Hybrid energy systems (HES) have emerged as key players in addressing the global demand for electrical power, particularly as societies grapple with climate change and the environmental impact of traditional energy sources. A recent study has proposed new control strategies aimed at enhancing the effectiveness and efficiency of HES, particularly those leveraging battery storage systems integrated with photovoltaic (PV) and wind energy sources. By optimizing these hybrid systems, researchers hope to create more reliable and eco-friendly energy solutions.
This research focuses on innovative maximum power point tracking (MPPT) strategies using adaptive neuro-fuzzy inference system (ANFIS) methods for PV systems and artificial neural networks for wind energy systems. Both techniques allow for the efficient extraction of power from renewables without necessitating precise mathematical modeling of the systems, potentially increasing robustness and stability.
Understanding the urgency behind such advancements is rooted not only in energy demands but also environmental concerns. Traditional energy systems, primarily reliant on fossil fuels, have led to harmful emissions and rising energy costs. The researchers highlight, "The integration of the three systems is of great importance to meet the energy demands and reduce reliance on traditional energy sources." This calls for urgent attention toward developing sustainable renewable energy systems to counteract those impacts.
The authors set up simulations through MATLAB to evaluate the performance of various control techniques under different working conditions. They found remarkable improvements with their new methods. For example, the MPPT-ANFIS strategy demonstrated significant reductions in rise time and steady-state error compared to traditional methods, with efficiencies improving by over 60% for various metrics during the study. Specifically, they noted improvements as high as 94.70% for changes under varying weather and irradiation conditions.
Utilizing particle swarm optimization (PSO), the study also combined fractional-order proportional-integral (FOPI) control and integral sliding mode control (ISMC) to regulate the battery storage systems. This approach not only enhanced the overall power quality but also effectively reduced total harmonic distortion (THD) levels. The efficiency gained here can be pivotal for industries and businesses aiming to implement cleaner energy systems.
Research findings offer hope for wider applications of HES, showcasing their adaptability and effectiveness under fluctuated environments. The LP model employed within the simulations allowed for the precise evaluation of energy distribution, leading to real-time adjustments based on system performance.
Concluding their research, the authors express eagerness for future developments, asserting, "Our proposed model significantly improves power quality compared to using classical techniques and other works." They anticipate this study will pave the way for enhanced hybrid systems capable of meeting tomorrow's electrical demands sustainably. Continued innovation and research are necessary to refine these technologies, ensuring they align with both efficiency goals and environmental stewardship.