Avoiding solar thermal energy storage reduces efficiency in hybrid solar-geothermal energy systems, making them impractical. To address this challenge, a synergistic approach involves the integration of these resources in the construction of hybrid power plants. An experiment is conducted with five independent variables: evaporator temperature, separator pressure, inlet pressure of turbine 2, effectiveness of vapor generator 1, and desalination mass ratio. Unlike most energy systems that rely on a single optimization pattern, this study utilizes response surface methodology (RSM) to design and gather data through simulation. Additionally, an artificial neural network (ANN) is employed alongside RSM to establish mappings from independent variables to response variables, including thermal efficiency and levelized cost of product. The selection of objective functions derived from ANN is predicated on their commendable performance, denoted by an R-squared value of 1. Furthermore, a cost function is formulated with the dual aims of maximizing thermal efficiency and minimizing the levelized cost of product. This function is subsequently optimized through the application of genetic algorithms (GAs). The findings elucidate that specific parameter values—namely, a desalination mass ratio of 2.43, separator pressure of 455.77 kPa, effectiveness of vapor generator 1 of 0.82, inlet pressure of turbine 2 of 12000 kPa, and evaporator temperature of −11.51 —conducive to the optimal condition are identified, yielding a thermal efficiency of 30.47% and a levelized cost of product of 13.04 . This endeavor is anticipated to furnish an algorithmic framework not only for modeling hybrid plants but also for optimizing electrical power generation processes.

中文翻译：

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使用耦合人工神经网络和遗传算法对地热-太阳能混合发电厂进行建模和优化

避免太阳能热能存储会降低太阳能-地热能混合系统的效率，使其不切实际。为了应对这一挑战，协同方法涉及将这些资源整合到混合发电厂的建设中。使用五个自变量进行实验：蒸发器温度、分离器压力、涡轮2入口压力、蒸汽发生器1的效率和脱盐质量比。与大多数依赖单一优化模式的能源系统不同，本研究利用响应面方法 (RSM) 通过模拟来设计和收集数据。此外，还采用人工神经网络 (ANN) 与 RSM 一起建立从自变量到响应变量的映射，包括热效率和产品的平准化成本。从 ANN 导出的目标函数的选择取决于其值得称赞的性能，用 R 平方值 1 表示。此外，制定成本函数的双重目标是最大化热效率和最小化产品的平准化成本。随后通过遗传算法（GA）的应用对该功能进行优化。结果表明，具体参数值，即海水淡化质量比为2.43，分离器压力为455.77 kPa，蒸汽发生器1的效率为0.82，涡轮2入口压力为12000 kPa，蒸发器温度为-11.51，有利于实现确定了最佳条件，热效率为 30.47%，产品平准成本为 13.04 。这项工作预计将提供一个算法框架，不仅用于混合动力发电厂建模，还用于优化发电过程。