基于智能负荷控制的分布式能源系统调控策略研究

Regulation Strategy for Distributed Energy System Based on Intelligent Load Control

为提高分布式能源系统(DES)经济性及运行效率,提出基于智能负荷控制的DES调控策略。首先,基于智能电表的测量数据,采用监督学习技术训练神经网络,构建智能负荷神经网络模型计算住宅可控有功功率;其次,考虑智能负荷与DES之间的互动关系,结合DES各设备运行及互补特性,构建智能分布式能源管理系统模型,分析其可控负荷的调度策略,并给出求解流程,利用IEEE-6节点系统进行算例分析。结果表明,DES总运行成本将随着需求响应控制的加强而降低,考虑智能负荷参与能源系统互动,可有效改善系统负荷曲线,减少切负荷现象,促进风电、光伏清洁能源的消纳。

In order to improve the economy and operation efficiency of distributed energy system(DES),the paper proposes the regulation strategy of DES based on intelligent load control.Firstly,based on the measurement data of smart meter,the neural network is trained by supervised learning technology,and the intelligent load neural network model is constructed to calculate the controllable active power of residence.Secondly,considering the interactive relationship between the intelligent load and DES,combined with the operation and complementary characteristics of various equipment,the intelligent DES model is constructed,the scheduling strategy of controllable load is analyzed,and the solution process is given.Finally,an example is analyzed by using IEEE6-node system.The results show that the total operation cost of DES will decrease with the strengthening of demand response control.Considering the participation of the intelligent load in the interaction of energy system can effectively improve the system load curve,reduce the load shedding phenomenon and promote the integration of wind power and photovoltaic clean energy.