The automatic load frequency control for multi-area power systems has been a challenging task for power system engineers. The complexity of this task further increases with the incorporation of multiple sources of power generation. For multi-source power system, this paper presents a new heuristic-based hybrid optimization technique to achieve the objective of automatic load frequency control. In particular, the proposed optimization technique regulates the frequency deviation and the tie-line power in multi-source power system. The proposed optimization technique uses the main features of three different optimization techniques, namely, the Firefly Algorithm (FA), the Particle Swarm Optimization (PSO), and the Gravitational Search Algorithm (GSA). The proposed algorithm was used to tune the parameters of a Proportional Integral Derivative (PID) controller to achieve the automatic load frequency control of the multi-source power system. The integral time absolute error was used as the objective function. Moreover, the controller was also tuned to ensure that the tie-line power and the frequency of the multi-source power system were within the acceptable limits. A two-area power system was designed using MATLAB-Simulink tool, consisting of three types of power sources, viz., thermal power plant, hydro power plant, and gas-turbine power plant. The overall efficacy of the proposed algorithm was tested for two different case studies. In the first case study, both the areas were subjected to a load increment of 0.01 p.u. In the second case, the two areas were subjected to different load increments of 0.03 p.u and 0.02 p.u, respectively. Furthermore, the settling time and the peak overshoot were considered to measure the effect on the frequency deviation and on the tie-line response. For the first case study, the settling times for the frequency deviation in area-1, the frequency deviation in area-2, and the tie-line power flow were 8.5 s, 5.5 s, and 3.0 s, respectively. In comparison, these values were 8.7 s, 6.1 s, and 5.5 s, using PSO; 8.7 s, 7.2 s, and 6.5 s, using FA; and 9.0 s, 8.0 s, and 11.0 s using GSA. Similarly, for case study II, these values were: 5.5 s, 5.6 s, and 5.1 s, using the proposed algorithm; 6.2 s, 6.3 s, and 5.3 s, using PSO; 7.0 s, 6.5 s, and 10.0 s, using FA; and 8.5 s, 7.5 s, and 12.0 s, using GSA. Thus, the proposed algorithm performed better than the other techniques.

The load frequency control (LFC) and tie-line power are the key deciding factors to evaluate the performance of a multiarea power system. In this paper, the performance analysis of a two-area power system is presented. This analysis is based on two performance metrics: LFC and tie-line power. The power system consists of a thermal plant generation system and a hydro plant generation system. The performance is evaluated by designing a proportional plus integral (PI) controller. The hybrid gravitational search with firefly algorithm (hGFA) has been devised to achieve proper tuning of the controller parameter. The designed algorithm involves integral time absolute error (ITAE) as an objective function. For two-area hydrothermal power systems, the load frequency and tie-line power are correlated with the system generation capacity and the load. Any deviation in the generation and in the load capacity causes variations in the load frequencies, as well as in the tie-line power. Variations from the nominal value may hamper the operation of the power system with adverse consequences. Hence, performance of the hydrothermal power system is analyzed using the simulations based on the step load change. To elucidate the efficacy of the hGFA, the performance is compared with some of the well-known optimization techniques, namely, particle swarm optimization (PSO), genetic algorithm (GA), gravitational search algorithm (GSA) and the firefly algorithm (FA).

Multi Verse Optimization (MVO) technique is a newly developed population based algorithm established on the perception of white holes and black holes in a multi-verse for finding out all possibilities of search spaces. This algorithm is applied to tune the proposed Fractional Order Proportional Derivative Proportional Integral (PPDPI) controller of a multi area power system having hydro, thermal, and gas power plants in each area and the system is analysed in two different prospects i.e. with HVDC link and without HVDC link to have realistic situation. The effectiveness of the proposed controller tuned with MVO for load frequency control under small load perturbation (SLP) is compared with other well-known optimization algorithm for the same power system. In this paper, a Fractional-Order Cascade Controller (FOCC) is proposed which is the combination of both the concepts of fractional calculus and cascade control. Cascade connection of FOPI and FOPD are combined in the proposed controller. Change in load and change in system parameters are also incorporated to verify the robustness of the power system where the controllers tuned by the proposed MVO technique. It is observed that the MVO tuned controllers outperformed the performance of recently published TLBO and DE tuned controllers in the area of dynamic performance, stability, and robustness.

