# Development of an Intelligent Solution for the Optimization of Hybrid Energy Systems
###### Abstract
Furthermore, a statistical study was conducted to determine the stability of the performance of each optimization strategy. The results revealed optimal variables, including the number of photovoltaic panels, wind turbines, batteries, and diesel generator capacity. Over the course of a year, the optimal configuration was tested against the study of capital and fuel expenditures. The statistical results also proved the robustness of the developed algorithm. [PERSON] et al. presented a comparative study between four optimization methods for autonomous HES, which include two energy sources (PV, wind), storage batteries, and fuel cells [13]. The first method is based on mathematical equations, the second uses the SDO program, the third uses the Homer optimization software, and the last uses genetic algorithms based on the HOGA software. The results show that the HES designed by each method guarantees reliability in the energy supply, and SDO is the best HES optimization method. Other contributions use AI approaches. [PERSON] and [PERSON] propose four heuristic algorithms, PSO, TS, SA, and HS, on an autonomous HES (PV, wind turbine, fuel cell) [14]. This study showed that HES with energy storage batteries is the best choice economically, and PSO is the most reliable optimization technique. In the same context, [PERSON] et al. were interested in the development of a methodology based on the information of a GIS to identify the preferred sites for an HES based on wind and PV [15]. This study used the principle of fuzzy logic and the MCDM approach to determine the economic feasibility of wind and solar energy. Finally, the associated maps were superimposed to obtain the most feasible places for HES. The proposed methodology can help policymakers and investors easily adapt to other types of energy resources. [PERSON] et al. provided an overview of optimization methods. The study presented the different architectures of stand-alone or network-connected HES [16]. This study concluded with the following submissions:  
* HES that includes solar and wind energy sources requires the addition of storage batteries or integration into the grid. * Among the methods proposed (probabilistic, analytical, iterative, and hybrid), the hybrid methods are the most powerful for HES optimizing. [PERSON] et al. provided an optimization solution for HES based on the intelligent reasoning rules [17; 18]. Then, this solution was improved to be generic. After implementing it, the results showed its effectiveness in choosing the optimization technique. In addition, it is characterized by various advantages, such as flexibility in its operation and updating [19]. [PERSON] et al. proposed a MAS solution to optimize the HES [20]. [PERSON] et al. proposed to minimize the costs, maximize the use of RE, and simplify the integration into the grid [21]. [PERSON] et al. present an intelligent method of HES energy management [22]. The solution is based on the MAS. This concept allows the different units of the MAS to work together to achieve the objectives. [PERSON] et al. studied the performance of the control strategy by verification on a microgrid that includes storage batteries and a PV generator [23]. This study allows us to know the advantages that characterize distributed management compared with centralized management. [PERSON] et al. developed a MAS for managing the energy of a microgrid containing two PV systems, two wind turbines, a battery unit, and a diesel plant [24]. This solution offers the consumer the possibility of choosing the actions to increase energy efficiency. A study adapted to the west African climate was conducted by [PERSON] et al. It is based on the MAS for the control, where it was applied on an HES (PV, wind, batteries) [25]. In this study, three load profiles were considered in the simulations. Then, the control made it possible to reduce battery use by 3%, 5%, and 6%, for profiles 1, 2, and 3, respectively. It was made in 1 day to obtain 35 kWh for profile 1 and 20 kWh for profile 3.