新型电力系统建模、控制与源荷预测研究进展

doi:10.3969/j.issn.2097-0706.2026.03.001

综合智慧能源 ›› 2026, Vol. 48 ›› Issue (3): 1-14.doi: 10.3969/j.issn.2097-0706.2026.03.001

• 电力系统建模与调控 • 下一篇

新型电力系统建模、控制与源荷预测研究进展

丁新宇^a(), 周庆才^b^,^*(), 迟耀丹^a^,^b(), 张尧^b(), 王俊喜^b(), 王超^a^,^b(), 贾红丹^b(), 林国雄^a()

^a 吉林建筑大学电气与计算机学院长春 130118
^b 吉林建筑大学寒地建筑综合节能教育部重点实验室，长春 130118

收稿日期:2025-06-18 修回日期:2025-08-11 出版日期:2026-03-25
通讯作者: *周庆才（1973），男，教授，博士，从事清洁能源获取、多能互补及优化计算等方面的研究，zhouqingcai@jlju.edu.cn。
作者简介:丁新宇（2001），男，硕士生，从事微电网优化与运行等方面的研究，117526719@qq.com；
迟耀丹（1974），女，教授，博士，从事建筑智能节能技术、新能源与并网技术等方面的研究，chiyaodan@jlju.edu.cn；
张尧（1993），男，助理研究员，博士，从事新能源与并网技术等方面的研究，zhangyao@jlju.edu.cn；
王俊喜（1996），男，助理研究员，博士，从事无线传感网与计算机视觉等方面的研究，wangjunxi@jlju.edu.cn；
王超（1981），女，教授，博士，从事氧化物半导体薄膜材料生长与性能等方面的研究，wangchao@jlju.edu.cn；
贾红丹（1995），女，讲师，硕士，从事综合能源系统优化调度方面的研究，771553364@qq.com；
林国雄（2000），男，硕士生，从事微电网优化与运行等方面的研究，1619139893@qq.com。
基金资助:
吉林省科学技术厅基金项目(20240304183SF)

Research progress on modeling， control， and source-load prediction of new-type power systems

DING Xinyu^a(), ZHOU Qingcai^b^,^*(), CHI Yaodan^a^,^b(), ZHANG Yao^b(), WANG Junxi^b(), WANG Chao^a^,^b(), JIA Hongdan^b(), LIN Guoxiong^a()

^a School of Electrical and Computer Engineering， Jilin Jianzhu University， Changchun 130118， China
^b Key Laboratory of Comprehensive Energy Saving of Buildings in Cold Regions， Ministry of Education，Jilin Jianzhu University， Changchun 130118， China

Received:2025-06-18 Revised:2025-08-11 Published:2026-03-25
Supported by:
Jilin Provincial Science and Technology Department Fund Project(20240304183SF)

摘要/Abstract

摘要：

当前，全球能源结构正经历深刻转型，以风能、太阳能为代表的可再生能源凭借其清洁性和可持续性，日益成为电力供应的关键组成部分。但这类能源固有的间歇性和波动性对电力系统的频率、电压及整体稳定性带来严峻挑战，威胁供电的安全性与可靠性。为有效应对上述挑战，电力系统亟须引入先进的技术与方法，以保障其运行的可靠性与效率。系统分析了仿真技术、频率调节策略以及人工智能在新型电力系统中的作用。深入剖析了混合仿真从传统串行到高效并行及智能化的发展路径，探讨了虚拟同步发电机与多类型储能在应对惯量下降和频率调控中的关键作用，并全面评估了人工智能在新能源发电预测、负荷预测及智能微电网调度中的最新进展与潜力。最后指出，应充分发挥储能技术和人工智能的支撑作用，构建更加灵活的市场机制和资源配置体系，为新型电力系统的稳定运行奠定基础。

关键词: 新型电力系统, 可再生能源, 电力系统仿真, 调频, 发电预测, 能源管理系统, 人工智能, 储能, 智能微电网调度

Abstract:

Currently， the global energy structure is undergoing a profound transformation. Renewable energy sources represented by wind and solar power are increasingly becoming key components of power supply due to their cleanliness and sustainability. However， the inherent intermittency and volatility of these energy resources pose significant challenges to the frequency， voltage， and overall stability of power systems， threatening the security and reliability of power supply. To address these challenges effectively， it is crucial to integrate advanced technologies and methods to ensure reliable and efficient operation of power systems. The roles of simulation technologies， frequency regulation strategies， and artificial intelligence in new-type power systems are systematically analyzed. The evolution of hybrid simulation， from traditional serial methods to high-performance parallel and intelligent approaches， is analyzed in depth. The key roles of virtual synchronous generators and multi-type energy storage in mitigating inertia reduction and frequency regulation are examined. Additionally， recent advances and potential of artificial intelligence in renewable energy power generation prediction， load prediction， and smart microgrid scheduling are comprehensively evaluated. It is emphasized that the supporting roles of energy storage technologies and artificial intelligence should be fully leveraged， and more flexible market mechanisms and resource allocation systems should be established， thereby laying a foundation for the stable operation of new-type power systems.

Key words: new-type power system, renewable energy, power system simulation, frequency regulation, power generation regulation, energy management system, artificial intelligence, energy storage, smart microgrid scheduling

中图分类号:

TK 01：TM 734

丁新宇, 周庆才, 迟耀丹, 张尧, 王俊喜, 王超, 贾红丹, 林国雄. 新型电力系统建模、控制与源荷预测研究进展[J]. 综合智慧能源, 2026, 48(3): 1-14.

