This article proposes a tidal resource evaluation-based planning method for profitable and reasonable allocations of tidal current generation farms (TCGFs). The directionality and variability of tidal current velocity (TCV) and their correlation are addressed. First, a TCGF power output model is developed. The correlations between tidal current directions and the probability distributions of TCV magnitudes are modeled by rose diagram, and wake effects in TCGFs are simulated by the semi-analytical wake model. Second, a tidal current resource evaluation method is proposed to determine the TCGF's capacity. An optimization planning model is proposed to optimally coordinate tidal current turbine (TCT) micrositing, offshore substation position selection and the ECS planning scheme. The positions of TCTs and substation are modeled by continuous coordinates. The objective is to minimize the levelized cost of energy, subject to the geographical boundary and security operation requirements of the TCGF. Third, the Prufer number-based particle swarm optimization (PN-PSO) algorithm is developed to solve the proposed planning model. The requirements of ECS radial topology and no cable crossings can be fully satisfied. Finally, the measured TCV data collected from Alaska and North Orkney Islands were used to verify the effectiveness and adaptability of the proposed method.

中文翻译：

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考虑潮汐流方向性的基于潮汐资源评估的潮汐发电场分配方法

本文提出了一种基于潮汐资源评估的规划方法，用于对潮汐发电场（TCGF）进行有利可图的合理分配。解决了潮流速度（TCV）的方向性和变异性及其相关性。首先，开发了TCGF功率输出模型。利用玫瑰图对潮流方向与TCV幅值的概率分布之间的相关性进行建模，并通过半解析苏醒模型对TCGF中的苏醒效应进行模拟。其次，提出了一种潮汐资源评估方法来确定TCGF的能力。提出了一种优化计划模型，以最佳地协调潮流涡轮机（TCT）的选址，海上变电站的位置选择和ECS计划方案。TCT和变电站的位置通过连续坐标建模。目的是在符合TCGF的地理边界和安全操作要求的情况下，使能源的平均成本最小化。第三，开发了基于Prufer数的粒子群优化（PN-PSO）算法来解决所提出的规划模型。完全满足ECS径向拓扑和无电缆交叉的要求。最后，从阿拉斯加和北奥克尼群岛收集的实测TCV数据用于验证所提方法的有效性和适应性。提出了基于Prufer数的粒子群算法（PN-PSO），用于求解所提出的规划模型。完全满足ECS径向拓扑和无电缆交叉的要求。最后，从阿拉斯加和北奥克尼群岛收集的实测TCV数据用于验证所提方法的有效性和适应性。提出了基于Prufer数的粒子群算法（PN-PSO），用于求解所提出的规划模型。完全满足ECS径向拓扑和无电缆交叉的要求。最后，从阿拉斯加和北奥克尼群岛收集的实测TCV数据用于验证所提方法的有效性和适应性。