Having continuous decrease in inertia and being sensitive to load/generation variation are considered crucial challenging problems for modern power grids. The main cause of these problems is the increased penetration capacities of renewables. An unbalanced load with generation power largely affects grids’ frequency and voltage profiles. Load frequency control (LFC) mechanisms are extensively presented to solve these problems. In the literature, LFC methods are still lacking in dealing with system uncertainty, parameter variation, structure changes, and/or disturbance rejection. Therefore, this paper proposes an improved LFC methodology using the hybrid one plus proportional integral double-integral derivative (1+PII2D) cascaded with fractional order proportional-integral-derivative (FOPID), namely, the proposed 1+PII2D/FOPID controller. The contribution of superconducting magnetic energy storage devices (SMES) is considered in the proposed design, also considering hybrid high-voltage DC and AC transmission lines (hybrid HVDC/HVAC). An optimized design of proposed 1+PII2D/FOPID controller is proposed using a new application of the recently presented powerful artificial rabbits optimizers (ARO) algorithm. Various performance comparisons, system changes, parameter uncertainties, and load/generation profiles and changes are considered in the proposed case study. The results proved superior regulation of frequency using proposed 1+PII2D/FOPID control and the ARO optimum parameters.

Recent developments in electrical power grids have witnessed high utilization levels of renewable energy sources (RESs) and increased trends that benefit the batteries of electric vehicles (EVs). However, modern electrical power grids cause increased concerns due to their continuously reduced inertia resulting from RES characteristics. Therefore, this paper proposes an improved fractional-order frequency controller with a design optimization methodology. The proposed controller is represented by two cascaded control loops using the one-plus-proportional derivative (1 + PD) in the outer loop and a fractional-order proportional integral derivative (FOPID) in the inner loop, which form the proposed improved 1 + PD/FOPID. The main superior performance characteristics of the proposed 1 + PD/FOPID fractional-order frequency controller over existing methods include a faster response time with minimized overshoot/undershoot peaks, an ability for mitigating both high- and low-frequency disturbances, and coordination of EV participation in regulating electrical power grid frequency. Moreover, simultaneous determination of the proposed fractional-order frequency controller parameters is proposed using the recent manta ray foraging optimization (MRFO) algorithm. Performance comparisons of the proposed 1 + PD/FOPID fractional-order frequency controller with existing PID, FOPID, and PD/FOPID controllers are presented in the paper. The results show an improved response, and the disturbance mitigation is also obtained using the proposed MRFO-based 1 + PD/FOPID control and design optimization methodology.

An important issue in interconnected microgrids (MGs) is the realization of balance between the generation side and the demand side. Imbalanced generation and load demands lead to security, power quality, and reliability issues. The load frequency control (LFC) is accountable for regulating MG frequency against generation/load disturbances. This paper proposed an optimized fractional order (FO) LFC scheme with cascaded outer and inner control loops. The proposed controller is based on a cascaded one plus tilt derivative (1+TD) in the outer loop and an FO tilt integrator-derivative with a filter (FOTIDF) in the inner loop, forming the cascaded (1+TD/FOTIDF) controller. The proposed 1+TD/FOTIDF achieves better disturbance rejection compared with traditional LFC methods. The proposed 1+TD/FOTIDF scheme is optimally designed using a modified version of the liver cancer optimization algorithm (MLCA). In this paper, a new modified liver cancer optimization algorithm (MLCA) is proposed to overcome the shortcomings of the standard Liver cancer optimization algorithm (LCA), which contains the early convergence to local optima and the debility of its exploration process. The proposed MLCA is based on three improvement mechanisms, including chaotic mutation (CM), quasi-oppositional based learning (QOBL), and the fitness distance balance (FDB). The proposed MLCA method simultaneously adjusts and selects the best 1+TD/FOTIDF parameters to achieve the best control performance of MGs. Obtained results are compared to other designed FOTID, TI/FOTID, and TD/FOTID controllers. Moreover, the contribution of electric vehicles and the high penetration of renewables are considered with power system parameter uncertainty to test the stability of the proposed 1+TD/FOTIDF LFC technique. The obtained results under different possible load/generation disturbance scenarios confirm a superior response and improved performance of the proposed 1+TD/FOTIDF and the proposed MLCA-based optimized LFC controller.

