基于数据驱动代理模型的城市输电网运行品质调节控制策略

传统电网运行品质调节控制忽略了高压配电网拓扑结构对潮流转供的作用。将高压配电网计入所提城市输电网运行品质调节控制策略中,通过高压配电网的拓扑重构,提升输电网运行品质。通过马尔科夫链蒙特卡洛抽样生成大量高压配电网拓扑数据,计算每种拓扑下的运行品质,包括线路损耗、母线电压、线路负载率和断面负载率。使用深度神经网络拟合高压配电网拓扑和以上输电网状态参数之间非线性关系,生成基于深度神经网络的城市输电网运行品质评估代理模型。该数据驱动代理模型可以实现快速高效的输电网状态评估。之后将数据驱动代理模型嵌入非支配排序遗传算法(NSGA-Ⅱ)的寻优计算中,对高压配电网拓扑结构进行迭代,寻找到能提高城市输电网运行品质的拓扑重构策略。所提算法在某城市电网进行验证,显著提高了城市输电网运行品质。

Data driven surrogate model-based operation quality control strategy of an urban transmission network

Traditional power system operation quality control ignores the role of high-voltage distribution network topology on power flow transfer.In this paper,the high-voltage distribution network is included in the proposed algorithm of urban transmission network operation quality control,and the operation quality of the transmission network will be improved through the reconstruction of the topology of the high-voltage distribution network.A large amount of topology data of high-voltage distribution network is generated by Markov chain Monte Carlo sampling,and the operational quality of each topology is calculated,including line loss,bus voltage,line load ratio and section load ratio.A deep neural network is used to fit the nonlinear relationship between the topology of a high-voltage distribution network and the above state parameters,and the deep neural network-based surrogate model of urban transmission network operation quality estimation is generated.The data driven surrogate model can realize fast and efficient transmission network state estimation.Then,the model is embedded in the optimization calculation of the Non-Dominated Sorting Genetic Algorithm(NSGA-II),and the topology of the high-voltage distribution network is iterated to find the topology reconstruction strategy which can improve the operational quality of the urban transmission network.The algorithm is verified in a city power grid,which improves the operational quality of the city power grid significantly.