# Hybrid Renewable Energy Systems--A Review of Optimization Approaches and Future Challenges
## 6 Discussion  
The reviewed articles offer a comprehensive analysis of HRES design, optimization techniques, and future insights. Its scope is illustrated in Figure 8.  
**Optimization approaches advantages:**  
* This research highlights the trade-offs between complexity, scalability, hardware dependence, and accuracy for each optimization technique. MILP and MATLAB 9.1-9.13 are the most flexible and suitable for HRES multi-objective optimization for their precision and versatility, but they require significant computational resources (see Section 4.4). * Four scientific principles for HRES optimization are revealed: simulation tools are combined with optimization techniques, metaheuristic algorithms are blended with decision-making methods, metaheuristic methods are combined in between, and multi-objective optimization is used together with Pareto Analysis (see Section 4.2). These principles are crucial for HRES optimization as they enable precise modeling of complex systems, efficient exploration of solution spaces, and practical decision-making. * Four relationships between criteria and optimization methods were identified: economic indicators for multi-objective approaches, environmental criteria for evolutionary algorithms, reliability criteria for simulation-driven techniques, and social criteria for emerging frameworks (see Section 4.3). Incorporating social criteria, such as job creation and public acceptability, alongside traditional metrics like carbon emissions ensures technically robust and socially beneficial HRES designs. Balancing competing objectives like cost, efficiency, and sustainability ensures innovative, scalable, and actionable solutions for real-world energy challenges. **Optimization techniques challenges:**  
* As the diversity of renewable energy sources in HRESs increases, along with the integration of more energy storage solutions and the development of both islanded and grid-connected HRESs (see Section 3), the demand for innovative optimization solutions, their integration, and the selection of appropriate criteria is growing. * This article collects challenges (see Table 1) that researchers faced, so the directions clearly emphasize advancing HRES optimization, control, and integration of renewable resources [62; 68; 90]. Reports are increasingly addressing uncertainties [50; 67; 91], improving demand-side management [47; 60], and developing advanced optimization techniques [20; 40; 92] to enhance system efficiency and scalability. HRES integration is often coupled with innovative technologies like advanced batteries and AI-based optimization [44; 48; 63]. Optimization challenges underscore a growing need for sustainable and resilient solutions. Economic and policy considerations are also gaining prominence, particularly in evaluating job creation [55; 93]. Robust monitoring systems and dynamic modeling remain crucial for ensuring adaptability and precision in HRES performance [38; 48]. **Future perspectives:**  
* The roadmap for future research consisting of nine areas (see Table 2) was developed to address existing issues and foster advancements in HRESs, enabling efficient integration, improved reliability, and broader adoption in diverse energy landscapes. * Future advancements in HRESs will likely focus on dynamic optimization that incorporates high-resolution renewable resource forecasting and real-time adaptability of HRESs [31; 68]. Scaling these systems to integrate diverse energy sectors, including industrial and agricultural loads, will further enhance their applicability and impact [57; 68]. Efficient algorithm designs will play a role as HRESs expand globally, emphasizing the need for lightweight and scalable algorithms. * The future lies in integrating quantum computing, which can drastically accelerate complex tasks and enable the exploration of previously unfeasible problems such as real-time dynamic pricing in HRESs, microgrid control, or detailed life cycle assessments of HRESs [79; 80].