# Hybrid Renewable Energy Systems--A Review of Optimization Approaches and Future Challenges
## 4 HRES Optimization
### General Principle of Energy System Optimization
#### 4.1.2 Pseudocode  
The second tool used to describe the operation of the system is the following pseudo-code: PSO algorithm processes [24], GA-PSO algorithm processes [29], PSO algorithm processes [41], Improved CSA algorithm process [31], Temporal Difference (TD) Lambda within a Reinforcement Learning framework for HRES optimization [42], MOPSO solving procedure [34], HGWO-SCA algorithm implementation methodology [35], and LF-SSA implementation methodology [36]. It bridges the gap between visual diagrams and detailed programming, offering a simplified, language-independent outline of the computational logic behind the optimization. As a result of the literature analysis, the pseudo-code for the same system as the flowchart is provided in Figure 6.  
Figure 5: Energy system sizing flowchart (developed by the authors). In detail, the flowchart and the pseudo-code illustrate a simplified process for optimizing an HRES by balancing energy supply, demand, and storage. The process starts by initializing input data: P_(Load(t)), which represents the power demand, and P_(Ren(t)), the renewable energy generation at a given time step. The iteration begins with i = 1, showing the first step. The system first checks if the renewable energy generation is enough to meet the load P_(Ren(t)) \(\geq\) P_(Load(t)). If the generation exceeds the demand, the surplus energy is used to charge the battery, provided the battery's state of charge (SOC) is below its maximum capacity SOC_Max. The excess power generation is dumped if the battery is fully charged [52]. When renewable energy is insufficient to meet the load P_(Load(t)) \(<\) P_(Ren(t)), the system evaluates whether the battery can discharge enough energy to cover the deficit SOC \(\geq\) P_(Load(t)) \(-\) P_(Ren(t)). If not, the unmet load is recorded for later analysis. After each time step, metrics such as unmet load and excess generation power are updated, and the system moves to the next step i = i + 1. Once all time steps have been processed, the system evaluates whether the combined excess and unmet power meet predefined operational requirements. If the requirements are satisfied, then the process ends; otherwise, the system is resized, components like battery capacity or renewable generation are adjusted, and the process begins again. This iterative framework efficiently balances energy supply, battery storage, and demand, minimizing energy losses and enhancing reliability. In detail, the flowchart and the pseudo-code illustrate a simplified process for optimizing an HRES by balancing energy supply, demand, and storage. The process starts by initializing input data: P_(Load(t)), which represents the power demand, and P_(Ren(t)), the renewable energy generation at a given time step. The iteration begins with i = 1, showing the first step. The system first checks if the renewable energy generation is enough to meet the load P_(Ren(t)) \(\geq\) P_(Load(t)). If the generation exceeds the demand, the surplus energy is used to charge the battery, provided the battery's state of charge (SOC) is below its maxi  
Figure 6: Energy system sizing pseudo-code (developed by the authors). Moreover, the analyzed articles reveal scientific principles for HRES optimization techniques:
* _Simulation tools are combined with optimization techniques_. MATLAB 9.1-9.13 is paired with algorithms like MOPSO, demonstrating their utility in combining simulation-based scenario analysis with robust optimization for better system design [58]. Also, its HOMER Pro 3.12-3.16 is used to solve the optimization problem using GA [49]. HOMER 3.14 simulates system configurations and compares the results with GWO [37]. MATLAB's 9.1-9.13 computational capabilities are incorporated with PSO to optimize energy system planning parameters [8]. * _Metaheuristic algorithms are blended with decision-making methods_. Firstly, the MOPSO and TOPSIS combination effectively balances optimization with multi-criteria decision-making, highlighting its use for selecting optimal Pareto solutions in multi-objective problems [62].