Conference Agenda

The increase in the share of renewable energy sources, driven by CO₂ emission reduction targets, lower generation costs, and energy policies, has introduced a significant level of uncertainty into the operation of electrical systems. This variability generates discrepancies between scheduled generation and dispatched generation, increasing the need for backup systems to ensure the security and stability of supply, which makes the operation of the electrical system vulnerable. This paper develops a cost optimization model for economic dispatch based on the gravitational search algorithm (GSA), which considers the uncertainty associated with a renewable generation source using Monte Carlo simulation. Thirty scenarios are generated over a 24-hour horizon, from which the minimum, average, and maximum costs per hour are obtained. A performance comparison of each algorithm is also carried out with the particle swarm optimization (PSO) method, widely used in the literature. The performance comparison shows that both methods produce high-quality solutions; however, the GSA obtains lower costs in most scenarios and presents less dispersion of results, demonstrating greater robustness and stability in the face of renewable uncertainty