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

doi:10.3969/j.issn.2097-0706.2026.03.001

Abstract

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

CLC Number:

TK 01：TM 734

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.

Figures/Tables 8

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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