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Crisscross differential evolution algorithm for constrained hydrothermal scheduling
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DOI：10.1016/j.asoc.2020.106393.png)
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Abstract
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This paper proposes a novel chaotic-crisscross differential evolution (CCDE) algorithm to realize an optimal generation schedule of multi-chain short-term hydrothermal system over 24 hours' time-horizon in a multi-objective framework, considering conflicting economic-environmental aspects of thermal units. The equality constraints of active power balance and the amount of available water are independently handled using variable elimination method. However, the statistical uncertainties called residues arise due to infringements of equality constraints while adjusting the violated dependent variables within their boundaries. These residues are fuzzy quantified within their prescribed bounds, and are embedded as objectives to be optimized. An interactive unified fuzzy satisfying function is aimed to solve the conflict of three objectives. The global solution accuracy and convergence rate of stochastic algorithms are significantly affected by parameter-tuning, exploration and exploitation strategies. The proposed algorithm integrates dual crisscross mechanism orthogonally with chaotically tuned DE to balance exploration and exploitation. Information collected about non-dominated solutions from search space is processed using opposition-based learning for better accuracy of global solution in three-dimensional objective function hyperspace. The numerical results show improvement in unified satisfying objective function and convergence performance metrics over the existing methods. The competence of the proposed algorithm is confirmed through illustrations on benchmark functions and is substantiated through statistical significance tests. (C) 2020 Published by Elsevier B.V.
Keywords:
Crisscross differential evolution
Fuzzy set theory
Hydrothermal scheduling
Multi-objective optimization problem
Opposition-based learning
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