考虑马尔可夫过程的风电预测随机机组承诺

Nhung Nguyen-Hong, Nakanishi Yosuke
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引用次数: 3

摘要

机组承诺是电力系统运行中的一个主要问题,它决定了发电机组的运行计划,从而使系统运行成本最小化。由于风电的不确定性,UC问题需要作为多周期随机优化来解决。在这个随机问题中,会产生一个场景树,当时间范围较长时,可能会产生一个太大而无法求解的场景树。本文提出了一种基于最大熵原理的方法,通过将随机过程转化为有限状态马尔可夫链过程并求转移概率矩阵来生成和约简场景。将该方法应用于具有随机波动率的ARMA(1,1)模型的风电过程变换。本文解决了一个简单的随机单元承诺问题。由于电力系统的安全性,还考虑了备用约束。
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Stochastic unit commitment considering Markov process of wind power forecast
Unit commitment (UC) is a major problem in power system operation which determines the operation schedule of the generating units by minimizing system operation cost. Because of the uncertainty of wind power, the UC problem needs to solve as a multi-period stochastic optimization. In this stochastic problem, scenarios tree is generated and may be too large to be solved when time horizon is longer. This paper presents an approach based on Maximum Entropy principle to generate and reduce scenarios by transforming a stochastic process to a finite-state Markov chain process and finding transition probability matrix. This approach is applied to transform a wind power process modeled by ARMA(1,1) model with Stochastic Volatility. A simple stochastic unit commitment is solved in this article. Because of power system security, reserve constraints also considered.
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