Prediction of Large-Scale Renewable Energy Access under Steady State of Electric Power System

doi:10.11930/j.issn.1004-9649.202405113

Abstract

Abstract:

The large-scale increase in renewable energy generation and the superposition of various uncertainties have posed severe challenges to power systems. This paper studies representative energy sources, analyzes the factors affecting the large-scale integration of renewable energy under power system stability conditions, screens key factors based on grey correlation analysis and XGBoost, and constructs a prediction model for the large-scale integration of renewable energy. The proposed model is verified by a regional power system. The results show that the proposed is feasible and can provide a reference for the reasonable and effective power planning of new power systems.

Key words: electric power system, renewable energy, access volume

Mingrun TANG, Ruoyang LI, Muran LIU, Xiaoyu CHENG, Yao LIU, Shuxia YANG. Prediction of Large-Scale Renewable Energy Access under Steady State of Electric Power System[J]. Electric Power, 2025, 58(2): 126-132.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202405113

https://www.electricpower.com.cn/EN/Y2025/V58/I2/126

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References 22

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影响因素		关联度
容载比/(MA·V·MW^–1)		0.533 482
负备用容量/MW		0.553 766
下调旋转备用要求/MW		0.594 132
电网电压越下限/kV		0.590 086
持续充电时间下限/h		0.589 064

影响因素		关联度
容载比/(MA·V·MW^–1)		0.533 482
负备用容量/MW		0.553 766
下调旋转备用要求/MW		0.594 132
电网电压越下限/kV		0.590 086
持续充电时间下限/h		0.589 064

年份	实际值/万kW	XGBoost预测值/万kW	准确度/%
2022	1 880	1 889.67	99.5
2023	2 100	2 084.48	99.3

年份	实际值/万kW	XGBoost预测值/万kW	准确度/%
2022	1 880	1 889.67	99.5
2023	2 100	2 084.48	99.3

精度指标	支持向量回归方法	思维进化算法	长短期记忆方法	XGBoost
RMSE/万kW	32,3	22.47	26.92	12.93
MAPE/%	2.16	1.23	1.64	0.63