深度神经网络在新型能源系统中的应用及展望

doi:10.3969/j.issn.2097-0706.2025.02.009

综合智慧能源 ›› 2025, Vol. 47 ›› Issue (2): 88-101.doi: 10.3969/j.issn.2097-0706.2025.02.009

• 基于AI的新型电力系统调度 • 上一篇

深度神经网络在新型能源系统中的应用及展望

石鑫¹(), 刘奇央², 高峰¹^,^*()

1.清华大学能源互联网创新研究院，北京 100085
2.港华能源投资有限公司，广东深圳 518066

收稿日期:2024-09-05 修回日期:2024-10-29 出版日期:2025-01-03
通讯作者: ^* 高峰（1977），男，正高级工程师，博士，从事能源互联网与能源行业数字化转型等方面的研究，fgao@tsinghua.edu.cn。
作者简介:石鑫（1988），男，副研究员，博士，从事新型能源系统网络化监控及智能信息处理等方面的研究，xinshi_bjcy@163.com；
刘奇央（1967），男，正高级工程师，硕士，从事综合能源规划研究及项目建设等方面的研究。
基金资助:
广州市重点领域研发计划项目(20220602JBGS04);清华大学（电机系）-港华能源投资有限公司零碳智慧园区虚拟电厂技术联合研究中心项目(20216701035)

Application and prospects of deep neural network in new energy systems

SHI Xin¹(), LIU Qiyang², GAO Feng¹^,^*()

1. Energy Internet Research Institute， Tsinghua University， Beijing 100085， China
2. Towngas Energy Investment Limited， Shenzhen 518066， China

Received:2024-09-05 Revised:2024-10-29 Published:2025-01-03
Supported by:
Guangzhou Key Research and Development Program(20220602JBGS04);Tsinghua University（Department of Electrical Engineering）-Towngas Energy Investment Limited Zero Carbon Smart Park Virtual Power Plant Technology Joint Research Project(20216701035)

摘要/Abstract

摘要：

在“双碳”目标的推动下，风能和光能等新能源迅速发展，但能源生产、消费和存储环节面临弃风弃光、资源浪费和低储存效率等挑战，为此，亟须发展更加智能的新型能源系统。深度神经网络（DNN）是新一代人工智能发展的一个重要方向，网络的深层结构使得其对复杂函数具有强大的拟合能力，解决了传统机器学习算法在进行大数据建模分析时由于模型自身学习能力局限而无法提取数据最具表征力特征的问题。重点对DNN在新型能源系统的应用进行研究，主要从DNN概述，新能源系统对DNN的需求以及DNN在新型能源系统建模仿真、规划优化、运行维护、运行控制和系统管理中的应用等层面进行综述分析，对DNN在新型能源系统应用面临的挑战进行了总结展望，旨在为相关行业工作者提供参考。

关键词: “双碳”目标, 新型能源系统, 深度神经网络, 人工智能, 机器学习, 大数据分析, 大语言模型

Abstract:

Driven by the "dual carbon" goal， new energy sources such as wind and solar energy are developing rapidly. However， challenges such as wind and solar curtailment， resource waste， and low storage efficiency persist in energy production， consumption， and storage processes. Therefore， it is imperative to develop more intelligent new energy systems. Deep neural network（DNN）， a key technology in the development of next-generation artificial intelligence， has powerful capabilities in fitting complex functions due to its deep structure. It addresses the problem that traditional machine learning algorithms face when modeling and analyzing big data due to their limited ability to extract the most representative features from the data. The focus is on the application of DNN in new energy systems， providing an overview of DNN， the demand for DNN in new energy system， its applications in modeling and simulation， planning and optimization， operation and maintenance， operational control， and system management of new energy systems. The challenges of applying DNN in new energy systems are summarized and prospects for future development are outlined， aiming to provide reference for professionals in related industries.

Key words: "dual carbon" goal, new energy system, deep neural network, artificial intelligence, machine learning, big data analysis, large language model

中图分类号:

TK01：TP18

石鑫, 刘奇央, 高峰. 深度神经网络在新型能源系统中的应用及展望[J]. 综合智慧能源, 2025, 47(2): 88-101.

SHI Xin, LIU Qiyang, GAO Feng. Application and prospects of deep neural network in new energy systems[J]. Integrated Intelligent Energy, 2025, 47(2): 88-101.

图/表 11

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深度神经网络在新型能源系统中的应用及展望

Application and prospects of deep neural network in new energy systems

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