Integrated techno-economic optimization and metaheuristic benchmarking of grid-connected hybrid renewable energy systems using real-world load and climate data

• Developed a techno-economic model optimizing PV/WT/DG/BT hybrid systems using real annual meteorological and load data. • Introduced a rule-based energy management strategy tailored for grid-connected HRES with strict reliability and REF constraints. • Benchmarked four metaheuristic algorithms under identical conditions; MFOA outperformed in speed, accuracy, and sustainability. • Achieved 100% renewable fraction with zero diesel use while minimizing LCOE to $0.1443/kWh. • Proposed a replicable optimization framework adaptable to regions with high tariffs and renewable integration targets. This study proposes an integrated optimization framework for the techno-economic sizing and performance evaluation of a grid-connected hybrid renewable energy system (HRES) comprising photovoltaic (PV) panels, wind turbines (WT), battery storage (BTS), and a diesel generator (DG). A real-world case study is conducted on a university campus in Turkey using high-resolution hourly meteorological and load data over a full year (8760 hours). The objective is to minimize the annualized cost of the system (ACS), levelized cost of energy (LCOE), and total net present cost (TNPC), while ensuring high reliability through a constraint on the loss of power supply probability (LPSP) at 0.5%. The decision variables include the optimal capacities of PV, WT, DG, BT, and inverter components, bounded by technical, economic, and operational constraints, including a minimum renewable energy fraction (REF) requirement. The system's energy production, storage, and grid interactions are modeled using detailed mathematical formulations. Optimization is performed using the Moth-Flame Optimization Algorithm (MFOA) and benchmarked against the Whale Optimization Algorithm (WOA), Flower Pollination Algorithm (FPA), and Genetic Algorithm (GA). Simulation results identify the PV/WT/BT configuration as the most cost-effective and reliable, achieving an LCOE of $0.1342/kWh, a TNPC of $3.2542 × 10⁶, and an ACS of $2.9214 × 10⁵. These values reflect a 33% cost reduction compared to the off-grid configuration. Additionally, the system enables annual grid electricity purchases of up to 4.4086 × 10⁵ kWh and sales of up to 1.2114 × 10⁶ kWh. Notably, the achieved LCOE is significantly lower than the prevailing commercial grid tariff of $0.35/kWh in Turkey, demonstrating the financial competitiveness of the proposed system for institutional and commercial users. In terms of algorithmic performance, MFOA outperforms the other methods by delivering the fastest convergence, highest optimization stability, and a fully renewable solution (REF = 100%) without DG operation. This solution achieves an LCOE of $0.1443/kWh and a TNPC of $3.5085 × 10⁶, which is slightly higher than the absolute minimum cost but demonstrates the ability to reach 100% renewable penetration without diesel usage. The system also reports the shortest execution time (336.5 seconds), confirming its suitability for real-time or iterative design tasks. Overall, the proposed HRES configuration offers a technically feasible, economically advantageous, and environmentally sustainable solution for campus electrification and broader smart grid applications, and serves as a replicable decision-support model for renewable energy planning in regions with high electricity tariffs.