Optimizing PV power utilization in standalone battery systems with forecast-based charging management strategy

IF 2.6 Q4 ENERGY & FUELS
Utpal Kumar Das , Ashish Kumar Karmaker
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引用次数: 0

Abstract

Optimizing photovoltaic (PV) power utilization in battery systems is challenging due to solar intermittency, battery efficiency, and lifespan management. This paper proposes a novel forecast-based battery charging management (BCM) strategy to enhance PV power utilization. A string of Li-ion battery cells with diverse capacities and states of charge (SOC) is contemplated in this constant current/constant voltage (CC/CV) battery-charging scheme. Significant amounts of PV power are often wasted because the CC/CV mode cannot fully exploit the available power to maintain appropriate charging rates. To address this issue, the proposed BCM algorithm selects an optimal set of battery cells for charging at any given time based on forecasted PV power generation, ensuring maximum power is obtained from the PV system. Additionally, a support vector regression (SVR)-based forecasting model is developed to predict PV power generation precisely. The results indicate that the anticipated BCM strategy achieves an overall utilization rate of 87.47% of the PV-generated power for battery charging under various weather conditions.
基于预测的充电管理策略优化独立电池系统的光伏发电利用率
由于太阳能间歇性、电池效率和寿命管理,优化电池系统中的光伏(PV)功率利用率具有挑战性。本文提出了一种基于预测的电池充电管理策略,以提高光伏发电的利用率。在这种恒流/恒压(CC/CV)电池充电方案中,考虑了一系列具有不同容量和充电状态(SOC)的锂离子电池。由于CC/CV模式不能充分利用可用功率来维持适当的充电速率,因此经常浪费大量的PV功率。为了解决这一问题,本文提出的BCM算法根据预测的光伏发电量,在任意给定的时间选择一组最优的电池进行充电,以确保光伏系统获得最大功率。在此基础上,建立了基于支持向量回归(SVR)的光伏发电预测模型。结果表明,在各种天气条件下,预期BCM策略实现了87.47%的光伏发电电池充电总利用率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Global Energy Interconnection
Global Energy Interconnection Engineering-Automotive Engineering
CiteScore
5.70
自引率
0.00%
发文量
985
审稿时长
15 weeks
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