微电网
储能
人工神经网络
计算机科学
电气工程
电子工程
工程类
可再生能源
功率(物理)
物理
人工智能
量子力学
作者
Senthil Kumar R,V. Indragandhi,Balakumar Palaniyappan,B. Ashok,Belqasem Aljafari
标识
DOI:10.1016/j.est.2024.111562
摘要
Standalone microgrids using Photovoltaic (PV) systems might be a feasible alternative for powering off-grid populations. However, this form of application necessitates the use of energy storage systems (ESS) to control the intermittent nature of PV production. This paper proposes a novel energy management strategy (EMS) based on Artificial Neural Network (ANN) for controlling a DC microgrid using a hybrid energy storage system (HESS). The HESS connects to the DC Microgrid using a bidirectional converter (BC), that enables energy exchange between the battery and supercapacitor (SC). The effectiveness of the proposed approach in relation to the traditional control method is based on performance metrics such as overshoot and settling time. The proposed ANN control attains 18 % overshoot while step increases in PV generation and attains 21 % overshoot while decreasing PV generation. The results show that the proposed control method ensures continuous power supply from the PV system by optimising load balancing under various operating conditions, maintaining state-of-charge (SoC), and improving the voltage profile.
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