基于集群负荷预测的主动配电网多目标优化调度

常规的配电网调度模式中,往往通过可控分布式电源、储能和柔性负荷来调节预测误差和实时波动,粗略地预测负荷值,这使得负荷预测往往不够精准,而且用可控分布式电源、柔性负荷或储能平衡配电网负荷波动,会造成较大的波动成本和备用成本。对此提出一种基于集群负荷预测的主动配电网多目标优化调度方法。采用模糊聚类的方法,对负荷进行集群划分,利用极限学习机对负荷进行集群预测。基于预测值,先以有功调度成本最低进行日前调度,再在日前调度的基础上进行修正,以可控分布式出力修正量最小、储能出力修正量最小、柔性负荷修正量最小为目标进行实时调度。

Multi-objective Optimal Dispatching of Active Distribution Network based on Cluster Load Prediction

In the conventional distribution network dispatching mode,the forecasting error and real-time fluctuation are regulated by controllable distributed power supply,energy storage and flexible load,and the load values are predicted roughly,which makes the load forecasting often inaccurate.Moreover,using controllable distributed power supply,flexible load or energy storage device to balance load fluctuation of distribution network will result in large fluctuation cost and reserve cost.A multi-objective optimal dispatching method for active distribution network based on cluster load prediction is proposed in this paper.The fuzzy clustering method is adopted to cluster the load of the active distribution network,and the extreme learning machine is utilized to predict the load cluster.Based on the predicted value,the day-ahead dispatching is firstly carried out with the lowest active power dispatching cost,and then modified on the basis of day-ahead dispatching.Real-time dispatching is carried out with the objective of the minimum controllable distributed output correction,the minimum energy storage output correction,and the minimum flexible load correction.