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The current paper presents a multi‐objective manta ray foraging algorithm (MO‐MRFA) for efficient operation of hybrid AC and multi‐terminal direct current (MTDC) power grids. The multi‐objective framework aims at achieving economical, technical and environmental benefits by minimising the total production fuel costs, minimising the transmission power losses and minimising the environmental emissions in the AC/MTDC transmission systems. The MRFA imitates three separate independent foraging organisations of the manta rays. It is updated incorporating an additional Pareto archive to preserve the non‐dominated solutions. A dynamic adaptation of the fitness feature is employed by iteratively varying the form of the employed fitness function. Furthermore, a fuzzy decision‐making technique is activated to finally pick the appropriate operating point of the AC/MTDC power grids. The proposed technique is compared with other reported algorithms in the literatures. The applications are conducted on three test systems. These systems are IEEE 30‐bus, IEEE 57‐bus test power systems in addition to real part of the Egyptian grid at West Delta region. Numerical results demonstrate that the proposed MO‐MRFA has great effectiveness and robustness indices over the others. Nevertheless, the proposed MO‐MRFA is successfully extracting several Pareto solutions that meet the techno‐economic requirements with accepted environmental concerns. [ABSTRACT FROM AUTHOR]

In recent years, optimal sizing and location of reactive power resources are drawing much attention to help the operators of the utilities to enhance the power system operations. Therefore, this work presents a new version of grey wolf optimiser (GWO) to solve the problem of optimal reactive power resources sizing for power system operation enhancement. The proposed method which called improved grey wolf optimiser (IGWO) can be derived by modifying the exploration-exploitation balance in the conventional GWO to enhance its rate of convergence. Also, the weighted distance strategy is employed in the proposed IGWO to overcome the drawback of the conventional GWO. Optimal reactive power resources sizing problem is nonlinear and non-convex optimisation problem. To solve this problem, different objective functions are used. These objective functions are minimisation of generating cost, minimisation of transmission power loss and voltage profile improvement. The validity and superiority of the proposed IGWO method are tested using three standard IEEE systems for normal and contingency conditions. Then the results are compared with those obtained from other recently published algorithms. The simulation results show that the proposed IGWO method is more accurate and efficient than other recently published algorithms. [ABSTRACT FROM AUTHOR]