论文标题
尺寸与网格连接的风力发电和能源存储，具有唤醒效果和内源性不确定性：分布强大的方法
Sizing Grid-Connected Wind Power Generation and Energy Storage with Wake Effect and Endogenous Uncertainty: A Distributionally Robust Method
论文作者
论文摘要
作为一种绿色能源资源，风能以及储能系统（ESSS）正在迅速增长，以减轻其波动。风力发电和ESS的规模已成为要解决的重要问题。风电场中的唤醒效果会导致风速赤字和下游风力涡轮机发电的下降，但是在电力系统的尺寸问题中很少考虑。在本文中，提出了一个双目标分布强大的优化（DRO）模型，以确定风力发电的能力和ESS的能力考虑到唤醒效果。建立了基于Wasserstein指标的歧义设置，以表征风力和需求不确定性。特别是，风能不确定性受到第一阶段确定的风力发电能力的影响。因此，提出的模型是内源性不确定性（或决策依赖性不确定性）的DRO问题。为了解决提出的模型，开发了基于最小Lipschitz常数的随机编程近似方法，以将DRO模型转换为线性程序。然后，构建了一种迭代算法，并带有评估最小Lipschitz常数的方法。案例研究表明有必要考虑唤醒效应和所提出方法的有效性。
Wind power, as a green energy resource, is growing rapidly worldwide, along with energy storage systems (ESSs) to mitigate its volatility. Sizing of wind power generation and ESSs has become an important problem to be addressed. Wake effect in a wind farm can cause wind speed deficits and a drop in downstream wind turbine power generation, which however was rarely considered in the sizing problem in power systems. In this paper, a bi-objective distributionally robust optimization (DRO) model is proposed to determine the capacities of wind power generation and ESSs considering the wake effect. An ambiguity set based on Wasserstein metric is established to characterize the wind power and demand uncertainties. In particular, wind power uncertainty is affected by the wind power generation capacity which is determined in the first stage. Thus, the proposed model is a DRO problem with endogenous uncertainty (or decision-dependent uncertainty). To solve the proposed model, a stochastic programming approximation method based on minimum Lipschitz constants is developed to turn the DRO model into a linear program. Then, an iterative algorithm is built, embedded with methods for evaluating the minimum Lipschitz constants. Case studies demonstrate the necessity of considering wake effect and the effectiveness of the proposed method.
