电网技术2016，Vol.40Issue(12)：3880-3887,8.DOI:10.13335/j.1000-3673.pst.2016.12.034
考虑光伏不确定性的低压配电网分散无功补偿鲁棒优化配置
Robust Optimal Allocation of Reactive Power Compensation in Low Voltage Distribution Networks Considering Uncertainty of Photovoltaic Generation
摘要
Abstract
In order to improve voltage quality and reduce line loss,a robust optimization model of distributed reactive power compensation for low voltage (LV) distribution network is proposed,considering random fluctuation of photovoltaic (PV) output and forecasting error of load power.In this model,objective is to minimize total power loss of distribution network,with uncertain variables of load power,light intensity and temperature affecting PV output discribed as uncertain set.Its constraints include upper and lower limits of node voltage and reactive power compensation capacity,and limit of total number of installed compensation devices.A deformed S-function is used to approximate symbolic function to smooth non-differentiable function in the model.Bi-level optimization method is used to transform robust optimization model with uncertain variables to bi-level deterministic optimization model,which is solved alternately.Finally,interior point method is adopted to solve the deterministic model to obtain robust optimal allocation scheme of reactive power compensation.Analysis of a real LV distribution network demonstrates that the obtained robust optimal allocation scheme of reactive power compensation can ensure all node voltage qualified no matter how LV output and load power changes in their own fluctuation interval,so the scheme is more applicable in engineering.关键词
分布式光伏/配电网无功优化/分散补偿配置/符号函数逼近/鲁棒优化/双层优化Key words
distributed photovoltaic/reactive power optimization of distribution network/distributed compensation allocation/sign function approximation/robust optimization/bi-level optimization分类
信息技术与安全科学引用本文复制引用
张海鹏,林舜江,刘明波..考虑光伏不确定性的低压配电网分散无功补偿鲁棒优化配置[J].电网技术,2016,40(12):3880-3887,8.基金项目
国家自然科学基金项目(51207056) （51207056）
广东省自然科学基金资助项目(2015A030313233).Project Supported by National Natural Science Foundation of China (51207056) and Natural Science Foundation of Guangdong Province (2015A030313233). （2015A030313233）
