基于特征构建的区域电力负荷增长归因及量化分析方法

doi:10.11930/j.issn.1004-9649.202310056

摘要/Abstract

摘要：

电力负荷由于受到气温、经济、特殊事件等多种因素及多因素耦合影响，增长成因量化分析困难。同时，目前对于电力负荷研究多集中于预测方面，对负荷增长原因分析较少。通过研究电力负荷数据特征构建方法，提出一种电力负荷增长归因分析方法。首先，构建气象相关性指标、基于经济发展的自然负荷增长指标、基于电力电量修正的产业结构变化指标以及事件趋势一致性评价指标；在此基础上，分别提取气象负荷、自然经济负荷、业扩负荷、随机负荷，利用贡献率量化各因素对负荷增长的影响程度。最后，利用西北某2省的电力电量数据进行验证，结果显示所提方法能够很好地量化负荷增长的原因。

关键词: 负荷增长, 特征构建, 自然经济负荷, 产业结构变化, 特殊事件

Abstract:

The power load is affected by various factors such as temperature, economy, special events, and multi-factor coupling, which makes the quantitative analysis of the causes of the power load growth difficult. At the same time, current load analysis is mostly focused on prediction, and it is uncommon to analyze the causes of load growth.Therefore, based on a study on the construction method of power load data characteristics, a power load growth attribution analysis method is proposed. Firstly, the meteorological correlation indicators, the economic development-based natural load growth indicators, the electrical quantity correction-based industrial structure change indicators, and the event trend consistency evaluation indicators are constructed. And then, the meteorological load, natural economic load, industrial expansion load and random load are respectively extracted, and the contribution rate is used to quantify the influence degree of each factor on the load growth. Finally, the power consumption data of two northwestern provinces are used to verify the proposed method, which indicates that the proposed method is effectively quantify the causes of load growth.

Key words: load growth, feature construction, natural economic load, industrial structure changes, special events

邱敏, 周颖, 赵伟博, 王阳, 陈宋宋, 郭耀扬, 赵波. 基于特征构建的区域电力负荷增长归因及量化分析方法[J]. 中国电力, 2024, 57(8): 190-205.

Min QIU, Ying ZHOU, Weibo ZHAO, Yang WANG, Songsong CHEN, Yaoyang GUO, Bo ZHAO. Attribution and Quantitative Analysis Method for Regional Power Load Growth Based on Feature Construction[J]. Electric Power, 2024, 57(8): 190-205.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202310056

https://www.electricpower.com.cn/CN/Y2024/V57/I8/190

图/表 29

图 1 总负荷变化影响因素

Fig.1 Influencing factors of total load change

图 2 基于特征构建的负荷增长量化分析研究思路

Fig.2 Research frame for quantitative analysis of load growth based on feature construction

图 3 负荷增长量化分析模型架构

Fig.3 Architecture of load growth quantitative analysis model

图 4 基于气象相关性指标的气象负荷分析流程

Fig.4 Flow chart of meteorological load analysis based on meteorological correlation index

表 1 2022年区域1气象因素与最大用电负荷相关性

Table 1 Correlation between meteorological factors and maximum electricity load in area 1 in 2022

月份	与最大用电负荷相关系数
月份	最高温度	最低温度	平均温度	风速
1	–0.16	0.46	0.22	–0.16
2	0.42	0.65	0.56	0.38
3	–0.04	–0.38	–0.21	–0.04
4	–0.19	–0.19	–0.22	0.15
5	0.11	0.43	0.29	–0.10
6	0.58	0.15	0.50	0.18
7	0.71	0.84	0.87	0.18
8	0.96	0.96	0.98	0.42
9	0.41	0.60	0.63	0.00
10	–0.19	–0.50	–0.44	–0.44
11	–0.69	–0.53	–0.63	0.08
12	–0.13	0.25	0.08	–0.17

