Enhanced RIME-Based Energy Management Strategy for Techno-Economic Optimization of Standalone Hybrid Micro-grid

Technical Summary: Enhanced RIME-Based Energy Management Strategy for Techno-Economic Optimization of Standalone Hybrid Micro-grid

Problem Statement
The integration of renewable energy resources (RES) into isolated, off-grid communities necessitates efficient methodologies for the optimal sizing of standalone hybrid micro-grids. These systems face significant challenges due to the intermittent nature of solar and wind resources, which complicates the balance between supply and demand. The core technical challenge addressed in this study is the optimal sizing of a hybrid system comprising photovoltaic (PV) modules, wind turbines (WT), fuel cells (FC), and battery energy storage systems (BESS). This constitutes a complex, non-linear, non-convex, mixed-integer, and constrained optimization problem. Classical optimization methods are often inefficient in finding globally optimal solutions within reasonable computational time for such high-dimensional problems (specifically a 42-dimensional integer model in this case). While metaheuristic algorithms like Particle Swarm Optimization (PSO) and the recently proposed RIME (Rime Optimization) algorithm offer alternatives, they can suffer from premature convergence, loss of population diversity, and stagnation in local optima when applied to complex, constrained design spaces.

Methodology
The study proposes a comprehensive techno-economic framework applied to a standalone hybrid micro-grid located at the Zafarana site in the Gulf of Suez, Egypt. The system architecture connects generation and storage subsystems (PV, WT, FC, BESS) via a common DC bus, utilizing specific converters and inverters to manage power flow.

1. System Modeling:

   • Generation: PV output is modeled based on irradiance and temperature; WT output follows a piecewise power curve; the FC acts as a dispatchable generator.
   • Storage: A dual-storage approach is modeled, including a BESS for short-term buffering and a hydrogen storage system (electrolyzer and H₂ tank) for long-duration storage.
   • Constraints: The optimization enforces power balance across four representative operating scenarios (summer/winter, day/night), annual energy adequacy, component compatibility, and a minimum 3-day battery backup duration.
   • Objective: The goal is to minimize the total lifecycle cost (capital, installation, and O&M) over a 20-year project horizon.

2. Proposed Algorithm (E-RIME):
   The authors introduce an Enhanced RIME (E-RIME) algorithm to address the limitations of the baseline RIME and PSO. E-RIME integrates four specific enhancement mechanisms into the standard RIME search cycle:

   • Tent Chaotic Initialization: Replaces random initialization to improve population diversity and search-space coverage.
   • Opposition-Based Learning (OBL): Evaluates opposite candidate solutions during initialization to retain higher-quality individuals.
   • Adaptive Non-Linear Parameter Control: Utilizes a cosine decay function to manage the transition from exploration to exploitation.
   • Stagnation Restart Mechanism: Periodically reinitializes poorly performing agents to prevent premature convergence and maintain diversity.

3. Experimental Setup:
   The performance of E-RIME was compared against baseline RIME and PSO under identical conditions: 60 search agents, 30 independent runs, and varying iteration budgets (20, 100, and 300 iterations).

Key Results

• Convergence Performance: E-RIME demonstrated the fastest convergence speed, particularly under limited computational budgets (20 iterations). While baseline RIME and PSO showed slower initial improvements, E-RIME consistently reached lower lifecycle costs early in the search process.
• Lifecycle Cost Minimization: Across all iteration budgets, E-RIME achieved the lowest total lifecycle cost.
  • At 20 iterations: E-RIME ($2.0967 \times 10^6$) significantly outperformed PSO ($2.3787 \times 10^6$) and RIME ($2.5613 \times 10^6$).
  • At 300 iterations: E-RIME achieved the final lowest cost ($1.2581 \times 10^6$), followed by RIME ($1.4129 \times 10^6$) and PSO ($2.1805 \times 10^6$). Notably, PSO's cost increased slightly between 100 and 300 iterations, suggesting difficulty in refining solutions over extended searches.
• Optimal Component Sizing: The E-RIME algorithm determined an optimal configuration dominated by PV modules (Type 3) and Fuel Cells (Type 1). Crucially, the optimizer selected zero wind turbine units, indicating that under the specific techno-economic assumptions and Zafarana site conditions, wind generation was not cost-effective. The solution included significant battery storage (Types 1–3) and full utilization of inverter and converter capacities.
• System Operation: The 24-hour power balance analysis showed that the fuel cell provided a constant baseload, while PV contributed during daylight hours. The battery system operated primarily in a continuous charging mode, absorbing surplus generation, confirming the system's ability to meet load demands with a stable supply.

Significance and Claims
The paper claims that the proposed E-RIME framework is an effective and robust solution for large-scale, mixed-integer hybrid micro-grid sizing problems. The study asserts that the integration of chaotic initialization, OBL, adaptive parameter control, and stagnation restart mechanisms successfully enhances the balance between exploration and exploitation, leading to superior solution quality and faster convergence compared to established metaheuristics like PSO and baseline RIME.

The authors highlight that E-RIME is particularly suitable for applications with restricted computational budgets where rapid convergence is required, while also maintaining the ability to find superior global optima in extended search scenarios. The findings confirm the effectiveness of the approach in minimizing lifecycle costs for standalone renewable energy systems.

Limitations and Future Work
The study acknowledges that its results are based on deterministic renewable resource and load data, without explicitly modeling component degradation, uncertainty, or stochastic operating conditions. The authors suggest that future research should incorporate uncertainty modeling, degradation-aware lifecycle assessments, and real-time energy management strategies to further enhance the framework's practical applicability.