基于动态分区的配电网日前优化调度研究

为了提高对分布式可再生能源的就地消纳能力，实现配电网分层分区调度，提出了基于MOEA/D的多目标蚁群动态分区算法和基于动态分区的配电网日前优化调度模型。利用潮流追踪算法与复杂网络理论中的二分模块度，提出了量化分区间能量耦合程度的能量二分模块度指标。基于电力系统潮流方程雅克比矩阵推导蚁群算法中的启发式信息，结合预测场景集以分区的能量二分模块度与功率储备为目标函数，利用多目标蚁群算法生成动态分区方案。建立以联络线功率、灵活性不足率以及成本最低为目标的基于动态分区日前优化调度模型，并利用NSGA-II算法求解Pareto最优解集。最后基于IEEE33节点网络对所提模型与方法进行验证。结果表明，采用该方法进行动态分区与日前调度可有效提高系统应对可再生能源不确定性的能力，为就地平抑可再生能源波动奠定基础。

Day-ahead optimal dispatch for a distribution network based on dynamic partitioning

To improve the local accommodation of distributed renewable energy and realize hierarchical optimal scheduling model for a distribution network, this paper proposes a multi-objective ant colony dynamic partitioning algorithm based on MOEA/D and a day-ahead optimal dispatching model based on dynamic partitioning. Using a power flow tracing algorithm and bipartite modularity in complex network theory, an energy bipartite modularity index that quantifies the degree of energy coupling between partitions is proposed. Based on a Jacobian matrix of power flow calculation, heuristic information in the ant colony algorithm is derived. Combined with the prediction scenarios, the energy bipartite modularity and power reserve of the partitions are used as the objective function, and the multi-objective ant colony algorithm is used to generate dynamic partitions. A day-ahead optimal scheduling model based on dynamic partitions is established with the objectives of the partitions'' communication line power, insufficient flexibility rate and lowest cost. The Pareto optimum is determined based on the NSGA-II algorithm. Finally, based on the IEEE33 bus distribution network, the proposed model and method are verified. The results show that the dynamic partitioning and day-ahead scheduling using this method can effectively improve the system''s ability to deal with the uncertainty of renewable energy, and lay the foundation for suppressing the fluctuation of renewable energy locally. This work is supported by the Natural Science Foundation of Fujian Province (No. 2017J01480).