Citation
Abstract
This paper introduces an optimal bi-objective optimization methodology customized for microgrid systems, encompassing economic, technological, and environmental considerations. The framework portrays the objectives of an intelligent microgrid, aiming to minimize operational costs, CO2 emissions, peak-to-average ratio (PAR), and energy consumption while concurrently enhancing user comfort (UC). A scheduled power allocation strategy is formulated to efficiently cater to the energy needs of residential loads. The stochastic nature of wind and solar resources is characterized by modeling wind speed and solar radiation intensity using a beta probability density function (PDF). The non-dominated sorting genetic algorithm II (NSGA-II) is employed to address optimization challenges. A decision-making process is implemented to select the optimal solution from the non-dominated alternatives. The study presents three scenarios illustrating the optimal operational values for various parameters and energy consumption, providing a comprehensive analysis of the proposed algorithm's efficacy. Leveraging the NSGA-II algorithm, coupled with renewable energy resources and optimal energy storage system scheduling, yielded significant reductions in overall expenses, PAR, CO2 emissions, user discomfort, and energy consumption. MATLAB simulations were conducted to substantiate the efficacy of our proposed approach. The obtained results underscore the effectiveness and productivity of our devised NSGA-II-based approach. Notably, the proposed algorithm demonstrated a substantial reduction in electricity costs by 19.0%, peak-to-average ratio (PAR) by 30.7%, and carbon emissions by 21.7% in scenario-3, as evidenced by a comparative analysis with the unscheduled case.
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Official URL or Download Paper: https://ieeexplore.ieee.org/document/10399640/
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Additional Metadata
Item Type: | Article |
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Divisions: | Faculty of Engineering |
DOI Number: | https://doi.org/10.1109/access.2024.3354065 |
Publisher: | Institute of Electrical and Electronics Engineers |
Keywords: | Carbon dioxide; Decision making; Electric loads; Energy utilization; Genetic algorithms; MATLAB; Operating costs; Probability density function; Renewable energy resources; Smart power grids; Stochastic systems; Uranium compounds; Wind; Carbon emissions; Energy; Energy-consumption; Heuristic; Heuristics algorithm; Load modeling; Optimisations; Peakto-average power ratios (PAPR); Renewable energy source; Smart grid; Smart grid energy; Storage systems; Heuristic algorithms |
Depositing User: | Mohamad Jefri Mohamed Fauzi |
Date Deposited: | 02 Jul 2024 02:37 |
Last Modified: | 02 Jul 2024 02:37 |
Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/access.2024.3354065 |
URI: | http://psasir.upm.edu.my/id/eprint/105750 |
Statistic Details: | View Download Statistic |
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