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An efficient technique-based distributed energy management for hybrid MG system: A hybrid QOCSOS-RF technique

机译:基于高效技术的混合MG系统分布式能源管理:混合QOCSOS-RF技术

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摘要

This paper proposes an efficient hybrid approach-based energy management strategy (EMS) for grid-connected microgrid (MG) system. The primary objective of the proposed technique is to reduce the operational electricity cost and enhanced power flow between the source side and load side subject to power flow constraints. The proposed control scheme is a consolidated execution of both the random forest (RF) and quasi-oppositional-chaotic symbiotic organisms search algorithm (QOCSOS), and it is named as QOCSOS-RF. Here, the QOCSOS can have the capacity to enhance the underlying irregular arrangements and joining to a superior point in the pursuit space. Likewise, the QOCSOS has prevalence in nonlinear frameworks due over the way that can insert and extrapolate the arbitrary information with high exactness. Here, the required load demand of the grid-connected MG system is continuously tracked by the RF technique. The QOCSOS optimized the perfect combination of the MG with the consideration of the predicted load demand. Furthermore, in order to reduce the influence of renewable energy forecasting errors, a two-strategy for energy management of the MG is employed. At that point, proposed model is executed in MATLAB/Simulink working platform, and the execution is assessed with the existing techniques.
机译:本文提出了一种高效的基于混合方法的并网微电网(MG)系统的能源管理策略(EMS)。所提出的技术的主要目的是在受到功率流约束的情况下减少运行电力成本并增加源侧和负载侧之间的功率流。所提出的控制方案是对随机森林(RF)和准对立混沌共生生物搜索算法(QOCSOS)的合并执行,并被称为QOCSOS-RF。在这里,QOCSOS可以增强潜在的不规则安排并加入追逐空间的优势。同样,由于可以高度精确地插入和外推任意信息的方式,QOCSOS在非线性框架中也很普遍。在此,通过RF技术不断跟踪并网MG系统的所需负载需求。 QOCSOS考虑了预期的负载需求,优化了MG的完美组合。此外,为了减少可再生能源预测误差的影响,MG的能源管理采用了两种策略。到那时,在MATLAB / Simulink工作平台上执行所提出的模型,并使用现有技术对执行情况进行评估。

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