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Genetic Algorithm Based Power Control Strategies of a Grid Integrated Hybrid Distributed Generation System

Genetic Algorithm Based Power Control Strategies of a Grid Integrated Hybrid Distributed... In this manuscript, a genetic algorithm (GA) is proposed for the power management (PM) of a grid integrated hybrid distributed generation (DG) system. The hybrid distribution generation (DG) system incorporates photovoltaic (PV), wind turbine (WT), fuel cell (FC) and battery. The power fluctuations are produced in the distributed generation system, because the hybrid resource utilization and the generation of power is changeable. The major purpose of the proposed control method is “to control the power flow (PF) of active with reactive energy amid the source and grid side. The proposed GA-based power control system is mainly utilized for optimizing active with reactive power flow controllers. By controlling charge with discharge of battery, the proposed system met the energy requirement of the charge and managed the sensitivity of the charge. The proposed method provides an optimal power flow in DG systems. To evaluate the management of PF, the equality with inequality constraints is determined that is used to specify the accessibility of renewable energy sources (RES), the demand for electricity and storage components load level. The security of power system is improved with the help of proposed control system. Moreover, the battery is used to allow the renewable energy system and maintain a stable power output. The proposed method is activated in MATLAB / Simulink work site and the performance is compared with existing methods. The statistical analysis of mean, median and standard deviation (SD) are also analyzed for proposed with existing methods. The proposed technique mean value is 1.5784, median is 1.4892, SD is 0.5883. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Technology and Economics of Smart Grids and Sustainable Energy Springer Journals

Genetic Algorithm Based Power Control Strategies of a Grid Integrated Hybrid Distributed Generation System

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2021
eISSN
2199-4706
DOI
10.1007/s40866-021-00109-8
Publisher site
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Abstract

In this manuscript, a genetic algorithm (GA) is proposed for the power management (PM) of a grid integrated hybrid distributed generation (DG) system. The hybrid distribution generation (DG) system incorporates photovoltaic (PV), wind turbine (WT), fuel cell (FC) and battery. The power fluctuations are produced in the distributed generation system, because the hybrid resource utilization and the generation of power is changeable. The major purpose of the proposed control method is “to control the power flow (PF) of active with reactive energy amid the source and grid side. The proposed GA-based power control system is mainly utilized for optimizing active with reactive power flow controllers. By controlling charge with discharge of battery, the proposed system met the energy requirement of the charge and managed the sensitivity of the charge. The proposed method provides an optimal power flow in DG systems. To evaluate the management of PF, the equality with inequality constraints is determined that is used to specify the accessibility of renewable energy sources (RES), the demand for electricity and storage components load level. The security of power system is improved with the help of proposed control system. Moreover, the battery is used to allow the renewable energy system and maintain a stable power output. The proposed method is activated in MATLAB / Simulink work site and the performance is compared with existing methods. The statistical analysis of mean, median and standard deviation (SD) are also analyzed for proposed with existing methods. The proposed technique mean value is 1.5784, median is 1.4892, SD is 0.5883.

Journal

Technology and Economics of Smart Grids and Sustainable Energy – Springer Journals

Published: Aug 26, 2021

Keywords: Photovoltaic; Wind turbine; Fuel cell; Battery; Distributed generators; Genetic algorithm; Power flow management; Source side control; Grid side control

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