CN104751255A - Distribution unit-area maximum load forecasting method - Google Patents
Distribution unit-area maximum load forecasting method Download PDFInfo
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- CN104751255A CN104751255A CN201510199762.3A CN201510199762A CN104751255A CN 104751255 A CN104751255 A CN 104751255A CN 201510199762 A CN201510199762 A CN 201510199762A CN 104751255 A CN104751255 A CN 104751255A
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Abstract
The invention discloses a distribution unit-area maximum load forecasting method, which comprises the following steps of dividing China into five areas, i.e. a severe cold area, a cold area, a hot-summer and cold-winter area, a mild area and a hot summer and warm winter area according to a building climate division standard in China; respectively dividing urban per capita disposable income and rural resident annual net income of the five areas into five levels, i.e. high, above the average, middle, below the average and low; aiming at the five areas, respectively surveying and counting the holding quantity of household appliances per 100 urban households and rural households in every kind of economic level in each area; aiming at the five areas, respectively surveying and counting the use ratio of each household appliance of every kind of economic level in each area; combining the maximum load and the growth rate to forecast the distribution unit-area maximum load forecasting. The method is simple, convenient, effective and favorable for actual operation, lays a foundation for reasonable configuration of unit-area distribution transformer capacity, and is beneficial to improvement of the economic operation level and the construction efficiency of the distribution unit-area.
Description
Technical field
The present invention relates to a kind of power distribution station peak load Forecasting Methodology.
Background technology
Along with improving constantly of China's Living consumption, residential households household electrical appliance owning amount and kind constantly increase, and residential electricity consumption load constantly changes.Accurate Prediction power distribution station load variations is the basis of carrying out power distribution station planning, construction, particularly predicts it is the key that distribution transformer capacity configures to power distribution station peak load.At present in China's power distribution network physical planning, process of construction, usually rule of thumb estimation is carried out to determine distribution transformer capacity to the change of power distribution station peak load, or directly select distribution transformer capacity according to directive/guide Plays class capabilities principle.China is vast in territory, and Regional Economic development level, climatic environment, residential electricity consumption custom difference is comparatively large, and power distribution station load variations otherness is also larger.Only rule of thumb estimate peak load or according to directive/guide standard class Capacity Selection distribution transformer capacity, usually cause capacity configuration unreasonable.Capacity Selection is excessive, and substation transformer load factor is on the low side; Capacity Selection is too small, and substation transformer puts into operation shortly to be needed to carry out increase-volume, the raising having a strong impact on power distribution station economic operation level and construction efficiency of above-mentioned phenomenon.
Therefore, be necessary to provide a kind of simple and effective power distribution station peak load Forecasting Methodology, Accurate Prediction power distribution station peak load changes, and then distribution transformer capacity in reasonable disposition power distribution station.First the present invention carries out subregion according to climate characteristic and the level of economic development to China, and then according to resident's household electrical appliance owning amount of each subregion and probability of use situation, matching is when the power distribution station peak load the year before last, then according to working as power distribution station peak load and history year load growth rate in the year before last, logtis curvilinear function is used to carry out matching to the following annual peak load in power distribution station.
Summary of the invention
The present invention is in order to solve the problem, propose a kind of power distribution station peak load Forecasting Methodology, the method carries out subregion according to climate characteristic and the level of economic development, according to resident's household electrical appliance owning amount of each subregion and the matching of probability of use situation when the power distribution station peak load the year before last, then according to working as power distribution station peak load and history year load growth rate in the year before last, logtis curvilinear function is used to carry out matching to the following annual peak load in power distribution station.And then lay the foundation for the reasonable disposition of platform district distribution transformer capacity, improve power distribution station economic operation level and construction efficiency.
To achieve these goals, the present invention adopts following technical scheme:
A kind of power distribution station peak load Forecasting Methodology, comprises the following steps:
(1) according to China's Standard for climatic regionalization for building and civil engineering, China is divided into severe cold area, cold district, hot-summer and cold-winter area, temperate zone and hot summer and warm winter region five regions;
(2) urban residents' disposable income per capita of five regions and urban residents' year net return are divided into respectively height, in upper, in, in lower and low 5 grades;
(3) for severe cold area, cold district, hot-summer and cold-winter area, temperate zone and hot summer and warm winter region five regions, urban households and rural households every one hundred houses household electrical appliance owning amount of economic level is often planted in the every class region of investigation statistics respectively;
(4) for severe cold area, cold district, hot-summer and cold-winter area, temperate zone and hot summer and warm winter region five regions, the probability of use of often kind of household electrical appliance of economic level is often planted in the every class region of investigation statistics respectively;
(5) in conjunction with current annual peak load and history annual growth rate, the following biggest yearly load prediction of this power distribution station is predicted.
In described step (2), the height of urban residents' disposable income per capita and urban residents' year net return, in upper, in, in lower and low 5 grades all divide according to 5 equal proportions, each grade accounting is 20%.
