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Identification of Critical Parameters for the Design of Energy Management Algorithms for Li-Ion Batteries Operating in PV Power Plants

机译:光伏发电厂运行中锂离子电池能量管理算法设计临界参数的识别

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摘要

Lithium-ion batteries are gaining importance for a variety of applications due to their price decrease and characteristics improvement. For a proper use of such storage systems, an energy management algorithm (EMA) is required. A number of EMAs, with various characteristics, have been published recently, given the diverse nature of battery problems. The EMA of deterministic battery problems is usually based on an optimization algorithm. The selection of such an algorithm depends on a few problem characteristics, which need to be identified and closely analyzed. The aim of this article is to identify the critical optimization problem parameters that determine the most suitable EMA for a Li-ion battery. With this purpose, the starting point is a detailed model of a Li-ion battery. Three EMAs based on the algorithms used to face deterministic problems, namely dynamic, linear, and quadratic programming, are designed to optimize the energy dispatch of such a battery. Using real irradiation and power price data, the results of these EMAs are compared for various case studies. Given that none of the EMAs achieves the best results for all analyzed cases, the problem parameters that determine the most suitable algorithm are identified to be four, i.e., desired computation intensity, characteristics of the battery aging model, battery energy and power capabilities, and the number of optimization variables, which are determined by the number of energy storage systems, the length of the optimization problem, and the desired time step.
机译:由于其价格降低和特性改善,锂离子电池对各种应用进行了重要性。为了适当地使用这种存储系统,需要一种能量管理算法(EMA)。鉴于电池问题的不同性质,最近发表了许多EMA,具有各种特征。确定性电池问题的EMA通常基于优化算法。这种算法的选择取决于一些问题特征,需要识别和密切地分析。本文的目的是识别确定最合适的EMA的关键优化问题参数,用于锂离子电池。为此目的,起始点是锂离子电池的详细模型。三个基于用于面对确定性问题的算法,即动态,线性和二次编程的三个EMA,旨在优化这种电池的能量调度。使用真实的辐照和电源数据,比较各种案例研究的这些EMA的结果。鉴于没有任何EMAS实现所有分析的情况的最佳结果,确定最适合算法的问题参数被识别为四个,即所需的计算强度,电池老化模型的特性,电池能量和功率能力,以及优化变量的数量由能量存储系统的数量,优化问题的长度和所需的时间步长决定。

著录项

  • 来源
    《Industry Applications, IEEE Transactions on》 |2020年第5期|4670-4678|共9页
  • 作者单位

    Department of Electrical Electronic and Communications Engineering Institute of Smart Cities Public University of Navarre Pamplona Spain;

    Department of Electrical Electronic and Communications Engineering Institute of Smart Cities Public University of Navarre Pamplona Spain;

    Department of Electrical Electronic and Communications Engineering Institute of Smart Cities Public University of Navarre Pamplona Spain;

    Department of Electrical Electronic and Communications Engineering Institute of Smart Cities Public University of Navarre Pamplona Spain;

    Department of Electrical Electronic and Communications Engineering Institute of Smart Cities Public University of Navarre Pamplona Spain;

    Department of Electrical Electronic and Communications Engineering Institute of Smart Cities Public University of Navarre Pamplona Spain;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Aging; Heuristic algorithms; Energy management; Lithium-ion batteries; Optimization; Inverters;

    机译:老化;启发式算法;能源管理;锂离子电池;优化;逆变器;

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