Genetic algorithm based optimized load frequency control for storageless photo voltaic generation in a two area multi-agent system

Rereloluwa O. Fatunmbi, R. Belkacemi, F. Ariyo, G. Radman
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引用次数: 1

Abstract

In this paper, a multi-agent load frequency balancing control algorithm based on Genetic Algorithm for a storage-less Photo Voltaic (PV) generation is proposed. For maximum deployment of available renewable energy, the PV generation is tracked at its maximum power point however through the use of a virtual synchronous generator converter control, the PV generator is able to follow the load demand so far its possible maximum power remains in excess of the load demand. When the maximum power falls below demand, the conventional synchronous generator in the second area covers the power deficit. Through Genetic Algorithm based Optimization, the virtual inertia parameter is determined for optimum performance of the PV load following capability. Through the Universal Multi Agent Platform (UMAP), we are able to communicate the frequencies of the two areas (representing two agents) to a central agent which computes the Area Control Error (ACE) and therefore take frequency bias into account in our system model. By implementing load following without any energy storage, we significantly reduce costs accrued from energy storage installations.
基于遗传算法的二区多智能体无存储光伏发电负荷频率优化控制
针对无存储光伏发电系统,提出了一种基于遗传算法的多智能体负载频率平衡控制算法。为了最大限度地利用可用的可再生能源,光伏发电在其最大功率点被跟踪,但通过使用虚拟同步发电机转换器控制,光伏发电机能够跟随负载需求,直到其可能的最大功率仍然超过负载需求。当最大功率低于需求时,第二区域的常规同步发电机弥补功率不足。通过基于遗传算法的优化,确定虚拟惯性参数,使光伏负荷跟随能力达到最优性能。通过通用多代理平台(UMAP),我们能够将两个区域(代表两个代理)的频率传达给计算区域控制误差(ACE)的中央代理,从而在我们的系统模型中考虑频率偏差。通过在没有任何储能的情况下实施负荷跟踪,我们显著降低了储能装置产生的成本。
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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