In described step (3), household electrical appliance comprise cooling electric heating appliance, kitchen appliance, living electric apparatus and traffic electrical equipment, and wherein, cooling electric heating appliance comprises electric fan, air conditioner and space heater; Kitchen appliance comprises smoke exhaust ventilator, electromagnetic oven, electric cooker, electric kettle and micro-wave oven; Living electric apparatus comprises electric heater, televisor, washing machine, refrigerator and home computer; Traffic electrical equipment comprises electric bicycle.
In described step (5), comprise the following steps:
Step 5-1: select certain power distribution station, determines that this power distribution station belongs to that class subregion in severe cold area, cold district, hot-summer and cold-winter area, temperate zone and hot summer and warm winter region five regions;
Step 5-2: determine that this power distribution station belongs to the power distribution station of town-wide or belongs to the power distribution station of rural area scope;
Step 5-3: determine this power distribution station power supply electric client's number N;
Step 5-4: to determine in this power distribution station high, in upper, in, in the proportion of composing of power supply client of lower and low 5 kinds of different economic levels;
Step 5-5: calculate the peak load P when this power distribution station the year before last
0;
Step 5-6: determine that this power distribution station was respectively v when the year before last to the growth rate of peak load during first 4 years
1, v
2, v
3, v
4;
Step 5-7: the growth rate according to the current annual peak load of this power distribution station and the peak load during the year before last was to first 4 years is respectively v
1, v
2, v
3, v
4, calculate before this power distribution station during 1 to first 4 years peak load p
1, p
2, p
3, p
4, wherein p
1=p
0/ (1+v
1), p
2=p0/ ((1+v
1) (1+v
2)), p
3=p0/ ((1+v
1) (1+v
2) (1+v
3)), p
4=p
0/ ((1+v
1) (1+v
2) (1+v
3) (1+v
4));
Step 5-8: according to the peak load p0 of this power distribution station during the year before last was to first 4 years, p1, p2, p3, p4, parameter L, b and c of matching logtis curvilinear function y=L/ (1+b × e-c × x);
Step 5-9: in the situation of L, b and c of acquisition, according to y=L/ (1+b × e-c × x), calculate current year next year to target year peak load p (1) ..., p (i) ..., p (n).
Beneficial effect of the present invention is:
(1) the present invention carries out subregion according to climate characteristic and the level of economic development, according to resident's household electrical appliance owning amount of each subregion and the matching of probability of use situation when the power distribution station peak load the year before last, then according to working as power distribution station peak load and history year load growth rate in the year before last, logtis curvilinear function is used to carry out matching to the following annual peak load in power distribution station; Namely method considers historical load characteristic, has taken into account the saturation characteristic of following load simultaneously.
(2) method easy, effective, be beneficial to practical operation, for the reasonable disposition of platform district distribution transformer capacity lays the foundation, power distribution station economic operation level and construction efficiency can be improved.
Accompanying drawing explanation
Fig. 1 is power distribution station of the present invention peak load process flow diagram.
Embodiment:
Below in conjunction with accompanying drawing and embodiment, the invention will be further described.
As shown in Figure 1, a kind of power distribution station peak load Forecasting Methodology is as follows:
Step 1: according to China's Standard for climatic regionalization for building and civil engineering, is divided into severe cold area, cold district, hot-summer and cold-winter area, temperate zone and hot summer and warm winter region five regions by China.
Step 2: the urban residents' disposable income per capita of five regions and urban residents' year net return are divided into respectively height, in upper, in, in lower and low 5 grades.
Step 3: for severe cold area, cold district, hot-summer and cold-winter area, temperate zone and hot summer and warm winter region five regions, urban households and rural households every one hundred houses household electrical appliance owning amount of economic level is often planted in the every class region of investigation statistics respectively.Household electrical appliance comprise cooling electric heating appliance, kitchen appliance, living electric apparatus and traffic electrical equipment.Wherein, electric heating appliance of lowering the temperature comprises electric fan, air conditioner and space heater; Kitchen appliance comprises smoke exhaust ventilator, electromagnetic oven, electric cooker, electric kettle and micro-wave oven; Living electric apparatus comprises electric heater, televisor, washing machine, refrigerator and home computer; Traffic electrical equipment comprises electric bicycle.
Step 4: for severe cold area, cold district, hot-summer and cold-winter area, temperate zone and hot summer and warm winter region five regions, the probability of use of often kind of household electrical appliance of economic level is often planted in the every class region of investigation statistics respectively.
Step 5: select certain power distribution station to carry out the peak load prediction of platform district.
Wherein step 5 is selected certain power distribution station to carry out the prediction of platform district peak load to comprise the following steps:
Step 5-1: select certain power distribution station, determines that this power distribution station belongs to that class subregion in severe cold area, cold district, hot-summer and cold-winter area, temperate zone and hot summer and warm winter region five regions.
