基于数字孪生和深度学习的新一代调控系统预调度方法

doi:10.16183/j.cnki.jsjtu.2021.S2.006

上海交通大学学报 ›› 2021, Vol. 55 ›› Issue (S2): 37-41.doi: 10.16183/j.cnki.jsjtu.2021.S2.006

基于数字孪生和深度学习的新一代调控系统预调度方法

王兴志¹(), 翟海保¹, 严亚勤¹^,², 吴庆曦³

1.国家电网有限公司华东分部,上海 200120
2.国家电力调度控制中心,北京 100031
3.南瑞集团有限公司(国网电力科学研究院有限公司),南京 211106

收稿日期:2021-10-26 出版日期:2021-12-28 发布日期:2022-01-24
作者简介:王兴志(1979-),男,江苏省连云港市人,高级工程师,从事电力系统自动化等研究.电话(Tel.):13671878688;E-mail: wang_xz@ec.sgcc.com.cn.
基金资助:
国家重点研发计划资助项目(2017YFB0902600)

Pre-Dispatching Method of New Generation Dispatching and Control System Based on Digital Twin and Deep Learning

WANG Xingzhi¹(), ZHAI Haibao¹, YAN Yaqin¹^,², WU Qingxi³

1. East Branch of State Grid Corporation of China, Shanghai 200120, China
2. National Power Dispatching Control Center, Beijing 100031, China
3. Nari Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China

Received:2021-10-26 Online:2021-12-28 Published:2022-01-24

摘要/Abstract

摘要：

针对新型电力系统下传统调度自动化系统可扩展性和决策前瞻性不足等问题,提出新一代调控系统预调度方法.在描述子系统层建立能够反映电网一次设备、二次设备和环境等状态的电网数字孪生体;在预测子系统层,电网数字孪生体基于电网运行数据进行深度学习,并预测电网运行的未来态势和事故风险;以华东电网新一代调控系统的预调度试点应用为例,验证所提方法的可行性.应用结果表明:该预调度方法提高了系统处理新型电力系统运行控制问题的效率,可以为新一代调控系统的全面建设和推广应用提供有益参考.

关键词: 数字孪生, 深度学习, 预调度, 新一代调控系统

Abstract:

To meet the demand of scalability and decision-making foresight of the traditional dispatching automation system in the new power system, a novel pre-dispatching method of new generation dispatching and control was proposed. First, the power grid digital twin was established in the description subsystem layer, which can reflect the state of power grid primary equipment, secondary equipment, and environment. Then, in the prediction subsystem level, the deep learning models were used to learn and predict future situation or accident risk of power grid operation in power grid digital twin. Finally, the feasibility of the proposed method was verified by the implementation example of East China Grid. The application results show that the pre-dispatching method improves the efficiency of system in dealing with the operation control problems of the new power system, which also provides a useful reference for comprehensive construction, popularization, and application of new generation power systems.

Key words: digital twin, deep learning, pre-dispatching, new generation dispatching and control system

中图分类号:

TM734

王兴志, 翟海保, 严亚勤, 吴庆曦. 基于数字孪生和深度学习的新一代调控系统预调度方法[J]. 上海交通大学学报, 2021, 55(S2): 37-41.

WANG Xingzhi, ZHAI Haibao, YAN Yaqin, WU Qingxi. Pre-Dispatching Method of New Generation Dispatching and Control System Based on Digital Twin and Deep Learning[J]. Journal of Shanghai Jiao Tong University, 2021, 55(S2): 37-41.

图/表 3

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算例	算法	预测准确度/%
全口径系统负荷	深度森林	97.43
全口径系统负荷	多项式回归	96.76
全口径新能源负荷	深度森林	83.27
全口径新能源负荷	多项式回归	83.21
调度口径系统负荷	深度森林	98.72
调度口径系统负荷	多项式回归	97.65
调度口径新能源负荷	深度森林	86.58
调度口径新能源负荷	多项式回归	85.88

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基于数字孪生和深度学习的新一代调控系统预调度方法

Pre-Dispatching Method of New Generation Dispatching and Control System Based on Digital Twin and Deep Learning

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