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RSS FeedsEnergies, Vol. 15, Pages 5891: Optimal Sizing and Allocation of Distributed Generation in the Radial Power Distribution System Using Honey Badger Algorithm (Energies)

 
 

14 august 2022 08:24:57

 
Energies, Vol. 15, Pages 5891: Optimal Sizing and Allocation of Distributed Generation in the Radial Power Distribution System Using Honey Badger Algorithm (Energies)
 


There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial power distribution system. It is observed from the simulation results that honey badger algorithm is faster than grey wolf optimization and whale optimization algorithm in reaching accurate and optimum results in a mere one and two iterations for IEEE 33-bus and 69-bus systems, respectively. Additionally, power losses are reduced to 71% and 70% for IEEE 33-bus and 69-bus, respectively.


 
78 viewsCategory: Biophysics, Biotechnology, Physics
 
Energies, Vol. 15, Pages 5890: Super-Twisting Algorithm-Based Virtual Synchronous Generator in Inverter Interfaced Distributed Generation (IIDG) (Energies)
Energies, Vol. 15, Pages 5892: An Auto-Extraction Framework for CEP Rules Based on the Two-layer LSTM Attention Mechanism: A Case Study on City Air Pollution Forecasting (Energies)
 
 
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