摘要
随着环境问题日益突出和“双碳”目标的推进,需求侧响应资源参与负荷调峰和填谷对电力行业发展具有重要意义。针对我国需求侧响应快速发展的情况,首先考虑了发电机(PG)、电网公司和服务厂商(SPs)之间的相互作用;然后利用深度神经网络(DNNs)收集数据,并通过开发精确的需求价格弹性矩阵(PEMD)来模拟客户行为;最后基于该模型预测了未来每小时短期负荷预测以及前一天每小时的电力需求。结果表明,此模型可以减小电网负荷峰谷差,使发电、用电趋于平衡。
As environmental issues become more prominent and the“double carbon”goal is promoted,the participation of demand-side response resources in load peaking and valley filling is of great importance to the development of the power industry.Based on the rapid development of demand-side response in China,this paper first considers the interactions between generators(PG),Power grid companies and service providers(SPs),then collects data using deep neural networks(DNNs)and simulates customer behavior by developing an accurate price elasticity of demand matrix(PEMD).Finally,based on this model,future hourly short-term load forecasts and hourly electricity demand for the previous day are predicted.The results show that the model can reduce the peak-to-valley load differential on the grid and bring the generation and consumption of electricity into balance.
作者
王永彬
贾凤
朱国梁
WANG Yongbin;JIA Feng;ZHU Guoliang(Shandong Huizhi Electric Power Technology Co.,Ltd.,Jinan 250000,China;State Grid Shandong Electric Power Company Materials Company,Jinan 250000,China;State Grid Shandong Comprehensive Energy Service Co.,Ltd.,Jinan 250000,China)
出处
《电气应用》
2023年第8期69-76,共8页
Electrotechnical Application
关键词
深度神经网络
需求响应系统
需求价格弹性矩阵
deep neural networks
demand response systems
demand price elasticity matrix
