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Maintaining stability in modern power systems is challenging due to complex structures, rising power demand, and load disturbances. The integration of renewable energy sources further threatens stability by causing imbalances between generation and demand. Conventional load frequency stabilization methods fall short in such scenarios. This paper proposes an optimal fractional‐order proportional‐integral‐derivative‐acceleration (FOPIDA) controller, enhanced by a robust adaptive neuro‐fuzzy inference system (ANFIS), to improve load frequency control and reliability in power systems with wind and solar generators. First, the dynamical model of a multi‐area interconnected power system, including a thermal power plant, wind turbine, and solar photovoltaic generators, is developed. A decentralized ANFIS‐FOPIDA controller is then designed for load frequency control objectives. The gains of this controller are optimized using the whale optimization algorithm (WOA), focusing on frequency deviation and tie‐line power exchange. Simulations on a New England IEEE 10‐generator 39‐bus power system demonstrate the approach's effectiveness under various disturbances, including random load‐generation disturbances and nonlinear generation behaviors. Comparisons with other strategies, such as fractional order (FO) beetle swarm optimization algorithm (FOBSOA)‐FOPIDA, WOA‐PIDA, and WOA‐ANFIS‐PIDA, and recent control approaches highlight the superior performance of the WOA‐ANFIS‐FOPIDA method in enhancing power system stability. [ABSTRACT FROM AUTHOR]

This paper aims to improve transient stability using the Ant Colony Optimization for Continuous Domains (ACOR). This improvement is obtained by solving the Transient Stability Constrained Optimal Power Flow (TSCOPF) problem and extracting the sensitivity coefficients. The presented costs minimization approach requires less execution time to manage energy resources efficiently and compared to other conventional methods, it also outperforms based on statistical indicators such as mean and standard deviation. Furthermore, the sensitivity analysis of the suggested method considers the active power generation of the generating units along with their terminal voltage, which notably decreases the operation cost and reduces risk to the power system. On the other hand, the application of ACOR algorithm can reduce the fuel cost of power system operation to 60,928.36 $/h with a decrease of 15.33 $/h compared to the conventional method with a standard deviation equal to 2.51 $/h and an execution time of 7.66 s. This strategy could be used in power system dispatching. This method is implemented on the New England 39 bus system, and the results demonstrate the method's efficiency compared to other conventional methods. [ABSTRACT FROM AUTHOR]

Transient stability is one of the major features of power systems operation which can be also considered an objective function in the flexible AC transmission systems (FACTS) allocation problem. This paper introduces a general multi‐level multi‐objective optimisation framework and its application on optimising the size and location of the improved hybrid flow controller (IHFC). IHFC is a new member of the FACTS family whose ability to control power flow and improve power system stability has been investigated. The optimisation problem considers economic and stability‐based objective functions simultaneously, and the allocation problem is formulated as a non‐linear programming (NLP) problem. The New England 39‐bus system has been used as a case study to demonstrate that the proposed framework can provide efficient and economically viable solutions to the allocation problem. [ABSTRACT FROM AUTHOR]

This paper aims to improve transient stability using the Ant Colony Optimization for Continuous Domains (ACOR). This improvement is obtained by solving the Transient Stability Constrained Optimal Power Flow (TSCOPF) problem and extracting the sensitivity coefficients. The presented costs minimization approach requires less execution time to manage energy resources efficiently and compared to other conventional methods, it also outperforms based on statistical indicators such as mean and standard deviation. Furthermore, the sensitivity analysis of the suggested method considers the active power generation of the generating units along with their terminal voltage, which notably decreases the operation cost and reduces risk to the power system. On the other hand, the application of ACOR algorithm can reduce the fuel cost of power system operation to 60,928.36 $/h with a decrease of 15.33 $/h compared to the conventional method with a standard deviation equal to 2.51 $/h and an execution time of 7.66 s. This strategy could be used in power system dispatching. This method is implemented on the New England 39 bus system, and the results demonstrate the method's efficiency compared to other conventional methods. [ABSTRACT FROM AUTHOR]