基于深度学习的电力供需双侧协同优化的研究与应用

Research and Application of Power Supply and Demand Cooperative Optimization Based on Deep Learning

在光伏发电等可再生能源发电下,供需平衡能力问题应该在未来的电力系统中进行评估和解决。改进现有的平衡措施和新技术,如电力供需双侧协同和储能将解决这个问题。在这种情况下,远程电力系统供需分析应具有评估平衡对策的能力。基于负荷持续时间曲线的供需分析,与时间序列分析相比该方法具有一定的局限性。但有一个很大的优点是在维护各种电气设备时可以进行供需评估。采用机器学习和深度学习的模型,为预测消费者需求和分布式可再生能源提供了新的解决方案。提出的动力系统供需分析模型ESPRIT为电力供需双侧平衡提供了新的解决方案。

In the case of renewable energy generation such as photovoltaic power generation,the problem of supplydemand balance capacity should be evaluated and solved in the future power system.Improving existing balancing measures and new technologies such as power supply and demand synergy and energy storage will solve this problem.In this case,the remote power system supply and demand analysis should have the ability to evaluate the equilibrium countermeasures.Compared with time series analysis,this method has some limitations.But there is a big advantage in the maintenance of various electrical equipment can be supply and demand assessment.This paper uses machine learning and deep learning models to provide a new solution for predicting consumer demand and distributed renewable energy.The proposed power system supply and demand analysis model ESPRIT provides a new solution for power supply and demand balance.