スタンドアローン型ハイブリッドマイクログリッドの技術経済最適化のための拡張RIMEベースのエネルギー管理戦略

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a novel optimization algorithm (E-RIME) with comparative analysis, useful for researchers in renewable system design.

🏢実務担当者:Offers a cost-effective sizing methodology for hybrid micro-grids, applicable to remote or off-grid projects.

🏛政策担当者:Supports policy on rural electrification and renewable integration, though specific policy implications are limited.

<title>Abstract</title> <p>The increasing integration of renewable energy resources into isolated and off-grid communities has intensified the need for efficient and cost-effective optimal sizing methodologies for standalone hybrid micro-grids. This study presents a comparative techno-economic evaluation of three metaheuristic optimization algorithms—Enhanced RIME Optimization (E-RIME), Particle Swarm Optimization (PSO), and baseline RIME—for the optimal sizing of a standalone hybrid micro-grid located at the Zafarana site, Gulf of Suez, Egypt. The investigated system consists of photovoltaic PV modules, wind turbines (WT) as well as a fuel cell (FC) which is supplemented by a DC/DC-converter and an inverter. To increase the efficiency of regenerative energy systems, a battery energy storage system (BESS) is integrated. The optimization problem is formulated as a 42-dimensional integer optimization model that minimizes the total 20-year lifecycle cost while satisfying power balance, energy adequacy, component compatibility, and battery backup constraints. To enhance the search capability of the original RIME algorithm, E-RIME incorporates Tent chaotic initialization, Opposition-Based Learning (OBL), adaptive non-linear parameter control, and a stagnation restart mechanism. Comparative simulations were performed under identical optimization settings to evaluate convergence characteristics, solution quality, and robustness. The results demonstrate that E-RIME achieves faster convergence and consistently obtains lower lifecycle costs than both PSO and baseline RIME across different iteration budgets. Furthermore, E-RIME exhibits improved solution stability and a better balance between exploration and exploitation, enabling superior search performance in the high-dimensional optimization space. These findings confirm the effectiveness of the proposed E-RIME approach and highlight its suitability for optimal sizing of standalone hybrid renewable energy systems.</p>

• Research Square https://doi.org/10.21203/rs.3.rs-10718551/v1first seen 2026-09-02 04:33:49 · last seen 2026-09-15 04:22:16