基于数据驱动的园区综合能源系统优化调控方法

Data-driven optimal dispatching method for regional integrated energy system

针对目前园区综合能源系统管理中分布式本地可再生能源利用率低、用户侧购电成本高、源-储-荷协调不充分等现实问题,提出了数据驱动的实时调度决策方法和模型。该混合模型首先基于大量用能数据,应用K-means算法对其特性指标与时间序列特征进行聚类分析,得出具有代表性的用能模式。然后引入参数化模糊推理系统与启发式优化算法,依据代表样本对模糊逻辑推理规则进行自趋优设计,从而得出实现园区综合能源系统经济化调度的实时控制模型。再通过实际数据集仿真计算验证了所提出方法和模型的有效性。

Aiming at the practical problems of low self-sufficient renewable energy utilization, high power purchase cost and inadequate source-storage-charge coordination in the current energy management of active integrated energy systems, a data-driven realtime dispatching decision model is proposed. The hybrid model is firstly based on a large amount of energy consumption data for its characteristic indicators and time series characteristics, and the K-means algorithm is used for cluster analysis to obtain a representative typical energy consumption pattern. Then the parametric fuzzy inference system combined with the heuristic optimization algorithm is introduced, and the fuzzy logic inference rules are self-optimized based on the representative samples to obtain a real-time control model for the economical scheduling of energy-using systems.In the simulation calculation through the actual data set, the validity and feasibility of the model are verified.