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Smart control for battery energy storage system in a community grid

机译:社区电网中电池储能系统的智能控制

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摘要

Distributed renewable energy generators: wind turbines, photovoltaics and other Renewable Energy Source of Electricity (RES-E) are coupled with energy storage system to supply power to the local consumers in the micro-grid network. This system requires a wide-range of control to ensure system security, optimal operation and emission reduction. This paper proposes a variable-threshold methodology for control of energy storage unit within a micro-grid. With each stage of the decision making for optimal energy distribution adopting, an Adaptive Intelligence Technique (AIT) was applied on system variables for efficient power management. The AIT brings about optimal energy distribution across peak period, demand smoothening and high efficiency battery utilization. The proposed method was evaluated using onsite measured RES output data. The results show that this method can achieve load curve smoothing and maximum local utilization of the RES energy without requirement of precise load and RES forecasting. Compared with the traditional methods which either uses a fixed threshold or requires forecasting for battery charging/discharging, the proposed algorithm provides a variable energy regulation reference which is intelligently updated every sample step. The improvement is demonstrated using the simulation testing results.
机译:分布式可再生能源发电器:风力涡轮机,光伏发电设备和其他可再生能源(RES-E)与能源存储系统结合使用,为微电网中的本地消费者供电。该系统需要广泛的控制,以确保系统安全,最佳运行和减少排放。本文提出了一种可变阈值方法来控制微电网中的储能单元。在最佳能源分配决策的每个阶段都采用了自适应智能技术(AIT)来对系统变量进行有效的电源管理。 AIT可以在整个高峰时段实现最佳的能源分配,需求平滑和高效的电池利用率。使用现场测量的RES输出数据对提出的方法进行了评估。结果表明,该方法可以实现负载曲线的平滑和RES能量的最大局部利用,而无需精确的负载和RES预测。与使用固定阈值或需要对电池充电/放电进行预测的传统方法相比,该算法提供了一个可变能量调节参考,该参考在每个采样步骤中都会智能更新。使用模拟测试结果证明了这一改进。

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