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ACO and CS-based hybrid optimisation method for optimum sizing of the SHES

机译：基于ACO和CS的混合优化方法以优化SHES的尺寸

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摘要

This study proposes an efficient novel approach for achieving optimal economic operation of a standalone hybrid energy system (SHES). Optimal economic operation is obtained through a balance between power outages, carbon emission footprint left by non-renewable resources and cost per unit of energy production of renewable energy resources. SHES with battery and without battery has been considered as two different strategies to examine the impact of the energy storage element. The resulting multi-objective function is optimised using a novel combination of ant colony optimisation (ACO) and cuckoo search (CS) algorithms. The proposed hybrid algorithm is verified against a set of well-known benchmark functions and the results are compared with ACO and CS optimisation techniques. To validate the performance of the proposed algorithm, simulations have been carried out using hourly meteorological data and load data for both strategies (without and with energy storage elements) called as strategies 1 and 2, respectively. It has been observed that strategy 2 exhibits superior performance. Comparative performance analysis of the proposed technique with ACO and CS technique has been analysed and better results have been exhibited with the proposed ACO-CS algorithm. Besides the system with energy storage element yields lesser cost.
机译：这项研究提出了一种有效的新颖方法，可实现独立混合能源系统（SHES）的最佳经济运行。通过在停电，不可再生资源留下的碳排放足迹与可再生能源资源的单位能源生产成本之间取得平衡，可以实现最佳的经济运行。带电池和不带电池的SHES被认为是检查储能元件影响的两种不同策略。使用蚁群优化（ACO）和布谷鸟搜索（CS）算法的新颖组合来优化所得的多目标函数。针对一组著名的基准函数对提出的混合算法进行了验证，并将结果与​​ACO和CS优化技术进行了比较。为了验证所提出算法的性能，已使用小时气象数据和负荷数据分别对两种策略（不带有和带有能量存储元素）分别称为策略1和2进行了仿真。已经观察到策略2表现出优异的性能。分析了该技术与ACO和CS技术的比较性能分析，并通过ACO-CS算法展示了更好的结果。此外，带有储能元件的系统成本更低。

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