摘要
随着现代电力系统的发展,其失负荷概率逐渐降低。而风电大规模接入系统后,发输电系统中的不确定因素进一步增多。基于上述原因,现代电力系统可靠性评估的难度也相应地加大。该文以此为研究目标,依据交叉熵思想,提出一种能够计及风速波动性和风电机组降额状态的加速采样的改进交叉熵重要性采样法,称为IKL-IS法。该方法通过求取和利用发输电系统内各随机性元件的最优概率分布,降低了待评估的系统状态示性函数的方差,从而减小了评估指标的数值波动,加快了其收敛速度。最后根据调整后的IEEE-RTS 79发输电系统设计了算例,算例内部同时考虑了风速、负荷的波动性以及风电机组的多状态特性。同时将所提IKL-IS算法和传统蒙特卡洛采样法(MCS)、分散蒙特卡洛抽样法以及既有重要性采样法(KL-IS)应用于该算例,并比较4种方法评估得到的测试系统可靠性指标,验证了该算法的正确性和有效性。
With the development of modern power system, the probability of loss of load becomes much smaller and the system is much more robust. With the increasing capacity of integrated wind power, uncertainty in composite power system will be aggravated. Under the background, the difficulties in evaluating system reliability are significantly increased. To cope with the problem, an improved importance sampling method was developed and called IKL-IS method. IKL-IS method was capable of acquiring the optimal probability distribution of all random variables in the system, including wind speeds and the states of wind turbines. The optimal distribution was utilized to reduce the variance of the function indicating the system state. Therefore, the fluctuation of the reliability indices was decreased and the convergence process was accelerated. An adjusted IEEE-RTS 79 test system was designed by incorporating fluctuated wind speeds/load demands and multi-state wind turbines. Traditional Monte Carlo sampling method, scattered sampling technique, pre-existing KL-IS method and the proposed IKL-IS method were applied to evaluate the reliability of the adjusted system. The reliability indices obtained by the four methods were compared to validate the proposed method.
作者
徐青山
蔡霁霖
李淋
郑爱霞
XU Qingshan;CAI Jilin;LI Lin;ZHENG Aixia(School of Electrical Engineering,Southeast University,Nanjing 210096,Jiangsu Province,China;State Grid Jiangsu Electric Power Company,Nanjing 210024,Jiangsu Province,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2018年第24期7248-7257,共10页
Proceedings of the CSEE
基金
国家电网公司总部科技项目(SGJSJX00YJJS1800721)
国家自然科学基金项目(51877044)~~
关键词
发输电系统
可靠性评估
风速波动
多状态风电机组
重要性采样
composite power system
reliability evaluation
wind speed fluctuation
multi-state wind turbines
importance sampling
