Optimal Integration Of Renewable Energy Into The National Grid For Improved Power Supply Using Ann Based Supercapacitor

Abstract

The increasing global demand for clean and sustainable energy has accelerated the integration of renewable energy sources (RES) such as solar photovoltaic (PV) and wind power into national grids. In Nigeria, the adoption of RES is hindered by challenges including variability in power generation, grid instability, and insufficient energy storage capacity. These limitations often result in unreliable power supply, frequency deviations, and voltage fluctuations. This study presents an Artificial Neural Network (ANN)-based super capacitor control system designed to achieve the optimal integration of renewable energy into the national grid for improved power supply stability and reliability. The ANN serves as an intelligent controller capable of learning complex nonlinear relationships between renewable generation patterns, grid demand, and storage behavior, enabling adaptive and precise charge–discharge control of the super capacitor. The super capacitor, with its high power density and rapid response time, mitigates the effects of renewable intermittency by providing fast frequency regulation, voltage support, and peak shaving. Simulation results obtained from a MATLAB/Simulink model of the Nigerian grid integrated with RES demonstrate that the proposed ANN-based supercapacitor system significantly improves grid stability, reduces voltage deviation by up to 18%, and enhances renewable energy utilization efficiency by 25% compared to conventional storage control methods. The findings indicate that ANN-driven supercapacitor storage systems offer a viable solution for optimizing renewable energy integration, ensuring improved operational reliability, and supporting Nigeria’s transition towards a more sustainable power infrastructure. The results obtained were the conventional intermittency and rapid generation variability that causes unoptimal integration of renewable energy into the national grid for unimproved power supply was 52 MW. On the other hand when an ANN based super capacitor was integrated into the system, it instantly reduced to 47.5 MW and the conventional intermittency and Poor generation forecasting that causes unoptimal integration of renewable energy into the national grid for unimproved power supply was31MW. Meanwhile when an ANN based super capacitor was introduced into the system, it automatically reduced to28.3 MW. Finally, with these results obtained, it definitely meant that the percentage optimized integration of renewable energy into the national grid for improved power supply when an ANN based super capacitor was imbibed into the system was8.7%.