The solution of the short-term hydrothermal coordination problem by improved incremental gravitational search algorithm

There are hydraulic constraints as well as electrical constraints that must be satisfied in systems with hydraulic and thermal generation units. Therefore, the operation of energy systems with both thermal and hydraulic generation units for a defined period of time (hydrothermal coordination) is more difficult and more complicated with respect to a system composed of only thermal generation units. If the operation period is from one day to a week, the hydrothermal coordination problem is called short-term hydrothermal coordination problem (STHCP). Heuristic algorithms are frequently used in the solution of engineering problems like STHCP which are complicated and difficult to solve. The reason is that these type of problems are highly nonlinear and multi-dimensional having large search space. In recent years, a lot of heuristic algorithms have been developed for faster solution of these types of problems. In this work, gravitational search algorithm (GSA) has been selected as the heuristic algorithm for the solution of STHCP. In order to improve the performance of GSA, incremental social learning structure (ISL) has been applied to the algorithm in three different ways. This algorithm where the performance of GSA is improved has been called Incremental GSA (IGSA). Due to ISL applied in three different ways, three different algorithms were developed. Those newly developed algorithms were successfully applied to 23 benchmark test functions. Also, offline parameter adaptation was accomplished in order to find the most suitable parameter values for IGSAs. IGSA in which the calculated parameter values were used, was applied to STHCPs with fixed and variable heads. The constraints in the solution of the problems were handled by means of the penalty function approach and the obtained results were compared with those given in the late literature.