Deep learning based optimal energy management for photovoltaic and battery energy storage integrated home micro-grid system

被引:20
作者
Alam, Md Morshed [1 ]
Rahman, Md Habibur [1 ]
Ahmed, Md Faisal [2 ]
Chowdhury, Mostafa Zaman [3 ]
Jang, Yeong Min [1 ]
机构
[1] Kookmin Univ, Dept Elect Engn, Seoul 02707, South Korea
[2] Noakhali Sci & Technol Univ, Dept Elect & Elect Engn, Noakhali 3814, Bangladesh
[3] Khulna Univ Engn & Technol, Dept Elect & Elect Engn, Khulna 9203, Bangladesh
关键词
D O I
10.1038/s41598-022-19147-y
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The development of the advanced metering infrastructure (AMI) and the application of artificial intelligence (AI) enable electrical systems to actively engage in smart grid systems. Smart homes with energy storage systems (ESS) and renewable energy sources (RES)-known as home microgrids-have become a critical enabling technology for the smart grid. This article proposes a new model for the energy management system of a home microgrid integrated with a battery ESS (BESS). The proposed dynamic model integrates a deep learning (DL)-based predictive model, bidirectional long short-term memory (Bi-LSTM), with an optimization algorithm for optimal energy distribution and scheduling of a BESS-by determining the characteristics of distributed resources, BESS properties, and the user's lifestyle. The aim is to minimize the per-day electricity cost charged by time-of-use (TOU) pricing while considering the day-basis peak demand penalty. The proposed system also considers the operational constraints of renewable resources, the BESS, and electrical appliances. The simulation results from realistic case studies demonstrate the validation and responsibility of the proposed system in reducing a household's daily electricity cost.
引用
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页数:19
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