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Purpose In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems. Multi-objective ORPD (MOORPD) aims to minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale power system in an effort to achieve a good performance with stable and secure operation of electric power systems. Design/methodology/approach A MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm confirms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully verified and discussed. Findings Three test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the efficiency of the proposed algorithm. The findings obtained amply confirmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is benefitted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions. Originality/value The proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.

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Nowadays, electrical power grids are facing increased penetration of renewable energy sources (RES), which result in increasing level of randomness and uncertainties for its operational quality. In addition, emerging need for efficient solutions to stochastic optimal power flow (OPF) problem has attracted considerable attention to ensure optimal and reliable grid operations in the presence of generation uncertainty and increasing demand. Therefore, this paper proposes an efficient slime mould-inspired algorithm that aims to minimize overall operating cost of main grid by managing the power flow among different generating resources. The problem is formulated as large-scale constrained optimization problem with non-linear characteristics. Its degree of complexity increases with incorporation of intermittent energy sources, making it harder to be solved using conventional optimization techniques. However, could be efficiently resolved by nature-inspired optimization techniques without any modification or approximation into the originalformulation. The objective function is the overall cost of system, including reserve cost for over-estimation and penalty cost for under-estimation of both PV-solar and wind energy. The SMA performance is evaluated on the IEEE 30-bus test system and Algerian power system, DZA 114-bus. The SMA is compared with four optimization algorithms: i) The well-studied meta-heuristics, i.e., Gorilla troops optimizer (GTO), and Orca predation algorithm (OPA), ii) Recently developed meta-heuristics, i.e., Artificial ecosystem optimizer (AEO), Hunger games search (HGS), and Jellyfish search (JS) optimizer, iii) ad high-performance metaheuristics, Success-History based parameter adaptation for differential evolution method. The overall simulation results reveal that the SMA ranked first among the compared algorithms, and so, over and so, over different function landscapes.

In this paper one of the reliable and effective optimization algorithms called "artificial bee colony" (ABC) for solving the optimal reactive power dispatch (ORPD) problem with the discrete and continuous control variables in an electric power system is proposed. In this work ABC algorithm is used to find the settings of control variables such as generator voltages, tap positions of tap changing transformers and the number of capacitors banks to be switched, for optimal reactive power dispatch. The original ABC algorithm designed for the continuous nature of optimization problems, cannot be used for discrete cases; but the real ORPD problem has two different natures of control variables (discrete and continuous), for this reasons a simple rounding operator is included in the main steps of original ABC algorithm to ensure the discretization process. Then, the feasibility and performance of the proposed algorithm are verified by the serial simulations on the IEEE 14-bus, IEEE 30-bus and IEEE 57-bus power systems. The numerical results are compared to those yielded by the other recently published evolutionary optimization algorithms in the literature. This comparison shows that the ABC algorithm is superior to the other mentioned algorithms and can be efficiently used for solving the discrete ORPD problem.