含FACTS装置的风电并网VSC-MTDC系统的随机优化运行
Stochastic Optimal Operation of the VSC-MTDC System with FACTS Devices to Integrate Wind Power
摘要：
针对大规模风电接入对电力系统的挑战，提出了随机锥规划方法。多个风电场通过基于电压源换流器（VSC）的多端直流（VSC-MTDC）系统连接到由灵活交流输电系统（FACTS）支持的电力网络。在考虑风力发电不确定性的随机二次规划框架下，对包含VSC-MTDC系统和FACTS装置的电网进行了优化运行。提出了一种方法来产生代表性的情况下，从风力发电场的发电，风速测量和风力涡轮机模型。将包含VSC-MTDC系统和FACTS装置的非凸输电网络约束转化为二阶锥交流最优潮流模型（SOC-ACOPF），并利用内点法求解该模型的全局最优解。为了解决风电场景数量庞大带来的计算难题，提出了一种并行加速的改进Benders分解算法（M-BDA）。常规发电机组的能量调度问题被表述为M-BDA的主问题。最多50000个风电场景的数值计算结果表明，所提出的M-BDA方法来解决随机SOC-ACOPF优于传统的单阶段（无分解）的解决方案的方法在收敛能力和计算效率。本文还验证了所提出的随机SOC-ACOPF模型的可行性。
英文摘要：
This paper proposes to use stochastic conic programming to address the challenge of large-scale wind power integration to the power system. Multiple wind farms are connected through the voltage source converter (VSC) based multi-terminal DC (VSC-MTDC) system to the power network supported by the Flexible AC Transmission System (FACTS). The optimal operation of the power network incorporating the VSC-MTDC system and FACTS devices is formulated in a stochastic conic programming framework accounting the uncertainties of the wind power generation. A methodology to generate representative scenarios of power generations from the wind farms is proposed using wind speed measurements and wind turbine models. The nonconvex transmission network constraints including the VSC-MTDC system and FACTS devices are convexified through the proposed second-order cone AC optimal power flow model (SOC-ACOPF) that can be solved to the global optimality using interior point method. In order to tackle the computational challenge due to the large number of wind power scenarios, a modified Benders decomposition algorithm (M-BDA) accelerated by parallel computation is proposed. The energy dispatch of conventional power generators is formulated as the master problem of M-BDA. Numerical results for up to 50000 wind power scenarios show that the proposed M-BDA approach to solve stochastic SOC-ACOPF outperforms the traditional single-stage (without decomposition) solution approach in both convergence capability and computational efficiency. The feasibility performance of the proposed stochastic SOC-ACOPF model is also demonstrated.
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