基于BSA的电力系统概率暂态稳定约束最优潮流

Research on Probabilistic Transient Stability Constrained Optimal Power Flow for Power Systems Based on BSA

中文关键词:Nakagami分布  Beta分布  鸟群算法

keywords:Nakagami distribution  Beta distribution  bird swarm algorithm（BSA）

中文摘要:随着新能源占比的逐步增加，其不确定性问题成为暂态稳定约束最优潮流（transient stability constrained optimal power flow，TSCOPF）模型需要重点考虑的因素。针对传统TSCOPF模型不能很好地应对含新能源的电力系统这一问题，提出一种考虑新能源不确定性的概率暂态稳定约束最优潮流（probability transient stability constrained optimal power flow，PTSCOPF）模型。首先，利用Nakagami分布、Beta分布对风电、光伏发电的不确定性进行拟合；其次，根据风电、光伏机组的运行特性，构造相应约束，且对不等式约束进行概率化处理；再次，采用鸟群算法（bird swarm algorithm，BSA）对PTSCOPF进行求解，得到系统最优运行方式；最后，在改进的新英格兰68节点系统中进行仿真验证，证实所提模型和方法的有效性与优势。

Abstract:With the gradual increase in the proportion of new energy，its uncertainty has become a key factor to be considered in the transient stability constrained optimal power flow （TSCOPF） model. Given that the traditional TSCOPF model cannot well adapt to power systems with high penetration of new energy，a probabilistic transient stability constrained optimal power flow （PTSCOPF） model considering the uncertainty of new energy is proposed. First，the Nakagami distribution and Beta distribution are used to fit the uncertainties of wind power and photovoltaic power generation，respectively. Second，corresponding constraints are constructed according to the operating characteristics of wind and photovoltaic units，and the inequality constraints are probabilistically processed. Third，the bird swarm algorithm （BSA） is adopted to solve the PTSCOPF model so as to obtain the optimal operation mode of the power system. Finally，simulation verification is performed on the modified New England 68-bus system，which verifies the effectiveness and advantages of the proposed model and method.