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A genetic algorithm solution to a new fuzzy unit commitment model

机译：一种新的模糊单位承诺模型的遗传算法解决方案

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摘要

This paper proposes a new fuzzy model for the unit commitment problem (UCP). A solution method for the proposed UCP model based on the genetic algorithms (GAs) is presented (FZGA). The model treats the uncertainties in the load demand and the spinning reserve constraints in a new fuzzy logic (FL) frame. The proposed FL model is used to determine a penalty factor that could be used to guide the search for more practical optimal solution. The implemented fuzzy logic system consists of two inputs: the error in forecasted load demand and the amount of spinning reserve, and two outputs: a fuzzy load demand and a penalty factor. The obtained fuzzy load demand is more realistic than the forecasted crisp one; hence the solution of the UCP will be more accurate. In the proposed FZGA algorithm, coding of the solution is based on mixing binary and decimal representation. The fitness function is taken as the reciprocal of the total operating cost of the UCP in addition to penalty terms resulted from the fuzzy membership functions for both load demand and spinning reserve. Results show that the fuzzy-based penalty factor is directly related to the amount of shortage in the committed reserve; hence will properly guide the search, when added to the objective function, in the solution algorithm of the UCP. Accordingly, acceptable level of reserve with better-cost savings was achieved in the obtained results. Moreover, the proposed FZGA algorithm was capable of handling practical issues such as the uncertainties in the UCP. Numerical results show the superiority of solutions obtained compared to methods with traditional UCP models.
机译：本文为单位承诺问题（UCP）提出了一种新的模糊模型。提出了一种基于遗传算法（FZGA）的UCP模型的求解方法。该模型在新的模糊逻辑（FL）框架中处理负荷需求和旋转储备约束的不确定性。所提出的FL模型用于确定惩罚因子，该惩罚因子可用于指导寻找更实际的最佳解决方案。实施的模糊逻辑系统包括两个输入：预测的负载需求中的误差和旋转储备量，以及两个输出：模糊的负载需求和惩罚因子。所获得的模糊负荷需求比预测的明晰需求更为现实。因此，UCP的解决方案将更加准确。在提出的FZGA算法中，解决方案的编码基于混合的二进制和十进制表示形式。除对负荷需求和旋转储备的模糊隶属函数所导致的惩罚项外，适应度函数还用作UCP总运营成本的倒数。结果表明，基于模糊的惩罚因子与承诺准备金的短缺量直接相关。因此，当添加到目标函数中时，它将正确指导UCP的求解算法中的搜索。因此，在获得的结果中实现了可接受的储备金水平并节省了成本。此外，提出的FZGA算法能够处理实际问题，例如UCP中的不确定性。数值结果表明，与传统UCP模型相比，所获得的解决方案具有优越性。

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