Load factor optimization using intelligent shifting algorithms in a smart grid tree network



The major challenge at smart grid is to handle the load on peak demand. The demand on the smart grid varies with time, at peak and non-peak hours. In this paper, a new quantitative technique is proposed to balance the different category loads and optimize the energy distribution efficiency at various levels of the grid with high fairness. In this work, during high demand, the energy is allocated to primary and secondary substations using dynamic weight based on the load. A fine calibration is employed at the secondary substation to avoid the fractional wastage during allocation, and to optimize the distribution efficiency. Later, a horizontal block shifting (HBS) and vertical column by column shifting (VC2S) algorithms are proposed. The unallocated load at peak demand is shifted to nonpeak time slots with excess energy and reduces the demand in different time slots. The load request at various levels of the grid and energy demands in a day at the grid is analyzed. From the simulation results, it is observed that, the energy allocation is optimized with dynamic weights during high demand and yields high fairness index. The fine calibration improves the distribution efficiency of the smart grid network. The load request and load factor at grid without and with HBS, VC2S algorithms is analyzed. From the simulation results it is observed that, VC2S algorithm outperforms over HBS. The proposed shifting algorithms reduce the demand by shifting the load to various time slots, maximize the utilization, enhance the efficiency and load factor.


Distribution efficiency Fairness Fine calibration Horizontal block shifting Load factor Smart grid and vertical column by column shifting 


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Electronics and Communication EngineeringKakatiya Institute of Technology and ScienceWarangalIndia

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