1.国网浙江省电力有限公司电力科学研究院,杭州 310014
2.国网浙江省电力有限公司,杭州 310008
3.暨南大学 能源电力研究中心,广东 珠海 519070
王天宇(1991),男,博士研究生,高级工程师,研究方向为新能源电力系统调频。
綦晓(1992),男,讲师,硕士生导师,研究方向为新能源电力系统频率控制与优化调度等。
收稿:2025-11-26,
修回:2025-12-24,
纸质出版:2026-09-25
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王天宇,卢敏,张江丰等.多能源系统AGC可调容量评估及其准确性提升[J].浙江电力,2026,45(09):1-11.
WANG Tianyu,LU Min,ZHANG Jiangfeng,et al.Adjustable AGC regulation capacity assessment and accuracy enhancement of multi-energy power systems[J].ZHEJIANG ELECTRIC POWER,2026,45(09):1-11.
王天宇,卢敏,张江丰等.多能源系统AGC可调容量评估及其准确性提升[J].浙江电力,2026,45(09):1-11. DOI: 10.19585/j.zjdl.202609001.
WANG Tianyu,LU Min,ZHANG Jiangfeng,et al.Adjustable AGC regulation capacity assessment and accuracy enhancement of multi-energy power systems[J].ZHEJIANG ELECTRIC POWER,2026,45(09):1-11. DOI: 10.19585/j.zjdl.202609001.
由于风光等新能源出力不确定性强、不同能源协同调控策略复杂,多类型能源电力系统AGC(自动发电控制)可调容量难以准确评估。为此,提出多类型能源系统可调容量评估以及新能源-储能协同的可调容量准确性提升方法。采用概率区间预测方法,对新能源场站不确定性进行量化分析,实现多能源系统AGC可调容量确定性评估;提出一种新能源-储能协同调控方法,减小可调容量边界置信度区间范围,提高系统整体可调容量准确性。不同场景下的算例结果表明,所提方法有效解决了新能源可调容量评估准确性差、高置信度边界范围大造成的调控难题,提升了多类型能源系统AGC调节能力。
Due to the strong uncertainty in the output of renewable energy sources such as wind and photovoltaic power, as well as the complexity of coordinated control strategies among different energy types, the adjustable automatic generation control (AGC) regulation capacity of multi-energy power systems is difficult to assess accurately. To address this issue, a method for assessing the adjustable regulation capacity of multi-energy power systems and enhancing its accuracy through coordinated control of renewable energy and energy storage is proposed. A probabilistic interval prediction method is employed to quantify the uncertainty of renewable energy stations, enabling deterministic evaluation of adjustable AGC regulation capacity in multi-energy systems. Furthermore, a coordinated control strategy for renewable energy and energy storage is developed to reduce the confidence interval of the regulation capacity boundary and improve the overall accuracy of the system’s regulation capacity. Case studies under different scenarios demonstrate that the proposed method effectively addresses the problems of low accuracy in adjustable renewable energy regulation capacity evaluation and excessively wide high-confidence boundary intervals, thereby enhancing the AGC regulation performance of multi-energy power systems.
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