Improved differential evolution for short-term hydrothermal scheduling

This paper presents an improved differential evolution to determine the optimal hourly schedule of power generation in a hydrothermal system. Differential evolution (DE) exploits the differences of randomly sampled pairs of objective vectors for its mutation process. Consequently the variation between vectors will outfit the objective function toward the optimization process and therefore provides efficient global optimization capability. However, although DE is shown to be precise, fast as well as robust, its search efficiency will be impaired during solution process with fast descending diversity of population. This paper proposes Gaussian random variable instead of scaling factor which improves search efficiency. The algorithm is tested on two test problems and three hydrothermal multi-reservoir cascaded hydroelectric test systems having prohibited operating zones and thermal units with valve point loading. The ramp-rate limits of thermal generators are taken into consideration. The transmission losses are also accounted for through the use of loss coefficients. The results of the proposed approach are compared with those obtained by other evolutionary methods. It is found that the improved differential evolution based approach is able to provide better solution. (C) 2013 Elsevier Ltd. All rights reserved.