholds of any component, so guaranteeing a consistent power supply to meet demand. The findings demonstrate that the hybrid system can rapidly adapt to environmental changes and the requirements of the load. This outcome demonstrates that the proposed EHOA control, compared to other conventional and contemporary management techniques, may be effectively implemented in practical applications. This research has established a scalable and methodical framework for integrating hybrid renewable energy systems, supporting various topologies, sizes, and operating requirements, beyond basic technological efficiency. By providing a scalable framework for dependable, sustainable, and intelligent hybrid energy systems, the established solutions tackle fundamental issues in the integration of renewable energy.