Microgrid systems play a vital role in meeting local energy demands, particularly in the presence of unpredictable load variations. However, maintaining system stability under such conditions remains a key challenge. Despite several works implementing optimized control strategies, they are limited in their ability to offer robust solutions for addressing the complex interplay of power quality, stability, efficiency, and load variations in microgrids. This study addresses that limitation by presenting a hybrid Arithmetic Smell Agent Optimization (ASAO) algorithm, designed to optimize microgrid control performance by effectively tuning Proportional–Integral–Derivative (PID) parameters. A Simulink model was developed, incorporating solar Photovoltaic (PV) generation, battery energy storage, and grid interconnection, to simulate microgrid behavior. The control system adopted a two-level hierarchy, with a primary controller for rapid disturbance correction and a secondary controller for long-term stability. The primary and secondary controllers demonstrated rapid stabilization, with rise times of 4 ms and 50 ms, and settling times of 4.1 ms and 75 ms, respectively. This work provides a reliable foundation for the design of intelligent control systems in modern microgrids, offering scalable and efficient solutions for sustainable power delivery.

This work determines the reliability indices of the Nigeria 330kV electric power network, which is susceptible to disturbances. Besides, the network configuration is inadequate as it is vulnerable, resulting in transmission line outages. The cardinal object, therefore, is to benchmark the indices against established standards to enable effective operational improvement planning. First, a simulation was conducted using the Electrical Transient Analyzer Program (ETAP) and validated with the Power System Simulator for Engineering (PSS/E) software to assess bus voltages, line flows, and system losses. Subsequently, the ETAP software was applied to determine reliability indices such as the System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), Customer Average Interruption Duration Index (CAIDI), Average Service Availability Index (ASAI), Average Energy Not Supplied (AENS) and Expected Energy Not Supplied (EENS). The simulation results obtained for SAIFI, SAIDI, CAIDI, and ASAI on the test network are 3.2684 f/customer. yr, 9.4140 hours per customer in a year, 2.880 hours per customer interruption, and 0.9989 respectively. Likewise, the AENS with gave a high value of 1360.9340 MWh/customer. yr indicating that on the average, customer is are expected to lose access to 1360.9340 MWh of energy annually. Furthermore, the high value of EENS estimated at 55,798.300 MWh/yr means that the power system is expected to fail to supply 55,798.300 MWh of electricity in one year due to various incidents of failure. These values were compared with the standard IEEE values and were found to be outside the threshold; thus, making it imperative that the indices be utilized to undertake further work that would result in improved and efficient operation of the national grid.