Table 10 Previous studies on Hybrid renewable power systems with their strength and weaknesses.
From: Wind energy assessment and hybrid micro-grid optimization for selected regions of Saudi Arabia
Ref(s) | Objectives | Energy source(s) | Constraint(s) | software/Tool | merits/weaknesses | |
|---|---|---|---|---|---|---|
i. Reduction and savings on bills ii. Emission of Carbon | PV, FC and Battery | Cost | Formal mathematics techniques | Formal mathematical techniques are used to size HRESs and are effective in exploring the solution space to identify the optimal solution. However, these techniques have limitations when handling stochastic environments and managing a large number of design points. They are not ideally suited for such scenarios and may encounter challenges related to the curse of dimensionality. Additionally, these approaches can result in prolonged computation times and may struggle to model and optimize practical constraints effectively. Since the objective function and constraints are simple and linear, these methods may have limited applicability to complex or nonlinear systems. | ||
i. Minimize NPC ii. decrease system energy cost | PVs, WTs, Battery and DG | DPSP | Heuristic optimization technique | Heuristic optimization methods are effective for determining the unit sizing of implementation. However, these meta-heuristic techniques require specific algorithmic parameters. Poor adjustment of these parameters can result in the algorithm getting stuck in local optima or lead to lengthy computation times. Effective tuning of these parameters typically requires expertise. While heuristic algorithms may not guarantee an optimal solution, they provide an approximated solution. | ||
Proposed technique, MSSCS | i. cost and energy savings. ii. Reliability iii. Carbon emissions i. Minimization of NPC ii. Increased in reliability. | PVs, WTs, Battery and DG PVs, WTs and Battery. | LPSP LCOE and LPSP | HOMER Multi strategy serial cuckoo search algorithm | The software used in these studies is a useful tool for sizing HRES. However, it has some limitations of not supporting formulation of multi-objective problems, fails to account for within-hour variability or battery depth of discharge, and requires significant computation time for larger design scenarios. The tool used in these studies is highly effective for sizing HRES systems. However, it does not optimize or formulate multi-objective problems, nor does it account for short-term variability within an hour or battery depth of discharge. Additionally, it requires significant processing time for larger design scenarios. | |
