Hybrid Renewable Energy Systems (HRES), particularly those independent of the grid and powered by wind and solar energy, have gained increased interest as potential solutions to meet both potable water and electricity demand. Given the complex nature of these systems, achieving an optimal balance between different renewable energy resources, coupled with an appropriate energy storage system, necessitates precise consideration to devise an effective engineering solution. In this study, a HRES comprising photovoltaic panels, wind turbines, batteries, and a reverse osmosis desalination unit is designed and modeled to enhance the availability of potable water and meet the electricity demand. To optimize the proposed system properly, with the aim of minimizing costs and increasing reliability, six different algorithms are proposed: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harmony Search (HS), Non-dominated Sorting GA-II (NSGA-II), Multi-Objective PSO (MOPSO), and Multi-Objective HS (MOHS). The system is optimized using both single and multi-objective approaches with these proposed methods. The results are compared, and the best method is identified based on statistical analysis and each algorithm’s performance in finding the optimal solution. In single-objective optimization, the PSO algorithm outperforms the other methods, while in multi-objective optimization, after applying the Multi-Criteria Decision-Making method, MOPSO is identified as the best solution. Furthermore, a sensitivity analysis is conducted on some meteorological and economic parameters of the system. This validates the results obtained through the optimization process and identifies key parameters that have the most significant effect on the technical and economic aspects of the system. According to the results, the Total Annual Cost (TAC) of the optimal configuration for the proposed system is $4,368,500 at a 2% level of uncertainty using the single-objective approach, and $6,087,200 at a 0.82% level of power unavailability using the multi-objective approach. Additionally, sensitivity analysis reveals that among the parameters investigated, the solar panel price is the most influential, causing a 17% change in the TAC when varying it from 0% to −20%, and a 13% change in the TAC when varying it from 0% to 20%.

中文翻译：

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太阳能/风能/储能系统驱动的反渗透海水淡化装置优化设计：单目标和多目标优化


混合可再生能源系统（HRES），特别是那些独立于电网并由风能和太阳能供电的系统，作为满足饮用水和电力需求的潜在解决方案越来越受到人们的关注。考虑到这些系统的复杂性，要​​在不同的可再生能源之间实现最佳平衡，再加上适当的储能系统，需要精确考虑以设计有效的工程解决方案。在这项研究中，设计并建模了一个由光伏板、风力涡轮机、电池和反渗透海水淡化装置组成的 HRES，以提高饮用水的可用性并满足电力需求。为了正确优化所提出的系统，以最小化成本和提高可靠性为目标，提出了六种不同的算法：遗传算法（GA）、粒子群优化（PSO）、和谐搜索（HS）、非支配排序 GA-II（ NSGA-II)、多目标 PSO (MOPSO) 和多目标 HS (MOHS)。使用这些提出的方法，使用单目标和多目标方法来优化系统。对结果进行比较，并根据统计分析和每种算法在寻找最佳解决方案方面的性能来确定最佳方法。在单目标优化中，PSO算法优于其他方法，而在多目标优化中，应用多标准决策方法后，MOPSO被确定为最佳解决方案。此外，还对系统的一些气象和经济参数进行了敏感性分析。这验证了通过优化过程获得的结果，并确定了对系统技术和经济方面影响最显着的关键参数。 根据结果​​，使用单目标方法在 2% 的不确定性水平下，拟议系统最佳配置的年度总成本 (TAC) 为 4,368,500 美元，在使用多目标方法的 0.82% 电力不可用水平下，年度总成本 (TAC) 为 6,087,200 美元。客观的方法。此外，敏感性分析表明，在所研究的参数中，太阳能电池板价格影响最大，当其从 0% 变化到 -20% 时，导致 TAC 发生 17% 的变化，而当其变化时，TAC 会发生 13% 的变化。从 0% 到 20%。