An improved design optimization method for fractional-order-based proportional integral derivative (FOPID) controllers is proposed in this paper to enhance the stability and transient response of automatic voltage-regulator systems. The FOPID represents a higher degree-of-freedom controller through having five tunable parameters, compared with only three parameters in the integer-order PID controller. In the literature, the performance of the FOPID is highly determined through the design method and its parameter-determination process. Recently, optimum design of the FOPID has found wide employment in several engineering applications through using metaheuristic optimization algorithms. In this paper, an improved method for the FOPID’s parameter optimization is proposed for AVR applications using the marine predator optimization algorithm (MPA). The proposed MPA–FOPID controller is verified through comparing its performance with other featured and newly developed optimization algorithms. The proposed MPA–FOPID comparative analysis has been proven to have better stability, frequency response, robustness, faster response, and disturbance-rejection ability over the other studied methods in this paper.

Losses minimization in distribution networks (DNs) is a critical concern for power utilities worldwide, especially in both mature and developing power systems. The growing integration of distributed generation (DG) at the distribution level has transformed traditionally passive networks into active ones, introducing new challenges and opportunities. This paper presents a novel approach to optimally determine the placement, size, and number of various types of DG units—specifically Type-I, Type-II, and Type-III—to minimize real power losses (RPL) while adhering to system constraints. The proposed optimization technique proposes an Improved Manta Ray Foraging Optimizer (IMRFO) to achieve robust and effective results, maximizing the economic benefits for distribution companies deploying DGs. The IMRFO has been improved with a hybrid two-stage approach that combines traditional MRFO with a Genetic Algorithm (GA) to improve global search capabilities. The MRFO searches for the optimal solution, transitions to the second stage if no improvement is observed after five iterations, and if GA finds a better solution, restarts the search, ensuring adaptive exploration and exploitation. Additionally, IMRFO has been rigorously tested in comparison to the original MRFO on a range of standard benchmark functions, which serve to validate the robustness, efficiency, and reliability of the algorithm under different optimization scenarios. These benchmark tests, including unimodal, multimodal, and constrained functions, demonstrate IMRFO’s superior performance compared to traditional methods. The method’s efficacy is validated on standard IEEE radial DNs with 33 and 69 buses, utilizing forward-backward sweep power flow analysis. Comparative studies with existing algorithms demonstrate that the proposed approach offers superior solutions with consistent convergence, highlighting its potential to significantly reduce power losses and enhance voltage profiles. The results underscore the technique’s capability in achieving optimal DG placement, contributing to the advancement of efficient and reliable power DNs. The proposed IMRFO algorithm achieved significant reductions in active power losses, with a 1.45% reduction for three DG units of Type-I in the IEEE 33-bus DN and a 21.70% reduction for three DG units of Type-III. In the IEEE 69-bus DN, IMRFO achieved a remarkable reduction of over 98% in active losses for three Type-III DG units.

This study proposes an enhanced Kepler Optimization (EKO) algorithm, incorporating fractional-order components to develop a Proportional-Integral-First-Order Double Derivative (PI–(1+DD)) controller for frequency stability control in multi-area power systems with wind power integration. The fractional-order element facilitates efficient information and past experience sharing among participants, hence increasing the search efficiency of the EKO algorithm. Furthermore, a local escaping approach is included to improve the search process for avoiding local optimization. Applications were performed through comparisons with the 2020 IEEE Congress on Evolutionary Computation (CEC 2020) benchmark tests and applications in a two-area system, including thermal and wind power. In this regard, comparisons were implemented considering three different controllers of PI, PID, and PI–(1+DD) designs. The simulations show that the EKO algorithm demonstrates superior performance in optimizing load frequency control (LFC), significantly improving the stability of power systems with renewable energy systems (RES) integration.