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Grid Power Optimization Based on Adapting Load Forecasting and Weather Forecasting for System Which Involves Wind Power Systems

机译:基于自适应负荷预测和天气预报的风电系统并网优化

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This paper describes the performance, generated power flow distribution and redistribution for each power plant on the grid based on adapting load and weather forecasting data. Both load forecasting and weather forecasting are used for collecting predicting data which are required for optimizing the performance of the grid. The stability of each power systems on the grid highly affected by load varying, and with the presence of the wind power systems on the grid, the grid will be more exposed to lowering its performance and increase the instability to other power systems on the gird. This is because of the intermittence behavior of the generated power from wind turbines as they depend on the wind speed which is varying all the time. However, with a good prediction of the wind speed, a close to the actual power of the wind can be determined. Furthermore, with knowing the load characteristics in advance, the new load curve can be determined after being subtracted from the wind power. Thus, with having the knowledge of the new load curve, and data that collected from SACADA system of the status of all power plants, the power optimization, load distribution and redistribution of the power flows between power plants can be successfully achieved. That is, the improvement of performance, more reliable, and more stable power grid.
机译:本文根据适应的负荷和天气预报数据,描述了电网上每个发电厂的性能,产生的潮流分配和重新分配。负荷预测和天气预报都用于收集预测数据,这是优化电网性能所需的。电网中每个电力系统的稳定性都受到负载变化的严重影响,并且随着电网中风力发电系统的存在,电网将更容易降低其性能并增加电网上其他电力系统的不稳定性。这是由于风力涡轮机所产生的动力的间歇性,因为它们取决于始终在变化的风速。然而,通过良好的风速预测,可以确定接近风的实际功率。此外,在事先了解负载特性的情况下,可以在从风力中减去之后确定新的负载曲线。因此,通过了解新的负载曲线以及从SACADA系统收集的所有电厂状态的数据,可以成功实现电厂之间功率流的功率优化,负载分配和重新分配。即,性能的提高,更可靠和更稳定的电网。

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