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VOL. 10, ISSUE 1 (2024)
Microgrid fed optimisation from hybri energy sources using swarm intelligence
Authors
Sarika Nitin Kaple, Dr Vijayalaxmi Biradar, Dr Manoj Ramesh Tarambale
Abstract
An opportunity and a challenge for efficient
power generation, regulation, and distribution are presented by the rising
penetration of renewable energy resources in microgrids. Optimal operation is
complicated by the intermittency and nonlinearity of hybrid energy systems that
incorporate wind, solar photovoltaic (PV), and other renewable sources;
nonetheless, these systems provide improved sustainability and dependability.
An optimization framework for a microgrid that uses swarm intelligence
approaches to accomplish robust energy management and load balancing is
proposed in this paper. The microgrid would be fueled by hybrid renewable
energy sources. Taking into account the ever-changing nature of generation,
load demand, and storage state-of-charge, the best power dispatch method is
determined using Particle Swarm Optimization (PSO). To reduce the need for
backup systems powered by fossil fuels, the model incorporates real-time
weather forecasting data to enhance the accuracy of predictions for wind and
solar output. Under different load profiles and climatic circumstances, the
performance is assessed by MATLAB/Simulink simulations. When compared to more
traditional control strategies, the swarm intelligence method achieves better
results in reducing costs, reducing power loss, and improving voltage
stability. On top of that, the suggested solution meets demand-side needs and
keeps the grid stable by making the most of renewable resources. Intelligent
optimization algorithms have the ability to manage complicated hybrid microgrid
systems, encourage the integration of sustainable energy sources, and decrease
emissions of greenhouse gases, according to the research.
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Pages:48-53
How to cite this article:
Sarika Nitin Kaple, Dr Vijayalaxmi Biradar, Dr Manoj Ramesh Tarambale "Microgrid fed optimisation from hybri energy sources using swarm intelligence". International Journal of Research in Advanced Engineering and Technology, Vol 10, Issue 1, 2024, Pages 48-53
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