基于遗传算法和数据包络分析法的水火电力系统发电多目标经济调度

Multi-objective Economic Scheduling for Hydrothermal Power Systems Based on Genetic Algorithm and Data Envelopment Analysis

为充分提高水电站发电效益及达到节能降耗目的,建立了以梯级水电站发电量最大和火电机组煤耗量最小的水火电力系统短期发电多目标经济调度模型,并利用遗传算法与数据包络分析(data envelopment analysis,DEA)法求解该模型,并通过惩罚因子法将各约束条件融合到目标函数中,通过设置不同权重系数获得10种调度方案。比较不同调度方案的目标值和DEA评估值,并依据决策者喜好选取最优决策方案。算例结果验证了该方法的有效性。

To fully increase the benefit of electricity generation and implement energy saving and loss reduction, a multi-objective economic scheduling model, in which the generated energy by cascade hydroelectric stations is maximized and the coal consumption of thermal power generating units is minimized, is proposed, and genetic algorithm （GA） and data envelopment analysis （DEA） is used to solve the proposed model. By means of penalty factors, the constraint conditions are merged into the objective function, then through setting different weight coefficient ten scheduling schemes are obtained. Comparing objective values and DEA estimation values of different scheduling schemes and according to the preference of different decision-makers, the optimal decision-making scheme can be chosen. The effectiveness of the proposed method is verified by results of case calculation.