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Optimum utilization of grid connected hybrid renewable energy sources using hybrid algorithm
Transactions of the Institute of Measurement and Control ( IF 1.7 ) Pub Date : 2020-06-01 , DOI: 10.1177/0142331220913740
M. Suresh 1 , R. Meenakumari 1
Affiliation  

An optimal utilization of smart grid connected hybrid renewable energy sources is proposed in this paper. The hybrid technique is the combination of recurrent neural network and adaptive whale optimization algorithm plus tabu search, called AWOTS. The main objective is the RES optimum operation for decreasing the electricity production cost by hourly day-ahead and real time scheduling. Here, the load demands are predicted using AWOTS to develop the correct control signals based on power difference between source and load side. Adaptive whale optimization algorithm searching behaviour is adjusted by tabu search. The proposed technique is executed in the MATLAB/Simulink working platform. To test the performance of the proposed method, the load demand for the 24-hour time period is demonstrated. By then the power generated in the sources, such as photovoltaic, wind turbine, micro turbine and battery by the proposed technique, is analyzed and compared with existing techniques, such as genetic algorithm, particle swarm optimization and whale optimization algorithm. Furthermore, the state of charge of the battery for the 24-hour period is compared with existing techniques. Likewise, the cost of the system is compared and error in the sources also compared. The comparison results affirm that the proposed technique has less computational time (35.001703) than the existing techniques. Moreover, the proposed method is cost-effective power production of smart grid and effective utilization of renewable energy sources without wasting the available energy.

中文翻译:

使用混合算法优化并网混合可再生能源利用

本文提出了智能电网连接的混合可再生能源的优化利用。混合技术是循环神经网络和自适应鲸鱼优化算法加上禁忌搜索的组合,称为 AWOTS。主要目标是通过每小时日前和实时调度来降低电力生产成本的 RES 优化运行。在这里,使用 AWOTS 预测负载需求,以根据源侧和负载侧之间的功率差异开发正确的控制信号。自适应鲸鱼优化算法搜索行为通过禁忌搜索进行调整。所提出的技术在 MATLAB/Simulink 工作平台上执行。为了测试所提出方法的性能,演示了 24 小时时间段的负载需求。届时,源头产生的能量,对光伏、风力涡轮机、微型涡轮机和电池等提出的技术进行了分析,并与遗传算法、粒子群优化和鲸鱼优化算法等现有技术进行了比较。此外,将电池在 24 小时内的充电状态与现有技术进行比较。同样,比较系统的成本并比较来源中的误差。比较结果证实,所提出的技术比现有技术具有更少的计算时间 (35.001703)。此外,所提出的方法是具有成本效益的智能电网电力生产和可再生能源的有效利用,而不会浪费可用能源。如遗传算法、粒子群优化和鲸鱼优化算法。此外,将电池在 24 小时内的充电状态与现有技术进行比较。同样,比较系统的成本并比较来源中的误差。比较结果证实,所提出的技术比现有技术具有更少的计算时间(35.001703)。此外,所提出的方法是具有成本效益的智能电网电力生产和可再生能源的有效利用,而不会浪费可用能源。如遗传算法、粒子群优化和鲸鱼优化算法。此外,将电池在 24 小时内的充电状态与现有技术进行比较。同样,比较系统的成本并比较来源中的误差。比较结果证实,所提出的技术比现有技术具有更少的计算时间(35.001703)。此外,所提出的方法是具有成本效益的智能电网电力生产和可再生能源的有效利用,而不会浪费可用能源。比较结果证实,所提出的技术比现有技术具有更少的计算时间(35.001703)。此外,所提出的方法是具有成本效益的智能电网电力生产和可再生能源的有效利用,而不会浪费可用能源。比较结果证实,所提出的技术比现有技术具有更少的计算时间(35.001703)。此外,所提出的方法是具有成本效益的智能电网电力生产和可再生能源的有效利用,而不会浪费可用能源。
更新日期:2020-06-01
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