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作 者:卢锦玲[1] 颜禄涵 腊志源 刘晓鹏 任惠[1] LU Jinling;YAN Luhan;LA Zhiyuan;LIU Xiaopeng;REN Hui(Department of Electric Power Engineering,North China Electric Power University,Baoding 071001,Hebei Province,China)
机构地区:[1]华北电力大学电力工程系,河北省保定市071001
出 处:《电网技术》2023年第1期226-235,共10页Power System Technology
基 金:国家重点研发计划(2018YFE0122200)。
摘 要:传统综合能源系统(integrated energy system,IES)的日优化调度由于设备建模过于简化、预测模型精度低、信息捕获与传输不及时导致能源利用率低、经济与社会效益差。为解决上述问题,文章提出一种基于数字孪生与动态能效模型的综合能源系统实时优化调度策略。首先,针对传统预测方法精度不足的问题,构造长短期记忆神经网络与相似日气象搜索算法相结合的组合预测模型;其次,考虑到设备能效系数易受负载率及环境因素的影响发生扰动,建立设备的动态能效模型;然后,分别建立系统的物理模型与数字孪生体,通过环境、负荷等各项孪生数据,进行新能源机组出力与负荷的预测及IES实时优化;最后,基于Cloudpss平台建立园区综合能源系统测试模型,验证所提策略的有效性。结果表明,所提策略能实现信息的实时捕获与传输、提高模型计算精度,在兼顾运行成本的前提下提高能源利用率,对实际工程具有指导意义。The daily optimal scheduling of the traditional integrated energy system(IES) exists low energy utilization rate and poor economic and social benefits due to the over-simplification of equipment modeling, the low accuracy of the prediction model, and the delay of information capture and transmission. To solve the above problems, this paper proposes a real-time optimization scheduling strategy of the integrated energy system based on the digital twins and the dynamic energy efficiency model. Firstly, aiming at the problem of insufficient accuracy of the traditional predictions,a prediction model combining the long and short-term memory neural network and the similar day weather searching algorithm is constructed. Secondly, considering that the equipment energy efficiency coefficient is apt to be disturbed by the load rate and the environmental factors, a dynamic energy efficiency model of the equipment is established. Then,the physical model and the digital twins of the system are established respectively, and the output and load predictions and the IES real-time optimization of the new energy unit are carried out through the twin data of the environment and load.Finally, a test model of the integrated energy system in the park is established based on the Cloudpss platform to verify the effectiveness of the proposed strategy. The results show that the proposed strategy realizes the real-time information capture and transmission and improves the calculation accuracy of the model. It increases the energy efficiency while taking into account the operation costs, which has a guiding significance for the practical engineering.
分 类 号:TM721[电气工程—电力系统及自动化]
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