Multi-objective 3E-based sizing and energy scheduling of isolated hybrid microgrids for remote residential communities using Tianji’s Horse Racing Optimization

Remote coastal communities face persistent energy challenges, driving the transition toward renewable-based microgrids. This study develops a multi-objective techno-economic–environmental–employment optimization framework for an isolated three microgrid configurations that combine photovoltaic (PV) systems, wind turbines (WT), battery energy storage, and a diesel generator (DsG) for Yanbu, Saudi Arabia, using one year's real-world meteorological and load profiles. To determining the optimal size of these microgrid configurations, Tianji’s horse racing optimization (THRO) is applied and compared with five recent algorithms: dhole optimization, equilibrium optimizer, white shark optimizer, snake optimizer, and puma optimizer. The tri-objective formulation minimizes the levelized cost of energy and annual greenhouse gas emissions while maximizing job creation, subject to component limits and reliability constraint on the probability of power supply loss. The THRO-optimized hybrid PV/WT configuration achieved the best performance, with an LCOE of $0.11716/kWh, a net present value of $7.57 M, and a renewable fraction of 89.57%, satisfying the 2% reliability target. THRO outperforms all competing optimizers, demonstrating superior fitness, enhanced convergence stability, and strong statistical robustness. In comparison, PV-dominant and WT-dominant configurations showed higher costs($0.129/kWh and $0.142/kWh, respectively; 8.3M$ and 9.2 M$ net present cost). Despite its higher cost, the WT-dominant design exhibited the strongest investment profile, with a 5.5-year payback, 17.7% IRR, and 131% ROI. The optimal solution also limited annual emissions to 712,289 kg and created 3.93 jobs/year, while the PV-dominant system achieved the highest employment (5.24 jobs/year). These results validate THRO’s effectiveness for multi-dimensional optimization and provide a practical decision-support framework for sustainable microgrid planning in remote coastal regions.