图 5 2022年区域1工作日基线负荷曲线

Fig.5 Baseline load curves of working days in area 1 in 2022

图 6 2022年区域1休息日基线负荷曲线

Fig.6 Baseline load curves of holidays in area 1 in 2022

表 2 2022年区域2气象因素与最大用电负荷相关性

Table 2 Correlation between meteorological factors and maximum electricity load in area 2 in 2022

月份	与最大用电负荷相关系数
月份	最高温度	最低温度	平均温度	风速
1	0.22	0.21	0.21	0.05
2	–0.03	–0.22	–0.13	–0.21
3	–0.57	–0.71	–0.73	–0.39
4	–0.12	–0.71	–0.73	–0.39
5	0.16	0.24	0.21	0.16
6	0.79	0.82	0.85	–0.16
7	0.36	0.67	0.52	0.27
8	0.45	0.55	0.54	0.34
9	0.36	0.51	0.45	0.25
10	–0.87	–0.55	–0.81	0.04
11	–0.90	–0.85	–0.88	0.34
12	–0.21	–0.28	–0.29	0.03

图 7 2022年区域2工作日基线负荷曲线

Fig.7 Baseline load curves of working days in area 2 in 2022

图 8 2022年区域2休息日基线负荷曲线

Fig.8 Baseline load curves of holidays in area 2 in 2022

表 3 2022年区域1与区域2第1、2季度三次产业的产业增加值与人均消费支出情况

Table 3 Industrial added value and per capita consumption expenditure of the third industry in the first and second quarters of area 1 and area 2 in 2022

区域	W₁/ 亿元	$W'_1 $/ 亿元	W₂/ 亿元	$W'_2 $/ 亿元	W₃/ 亿元	$W'_3 $/ 亿元	y/ 元	y′/ 元
1	473.80	239.06	4160.71	3661.29	3352.42	3365.06	4589	4971
2	422.88	131.86	1828.26	1672.62	2052.60	2170.82	4717	4890

图 9 区域1产业结构变化值夹角变化趋势

Fig.9 The change trend of the included angle of industrial structure change value in region 1

图 10 2022年1—7月区域1负荷与电量占比变化情况

Fig.10 Changes in the proportion of load and electricity in area 1 from January to July 2022

图 11 区域2产业结构变化值夹角变化趋势

Fig.11 The change trend of the included angle of industrial structure change value in area 2

图 12 2022年1—7月区域2负荷与电量占比变化情况

Fig.12 Changes in the proportion of load and electricity in area 2 from January to July 2022

表 4 休息日与日负荷增长g值

Table 4 The correlation value g between holidays and daily load growth

区域	g值
区域	1月	2月	3月	4月	5月	6月	7月
1	0.05	0.04	0.03	0.15	–0.19	–0.15	0.34
2	0.31	0.11	0.33	0.15	–0.08	0.31	–0.12

表 5 基于贡献率的区域1负荷增长量化归因情况

Table 5 Quantitative attribution of load growth in area 1 based on contribution rate

负荷变量	增长量/万kW	贡献率/%
总负荷$ \Delta L $	1028.31	100.49
气象负荷$ \Delta {L_{\rm m}} $	820.52	79.79
自然经济负荷$ \Delta {L_{{t_1}}} $	139.91	13.61
业扩增长$ \Delta {L_{{t_2}}} $	22.59	2.20
休息日$ \Delta A $	50.34	4.90

表 6 基于贡献率的区域2负荷增长量化归因情况

Table 6 Quantitative attribution of load growth in area 2 based on contribution rate

负荷变量	增长量/万kW	贡献率/%
总负荷$ \Delta L $	610.33	100.95
气象负荷$ \Delta {L_{\rm m}} $	504.76	82.70
自然经济负荷$ \Delta {L_{{t_1}}} $	103.59	16.97
业扩增长$ \Delta {L_{{t_2}}} $	7.77	1.27
休息日$ \Delta A $	0.00	0.00

表 7 2020年区域1气象因素与最大用电负荷相关性

Table 7 Correlation between meteorological factors and maximum electricity load in area 1 in 2020

