Hybrid Ant Colony Optimisation-Random Forest Regression-Based Optimised Framework of An Intelligent Active Distribution Network

نویسندگان

چکیده

Abstract Measurement and model uncertainties in power grids lead to the unpredictability of its output, which brings great challenges safe sustainable system operation. Furthermore, optimal placement strategies measurement devices new energy sources have a impact maintain This study introduces constructed an active distribution network (ADN) including storage (ESS) is optimized by using hybrid Ant Colony Optimization-Random Forest Regression (HACORFR). Briefly operation combined modelled ESS-RDS (ERDS). Then HACORFR algorithm optimizes status RDS detects abnormality network. After applying proposed approach, if indicators network, don’t return normal operation, topology will further reconstruct optimize ERDS. The IEEE-28 bus testing was used validate method. Finally, results show that optimization method reduces loss 2.34%, voltage profile improved 2.94%. present makes full use flexible load realizes interaction between supply demand. It also greatly improves reliability economy.

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ژورنال

عنوان ژورنال: Journal of physics

سال: 2023

ISSN: ['0022-3700', '1747-3721', '0368-3508', '1747-3713']

DOI: https://doi.org/10.1088/1742-6596/2467/1/012020