This study evaluates three energy dispatch algorithms to find the best way to improve hybrid energy systems in rural areas: load following (LF), cycle charging (CC), and a novel customized strategy (CS). The study examined solar and wind resources as well as projected patterns of energy consumption with a particular focus on Bambur village in Taraba State, Nigeria. The Levy Flight Salp Swarm Algorithm (LFSSA) was used in the study to assess system configurations for each method, accounting for battery storage, wind turbines, diesel generators, and photovoltaic (PV) arrays. The most cost-effective and efficient approach was determined to be the Customized Strategy (CS), which produced the lowest Net Present Cost (NPC) of 0.119/kWh. The Load Following (LF) technique had the highest costs at 0.134/kWh, while the Cycle Charging (CC) method had intermediate costs with an NPC of 0.127/kWh. With a 580-kWh battery bank, a 10 kW wind turbine, 332 kW of PV capacity, and a 78 kW diesel generator, the CS approach showed better component sizing balance. The trade-offs between energy output, storage, and backup power in real-time were optimized using this design. Sensitivity analysis revealed that increasing interest rates from 10% to 18% led to a rise in LCOE, while diesel cost fluctuations showed a non-monotonic impact on LCOE, peaking at $0.79/L before declining due to increased reliance on renewable sources and storage. CS strategy’s balance of investment and efficiency makes it ideal for remote energy management, offering key insights for rural electrification.