DING Xinyu, ZHOU Qingcai, CHI Yaodan, ZHANG Yao, WANG Junxi, WANG Chao, JIA Hongdan, LIN Guoxiong. Research progress on modeling， control， and source-load prediction of new-type power systems[J]. Integrated Intelligent Energy, 2026, 48(3): 1-14.

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电磁模型工具	机电模型工具	接口方式	运算方式	主要用途
PSCAD	PSS/E	E-Tran Plus	串行计算	交直流系统动态特性分析，适合大规模交直流系统仿真
PSCAD	PSSE	E-Tran Plus	串行计算	CLCC多馈直流电网动态特性，新能源多点接入稳定性验证
ADPSS（EMT）	ADPSS（EMT分组）	分群解耦 + 并行接口	并行计算	大规模电力系统动态仿真，解决大量边界节点带来的计算瓶颈
RT-LAB	RT-LAB	高频等值阻抗接口	并行计算	大规模并网变流器与电网宽频交互仿真，提升精度
Simulink	Simulink	诺顿/戴维南接口+Dq-120相量提取	并行交互仿真	配电网多时间尺度暂态分析，提高接口处相量提取稳定性
RTDS	并行计算机	三序功率初始自校正计算方法	并行实时仿真	交直流系统不对称故障实时仿真，全序量交互，扩展SMRT平台功能

仿真改进方式	优点	应用场景
提出一种多层机电-电磁混合仿真并行仿真方法	不仅保证了仿真精度，而且通过网络解耦元件大大降低了仿真的计算时间消耗	大规模直流电网
根据潮流数据和指定分网方案自动定义接口	实现了直流输电电磁模型的自动初始化，提高了混合仿真初始化的效率和精度	大规模交直流电网
基于ADPSS平台的交直流系统混合仿真模型，支持电磁与机电子系统的并行仿真与数据交互	可真实反映交直流系统的运行状态，提高对快速暂态过程的仿真能力	超高压交直流输电系统
在实际电网中建立MTDC混合仿真模型，支持分析不同故障下的MTDC稳定性	可准确描述MTDC系统的动态特性，仿真效率高于传统模型	多端直流电网
提出了基于FDNE改进的混合仿真方法，结合矢量拟合技术提高仿真精度	提高了仿真精度和计算效率，适用于复杂电网的实时仿真	电力系统大规模实时仿真
提出HVDC混合仿真平台	实现一、二次系统的闭环仿真，提高了准确性与时效性	交直流电网交互
移频相量建模方法（希尔伯特变换）	电磁-机电子系统同一底层算法交互简单，提高了仿真速度	交直流电网交互

预测模型及方法	应用场景	模型优势	模型局限
ICEEMDAN-MFE-LSTM-Informer	复杂地形日前风电预测（24$\sim $48 h）	模块化设计便于针对特定问题调整	依赖NWP质量，缺乏绝对误差数据
WOA-VMD-SSA-LSTM	中长期风电规划	高精度，适于非平稳序列	计算复杂度高，参数优化依赖
GSA-VMD-BiLSTM-MHSA	复杂地形短期风电预测	特征选择高效，时间与特征建模能力强	计算复杂度高，MHSA训练时间长
PSO-CNN-LSTM, PCA, K-Means-GMM	短期风电功率预测	主成分分析用于特征变换；K-Means优化GMM初始参数实现聚类，提高预测准确性	计算复杂度高，CA，K-Means-GMM和PSO-CNN-LSTM的组合增加计算负担
GBDT-LightGBM-RF, PSO	多特征负荷预测	权重优化，集成效果好	机器学习模型的选择可能偏向于主观偏好
eEEMD-LSTM	海上超短期预测	高精度，模态优化，差异化训练，时间序列建模能力强	eEEMD的熵阈值和LSTM训练规则需人工调整
VMD-QPSO-LSTM	短期风电功率预测	参数优化高效，适于复杂波动环境	算法复杂度对成本有要求，VMD模态数需要人工调整
FCM-WOA-LSSVM-NPKDE^[63]	短期光伏功率预测	采用FCM对NWP功率数据聚类，增强对复杂环境的适应性	若数据缺失或噪声大，聚类和预测精度可能下降。
CNN-LSTM without max pooling layer^[64]	短期光伏功率预测	剔除最大池化层避免特征丢失	训练复杂度高，数据质量依赖

预测模型及方法	应用场景	模型优势	模型局限
TPE-PSO-BiGRU， Conv1D，注意力机制^[69]	短期风电功率预测	双重特征优化，鲁棒性好	VMD，TPE-PSO优化和BiGRU训练需高算力，应用成本高
VMD-SSA-CBiLSTM-RF	复杂地形长期预测	鲁棒性高，长期预测精准	数据依赖性强
TL， VMD-CNN-LSTM	短期光伏预测，适用于数据稀缺或欠发达地区	通过分解数据来减少非平稳性；使用迁移学习，数据稀缺适应性强	在高度波动的数据集中，算法的预测性能仍需改进
Seq2Seq-AE，注意力机制	短期风电调度	高精度误差修正，捕捉时间依赖性	高算力需求，未区分正负误差
Seq2LPP， NWP引导注意力, Patch特征	超短期风电预测（1$\sim $4 h）	NWP引导的注意力机制，模型显著提升LPPs预测精度	依赖高质量NWP

新型电力系统建模、控制与源荷预测研究进展

Research progress on modeling， control， and source-load prediction of new-type power systems

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