This paper presents an optimal design for ground-mounted grid-connected bifacial PV power plants using a Computational Intelligence (CI)- based Harris Hawks Optimization (HHO) algorithm. This HHO algorithm identifies the best configuration of components and installation parameters for the bifacial PV power plant, aiming to maximize the final yield, minimize the Levelized Cost of Electricity, and boost the Net Present Value. Four variables were optimized: the bifacial PV module model, inverter model, tilt angle, and module elevation. Furthermore, the paper introduces a Harris Hawks Optimization Sizing Algorithm (HHOSA) to address the sizing challenges. The presented HHOSA was purely developed in Matlab R2017b. The usage of PVsyst was only limited to the derivation of irradiation data at different tilt angle of PV array. These data were later used in HHOSA. To verify its effectiveness, HHOSA was benchmarked against other CI algorithms, including the Slime Mould Algorithm (SMA), Firefly Algorithm (FA), Manta Ray Foraging Optimization (MRFO), and Cuckoo Search Algorithm (COA). The evaluation considered the algorithm’s stability, local search capability, convergence rate, computation time, and required population size. Findings suggest that the HHOSA outperforms its peers, marking it as a potential leader for designing bifacial PV power plants. The results indicate that the HHOSA algorithm exhibits superior performance in these aspects, making it a promising approach for optimizing the design of bifacial PV power plants. Moreover, this study provides insights into the economic and technical viability of bifacial PV systems under various environmental and system conditions. A sensitivity analysis, focusing on the interplay of three decision variables − albedo values (25 %, 50 %, and 75 %), tilt angles (10°, 25°, and 35°), and module elevations (0.5 m, 1.5 m, and 2 m) − was conducted. It assessed their influence on final yield, additional bifacial PV module yield, Levelized Cost of Electricity, and the system’s Net Present Value. The results emphasize the importance of carefully considering the impacts of albedo, module elevation, and tilt angle on the financial performance of bifacial PV installations.

中文翻译：

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使用 Harris Hawks Optimization 优化具有双面模块的固定倾斜地面安装并网光伏系统的尺寸


本文提出了一种使用基于计算智能 (CI) 的 Harris Hawks 优化 (HHO) 算法的地面并网双面光伏电站的优化设计。该 HHO 算法确定了双面光伏电站的最佳组件配置和安装参数，旨在最大化最终产量、最小化电力平准化成本并提高净现值。优化了四个变量：双面光伏组件模型、逆变器模型、倾斜角度和组件仰角。此外，本文还介绍了 Harris Hawks 优化规模调整算法 (HHOSA) 来解决规模调整挑战。所提出的 HHOSA 纯粹是在 Matlab R2017b 中开发的。 PVsyst的用途仅限于导出光伏阵列不同倾斜角度下的辐照数据。这些数据后来被用于 HHOSA。为了验证其有效性，HHOSA 与其他 CI 算法进行了基准测试，包括史莱姆霉菌算法 (SMA)、萤火虫算法 (FA)、蝠鲼觅食优化 (MRFO) 和布谷鸟搜索算法 (COA)。评估考虑了算法的稳定性、局部搜索能力、收敛速度、计算时间和所需的种群规模。调查结果表明，HHOSA 的表现优于同行，使其成为设计双面光伏电站的潜在领导者。结果表明，HHOSA 算法在这些方面表现出优越的性能，使其成为优化双面光伏电站设计的一种有前途的方法。此外，这项研究还深入了解了双面光伏系统在各种环境和系统条件下的经济和技术可行性。 敏感性分析，重点关注三个决策变量的相互作用 - 反照率值（25%、50% 和 75%）、倾斜角度（10°、25° 和 35°）以及模块标高（0.5 m、1.5 m）和 2 m) − 进行。它评估了它们对最终产量、额外双面光伏组件产量、平准化电力成本和系统净现值的影响。结果强调了仔细考虑反照率、组件高度和倾斜角度对双面光伏装置财务绩效影响的重要性。