Renewable energy resources are often suffered from the challenges such as non-uniform power generation as a result of weather and seasonal variations. Hybrid renewable energy systems (HRES) is a solution for efficient energy intensification of renewable resources in which several of them are combined to overcome the challenges arising in operating these in a stand-alone mode. This study proposes a multi-objective dynamic optimization of a candidate HRES by using a genetic algorithm in which the operating cost of HRES, use of nonrenewable power, and fuel emission are minimized simultaneously over a finite time length, subject to operational constraints. In optimization, three strategies are evaluated by considering the wind, solar, and load profiles for 24 hours ahead (strategy 1), past and 1 hour ahead (strategy 2), and 1 hour ahead (strategy 3). Comparison of results illustrates that the power needs to be bought from the grid for strategy 1 is lower by 8.7 % and 10.7 % compared to strategy 2 and strategy 3 and also the power sold to the grid is 19 % and 22 % higher than strategy 2 and strategy 3, respectively while meeting the given load profile of 100 households.

Statement of significance

The paper proposes a novel dynamic multi-objective optimization of Hybrid Renewable Energy Systems (HRES). HRES are important for energy intensification of renewable energy systems. In this study, we try to solve three objectives, namely, the operating cost of HRES, use of nonrenewable power, and fuel emission are minimized simultaneously over a finite time length, subject to operational constraints. In optimization, three strategies are evaluated by considering the wind, solar, and load profiles. Overall, our results underlines that energy intensification when combined with intelligent decision making can help the processes to operate more flexibly and optimally

中文翻译：

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混合可再生能源系统的多目标动态优化

可再生能源资源经常受到天气和季节变化导致的发电不均匀等挑战。混合可再生能源系统 (HRES) 是一种用于可再生资源高效能源集约化的解决方案，其中几种可再生能源相结合，以克服以独立模式运行这些能源所带来的挑战。本研究通过使用遗传算法提出候选 HRES 的多目标动态优化，其中 HRES 的运行成本、不可再生能源的使用和燃料排放在有限的时间长度内同时最小化，受运行约束。在优化中，通过考虑提前 24 小时（策略 1）、过去和提前 1 小时（策略 2）和提前 1 小时（策略 3）的风能、太阳能和负载曲线来评估三种策略。

重要性声明

本文提出了一种新的混合可再生能源系统（HRES）的动态多目标优化。HRES 对于可再生能源系统的能源集约化很重要。在这项研究中，我们试图解决三个目标，即 HRES 的运营成本、不可再生能源的使用和燃料排放在有限的时间长度内同时最小化，受运营限制。在优化中，通过考虑风能、太阳能和负载曲线来评估三种策略。总体而言，我们的结果强调了能源集约化与智能决策相结合可以帮助流程更灵活、更优化地运行