Optimization of the economic load dispatch of regional power systems in turkey with different algorithms

Economic load dispatch appears as a problem whose importance has steadily increased from the past to the present. With today's conditions and the rapid growth of technology, energy demand is rising sharply. Because the available energy sources have proven insufficient to meet this demand, it has become important to operate power systems under the most favorable conditions. In an era when energy plays a critical role and given the serious impacts of war, embargoes, and economic pressures countries around the world are even working to bring previously decommissioned thermal power plants back online in order to satisfy their energy needs. For this reason, minimizing the operating costs of thermal power plants has also become important in our country. Taking energy costs into account, solid‐fuel plants are required to operate between their minimum and maximum active‐power limits. At this point, as economic load dispatch, reactive‐power optimization, and optimal‐power‐flow studies continue to yield effective results, their importance will only grow. In this thesis work, the Economic Load Dispatch (ELD) problem at regional power plants in Turkey is solved using the Sequential Least Squares Programming (SLSQP) optimization algorithm together with the Genetic Algorithm (GA), Artificial Bee Colony (ABC) algorithm, Tabu Search (TS) algorithm, and Simulated Annealing (SA) algorithm both those used in this study and those reported in the literature. First, for the 8‐generator, 22‐bus system, the consistent cost result of $80,530 $/h achieved by GA, ABC, and SLSQP is presented, along with the very short optimization time of just 0.00914 seconds for SLSQP. Next, for the 6‐generator, 14‐bus system, it is shown that GA, TS, SA, and SLSQP produce closely comparable cost outcomes. Moreover, tables detail that SLSQP attains the best cost of $47,660 $/h and reaches this solution in only 0.00706 seconds. In short, the algorithms' performances both in terms of lowest cost and convergence time are analyzed comparatively. Based on these analyses, the study seeks to determine the optimal operating conditions that minimize generator costs.