利用先进控制技术提高电池-超级电容器储能并网住宅光伏系统的稳定性和性能

Energy Storage Pub Date : 2025-06-26 DOI:10.1002/est2.70202
V. Pushpabala, C. Christober Asir Rajan
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引用次数: 0

摘要

由于发电的不可预测性,可再生能源技术的日益整合给电网带来了重大挑战。由于天气的不确定性导致发电量的变化,因此需要有效的储能解决方案来维持电网的稳定性和可靠性。本研究提出了一种结合混合储能系统(HESS)的并网住宅光伏(PV)系统的新方法,该系统由电池组和超级电容器(SC)组组成。本文的新颖之处在于对小熊猫优化算法(RPO)和高效预定义时间自适应神经网络(EPTANN)的创新。因此,该方法被命名为RPO-EPTANN。该方法的目的是增强稳定性,减少电压超调,提高效率,降低系统的整体成本。利用所提出的RPO对变换器的控制信号进行优化,EPTANN预测变换器的理想控制信号。然后,在MATLAB工作平台上对所提出的方法进行了实践,并利用已有的流程对所得结果进行了计算。该策略在粒子群优化(PSO)、人工神经网络(ANN)和人工兔子优化(ARO)方面优于现有的所有方法。现有方法的总系统成本分别为27 660美元、29 665美元和30 025美元,而提出的方法的成本明显较低,为24 540美元。将现有方法的效率从85%、75%和62%提高到98%。这些发现表明,所提出的RPO-EPTANN方法显著降低了操作成本,同时提高了整体系统效率。这反映了性能上的实质性进步,确保了并网住宅光伏系统的稳定性、可靠性和能源优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Stability and Performance of Grid-Connected Residential PV Systems With Battery-Super Capacitor Storage Using Advanced Control Techniques

The increasing integration of renewable energy technologies poses significant challenges to the power grid due to generation unpredictability. Variations in output, driven by weather uncertainties, highlight the need for effective storage solutions to maintain grid stability and reliability. This research proposes a novel approach for a grid-connected residential photovoltaic (PV) system incorporated with a hybrid energy storage system (HESS) comprising a battery bank and a super capacitor (SC) pack. The novelty of this paper lies in the innovation of the Red Panda Optimization (RPO) and Efficient Predefined Time Adaptive Neural Network (EPTANN). Hence, the method is named RPO-EPTANN. The objective of the proposed method is to enhance stability, reduce voltage overshoot, improve efficiency, and reduce the system's entire cost. The converter's control signal is optimized using the proposed RPO, and the EPTANN predicts the converter's ideal control signal. By then, the proposed approach is put into practice from the MATLAB working platform, and the findings are calculated using the existing process. The proposed strategy outperforms all current approaches in terms of Particle Swarm Optimization (PSO), Artificial Neural Networks (ANN), and Artificial Rabbits Optimization (ARO). The existing methods exhibit total system costs of 27 660$, 29 665$, and 30 025$, whereas the proposed method achieves a significantly lower cost of 24 540$. Efficiency of 85%, 75%, and 62% in the existing approaches are improved to 98% with the proposed method. These findings indicate that the proposed RPO-EPTANN method significantly reduces operational costs while enhancing overall system efficiency. This reflects a substantial advancement in performance, ensuring improved stability, reliability, and energy optimization in grid-connected residential PV systems.

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