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Impact of Wind Power Scenario Reduction Techniques on Stochastic Unit Commitment

机译:减少风能情景技术对随机机组承诺的影响

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摘要

Stochastic unit commitment (SUC) is an effective method widely used to cope with the uncertainty of wind power. For the limitation of computation capability, only limited members of representative scenario can be considered in SUC. It thus rises the concern that whether the selected scenarios can fully represent the uncertainty nature of wind power. In this paper, the performance of reduced scenarios is quantified by both its statistical quality and its economic value on the optimality of SUC. Two metrics are proposed to quantify the distortion of the stochastic quality of wind power during the scenario reduction process: output uncertainty and ramp diversity. The economic value of reduced scenarios is evaluated as the difference between the optimal cost of the SUC model associated with limited scenarios and the expected "actual" operating costs when considering all the possible scenarios. Then, this paper reviews several typical wind power scenario techniques and categorizes them by both the scenario clustering approach and scenario reduction criterion. The quality of each method is tested using the real wind power data from NREL database and the modified IEEE RTS-79 system. Results show that the performance of SUC is more sensitive to the output uncertainty approximation rather than the ramp diversity approximation of reduced scenarios.
机译:随机单位承诺(SUC)是一种广泛用于应对风力不确定性的有效方法。由于计算能力的限制,SUC中只能考虑代表性场景的有限成员。因此,引起了人们的关注,即所选方案是否可以完全代表风力发电的不确定性。在本文中,通过其统计质量和其在SUC最优性方面的经济价值来量化简化方案的性能。提出了两个指标来量化情景减少过程中风电随机质量的失真:输出不确定性和斜坡分集。当考虑所有可能的方案时,将减少的方案的经济价值评估为与有限的方案关联的SUC模型的最佳成本与预期的“实际”运营成本之间的差。然后,本文回顾了几种典型的风电情景技术,并通过情景聚类方法和情景减少准则对它们进行了分类。每种方法的质量都使用NREL数据库中的实际风力数据和经过修改的IEEE RTS-79系统进行测试。结果表明,SUC的性能对输出不确定度近似值更敏感,而不是简化方案的斜坡分集近似值。

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