Secondary frequency control with heuristic algorithms in a multi area power system

As a result of the advancement of technology and the increase in population, the demand for energy is increasing day by day. Although fossil fuel-based power systems are currently the most common in the power generation industry, the share of renewable energy sources and energy storage systems in future power systems is predicted to increase continuously. In interconnected power systems, in order to ensure synchronisation conditions in interconnected power systems, it is necessary to maintain the balance between generation and consumption. In order to maintain this balance, which changes as a result of the dynamic nature of the power system, the frequency of the system must be continuously controlled. The objective of primary frequency control is to autonomously and locally vary the grid frequency by adjusting the turbine speed of the respective generating unit, thus compensating for fluctuations in frequency. Secondary frequency control system is widely used in power systems to reduce active power fluctuations and maintain a balanced active power/frequency ratio within the control zones and along the inter-zonal tie lines. Power systems are becoming more complex and challenging due to the integration of renewable energy sources and battery energy storage technologies. The first test system chosen for the thesis work is a two-area thermal power system, which is widely referenced in the literature and provides a solid basis for evaluating the effectiveness of the present optimisation approach. The effectiveness of heuristic optimisation techniques known as Honey Badger Algorithm (HBA), Whale Algorithm (WA) and Grey Wolf Optimisation Algorithm (GWO) and Particle Swarm Optimisation (PSO), which are the most recent algorithms, in determining the optimum parameters for the selected PID controller is compared and analysed according to simulation results, system frequency and tie line power changes with respect to overshoot value and settling time. As other test systems, the performances of the controller and algorithms are tested under different operating conditions such as variable load demand, wind/PV power plant generation and energy storage in power systems, which are more complex and difficult systems. The results show that the Honey Badger Algorithm (BPA) significantly improves the overshoot value and settling time of the system frequency and tie line power variations compared to other techniques. In this thesis, an alternative study has been carried out by performing comprehensive analysis with heuristic methods for the solution of the load frequency control problem and contributed to the literature.