基于联络线扩展区域分解协调的分布式并行状态估计

为适应互联电网日益复杂的结构形式和分层分区管理模式,采用联络线分区解耦方式对互联系统进行分布式状态估计计算。考虑到估计计算精度与计算效率的均衡,提出了一种将联络线扩展区域状态估计和灵敏度矩阵协调算法结合的分布式状态估计算法。设计分布式状态估计整体思路,一方面,在实现联络线分区解耦的前提下,完成分区层各子区独立状态估计计算和灵敏度矩阵求解,利用子区计算时机动态构建协调层联络线估计区域并完成并行状态估计计算;另一方面,根据协调层下发的联络线估计值和灵敏度矩阵,分区层各子区并行实现边界估计值不匹配量的协调修正计算。最后,通过IEEE 118节点、IEEE 30节点和实际互联电网的模拟仿真,验证上述算法实现分布式状态估计计算的优越性。仿真结果表明,采用该算法进行分布式估计计算,既具有较高的估计精度、收敛速度和计算效率,又可实现并行独立计算,避免集中式状态估计计算规模大、无法解决局部不可观测或不收敛的瓶颈问题。

Distributed Parallel State Estimation Based on Decomposition and Coordination of Tie-line Extended Area

In order to adapt to the increasingly complex structure of interconnected power grid and the hierarchical and partitioned management modes, the distributed state estimation calculation for interconnected systems using partition and decoupling by tie-line are adopted. Considering the balance of estimation accuracy and efficiency, this paper proposes a distributed state estimation algorithm combining the state estimation of tie-line extended area and the sensitivity matrix coordination method. The whole idea of the distributed state estimation is designed in two parts. On one hand, the state estimation and the sensitivity matrix solution of each region in the partition layer is independently performed once the interconnected power grid is decoupled by regional tie-lines. Simultaneously, the tie-line estimation area in coordination layer is dynamically constructed and the parallel state estimation calculation is completed. On the other hand, the coordinated correction calculation for the mismatch of boundary estimation value in each sub-region is performed in parallel according to the tie-line estimated values and sensitivity matrixes distributed by the coordination layer. Finally, the calculation examples of the IEEE 118-bus, IEEE 30-bus and an actual interconnected power system show the superiority of the proposed algorithm, in terms of the estimation accuracy, the converging speed and the calculation efficiency. The proposed algorithm avoids the large-scale integrated state estimation calculation and solves the bottleneck problems of state estimation of large-scale power system with local observability or convergence issues by performing independent parallel calculation.