中国电机工程学报2016，Vol.36Issue(15)：4081-4089,9.DOI:10.13334/j.0258-8013.pcsee.150891
考虑预测误差时序分布特性的含风电机组组合模型
Model of Unit Commitment With Wind Farm Considering Time Series Characteristic of Wind Power Forecast Error
摘要
Abstract
There are two problems in unit commitment (UC) with wind power integration, high fitting precision of forecast error and appropriate selection of confidence interval. Aiming to solve the above problems, this paper proposed a new UC model with wind power integration. The first, based on the analysis of forecast error, a temporal segment method of forecast error was proposed, which usedt location-scale distribution for fat tail effect, improving the fitting accuracy, and also coordinated with day-ahead UC in time series. The second, a new UC model with double quantiles was proposed, which considered traditional coasts, extra reserve costs and risk costs. In this model, the confidence interval determined by the constrains among different costs, the reserve style sorted by dividing different confidence interval, the time-varying confidence level used to correspond to temporal segment distribution, make the model more applicable and economic. The last, the improved hybrid particle swarm optimization algorithm with heuristic searching strategy was proposed to solve the multivariate mixed integer programming problem, and the simulation results demonstrate the effectiveness of the proposed model.关键词
机组组合/预测误差/时序分段分布/置信水平/机会约束规划Key words
unit commitment/forecast error/time series segment distribution/confidence level/chance constrain programming分类
信息技术与安全科学引用本文复制引用
王成福,王昭卿,孙宏斌,梁正堂,梁军,韩学山..考虑预测误差时序分布特性的含风电机组组合模型[J].中国电机工程学报,2016,36(15):4081-4089,9.基金项目
国家重点基础研究发展计划项目(973项目)(2013CB228205)；国家自然科学基金项目(51177091,51477091)；山东省青年科学家奖励基金项目(BS2015NJ005)；山东大学基本科研业务费资助项目(2015GN001)。Project Supported by the National Basic Research Program of China (973 Program)(2013CB220205) （973项目）
National Natural Science Foundation of China(51177091,51477091) （51177091,51477091）
The Research Award Foundation for Outstanding Middle-aged and Young Scientist of Shandong Province, China (BS2015NJ005) （BS2015NJ005）
The Fundamental Research Funds of Shandong University (2015GN001) （2015GN001）
