The Particle Swarm Optimization (PSO) algorithm has been widely used in the field of optimization mainly due to its easy implementation, robustness, fast convergence, and low computational cost. However, due to its continuous nature, the PSO cannot be applied directly to real-life problems such as hybrid energy generating systems (HEGS) sizing, which contain continuous and discrete decision variables. In this context, the present work proposes the combination of the original version of the PSO with the binary version of the same algorithm (BPSO) for the sizing of HEGS. The transfer function is the main difference between these two algorithms. In this paper, an S-type transfer function is used to map the continuous space into a discrete space. All components of the HEGS are modeled and simulated during the optimization process. The net present value is defined as the unique objective function. The state of charge (SOC) of the batteries is the main constraint. The proposed PSO-BPSO is used for sizing hybrid power generating systems in the Galapagos Islands in Ecuador. Results show that the best configuration for the studied case is a hybrid system with solar panels, batteries, and diesel generators. Configurations that contain only photovoltaic panels and batteries imply a higher cost due to the oversizing of the battery bank. The proposed PSO-BPSO algorithm revealed to be a simple and powerful tool for efficient energy systems sizing.

中文翻译：

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一种用于混合发电系统选型的 PSO-BPSO 技术

粒子群优化（PSO）算法因其易于实现、鲁棒性强、收敛速度快、计算成本低等优点而被广泛应用于优化领域。然而，由于其连续性，PSO 不能直接应用于现实生活中的问题，例如混合能源发电系统 (HEGS) 的大小调整，其中包含连续和离散的决策变量。在这种情况下，目前的工作建议将原始版本的 PSO 与相同算法 (BPSO) 的二进制版本相结合，用于确定 HEGS 的大小。传递函数是这两种算法的主要区别。本文采用S型传递函数将连续空间映射到离散空间。在优化过程中对 HEGS 的所有组件进行建模和模拟。净现值被定义为唯一的目标函数。电池的荷电状态 (SOC) 是主要限制因素。拟议的 PSO-BPSO 用于确定厄瓜多尔加拉帕戈斯群岛的混合发电系统的规模。结果表明，所研究案例的最佳配置是带有太阳能电池板、电池和柴油发电机的混合系统。由于电池组的尺寸过大，仅包含光伏面板和电池的配置意味着更高的成本。所提出的 PSO-BPSO 算法被证明是一种用于高效能源系统规模调整的简单而强大的工具。结果表明，所研究案例的最佳配置是带有太阳能电池板、电池和柴油发电机的混合系统。由于电池组尺寸过大，仅包含光伏面板和电池的配置意味着更高的成本。所提出的 PSO-BPSO 算法被证明是一种用于高效能源系统规模调整的简单而强大的工具。结果表明，所研究案例的最佳配置是带有太阳能电池板、电池和柴油发电机的混合系统。由于电池组的尺寸过大，仅包含光伏面板和电池的配置意味着更高的成本。所提出的 PSO-BPSO 算法被证明是一种用于高效能源系统规模调整的简单而强大的工具。