Automation of Electric Power SystemsVol. 39, Issue 24, Pages: 16-22(2015)
作者机构:
南京理工大学自动化学院,江苏省,南京市,210094
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DOI:
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Published:2015
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ZHANG Liudong. [J]. Automation of electric power systems, 2015, 39(24): 16-22.
DOI:
ZHANG Liudong. [J]. Automation of electric power systems, 2015, 39(24): 16-22.DOI:
基于净负荷分步建模的旋转备用优化确定风险分析方法
摘要
由于采用净负荷预测误差模型描述风电和负荷共同预测误差
优化系统旋转备用配置的多场景概率风险分析方法尚难以全面考虑风电的不确定性
如从网络安全和经济性方面必须考虑的弃风因素以及风电功率预测误差存在的非正态分布情况。为此
基于分步建模的策略改进多场景概率风险分析方法中净负荷预测误差模型
使其在计算系统失负荷风险时能够计及弃风和考虑不同类型概率分布的风电功率预测误差
并将弃风惩罚成本引入机组组合优化模型的目标函数中
以实现风电功率的合理消纳和系统旋转备用的优化确定。采用混合整数线性规划方法
对含风电场的IEEE-RTS 26机测试系统进行仿真分析
验证了改进模型的有效性。
Abstract
Owing to use of the net demand forecast error in describing the common forecast errors of load and wind power
the risk analysing method based on multi-scenario probability while determining the optimal spinning reserve amount is unable to comprehensively consider wind power uncertainty. For instance
wind power spillage that must be taken into account to ensure network security and economical operation of power systems and the non-normal probability distributions of wind power forecast error. Therefore
the net demand forecast error model in the existing multi-scenario risk analysing methods by the strategy for multi-step modeling is improved to determine the loss-of-load risk of power systems with wind power spillage and different probability distributions of wind power forecast error taken into account. And the penalty cost of wind power spillage is added to the objective function of unit commitment to achieve rational wind power accommodation and optimization of spinning reserve. Simulation studies on a modified IEEE 26-generator reliability test system connected to a wind farm are conducted by the mixed integer linear programming method to verify the effectiveness of the improved model. This work is supported by National Natural Science Foundation of China (No. 61203129
No. 61174038
No. 61473151)
Jiangsu Planned Projects for Postdoctoral Research Funds (No. 1301014A)
and Fundamental Research Funds for the Central Universities (No. 30915011104