The reliability of a power system depends on its ability to handle fluctuations and varying load demands, as uncontrolled frequency deviations can lead to load-shedding and blackouts. Optimally tuned controllers are essential for Load Frequency Control (LFC) applications to efficiently stabilize the power system by minimizing frequency undershoots, overshoots, and settling time. This paper proposed the application of novel Golden Eagle Optimization (GEO) algorithm for the optimal tuning of the LFC controller, which has not been previously employed in any LFC applications. Moreover, this paper presents the first-ever implementation of a hybrid energy storage system consisting of Vanadium Redox Flow Battery (VRFB) and Super Magnetic Energy Storage System (SMES) coupled with AC/HVDC transmission lines in a multi-area power system. A GEO optimized Proportional-Integrative-Derivative (GEO-PID) robust controller is designed with the Integral Time Absolute Error (ITAE) objective function to enhance the power system’s stability. The proposed controller is tested on two and four areas power systems considering the sensitivity and nonlinearity of the power systems. A robustness test is also performed to verify the stability of the system under randomly chosen loading conditions. In comparison with particle swarm optimization, dragonfly algorithm, sine cosine algorithm, ant lion optimization, and whale optimization algorithm, the GEO-PID controller significantly reduced the settling time up to 80% for different area’s frequencies. Simulation results indicate that the proposed controller outperforms other recent optimization algorithms by effectively dampening the frequency and tie-line deviations with less settling times, as well as reduced frequency undershoots and overshoots.

This work provides an enhanced novel cascaded controller-based frequency stabilization of a two-region interconnected power system incorporating electric vehicles. The proposed controller combines a cascade structure comprising a fractional-order proportional integrator and a proportional derivative with a filter term to handle the frequency regulation challenges of a hybrid power system integrated with renewable energy sources. Driver training-based optimization, an advanced stochastic meta-heuristic method based on human learning, is employed to optimize the gains of the proposed cascaded controller. The performance of the proposed novel controller was compared to that of other control methods. In addition, the results of driver training-based optimization are compared to those of other recent meta-heuristic algorithms, such as the imperialist competitive algorithm and jellyfish swarm optimization. The suggested controller and design technique have been evaluated and validated under a variety of loading circumstances and scenarios, as well as their resistance to power system parameter uncertainties. The results indicate the new controller’s steady operation and frequency regulation capability with an optimal controller coefficient and without the prerequisite for a complex layout procedure.

In seaports, low-carbon energy systems and energy efficiency have become increasingly important as a result of the evolution of environmental and climate change challenges. In order to ensure the continued success of seaports, technological advancements must be introduced to a number of systems, such as seaport vehicles, harbor cranes, and the power sources of berthed ships. Harbor areas might need a microgrid to handle these aspects. Typically, microgrids that substitute conventional generator units with renewable energy sources (RES) suffer from system inertia problems, which adversely affect microgrid frequency stability. A load frequency controller (LFC) based on a novel modified proportional integral derivative with filter (MPIDF) is presented in this paper for enhancing the performance of marine microgrid system (MMS). The serval optimization algorithm (SOA), a recent bio-inspired optimization algorithm, is used to optimize the MPIDF controller coefficients. This controller is tested on a marine microgrid containing a number of RES such as wind turbine generators, sea wave energy, and solar generation. The efficacy of the proposed MPIDF controller is verified with respect to other controllers such as PIDF and PI. Similarly, the proposed meta-heuristic algorithm is validated as compared to other algorithms including particle swarm optimization (PSO), ant colony optimization (ACO), and jellyfish swarm optimization (JSO). This study also evaluates the robustness of the proposed controller to different perturbations in step load, changes in system parameters, and other parameter variations.

To sustain a system frequency within acceptable limits, it is widely conceded that retaining a power balance between generation and demand is necessary. In order to regulate the frequency of power systems (PSs) this article proposes a novel cascaded based fractional‐order controller termed as fractional‐order integer‐ (FOI‐) fractional‐order proportional integral with double derivative (FOPIDD2). In addition to redox flow batteries and capacitive energy storage, the recommended control strategy has been validated with gas, thermal reheat, hydro, and nuclear power systems. Additionally, a newly designed algorithm known as squid game optimizer (SGO) optimizes the gains of the new FOI‐FOPIDD2 controller. The squid game optimizer technique is inspired by the fundamental principles of a conventional Korean sport. It employs a population of candidate solutions and iteratively adjusts the control parameters to discover the optimal set that reduces frequency abnormalities and improves system stability. A comparison is also made between the controller’s performance and benchmarks, including the jellyfish search algorithm, the firefly algorithm, the grey wolf optimizer, and the particle swarm algorithm. The proposed algorithms reduced peak overshoot as compared to grey wolf optimizer algorithm by 35.34%, 46.78%, and 76.89%; jellyfish search optimization algorithm by 34.76%, 77.22%, and 82.56%; and firefly algorithm by 82.67%, 89.23%, and 29.67% for frequency variations in area 1, area 2 and tie line power, respectively. Furthermore, SGO‐FOI‐FOPIDD2 controllers under different loading circumstances and conditions were evaluated and endorsed for their ability to withstand uncertainties in power system parameters.

Recent widespread connections of renewable energy resource (RESs) in place of fossil fuel supplies and the adoption of electrical vehicles in place of gasoline-powered vehicles have given birth to a number of new concerns. The control architecture of linked power networks now faces an increasingly pressing challenge: tie-line power fluctuations and reducing frequency deviations. Because of their nature and dependence on external circumstances, RESs are analogous to continually fluctuating power generators. Using a fractional order-based frequency regulator, this work presents a new method for improving the frequency regulation in a two-area interconnected power system. In order to deal with the frequency regulation difficulties of the hybrid system integrated with RES, the suggested controller utilizes the modified form of fractional order proportional integral derivative (FOPID) controller known as FOI-PDN controller. The new proposed controllers are designed using the white shark optimizer (WSO), a current powerful bioinspired meta heuristic algorithm which has been motivated by the learning abilities of white sharks when actively hunting in the environment. The suggested FOI-PDN controller's performance was compared to that of various control methodologies such as FOPID, and PID. Furthermore, the WSO findings are compared to those of other techniques such as the salp swarm algorithm, sine cosine algorithm and fitness dependent optimizer. The recommended controller and design approach have been tested and validated at different loading conditions and different circumstances, as well as their robustness against system parameter suspicions. The simulation outcomes demonstrate that the WSO-based tuned FOI-PDN controller successfully reduces peak overshoot by 73.33%, 91.03%, and 77.21% for region-2, region-1, and link power variation respectively, and delivers minimum undershoot of 89.12%, 83.11%, and 78.10% for both regions and tie-line. The obtained findings demonstrate the new proposed controller's stable function and frequency controlling performance with optimal controller parameters and without the requirement for a sophisticated design process.