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Optimization coupling RO desalination unit to renewable energy by genetic algorithms

机译:利用遗传算法优化反渗透淡化装置与可再生能源的耦合

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摘要

Renewable energy sources (RES) for powering desalination processes is a promising option especially in remote and arid regions where the use of conventional energy is costly or unavailable. Reverse osmosis (RO) is one of the most suitable desalination processes to be coupled with different RES such as solar and wind. If RES/RO systems are optimally designed, some combinations can be cost effective and reliable. However, the design of such systems is complex because of uncertain renewable energy supplies, load demands, and the non-linear characteristics of some components. In such system, different scenarios can be suggested; i.e. combinations of Photovoltaic (PV) panels, type and number of batteries, type and number of turbines, etc. Therefore, it is difficult to determine the optimal configuration with classical techniques. The development of a tool to integrate all parameters involved and compare between the possible scenarios is very important. This paper presents a new model based on the genetic algorithms allowing for coupling small RO unit to RES. A particular interest is focused on the hybrid systems (PV/WIND/Batteries/RO). The objective function to minimize corresponds to the total water cost (capital cost plus operational costs). The feasible solutions (individuals in each generation) are obtained through simulations carried along a complete year.
机译:为海水淡化工艺提供动力的可再生能源(RES)是一种有前途的选择,尤其是在偏远和干旱地区,这些地区使用常规能源的成本很高或无法获得。反渗透(RO)是最适合与不同RES(例如太阳能和风能)结合使用的脱盐工艺之一。如果对RES / RO系统进行了优化设计,则某些组合可能具有成本效益和可靠性。但是,由于不确定的可再生能源供应,负载需求以及某些组件的非线性特性,此类系统的设计非常复杂。在这样的系统中,可以提出不同的方案。即光伏(PV)面板,电池的类型和数量,涡轮的类型和数量等的组合。因此,很难通过经典技术确定最佳配置。开发一种工具来集成涉及的所有参数并在可能的方案之间进行比较非常重要。本文提出了一种基于遗传算法的新模型,该模型允许将小型反渗透单元与RES耦合。特别关注混合动力系统(PV / WIND /电池/ RO)。最小化的目标函数对应于总水成本(资本成本加上运营成本)。通过一整年的模拟,可以获得可行的解决方案(每一代人)。

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