Modified Ant Lion Algorithms in Distributed Generations Systems
Optimization of Distributed Generations using intelligent Techniques
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The aim of this work is to improve the power quality of a radially configured system (RFS) using modified Ant Lion (MALO) algorithms. The size and appropriate location of distributed sources (DS) are determined to ensure system stability and reduce energy losses. This approach ensures that the demand of stochastic loads (DSL) is met without affecting the overall stability of the system. The energy quality and performance (PQP) of the algorithm are evaluated using standard IEEE 14 bus, IEEE 33 bus and IEEE 69 bus tests. Power profiles and regulated voltage profiles after optimal sizing and allo...
The aim of this work is to improve the power quality of a radially configured system (RFS) using modified Ant Lion (MALO) algorithms. The size and appropriate location of distributed sources (DS) are determined to ensure system stability and reduce energy losses. This approach ensures that the demand of stochastic loads (DSL) is met without affecting the overall stability of the system. The energy quality and performance (PQP) of the algorithm are evaluated using standard IEEE 14 bus, IEEE 33 bus and IEEE 69 bus tests. Power profiles and regulated voltage profiles after optimal sizing and allocation (OSA) of distributed sources demonstrate that this approach is suitable for optimizing systems when the nature of the demand and load is unknown. An objective function based on system stability and quality constraints (SSC) is used to establish system feasibility and robustness. Four configurations are used in terms of scenarios to assess the reliability of the system and the proposedmethod (RSPM).
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