article · Sensors
Combining solar photovoltaics and wind turbines offers an emissions-free alternative to conventional energy generation, but the intermittent nature of both sources creates challenges for grid integration and power regulation. To address these fluctuations, optimisation techniques including Particle Swarm Optimisation and Electric Eel Foraging Optimisation can be deployed within hybrid system architectures. Mathematical modelling and simulations conducted in MATLAB and Simulink demonstrate how these algorithms optimise operation and manage anticipated loading issues. The approaches enable rapid and precise compensation for connected loads, while regulating energy supply to keep delivered power at target levels. Consequently, these computational methods can improve overall system performance, maximise energy yield, and facilitate the seamless integration of combined solar and wind systems into the public electricity grid.
Solar and wind power fluctuate with the weather, making it difficult to maintain a steady electricity supply on public grids. Using advanced algorithms to balance both technologies simultaneously helps stabilise power delivery. This enables renewable installations to respond rapidly to changing electrical loads, supporting cleaner and more dependable energy networks without relying on fossil fuel backup.
This simulation-tested control approach could be adopted by microgrid operators, renewable energy developers, and utilities seeking to integrate co-located solar and wind assets into existing power networks. By optimising load compensation and energy yield, it supports grid-stability software tools. Because the findings are currently demonstrated solely through mathematical modelling and MATLAB or Simulink simulations, the technology remains at an early, computer-validated stage prior to physical prototype testing.
AI-generated from the published abstract. Always read the original work before citing.
This paper presents a comprehensive exploration of a hybrid energy system that integrates wind turbines with photovoltaics (PVs) to address the intermittent nature of electricity production from these sources. The necessity for such technology arises from the sporadic nature of electricity generated by PV cells and wind turbines. The envisioned outcome is an emissions-free, more efficient alternative to traditional energy sources. A variety of optimization techniques are utilized, specifically the Particle Swarm Optimization (PSO) algorithm and Electric Eel Foraging Optimization (EEFO), to achieve optimal power regulation and seamless integration with the public grid, as well as to mitigate anticipated loading issues. The employed mathematical modeling and simulation techniques are used to assess the effectiveness of EEFO in optimizing the operation of grid-connected PV and wind turbine hybrid systems. In this paper, the optimization methods applied to the system’s architecture are described in detail, providing a clear understanding of the intricate nature of the approach. The efficacy of these optimization strategies is rigorously evaluated through simulations of diverse operating scenarios using MATLAB/SIMULINK. The results demonstrate that the proposed optimization strategies are not only capable of precisely and swiftly compensating for linked loads, but also effectively controlling the energy supply to maintain the load’s power at the desired level. The findings underscore the potential of this hybrid energy system to offer a sustainable and reliable solution for meeting power demands, contributing to the advancement of clean and efficient energy technologies. The results demonstrate the capability of the proposed approach to improve system performance, maximize energy yield, and enhance grid integration, thereby contributing to the advancement of renewable energy technologies and sustainable energy systems.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.3390/s24072354
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
