基于分形理论的电力现货市场出清电价预测方法

doi:10.11930/j.issn.1004-9649.202502005

中国电力 ›› 2025, Vol. 58 ›› Issue (9): 183-193.DOI: 10.11930/j.issn.1004-9649.202502005

基于分形理论的电力现货市场出清电价预测方法

吴问足¹(), 王旭辉²(), 卢苑³(), 陈婉¹, 田石金⁴, 陈娴⁵

1. 南方电网电力调度控制中心，广东广州　510000
2. 南方电网云南电力调度控制中心，云南昆明　650000
3. 广东电力交易中心有限责任公司，广东广州　510699
4. 贵州电网有限责任公司电力调度控制中心，贵州贵阳　550000
5. 广东电网公司广州供电局，广东广州　510620

收稿日期:2025-02-07 发布日期:2025-09-26 出版日期:2025-09-28
作者简介:
吴问足（1993），男，通信作者，硕士，工程师，从事电力市场设计与运营，E-mail：Wuwenzu_1993@163.com
王旭辉（1994），男，硕士，工程师，从事电力市场设计与运营，E-mail：18468048370@163.com
卢苑（1994），女，硕士，工程师，从事电力市场设计与运营，E-mail：luyuan941227@163.com
基金资助:
国家重点研发计划资助项目（2024YFB2409000）；南方电网电力调度控制中心科技项目（0005KK52220042）。

A Forecast Method for Electricity Spot Market Clearing Prices Based on Fractal Theory

WU Wenzu¹(), WANG Xuhui²(), LU Yuan³(), CHEN Wan¹, TIAN Shijin⁴, CHEN Xian⁵

1. Power Dispatching and Control Center, China Southern Power Grid, Guangzhou 510000, China
2. Yunnan Power Dispatching and Control Center, China Southern Power Grid, Kunming 650000, China
3. Guangdong Power Exchange Center Co., Ltd., Guangzhou 510699, China
4. Electric Power Dispatching Control Center of Guizhou Power Grid Co., Ltd., Guiyang 550000, China
5. Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou 510620, China

Received:2025-02-07 Online:2025-09-26 Published:2025-09-28
Supported by:
This work is supported by National Key Research and Development Program of China (No.2024YFB2409000), Science and Technology Project of Southern Power Grid Power Dispatching and Control Center (No.0005KK52220042).

摘要/Abstract

摘要：

针对高比例新能源接入下电力现货市场出清电价的复杂波动特性，提出一种基于分形理论的出清电价预测方法。从单重分形与多重分形2个维度分析出清电价的波动特征，并构建相应的预测模型。单重分形预测方法通过计算hurst指数和盒维数，量化出清电价的长期记忆性和自相似性；多重分形预测方法通过相似模式匹配，精准应对新能源出力突变引发的局部波动异常。通过对5个地区的出清电价数据进行验证，结果表明，所提方法相比传统的“ARIMA+神经网络”方法，最大误差从43.95%降至8.41%，预测精度显著提升。进一步的适用性场景分析表明，当出清电价的hurst指数较大时，考虑单重分形特征的预测方法适用性较高；而当多重分形特征显著时，考虑多重分形特征的预测方法适用性较高。

关键词: 新能源, 现货市场, 出清电价, 分形理论, 单重分形, 多重分形

Abstract:

To address the complex fluctuations of electricity spot market clearing prices with high proportion integration of new energy, this paper proposes a clearing price prediction method based on fractal theory. Firstly, the clearing price fluctuations are analyzed from monofractal and multifractal dimensions, and corresponding prediction models are constructed. Secondly, based on the monofractal prediction method, the long-term memory and self-similarity of clearing prices are quantified using the Hurst indices and box dimension; based on multifractal method, the local fluctuation anomalies caused by sudden changes in new energy output are effectively captured via pattern matching. Thirdly, the proposed method is verified with clearing price data from five regions, which shows that, compared to the traditional "ARIMA + neural network" approach, the proposed method significantly improves the prediction accuracy, with the maximum error decreasing from 43.95% to 8.41%. In-depth applicability scenario analysis indicates that, when the Hurst index is large, the monofractal-based prediction method is more applicable; when multifractal features dominate, the multifractal-based prediction method is preferable.

