## 1 Introduction
Over the past decade, numerous studies have focused on the optimal sizing of hybrid renewable energy systems for agricultural electrification and irrigation [14; 15], as well as on system optimization [16; 17; 18; 19] and the development of optimization algorithms [20; 21; 22; 23; 24; 25]. This paper presents a methodology to optimize the sizing of a self-sustaining hybrid wind and solar power system. The approach employs metaheuristic techniques to determine the most suitable type and quantity of solar panels and wind turbines from a range of commercially available components. The objective is to minimize overall system costs while ensuring a stable energy supply, which is achieved through the integration of large water reservoirs for energy storage. Metaheuristic methods, such as genetic algorithms (GAs) and particle swarm optimization (PSO), are used to solve complex multi-objective optimization problems. Inspired by natural phenomena--such as biological evolution and swarm behavior--these techniques effectively explore large search spaces to identify optimal or near-optimal solutions. The main contributions of this work are as follows. * **Hydraulic Energy Storage Strategy:** A novel energy storage concept, replacing conventional electrochemical batteries with a high-capacity water reservoir, is proposed. In this approach, excess solar or wind energy is utilized to pump water into an elevated tank, thereby converting electrical energy into hydraulic potential. This storage method offers a cost-effective and environmentally friendly solution, making it particularly well suited to agricultural applications where the stored water simultaneously serves as a vital resource for irrigation. * **Gravity-Driven Irrigation:** During periods of low solar irradiance or wind availability, the system uses the stored hydraulic energy to deliver water purely through gravitational flow, entirely avoiding the need for electrical input. This contributes to improved energy autonomy and operational reliability, especially in off-grid rural settings. * The integrated system modeling framework explicitly incorporates the reservoir's storage capacity and hydraulic dynamics, enabling simulation and quantification of the impacts of the storage strategy. The results demonstrate that this framework effectively reduces dependence on electrical storage, improves irrigation reliability, and significantly lowers total system costs over the project's lifetime. * **Multi-Objective Optimization Framework:** A multi-objective optimization model was implemented to design and size the hybrid renewable energy system. This model concurrently minimizes system component costs and maximizes renewable electricity production while adhering to defined technical and operational constraints. The approach enables the generation of design configurations that are both economically efficient and technically robust. The rest of this paper is structured as follows: Section 2 presents the sizing methodology for a hybrid energy system in Skikda, Algeria. Section 3 presents the results. Section 4 provides a detailed discussion of the obtained results. Finally, Section 5 concludes this paper.