Glowworm Swarm Optimization Algorithm for Short-term Hydrothermal Scheduling

In order to address the issue of hydrothermal systems’ ideal power generation, this research provides an effective computationally based glowworm swarm optimization algorithm (GSO). Fundamentally, hydrothermal scheduling issues are thought of as nonlinear objective functions with a variety of active and running restrictions. This work fully considers a number of factors, including the stream nature of hydro plants, barriers to water transfer, capacity equilibrium constraints, water release limits, reservoir preservation limits, working restrictions for steam generating plant and hydraulic power station, fluid flow restriction, and previous and end reservoir container curtailments. In order to get better dynamic locations, glowworms’ step size is changed in the current work. Using well-known hydrothermal systems, the practicality and feasibility of the glowworm swarm optimization are demonstrated. Additionally, a comparison is made between the simulation findings and other evolutionary strategies that have been published in the literature. The comparisons indicate that the proposed method can achieve valuable results with minimal computational overhead.