Optimal Unit Generation With Renewable Energy Resources

Unit Commitment is a optimization problem utilized in power system operation planning. The primary objective of unit commitment (UC) is achieve an optimal commitment plan of the generators for predicted demand and spinning reserve. The Salp Swarm Algorithm (SSA), a metaheuristic algorithm, is inspired by the swarming behavior of salps in the ocean. In this paper, The Salp Swarm optimization algorithm is used to solve the thermal unit commitment problem based on the IEEE 30 Bus system’s parameters. The SSA algorithm is used to improve the system’s initial randomness and help in effective convergence towards the optimum. To verify the effectiveness of the suggested system, the outcomes are compared to those of other algorithms, such as the Genetic Algorithm (GA),Modified Imperialistic Competitive Algorithm (MICA), and Particle Swarm Optimization (PSO). The UC problem is formulated under constraints, taking into account inconsistent characteristics caused by the non-convexity of the binary state ON/OFF decision and irregular thermal generation cost. Due to climate impact and global catastrophic using non-renewable energy resources, researchers and engineers are significantly increasing the implementation of renewable energy sources (RES) into electrical power systems. In order to meet global constraints and optimize costs, this work investigates the window for the integration of renewable energy sources (RES) into power networks and extracts power from renewable sources. The paper covers the effects of integrating solar photovoltaic and wind energy resources and suggests a way to handle complex power optimization problems for further research.