# Hybrid Renewable Energy Systems--A Review of Optimization Approaches and Future Challenges
## 4 HRES Optimization
### General Principle of Energy System Optimization
#### 4.1.1 Flowchart  
The most widely used structure is the flowchart, it can visually represent many different processes, algorithms and strategies: energy management strategy [31,39]; Aspen Plus process flow diagram of the Anion Exchange Membrane (AEM) water electrolyzer [15]; structure of Back Propagation Neural Network (BPNN) algorithm [40]; solution flow of two-stage robust optimization model [41]; PV-wind-H2 system energy flow [42]; optimization and simulation flowchart [43]; strategy architecture of Bi-level gaming program [44]; Improved CSA implementation flowchart for solving the problem [45]; power management of the HRES with charge and discharge strategies; and Flowchart of the Archimedes optimization algorithm (AOA) [32]; block diagram of HRES energy production operation; and block diagram of whale optimization algorithm (WOA) implementation strategy for HRES designing [46]; 2 flowcharts of Monte Carlo simulation (MCS) method [35]; NSGA-II optimization flowchart [19]; flow diagram of the proposed design approach of 100% renewable electricity supply and the framework of the developed hybrid Multi-Criteria Decision Making (MCDM) approach [9]; Hybrid Grey Wolf Optimizer-Sine-Cosine Algorithm (HGWO-SCA) algorithm flowchart is employed for the design of a PV/WT/FC [47]; simplified flow chart of the proposed hybrid energy system [48], Gravity Energy Storage sizing and implementation methodology [49]; operation strategy of HRES composed of PV, WT, Bio-diesel generator, and battery; and flowchart and steps of the emerging metaheuristic optimization method based on Harmony Search (HS) [33]; proposed framework for the robust planning of an IHS; Flowchart of the solution process of the Hybrid Metaheuristic Algorithm; HMA-based Adaptive Robust Optimization (ARO) [50]; flowchart of the optimization procedure [51]; Modified Multi-Objective Salp Swarm Optimization Algorithm (MMOSSA) flowchart [31]. All mentioned flowcharts provide a high-level visual representation of the decision-making process.  
Flowcharts for HRES in the reviewed literature vary widely in design and focus, reflecting distinct methodologies and objectives. A comparison can be drawn based on three key aspects: optimization frameworks, energy management strategies, and component integration. For instance, flowcharts using metaheuristic algorithms like Particle Swarm Optimization (PSO) and genetic algorithms (GA) focus on cost and efficiency optimization, often visualizing iterative processes for the sizing and placement of components [20; 28]. In contrast, some studies emphasize energy management, illustrating real-time control strategies for balancing supply and demand in microgrids, such as using hybrid algorithms for load sharing and grid support [22; 48]. Lastly, regarding component integration, flowcharts differ by the extent of renewable and storage technologies included, with some incorporating advanced hydrogen storage and electrolyzers alongside traditional PV and wind setups [21; 22]. As a result of this analysis, the most straightforward energy system sizing flowchart of an off-grid 100 percent HRES is shown in Figure 5.