基于PCE的系统参数化暂态稳定约束最优潮流
Parameterized Transient Stability Constrained Optimal Power Flow of Power Systems Based on Polynomial Chaos Expansion
刘颂凯 1李正浩 1周倩 1杨超 1胡竞哲 1阮肇华2
作者信息
- 1. 三峡大学电气与新能源学院,湖北宜昌 443002;新能源微电网湖北省协同创新中心,湖北宜昌 443002
- 2. 国网福建省电力有限公司宁德供电公司,福建宁德 352100
- 折叠
摘要
为更好地应对电力系统中存在的不确定参数给暂态稳定性带来的影响,提出了一种基于多项式混沌展开(polynomial chaos expansion,PCE)和随机伽辽金法(stochastic Galerkin method,SGM)的参数化暂态稳定约束最优潮流(parameterized transient stability constrained optimal power flow,PTSCOPF)问题的求解方案.针对电力系统不确定参数建立PTSCOPF模型;基于PCE和SGM近似拟合参数化暂态稳定轨迹,将原始的微分代数方程组(differential algebraic equations,DAE)形式的PTSCOPF模型转化为代数方程组形式的参数化非线性规划(non-linear programming,NLP)问题;采用原始-对偶内点法求解该NLP问题.利用改进的新英格兰 10机39节点系统算例进行了各项测试,仿真结果表明,所提方法能够实现系统在预想故障下保持暂态稳定的优化效果.
Abstract
To better cope with the impact of uncertain parameters in the power system on transient stability,a solving scheme for parameterized transient stability constraint optimal power flow(PTSCOPF)based on polynomial chaos expansion(PCE)and stochastic Galerkin method(SGM)is proposed in this paper.First,the PTSCOPF model is established for the uncertain parameters of the power system;second,based on PCE and SGM to approximate the parameterized transient trajectory,the initial PTSCOPF model in the form of differential algebraic equations(DAE)is transformed into a parameterized non-linear programming(NLP)problem in the form of algebraic equations;finally,the primal-dual interior point method is applied to solve the NLP problem.The improved New England 10-machine 39-bus system being used to carry out various tests,the simulation results show that the proposed method can achieve the optimization effect of maintaining transient stability under the expected fault.
关键词
暂态稳定约束最优潮流/多项式混沌展开/随机伽辽金法/不确定参数Key words
transient stability constrained optimal power flow/polynomial chaos expansion/stochastic Galerkin method/uncertain parameters引用本文复制引用
基金项目
国家自然科学基金项目(62233006)
国家自然科学基金项目(52007103)
湖北省自然科学基金项目(2022CFB825)
电力系统智能运行与安全防御宜昌市重点实验室(三峡大学)开放基金项目(2020DLXY06)
梯级水电站运行与控制湖北省重点实验室(三峡大学)开放基金项目(2019KJX11)
出版年
2024
NETL
NSTL
万方数据