Abstract
The restructuring of power systems and the ever-increasing demand for electricity have given rise to congestion in power networks. The use of distributed generators (DGs) may play a significant role in tackling such issues. DGs may be integrated with electrical power networks to regulate the drift of power in the transmission lines, thereby increasing the power transfer capabilities of lines and improving the overall performance of electrical networks. In this article, an effective method based on the Harris hawks optimization (HHO) algorithm is used to select the optimum capacity, number, and site of solar-based DGs to reduce real power losses and voltage deviation. The proposed HHO has been tested with a complex benchmark function then applied to the IEEE 33 and IEEE 69 bus radial distribution systems. The single and multiple solar-based DGs are optimized for the optimum size and site with a unity power factor. It is observed that the overall performance of the systems is enhanced when additional DGs are installed. Moreover, considering the stochastic and sporadic nature of solar irradiance, the practical size of DG has been suggested based on analysis that may be adopted while designing the actual photovoltaic (PV) plant for usage. The obtained simulation outcomes are compared with the latest state-of-the-art literature and suggest that the proposed HHO is capable of processing complex high dimensional benchmark functions and has capability to handle problems pertaining to electrical distribution in an effective manner.
Highlights
In order to establish an algorithm, the proposed Harris hawks optimization (HHO) was tested with selected extremely complex benchmark functions taken from CEC-2014
The proposed HHO-based approach is significantly effective in finding the optimum number, optimal locations, and optimal sizes of distributed generators (DGs)
The comparison with the teaching–learning-based optimization (TLBO), genetic algorithm (GA), particle swarm optimization (PSO), QOTLBO, comprehensive teaching learning-based optimization (CTLBO), CTLBO ε-method, improved multiobjective elephant herding optimization (IMOEHO), improved decomposition-based evolutionary algorithm (I-DBEA), simulated annealing (SA), invasive weed optimization (IWO), bacterial foraging optimization algorithm (BFOA), moth–flame optimization (MFO), and bat algorithm (BA) methods shows that the proposed method performs comparatively better among all
Summary
Installing distributed generation (DG) sources in the distribution network system has been standard practice in recent years to minimize overall power losses and enhance the power quality [1,2]. The optimum sizing and positioning of DGs in power system networks are essential to maximize the benefits from those installations. The incorrect allocation and unreasonable sizing of DG units in the power system networks may increase voltage sags, voltage flickering, harmonic distortion, fault current, and power losses. With the application of DG units, the power system losses may be reduced by 13% [3,4]. In the functioning of power systems, economic damage and voltage collapse may be avoided through the reduction in power loss and voltage stability enhancement, respectively [5]
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