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Reducing Generation Uncertainty by Integrating CSP With Wind Power: An Adaptive Robust Optimization-Based Analysis

机译:通过将CSP与风电集成来减少发电不确定性:基于自适应鲁棒优化的分析

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The uncertainty of wind power generation brings problems in power system operation, such as requiring more reserves and possible frequency issues. In this paper, we propose an idea of combining concentrating solar power (CSP) plants with wind farms to reduce the overall uncertainty in the joint power output. Taking advantage of the dispatchability of CSP, the uncertainty of joint power generation is expected to decrease. Based on the operational model of CSP plants with thermal storage system, we search for the narrowest but robust bounds of the joint power output with a given uncertainty of the wind power output and solar power availability, and within operational constraints of CSP plants. The problem is formulated as an adaptive robust optimization (RO) problem, containing mixed-integer variables at the second stage. We introduce an algorithm that combines a nested column-and-constraint generation (C-CG) method and an outer approximation (OA) method to solve the problem. The case studies show that robust intervals for the joint power output can be obtained, and the obtained intervals can be significantly narrower than the original intervals of wind power.
机译:风力发电的不确定性给电力系统运行带来问题,例如需要更多的储备和可能的频率问题。在本文中,我们提出了将聚光太阳能发电厂与风电场相结合的想法,以减少联合发电量的总体不确定性。利用CSP的可调度性,联合发电的不确定性有望降低。基于带有蓄热系统的CSP电厂的运行模型,我们在给定的风能输出和太阳能可利用性不确定性的前提下,在CSP电厂的运行约束条件下,寻找联合电力输出的最窄但鲁棒的界限。该问题被表述为自适应鲁棒优化(RO)问题,在第二阶段包含混合整数变量。我们介绍了一种结合嵌套列和约束生成(C-CG)方法和外部逼近(OA)方法的算法来解决该问题。案例研究表明,可以获得联合功率输出的鲁棒间隔,并且所获得的间隔可以比风电的原始间隔明显更窄。

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