月份	与最大用电负荷相关系数
月份	最高温度	最低温度	平均温度	风速
1	–0.612	–0.264	–0.618	–0.158
2	–0.391	0.064	–0.250	0.170
3	–0.705	–0.721	–0.795	0.035
4	–0.470	–0.350	–0.465	0.213
5	0.254	0.160	0.260	–0.317
6	0.699	0.441	0.737	0.410
7	0.836	0.650	0.926	0.248
8	0.788	0.634	0.720	–0.556
9	0.726	0.569	0.790	–0.077
10	–0.010	–0.471	–0.393	0.188
11	–0.922	–0.880	–0.937	–0.293
12	–0.848	–0.426	–0.656	0.012

图 13 2020年区域1工作日基线负荷曲线

Fig.13 Baseline load curves of working days in area 1 in 2020

图 14 2020年区域1休息日基线负荷曲线

Fig.14 Baseline load curves of holidays in area 1 in 2020

表 8 2023年区域1气象因素与最大用电负荷相关性

Table 8 Correlation between meteorological factors and maximum electricity load in area 1 in 2023

月份	与最大用电负荷相关系数
月份	最高温度	最低温度	平均温度	风速
1	–0.514	–0.299	–0.452	0.225
2	–0.798	–0.001	–0.575	–0.244
3	–0.277	–0.306	–0.350	0.524
4	–0.124	0.033	–0.073	0.179
5	0.363	0.505	0.486	–0.145
6	0.656	0.764	0.785	0.443
7	0.689	0.646	0.864	0.092
8	0.825	0.815	0.936	0.361
9	0.819	0.870	0.911	0.625
10	0.233	–0.408	–0.148	–0.051
11	–0.730	–0.738	–0.837	0.048
12	–0.830	–0.830	–0.892	0.082

图 15 2023年区域1工作日基线负荷曲线

Fig.15 Baseline load curves of working days in area 1 in 2023

图 16 2023年区域1休息日基线负荷曲线

Fig.16 Baseline load curves of holidays in area 1 in 2023

表 9 2020年与2023年区域1第2季度三次产业的产业增加值与人均消费支出情况

Table 9 Industrial added value and per capita consumption expenditure of the third industries in the second quarters of area 1 in 2020 and 2023

${W_1}$/亿元		${W'_1}$/亿元		${W_2}$/亿元		$ {W'_2} $/亿元		$ {W_3} $/亿元		$ {W'_3} $/亿元		y/元		y′/元
508.24		398.62		3775.58		2931.88		3567.48		3024.76		10897		8027

图 17 区域1产业结构变化值夹角季度变化趋势

Fig.17 The quarterly change trend of the included angle of the industrial structure change value in the area 1

图 18 2020年7月至2023年7月区域1负荷与电量占比变化情况

Fig.18 Changes in the proportion of load and electricity in area 1 from July 2020 to July 2023

表 10 2020年第3季度至2023年第2季度区域1休息日与日负荷增长g值

Table 10 The correlation value g between holidays and daily load growth from the second quarter of 2020 to the second quarter of 2023

年份	季度	g值	年份	季度	g值
2020	3	0.213	2022	1	–0.048
2020	4	0.042		2	0.186
2021	1	–0.173		3	0.013
	2	0.107		4	0.123
	3	0.062	2023	1	0.164
	4	–0.032	2023	2	0.327

表 11 2020年7月至2023年7月区域1负荷变化量化归因

Table 11 Quantitative attribution of load changes in area 1 from July 2020 to July 2023

负荷变量	增长量/万kW	贡献率/%
总负荷$ \Delta L $	1441.58	98.60
气象负荷$ \Delta {L_{\rm m}} $	1004.41	69.67
自然经济负荷$ \Delta {L_{{t_1}}} $	417.03	28.93
业扩增长$ \Delta {L_{{t_2}}} $	0.00	0.00
休息日$ \Delta A $	0.00	0.00

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