Nonlinear model predictive control of a microgrid with a variable efficiency battery storage system

IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society Pub Date : 2022-10-17 DOI:10.1109/IECON49645.2022.9968434

Mateja Car, M. Vašak, Mojtaba Hajihosseini, V. Lešić

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Abstract

This paper presents a microgrid energy flow optimization algorithm with variable battery storage efficiency in order to achieve energy savings and expand the lifespan of the components. The converter efficiency curve is deduced from converter’s datasheets and approximated with mathematical functions. The power loss on the battery internal resistance is also included in order to achieve a more accurate model of the complete storage system. The obtained nonlinear model is used in model predictive control formulation and solved by using a sequential linear program (SLP) algorithm. The SLP algorithm iteratively linearizes the model around the current solution and uses corresponding efficiencies over the prediction horizon. Simulations in MATLAB are performed for a 7-day period and compared with a conventional, constant-efficiency battery system model. The results show an improved performance regarding the charging and discharging battery power and the overall savings of 7% in comparison with the conventional model used in model predictive control.

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微电网变效率电池储能系统的非线性模型预测控制

本文提出了一种可变电池储能效率的微电网能量流优化算法，以达到节能和延长组件寿命的目的。从变流器的数据表中推导出变流器的效率曲线，并用数学函数逼近。电池内阻上的功率损失也包括在内，以实现更准确的完整存储系统模型。将得到的非线性模型用于模型预测控制公式，并采用顺序线性规划(SLP)算法求解。SLP算法围绕当前解迭代线性化模型，并在预测范围内使用相应的效率。在MATLAB中进行了为期7天的仿真，并与传统的恒效率电池系统模型进行了比较。结果表明，与模型预测控制中使用的传统模型相比，该模型在电池充电和放电方面的性能有所提高，总体节省了7%。

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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IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society

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