Key words: new energy, spot market, clearing electricity price, fractal theory, monofractal, multifractal

吴问足, 王旭辉, 卢苑, 陈婉, 田石金, 陈娴. 基于分形理论的电力现货市场出清电价预测方法[J]. 中国电力, 2025, 58(9): 183-193.

WU Wenzu, WANG Xuhui, LU Yuan, CHEN Wan, TIAN Shijin, CHEN Xian. A Forecast Method for Electricity Spot Market Clearing Prices Based on Fractal Theory[J]. Electric Power, 2025, 58(9): 183-193.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202502005

https://www.electricpower.com.cn/CN/Y2025/V58/I9/183

图/表 9

图 1 基于分形理论预测出清电价的总体框架

Fig.1 The overall framework for clearing electricity price prediction based on fractal theory

图 2 基于盒维数的出清电价预测流程

Fig.2 Flowchart for clearing electricity price prediction based on box dimension

图 3 基于相似模式匹配的出清电价预测流程

Fig.3 Flowchart for clearing electricity price prediction based on similar pattern matching

表 1 各地区分形波动分析参数表

Table 1 Fractal wave analysis parameters for various regions

地区	$ \lambda $/%	$ h $	$ \Delta \alpha $	$ {f\left(\alpha \right)}_{\min} $	$ {f\left(\alpha \right)}_{\mathrm{m}\mathrm{a}\mathrm{x}} $	$ \Delta f\left(\alpha \right) $
A	58	0.9408	1.0116	0.6571	1.1770	0.5199
B	12	0.9846	0.4947	0.8618	1.1261	0.2643
C	32	0.8961	0.8423	0.6720	1.1263	0.4544
D	29	0.9805	0.8137	0.6802	1.0977	0.4147
E	63	0.9970	0.8708	0.6770	1.1493	0.4723

图 4 5个地区出清电价的预测结果

Fig.4 Forecast results of clearing electricity prices in five regions

表 2 各方法预测误差对比表

Table 2 Comparison of prediction errors by various methods

预测方法	误差类型	A	B	C	D	E
单重分形法	最大误差/%	8.41	7.48	13.94	6.04	4.84
	最小误差/%	0.03	0.21	0.00	1.07	0.09
	平均误差/%	4.13	3.69	5.16	3.14	2.39
多重分形法	最大误差/%	4.97	10.09	8.46	9.70	6.86
	最小误差/%	0.11	0.88	0.66	0.34	0.34
	平均误差/%	2.54	5.13	3.77	4.31	3.49
“ARIMA+ 神经网络”法	最大误差/%	43.95	23.72	30.26	59.84	77.91
	最小误差/%	0.54	0.11	0.19	0.52	1.10
	平均误差/%	16.88	6.23	9.80	12.65	24.46

表 3 考虑单重分形的各地区预测误差与hurst指数对照表

Table 3 Comparison of regional prediction errors and Hurst indices for monofractal models

地区	平均误差/%	h
E	2.39	0.9970
D	3.14	0.9805
B	3.69	0.9846
A	4.13	0.9408
C	5.16	0.8961

表 4 考虑多重分形的各地区预测误差与$\Delta {f}\left({\alpha }\right) $对照表

Table 4 Comparison of multifractal-based prediction errors and $ \Delta {f}\left({\alpha }\right) $ by various regions

地区	平均误差/%	$ \Delta f\left(\alpha \right) $
A	2.54	0.5199
E	3.49	0.4723
C	3.77	0.4544
D	4.31	0.4147
B	5.13	0.2643

表 5 两种预测方法的平均误差与分形参数对照表

Table 5 Comparison of average errors and fractal parameters by two prediction methods

地区	单重分形法平均误差/%	多重分形法平均误差/%	h	$ \Delta f\left(\alpha \right) $
A	4.13	2.54	0.9408	0.5199
B	3.69	5.13	0.9846	0.2643
C	5.16	3.77	0.8961	0.4544
D	3.14	4.31	0.9805	0.4147
E	2.39	3.19	0.9970	0.4723

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基于分形理论的电力现货市场出清电价预测方法

A Forecast Method for Electricity Spot Market Clearing Prices Based on Fractal Theory

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基于分形理论的电力现货市场出清电价预测方法

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