Abstract
The objective of a dynamic economic dispatch problem is to determine the optimal power generation from a number of generating units by minimizing the fuel cost. The problem is considered a high-dimensional complex constrained optimization problem. Over the last few decades, many differential evolution variants have been proposed to solve this problem. However, such variants were highly dependent on the search operators, control parameters and constraint handling techniques used. Therefore, to tackle with this shortcoming, in this paper, a new differential evolution framework is introduced. In it, the appropriate selection of differential evolution operators is linked to the proper combination of control parameters (scaling factor and crossover rate), while the population size is adaptively updated. To add to this, a heuristic repair approach is introduced to help obtaining feasible solutions from infeasible ones, and hence enhancing the convergence rate of the proposed algorithm. The algorithm is tested on three different dynamic dispatch problems with 12 and 24 hours planning horizons. The results demonstrate the superiority of the proposed algorithm to the state-of-the-art algorithms.
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Elsayed, S., Zaman, M.F., Sarker, R. (2016). Automated Differential Evolution for Solving Dynamic Economic Dispatch Problems. In: Lavangnananda, K., Phon-Amnuaisuk, S., Engchuan, W., Chan, J. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-27000-5_29
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DOI: https://doi.org/10.1007/978-3-319-27000-5_29
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