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Application meta-heuristics method for short-term unit commitment problem

机译:短期单位承诺问题的应用元启发式方法

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This work presents a hybrid chaos search genetic algorithm and simulated annealing method (CGA-SA) for solving short-term thermal generating unit commitment (UC) problems. The UC problem involves determining the start-up and shutdown schedules for generating units to meet the forecasted demand at the minimum cost. The commitment schedule must satisfy other constraints such as the generating limits per unit, reserve and individual units. We combined a genetic algorithm with the chaos search. First, it generates a set of feasible unit commitment schedules, and then puts the solution to the SA. The CCA has good global optima search capabilities, but poor local optima search capabilities. The SA method on the other hand, has good local optima search capabilities. Through this combined approach an optimal solution can be found.
机译:该工作提出了一种混合混沌搜索遗传算法和模拟退火方法(CGA-SA),用于求解短期热产生单元承诺(UC)问题。 UC问题涉及确定用于生成单元以满足最小成本的预测需求的启动和关闭计划。承诺计划必须满足其他限制,例如每单位的产生限制,储备和单位。我们将遗传算法与混沌搜索组合。首先,它生成一组可行的单位承诺计划,然后将解决方案放入SA。 CCA具有良好的全局Optima搜索功能,但本地Optima搜索功能差。另一方面,SA方法具有良好的本地Optima搜索功能。通过这种组合方法,可以找到最佳解决方案。

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