In this paper, a novel improved gravitational search algorithm–binary particle swarm optimization (IGSA-BPSO) driven proportional-integral-derivative (PID) controller is proposed to deal with issues of automatic generation control (AGC) of interconnected multi-source (thermal-hydro-gas) multi-area deregulated power systems. The effectiveness and robustness of the proposed controller is compared and analyzed with GSA and PSO-driven PID controllers. The simulated and mathematically formulated results show the superiority of the proposed IGSA-BPSO driven PID controller compared with the other two techniques in settling time, overshoot, and convergence time. The two-area test system considered in this article is integrated with a thermal, hydro, and gas turbine power plant. Integral time multiplied by absolute error (ITAE) is used as the objective function (minimization) by optimization techniques for getting optimum parameters of PID controllers installed in each area. The system’s dynamics are examined using poolco, bilateral, and contract violation cases under a deregulated environment, and the comparative results are shown to analyze the efficacy of the proposed concept. Physical constraints such as generation rate constraints (GRC) and time-delay (TD) have been considered in the system as a realistic approach. This paper considers an accurate AC-DC tie-link model for the proposed AGC mechanism. Dynamic load change condition is tested and verified. The variations of different parameters will be used in the robustness analysis of the proposed system. The comparison shows that the designed controllers are more robust and produce better results than those considered as references.

In the wake of the increasing integration of cyber-physical systems within power grids, ensuring the security and stability of these systems has become paramount. This paper presents a comprehensive security analysis of cyber-physical power systems (CPPS) employed for frequency control in deregulated power markets. In the era of evolving power grids, characterized by increasing renewable energy penetration and market deregulation, maintaining frequency stability becomes a critical challenge. To address this challenge, this study investigates the synergistic utilization of Unified Power Flow Controller (UPFC) device and Redox Flow Battery (RFB), to enhance the frequency control performance. A realistic two-area CPPS is considered where a cascaded Fractional-Order Proportional-Integral-Derivative-Fractional Integral (FOPID-FI) controller has been implemented for frequency control in both areas. Our analysis delves into the vulnerabilities inherent in CPPS, considering potential cyber threats and their impact on frequency control mechanisms. By employing a systematic approach, we identify critical points of vulnerability in the system architecture and propose countermeasures to mitigate cyber threats effectively. We include extensive simulation results to validate the efficacy of the proposed work by analyzing the performance of the system from vital perspective viz., sensitivity, scalability, robustness, stable, and resilient.

This paper presents an intelligent multi-area power control with dynamic knowledge domain inference concept. The ongoing operational shift leads to unacceptable states variation, which may result in power oscillations, and if the controllers are not suitably designed, the system may be interactive and oscillations can aggravate. The study reports a new concept of updating control parameters, which is linked with operational shift, initially in an offline mode in building respective knowledge domain that fits into the framework of changing situations, to ensure states regulation. The proposed concept also provides flexibility to update the knowledge domain over and above offline data with newer dataset combining the nearest data clusters to derive an averaged data (controller parameter) within predefined boundary to change the controller functioning. The knowledge retrieval, as operational shift proceeds, has been mapped utilizing dynamical inference concept. The control so derived, effectively ensures the best damping well within time for the large network reliability and security. The structure of the controller so obtained is termed as the intelligent controller. In the present investigation, parameters of the respective controllers are stored in their respective knowledge domain on the modular basis. Firefly Algorithm (FA) with integral time multiplied by absolute error has been used as the objective function to be minimized. FA is then used to develop knowledge domain structure by way of deriving optimal controller parameters for corresponding operational shift to ensure oscillation damping with minimum settling time as well as overshoot/undershoot. The study is performed on six area sample power system. The proposed concept demonstrates an intelligent control concept for quick oscillation damping as the operating condition changes. Unified power flow controller has been used as an ancillary device as power system stabilizer approaches to the onset of unacceptable response.