用电数据驱动的低压配电网负荷随机建模及不平衡评估

Electricity-consumption-data-driven Stochastic Modeling and Unbalance Assessment of Load in LowVoltage Distribution Network

户用光伏发电和居民用电等单相设备因运行随机不确定性造成配电网三相不平衡问题日益严重。针对该问题,提出了一种用电数据驱动的低压配电网负荷随机建模方法。该方法利用高斯混合分布和期望最大化算法描述典型居民用电行为状态;通过建立云层覆盖指标划分云层状态,结合Beta分布描述光伏集群出力行为状态;采用马尔可夫链挖掘单相设备集群历史运行数据,建立系统各时刻运行状态转移矩阵,并采用序贯蒙特卡洛模拟方法对行为状态持续时间进行抽样,并建立系统状态随机模型。考虑低压配电网结构特点以及该随机模型,采用拉丁超立方抽样对配电网节点信息进行采样。基于节点支路关联矩阵进行潮流计算,并对三相不平衡问题进行评估。最后,以配电网模型作为仿真测试系统,验证所提方法的合理性与有效性。

The problem of three-phase imbalance in distribution networks,caused by the random uncertainty in the operation of the single-phase equipment such as household photovoltaic power generation and residential power,becomes increasingly serious.Aiming at this problem,a method for stochastic modeling of load in low-voltage distribution networks driven by electricity consumption data is proposed.In this method,the Gaussian mixture distribution and the expectation maximization algorithms are used to describe the state of typical residential electricity consumption behaviors.The cloud states are divided through the establishment of cloud-cover level indices,and the power-output behavior states of the photovoltaic clusters are described with the Beta distribution.Markov chain is used to mine the historical operation data of the single-phase equipment cluster,and the transition matrix of the system operation states at each time is established.The sequential Monte-Carlo simulation method is used to sample the duration time of the behavior states,and a stochastic model of system operation states is established.Considering the structural characteristics and the stochastic model of the low-voltage distribution network,the Latin hypercube sampling is used to sample the node information of the distribution network.The power flow calculation is carried out based on the node-branch correlation matrix,and the assessment of three-phase unbalance problems is implemented.Finally,a distribution network model is adopted as a test simulation system to verify the rationality and effectiveness of the proposed method.