Modeling and Optimal Control of Power System Frequency Load Controller by Applying Disturbance in the System by a Modified Version of Firefly Algorithm

As shown by different studies in the electrical energy trade environment, frequency management is one of the maximum essential ancillary services of these methods. This operation provides a balance between the power and the frequency of the energy method. The most commonly used approaches to carry out frequency manage are load frequency manage and generator governor reaction (primary frequency control). The objective of the load frequency is to return the capability to regulate the starting frequency, restore the frequency to the minimal value and reduce the transmitting energy flow between areas; in other words, the principal duty of load frequency control is to make the steady state frequency mistake zero. Each control area in reorganized electrical energy systems contains different kinds of uncertainty and disruption that are brought on by escalating difficulty, miscalculations in the modeling process, and modifications to the design of the energy method. A flexible manager must be used because traditional controllers are unable to resolve the issue. Different decentralized robust methods, optimal control, and neural networks have all been employed in recent years to improve the scheme performance of automatic managers so they can adjust to variations in parameters. The load–frequency manage system s manager parameters are attempted to be improved and optimized in this study by using a novel approach. In this approach, the controller parameters are optimized using a modified version of the firefly algorithm. To demonstrate the effectiveness of this algorithm, the findings will be contrasted with those from the classical approach. The article s application and performance evaluation of the suggested system following modifications to load demand as an external factor is another objective. A two-area and a three-area interconnected energy system will use the suggested method. © The Author(s), under exclusive license to Springer Nature Switzerland AG. 2023.