The increasing penetration of renewable energy resources such as solar photovoltaic and wind power has introduced significant challenges in the operation and control of modern power systems. One of the critical issues is maintaining voltage stability and minimizing power losses while ensuring efficient reactive power management. Optimal Reactive Power Dispatch (ORPD) plays a vital role in enhancing the operational performance of power systems by optimally controlling reactive power sources, transformer tap settings, and voltage magnitudes of generators. However, the nonlinear, nonconvex, and highly constrained nature of the ORPD problem makes it difficult to solve using conventional optimization techniques.
This study presents an Improved Salp Swarm Algorithm (ISSA) for solving the optimal reactive power dispatch management problem in power systems integrated with renewable energy resources. The proposed ISSA is an enhanced version of the Salp Swarm Algorithm inspired by the swarming behavior of salps in the ocean. The algorithm improves the exploration and exploitation capability of the conventional SSA by incorporating adaptive control parameters, improved leader–follower updating strategies, and enhanced convergence mechanisms. These modifications help avoid premature convergence and improve global search ability when solving complex power system optimization problems.
In the proposed framework, renewable energy sources such as solar and wind generation units are integrated into the distribution network, and their uncertainties are considered during optimization. The ORPD problem is formulated as a multi-objective optimization problem with the primary goals of minimizing active power losses, improving voltage profile, and maintaining voltage stability across the system. Control variables include generator voltages, transformer tap positions, and reactive power compensation devices such as shunt capacitors. System constraints such as power balance equations, generator limits, voltage limits, and reactive power limits are also incorporated into the optimization model.
To validate the effectiveness of the proposed method, the Improved Salp Swarm Algorithm is applied to standard benchmark test systems including the IEEE 33-bus, IEEE 69-bus, and IEEE 85-bus distribution networks with renewable energy integration. Load flow analysis is performed using the backward–forward sweep method suitable for radial distribution networks. The results obtained using ISSA are compared with several well-known optimization algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Orangutan Optimization Algorithm (OOA). Simulation results demonstrate that the proposed ISSA achieves superior performance in terms of reducing power losses, improving voltage profiles, and achieving faster convergence compared to conventional algorithms.
The numerical results show that the ISSA-based reactive power dispatch strategy significantly enhances system performance under renewable energy integration scenarios. Furthermore, the algorithm provides stable and reliable solutions even under varying load conditions and renewable energy uncertainties. The improved convergence characteristics and robustness of the proposed approach make it a promising tool for solving complex optimization problems in modern smart grids.
Overall, this research highlights the potential of the Improved Salp Swarm Algorithm for efficient reactive power management in renewable energy-based power systems. The proposed approach can assist power system operators in maintaining voltage stability, reducing power losses, and improving overall system efficiency in future smart grid environments with high renewable energy penetration.
