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Application and Verification of Accumulated Temperature Effects on Daily Peak Load and Daily Valley Load of Power
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摘要: 从电力气象服务需求出发，利用2001—2010年5—9月河北省南电网逐日电力日峰负荷、日谷负荷与对应时间的气象资料，探讨晴热天气和闷热天气对电力日峰负荷、日谷负荷的影响特征。分析发现持续3 d以上的闷热天气相对晴热天气使电力日峰负荷、日谷负荷增长更显著；日最高气温32℃是引起河北省南电网日峰负荷增长的初始气温敏感点，35℃为强气温敏感点，38℃为极强气温敏感点，日最低气温25℃为引起日谷负荷增加的敏感气温临界点；建立了引入积温热累积效应的日峰负荷、日谷负荷多元回归气象预测模型，经2011—2013年应用检验，日峰负荷、日谷负荷预测平均相对误差分别为4.8%和3.5%，提高了预测准确率，对电力调度具有参考价值。Abstract: In order to meet needs for electric power of meteorological service, an analysis is made on the correlation between meteorological elements and electrical loads of electric network in Hebei Province. The meteorological data and electrical load data from May to September during 2001-2010 are used, and they are divided into sunny hot weather and muggy weather. Compared to the sunny hot weather, it shows that the daily peak load and daily valley load are increased significantly in muggy weather lasting three days or more. When daily maximum temperature reaches 32℃, daily peak load of power increase rapidly in Hebei Province, and 32, 35℃ and 38℃ of daily maximum temperature are three sensitive points for daily peak load of power to air temperature variation. During periods with daily maximum temperature more than or equal to 35℃, the daily peak load of power varies greatly according to the air temperature. When daily maximum temperature exceeds 38℃, considering 1℃ rising of daily maximum temperature, the daily peak load of power would increase 9.4%, and the air-conditioning cooling load of power would reach 50% of the daily peak load. When daily minimum temperature reaches 25℃, daily valley load of power increases rapidly, and 25℃ of daily minimum temperature is the sensitive point of daily valley load of power to air temperature variation. Introducing accumulated temperature effect as forecast factor, a meteorological electricity prediction model is established by using the multiple regression method, which can predict the peak and valley of meteorological electricity loads. The model is validated using historic data from 2011 to 2013, the average relative error of daily peak load is 4.8%, and that of the daily valley load is 3.5%, showing good prediction accuracy. The proposed model has reference significance for electric power dispatching.
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图 1 2001—2010年5—9月日最高气温、日最低气温及日峰负荷、日谷负荷年平均变化曲线
Fig. 1 The annual average curve of daily maximum temperature, daily minimum temperature and daily peak load, daily valley load of power from May to September during 2001－2010
图 2 2001—2010年5—9月日最高气温、日最低气温及日峰负荷、日谷负荷月平均曲线
Fig. 2 The monthly average curve of daily maximum temperature, daily minimum temperature and daily peak load, daily valley load of power from May to September during 2001-2010
图 3 2011—2013年5—9月日峰负荷、日谷负荷预测值与实况对比
Fig. 3 The contrast curve between forecast and real value of daily peak load and daily valley load from May to September during 2011-2013
表 1 2001—2010年5—9月晴热天气过程与日峰负荷、日谷负荷变化
Table 1 Variations of daily peak load and daily valley load of power in sunny hot weather from May to September during 2001-2010
时段 日峰负荷增量/(105 kW) 日谷负荷增量/(105 kW) 过程最高气温/℃ 过程最低气温/℃ 2001-07-11—13 102.8 29.9 37.1 23.5 2002-05-30—06-01 62.2 19.4 37.0 20.1 2002-06-03—07 62.0 14.1 38.1 22.0 2003-06-17—20 75.0 24.8 36.8 19.6 2004-06-09—11 53.1 41.4 37.5 19.7 2005-06-11—17 42.1 59.9 37.3 21.8 2005-06-19—21 107.1 85.7 40.3 24.1 2005-07-04—09 110.7 7.3 37.6 24.1 2007-06-06—10 91.9 31.0 37.8 22.0 2009-06-23—07-04 123.6 58.3 40.4 21.9 2010-06-27—29 67.5 27.5 38.1 23.1 平均 81.6 36.3 38.0 22.0 
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表 2 2001—2010年5—9月闷热天气过程与日峰负荷、日谷负荷变化
Table 2 Variations of daily peak load and daily valley load of power in muggy weather from May to September during 2001-2010
时段 日峰负荷增量/(105 kW) 日谷负荷增量/(105 kW) 过程最高气温/℃ 过程最低气温/℃ 2002-07-10—15 80.2 61.2 41.6 25.7 2002-07-30—08-04 81.1 38.6 36.2 24.8 2005-07-19—21 65.3 53.9 35.9 25.4 2005-08-11—15 120.7 66.5 35.1 25.8 2006-07-12—14 44.9 44.1 34.1 24.4 2006-08-08—13 26.5 39.4 34.8 25.0 2008-08-07—09 35.7 6.7 33.8 24.8 2009-07-20—22 184.6 142.2 33.9 26.0 2010-07-03—05 179.1 101.1 39.5 25.0 2010-07-22—31 123.6 37.3 25.4 平均 94.2 61.5 36.2 25.2
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表 3 日最高气温升高1℃对应的日峰负荷气象变化量 (K) 及增加值 (M)
Table 3 Increment of meteorological variations of daily peak power with 1℃ increment of daily maximum temperature
Tmax/℃ K/% M/% [27, 28) -18.8 [28, 29) -10.4 8.4 [29, 30) -9.3 1.1 [30, 31) 0.5 9.8 [31, 32) 3.3 2.8 [32, 33) 18.2 14.9 [33, 34) 19.6 1.4 [34, 35) 21.6 2.0 [35, 36) 27.6 6.0 [36, 37) 30.9 3.3 [37, 38) 34.5 3.6 [38, 39) 43.9 9.4 [39, 45*) 48.2 4.3 注：*历史记录中南电网区域夏季极端最高气温为44.4℃。
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表 4 日最低气温升高1℃对应的日谷负荷气象变化量及增加值
Table 4 Increment of meteorological variations of daily valley load of power with 1℃ increment daily minimum temperature
Tmin/℃ K/% M/% [20, 21) -7.0 [21, 22) -4.3 2.7 [22, 23) 5.0 9.3 [23, 24) 0.1 -4.9 [24, 25) 2.7 2.6 [25, 26) 17.3 14.6 [26, 27) 20.9 3.6 [27, 30*) 20.5 -0.4 注：*历史记录中南电网区域夏季日最低气温最大值为30.0℃。
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表 5 2001—2010年5—9月逐日电力气象负荷与气象要素的相关系数
Table 5 The correlation between power load and meteorological elements from May to September during 2001-2010
气象要素 Fmax Fmin T 0.551 0.561 Tmax 0.542 0.535 Tmin 0.426 0.431 Bmax 0.430 Bmin 0.353 f -0.361 -0.338 R -0.228 -0.183
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