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Bi-Level Optimization Model for Daily Operation with Heterogeneous Hydropower Units in Multiple Reservoirs with Application to the Three Gorges-Gezhouba Cascade Power Stations

机译:三峡-葛洲坝梯级电站多水库非均质水电站机组日常运行双层优化模型

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The complexity of the operation of the Three Gorges-Gezhouba cascade hydropower stations lies in not only the enormous unit installed capacity, but also the range of different characteristics from different types of hydropower generating units. The system has two reservoirs and the generation system consists of 47 power generating units. These units are of 10 different types that have different functions describing the relationships among power output, water head and turbine water discharge. A bi-level optimization framework is presented to determine the best hourly allocation of water to each of these types of power generating units to minimize the amount of water used while meeting load demand. The optimization has an outer global optimization loop that determines the amount of water allocation each time period. The inner loop uses linear programming to optimize the allocation of that water between reservoirs and among power generating units. The optimization methodology uses water heads and other seasonal data as input so that hourly allocation to power generating units can be updated daily. Dynamically Dimensioned Search (DDS) and Simulated Annealing (SA) are tested for global optimization in the outer loop, and DDS performs better than SA.
机译:三峡葛洲坝梯级水电站的运行复杂性不仅在于巨大的机组装机容量,还在于不同类型水力发电机组具有不同特性的范围。该系统有两个水库,发电系统由47个发电单元组成。这些单元有10种不同类型,它们具有不同的功能,描述了功率输出,水头和涡轮机排水之间的关系。提出了一个双层优化框架,以确定向这些类型的发电设备中的每一种发电的最佳小时分配,以在满足负载需求的同时最大程度地减少用水量。该优化具有外部全局优化循环,该循环确定每个时间段的水分配量。内循环使用线性编程来优化水在水库之间和发电单元之间的分配。优化方法使用水头和其他季节性数据作为输入,以便可以每天更新对发电单元的每小时分配。对动态尺寸搜索(DDS)和模拟退火(SA)进行了外部循环中的全局优化测试,并且DDS的性能优于SA。

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