Probabilistic Forecast of Real-Time LMP via Multiparametric Programming
Probabilistic Forecast of Real-Time LMP via Multiparametric Programming
复制标题
通过多参数规划实时末次月经概率预测
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
L. Tong
中科院分区：
文献类型：
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作者：
Yuting Ji;R. Thomas;L. Tong
研究了实时区位边际电价的短期概率预测问题。提出了一种新的基于多参数规划的预测方法，该方法将不确定参数空间划分为多个临界区，并利用蒙特卡罗方法估计实时LMP的条件概率质量函数。该方法融合了不确定性模型，如负荷和随机发电预测以及系统事故模型。利用多参数线性规划的离线计算，大大降低了在线计算的成本。
The problem of short-term probabilistic forecast of real-time locational marginal price (LMP) is considered. A new forecast technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability mass function of the real-time LMP is estimated using Monte Carlo techniques. The proposed methodology incorporates uncertainty models such as load and stochastic generation forecasts and system contingency models. With the use of offline computation of multiparametric linear programming, online computation cost is significantly reduced.