6/15/2023 0 Comments Gray wolf express dual admission![]() The probabilistic characteristics of wind and solar sources and random demand create power imbalance and produce frequency oscillation. To satisfy the demand of an isolated community, the expansion of DER (Distributed Energy Resources) can be done by interconnection with storage devices. Hence a clear shift can be observed from a centralized system to a decentralized mode. In addition, there is a lack of flexibility in conventional centralized electricity generation to adjust to the challenges posed by recent changes in energy consumption. This results in problems such as high power loss during transmission and distribution, deficiencies in the power grid, unreliable power supply, etc. In most countries, the majority of electricity supplies are through centralized power plants. ![]() This not only reduces the complexity and improves the execution time but also improves the solution quality. In the proposed Simplified GWO (SGWO), the least fit wolves, i.e., the delta wolves, are eliminated and more importance is attached to the α wolves to find updated positions. In the original GWO, four categories of grey wolves, namely alpha ( α), beta ( β), delta ( δ), and omega ( ω) are engaged in mimicking the leadership ladder and equal importance is given to the alpha, beta and delta wolves to compute the updated position of wolves throughout the iterations. To address such issues, this study simplifies the original GWO method as proposed in. However, modifications of the original GWO method to enhance its versatility increase its complexity. A lot of effort has been put in to modify the GWO to improve its performance on optimization problems. The performance of the GWO can be enhanced by maintaining an equilibrium amongst the exploration and exploitation stages in the course of searching. Because of its comprehensibility, high flexibility, and quick programmability features, and dealing with fewer algorithm parameters, it has attracted significant research interests from numerous fields over a short time. The Grey Wolf Optimizer (GWO) is a new optimization method applied to diversified objectives in different optimization tasks. Finally, the novelty of the paper is demonstrated by comparing with the existing publications in an extensively used two-area test system. A sensitivity study is also performed to examine the impact of the unpredictability in the parameters of the investigated system on system performance. ![]() It is also seen that SGWO based AFPID controller is highly efficacious in regulating the frequency compared to the standard PID controller. It is demonstrated that the SGWO method is superior to the GWO method in the optimal controller design task. The DPGS contains renewable generation such as photovoltaic, wind, and storage elements such as battery and flywheel, in addition to plug-in electric vehicles. Practical application in a Distributed Power Generation System (DPGS) with energy storage is then considered by designing an Adaptive Fuzzy PID (AFPID) controller using the suggested SGWO method for frequency control. The results are also contrasted to the Gravitational Search Algorithm, the Particle Swarm Optimization, and the Sine Cosine Algorithm and this shows the superiority of the proposed SGWO technique. The advantage of the presented SGWO over GWO is a better solution taking less execution time and is demonstrated by taking unimodal, multimodal, and fixed dimension test functions. The simplification in the original Grey Wolf Optimizer (GWO) method is introduced by ignoring the worst category wolves while giving priority to the better wolves during the search process. A Simplified Grey Wolf Optimizer (SGWO) is suggested for resolving optimization tasks.
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