非参数条件概率预测提高风电消纳的优化方法  

Optimal Method of Improving Wind Power Accommodation by Nonparametric Conditional Probabilistic Forecasting

作　　者：叶一达[1] 魏林君[2] 乔颖[1] 李琰[2] 鲁宗相[1] 

机构地区：[1]电力系统及发电设备控制和仿真国家重点实验室(清华大学电机系),北京市海淀区100084 [2]新能源与储能运行控制国家重点实验室(中国电力科学研究院),北京海淀区100192

出　　处：《电网技术》2017年第5期1443-1450,共8页Power System Technology

基　　金：国家重点研发计划支持项目(2016YFB0900105);国家电网公司科技项目(XTB17201500037)~~

摘　　要：功率点值预测信息未充分考虑可再生能源的不确定性及波动性,在日前发电计划和备用决策安排中存在保守或冒进的风险。引入非参数化方法,根据历史风资源状况得到风电出力的非参数条件概率预测结果,建立风电日前消纳调度模型。该模型在不同资源主导因素所划分的条件空间子集中得到预测功率条件概率分布,能够反映弃风和失负荷风险,可以根据调度需求选取不同的预测功率置信区间,计及风电不确定性完整的条件概率信息。仿真算例验证了选取合适置信水平的风电非参数化条件概率预测方法在日前优化运行中有效提高风电消纳水平,并降低系统运行风险。The day-ahead dispatch and reserve scheduling based on the point forecasting could be either conservative or aggressive as the intrinsic volatility and uncertainty of renewable energy are not fully considered. In this paper, the nonparametric conditional probabilistic forecasting is proposed to estimate the probability distribution of forecasted power in different conditional spatial subsets divided by dominant wind-resource factors. Then, a day-ahead unit commitment model with different wind power forecasting confidence intervals is presented to take a full probabilistic view of uncertainty, which can meet the demand of dispatching departments in various operational conditions, by considering wind curtailment and load shedding risks. Case study verifies that the proposed model can reduce both day-ahead wind power curtailment and operational risks of power systems.

关 键 词：风电日前消纳 非参数化 条件概率预测 系统运行风险 

分 类 号：TM76[电气工程—电力系统及自动化]