Introducing a Novel Controller for Combined Load Frequency Control and Automatic Voltage Regulation of Interconnected Microgrids

The microgrid exhibits low inertia levels and small X/R ratios. Consequently, load changes can adversely affect microgrid stability. However, frequency and voltage oscillations can be mitigated through the use of a load frequency control (LFC) system and an automatic voltage regulator (AVR), which serve as secondary control mechanisms. Additionally, the integration of photovoltaic (PV) and wind turbine (WT) in microgrids complicates the performance of LFC and voltage control due to the uncertainties associated with these renewable sources. Therefore, it is essential to employ a suitable controller with optimal parameters. To address this, this paper introduces a novel control technique known as the tilt‐proportional‐integral‐derivative second‐order derivative controller (TPIDD 2 ) to concurrently manage the voltage and frequency of microgrids. It also incorporates an intelligent optimisation algorithm integrated with quantum computing, referred to as quantum teaching‐learning‐based optimisation (QTLBO), to achieve optimal control parameters. The test system consists of a two‐area interconnected microgrid, where each area includes various sources such as PV, WT, fuel cell (FC), diesel generator, and battery energy storage system (BESS). The integral of time multiplied by the squared error (ITSE) is utilised as the objective function. To demonstrate the effectiveness of the proposed controller, it is compared with the proportional‐integral‐derivative (PID) controller. From the ITSE perspective, the proposed controller is 71.96% more effective than the PID controller. Furthermore, the results obtained using QTLBO are contrasted with those from teaching‐learning based optimization (TLBO), differential evolution (DE), and RCGA.