Vol.26, NO.4, 2015
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Article Navigation >  Journal of Applied Meteorological Science  > 2015  >  26(4): 492-499
Fu Guiqin, You Fengchun, Cao Xin, et al. Application and verification of accumulated temperature effects on daily peak load and daily valley load of power. J Appl Meteor Sci, 2015, 26(4): 492-499. DOI:  10.11898/1001-7313.20150411.
Citation: Fu Guiqin, You Fengchun, Cao Xin, et al. Application and verification of accumulated temperature effects on daily peak load and daily valley load of power. J Appl Meteor Sci, 2015, 26(4): 492-499. DOI:  10.11898/1001-7313.20150411.
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Application and Verification of Accumulated Temperature Effects on Daily Peak Load and Daily Valley Load of Power

DOI: 10.11898/1001-7313.20150411
  • 1.

    Hebei Provincal Meteorology Service Center, Shijiazhuang 050021

  • 2.

    Key Laboratory for Meteorology and Ecological Environment of Hebei Province, Shijiazhuang 050021

  • 3.

    Beijing Meteorological Observatory, Beijing 100089

  • 4.

    State Grid Hebei Electric Power Company, Shijiazhuang 050021

  • Received Date: 2014-10-20
  • Rev Recd Date: 2015-02-09
  • Publish Date: 2015-07-31
    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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  • 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

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    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

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    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

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    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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    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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    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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    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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    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
    DownLoad: Download CSV
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    • Received : 2014-10-20
    • Accepted : 2015-02-09
    • Published : 2015-07-31
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