Enhancement of power management in micro grid system using adaptive ALO technique

• Published in: Journal of ambient intelligence and humanized computing 2021-02, Vol.12 (2), p.2163-2182
• Main Authors: Sridhar, N., Kowsalya, M.
• Format: Article
• Language: English
• Subjects: Adaptive systems; Algorithms; Alternative energy sources; Artificial Intelligence; Computational Intelligence; Controllers; Cost control; Diesel generators; Distributed generation; Electric potential; Electric power demand; Energy management; Energy resources; Energy storage; Engineering; Fossil fuels; Fuzzy logic; Insects; Integer programming; Optimization; Original Research; Power management; Reactive power; Renewable resources; Robotics and Automation; User Interfaces and Human Computer Interaction; Voltage; Wind power
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-020-02313-3

Micro grids are drawing in more consideration since they can mitigate the worry of primary transmission systems, reduce feeder losses, enhance system power quality (PQ), and power management. Power management in MG is challengeable utilizing energy storage and generation components like PV, diesel generator, micro-hydro generator, battery bank with a bidirectional inverter. In this paper, the power management of the MG is investigated with the proposed droop controller. The droop controller is intended to get the optimal energy management of MG. The proposed method is the combination of Ant Lion Optimization (ALO) with a Bat algorithm to enhance the power management of MG. The ALO calculation is a nature-inspired algorithm. It imitates the chasing system of ant lions in nature. The Bat algorithm is utilized to refresh the insect lion position of the ALO algorithm. The droop control is sharing the power in the generation side depends upon the load demand of the grid side through the control action of the real and reactive power. The goal of the paper is used to enhance the real and reactive power of MG. With the assistance of the proposed technique, the real and reactive power is enhanced and the voltage is controlled. Based on the droop characteristics, power management is accomplished continuously by way of the upgraded proposed approach. The proposed strategy is executed in the MATLAB/Simulink platform and also analysis of the PV power, wind power, power demand, real power, reactive power, voltage, current, demand values, and grid power. The upgrading proposed framework is compared to current techniques such as CSO and PSO algorithms. Hence, the overall performance of the proposed approach will be effective.