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Parametric Studies on Artificial Intelligence Techniques for Battery SOC Management and Optimization of Renewable Power

机译:电池SOC管理人工智能技术的参数研究及可再生能力优化

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摘要

Drastic increases in oil prices and need to reduce global warming are issues that have raised huge public awareness, which in turn encouraged many developed and developing countries to adopt new energy policies. Renewable energy resources are gaining fast attention in solving power supply problems in almost every area to shield the environment. In solving power supply problem standalone microgrid systems including renewable energy sources plays a key role. For a stand-alone renewable power delivery system, battery is the crucial component which allows storing excess renewable energy and further using the energy as and when needed by the load. In order to achieve the best performance and utilization the state of charge of the batteries should be assessed. This paper presents heuristic approaches to evaluate the state of charge (SOC) of lead–acid battery through optimal power generation in a standalone hybrid wind-solar renewable power delivery system. The mathematical analysis is made by the application of modified heuristic techniques, i.e. tuned genetic algorithm (TGA) and improved colliding body optimization(ICBO), for a specific loading condition in India. More optimized power is generated by the renewable distributed energy sources when evaluated by ICBO than that achieved by TGA, for both winter and summer load demands. These optimized value of power generated by the application of ICBO, resulted in low average value of SOC. The low values of SOC further prevent the battery from malfunctioning for a longer period of time. In order, to explore more optimal generation pertaining to good value of SOC parametric studies of modified algorithms, i.e. TGA and ICBO have been carried out. A set of critical operational parameters have also been obtained through parametric study of TGO and ICBO for a specific real time load profiles. Thus the reliable and optimal values lead to a better prospect in utilizing standalone renewable power delivery system for Indian future power scenario. Moreover, a comparative study has also been performed in analysing the performances of conventional and non-conventional methods in this regard. It has been found that the heuristic techniques, viz ICBO and TGA delivered 13% and 17% better result in comparison to that obtained by linear programming method respectively. Thus it can be surmise that the heuristic techniques perform well in considering the uncertainties and approximations of real world problem of generation renewable power, pertaining to better values of SOC.
机译:油价的大幅增加,需要减少全球变暖的问题是提高了巨额公众意识的问题,这反过来鼓励许多发达国家和发展中国家采用新的能源政策。可再生能源资源在几乎每个区域求解电源问题时都会在掩盖环境中进行求解。在求解电源问题中,独立的微电网系统,包括可再生能源来源的关键作用。对于独立的可再生电力输送系统,电池是关键的组件,允许将多余的可再生能源存储多余的可再生能量,并进一步使用负载所需的能量。为了实现最佳性能和利用,应评估电池的充电状态。本文通过独立式混合风力 - 太阳能可再生电力输送系统中的最佳发电来评估铅酸电池的充电状态(SOC)的启发式方法。数学分析是通过应用修改的启发式技术,即调谐遗传算法(TGA)和改进的碰撞体优化(ICBO),在印度的特定负载条件。当冬季和夏季负荷需求的ICBO评估时,可再生分布式能源产生更优化的电源。这些通过ICBO应用产生的功率优化值,导致SoC的平均值低。 SOC的低值进一步防止电池在更长的时间内从故障发生故障。为了探讨改进算法的SoC参数研究的良好价值,即探索更优化的发电,即,已经进行了TGA和ICBO。还通过参数研究TGO和ICBO的参数研究获得了一组关键操作参数,以获得特定的实时负载轮廓。因此,可靠和最佳的值导致利用独立可再生电力输送系统进行印度未来电力方案的更好的前景。此外,还已经在分析了这方面的常规和非常规方法的性能方面进行了对比研究。已经发现,与线性编程方法分别获得的相比,启发式技术,viz Icbo和TGA提供了13%和17%的结果,结果更好。因此,它可以推动启发式技术在考虑到生成可再生能力的现实世界问题的不确定性和近似值时表现良好,这与SOC的更好值有关。

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