Step 5-2: determine that this power distribution station belongs to the power distribution station of town-wide or belongs to the power distribution station of rural area scope.
Step 5-3: determine this power distribution station power supply electric client's number N.
Step 5-4: to determine in this power distribution station high, in upper, in, in the proportion of composing of power supply client of lower and low 5 kinds of different economic levels.
Step 5-5: calculate the peak load P0 when this power distribution station the year before last.
Step 5-6: determine that this power distribution station was respectively v1, v2, v3, v4 when the year before last to the growth rate of the peak load during first 4 years.
Step 5-7: the growth rate according to the current annual peak load of this power distribution station and the peak load during the year before last was to first 4 years is respectively v1, v2, v3, v4, calculate before this power distribution station during 1 to first 4 years peak load p1, p2, p3, p4.Wherein p1=p0/ (1+v1), p2=p0/ ((1+v1) (1+v2)), p3=p0/ ((1+v1) (1+v2) (1+v3)), p4=p0/ ((1+v1) (1+v2) (1+v3) (1+v4)).
Step 5-8: according to the peak load p0 of this power distribution station during the year before last was to first 4 years, p1, p2, p3, p4, parameter L, b and c of matching logtis curvilinear function y=L/ (1+b × e-c × x).
Step 5-9: in the situation of L, b and c of acquisition, according to y=L/ (1+b × e-c × x), calculate current year next year to target year peak load p (1) ..., p (i) ..., p (n).
By reference to the accompanying drawings the specific embodiment of the present invention is described although above-mentioned; but not limiting the scope of the invention; one of ordinary skill in the art should be understood that; on the basis of technical scheme of the present invention, those skilled in the art do not need to pay various amendment or distortion that creative work can make still within protection scope of the present invention.
Claims (5)
1. a power distribution station peak load Forecasting Methodology, is characterized in that: comprise the following steps:
(1) according to China's Standard for climatic regionalization for building and civil engineering, China is divided into severe cold area, cold district, hot-summer and cold-winter area, temperate zone and hot summer and warm winter region five regions;
(2) urban residents' disposable income per capita of five regions and urban residents' year net return are divided into respectively height, in upper, in, in lower and low 5 grades;
(3) for severe cold area, cold district, hot-summer and cold-winter area, temperate zone and hot summer and warm winter region five regions, urban households and rural households every one hundred houses household electrical appliance owning amount of economic level is often planted in the every class region of investigation statistics respectively;
(4) for severe cold area, cold district, hot-summer and cold-winter area, temperate zone and hot summer and warm winter region five regions, the probability of use of often kind of household electrical appliance of economic level is often planted in the every class region of investigation statistics respectively;
(5) in conjunction with current annual peak load and history annual growth rate, the following biggest yearly load prediction of this power distribution station is predicted.
2. a kind of power distribution station as claimed in claim 1 peak load Forecasting Methodology, it is characterized in that: in described step (2), the height of urban residents' disposable income per capita and urban residents' year net return, in upper, in, in lower and low 5 grades all divide according to 5 equal proportions, each grade accounting is 20%.
3. a kind of power distribution station as claimed in claim 1 peak load Forecasting Methodology, it is characterized in that: in described step (3), household electrical appliance comprise cooling electric heating appliance, kitchen appliance, living electric apparatus and traffic electrical equipment, wherein, electric heating appliance of lowering the temperature comprises electric fan, air conditioner and space heater; Kitchen appliance comprises smoke exhaust ventilator, electromagnetic oven, electric cooker, electric kettle and micro-wave oven; Living electric apparatus comprises electric heater, televisor, washing machine, refrigerator and home computer; Traffic electrical equipment comprises electric bicycle.
4. a kind of power distribution station as claimed in claim 1 peak load Forecasting Methodology, is characterized in that: in described step (5), comprise the following steps:
Step 5-1: select certain power distribution station, determines that this power distribution station belongs to that class subregion in severe cold area, cold district, hot-summer and cold-winter area, temperate zone and hot summer and warm winter region five regions;
Step 5-2: determine that this power distribution station belongs to the power distribution station of town-wide or belongs to the power distribution station of rural area scope;
Step 5-3: determine this power distribution station power supply electric client's number N;
Step 5-4: to determine in this power distribution station high, in upper, in, in the proportion of composing of power supply client of lower and low 5 kinds of different economic levels;
Step 5-5: calculate the peak load P when this power distribution station the year before last
0;
Step 5-6: determine that this power distribution station was respectively v when the year before last to the growth rate of peak load during first 4 years
1, v
2, v
3, v
4;
Step 5-7: the growth rate according to the current annual peak load of this power distribution station and the peak load during the year before last was to first 4 years is respectively v
1, v
2, v
3, v
4, calculate before this power distribution station during 1 to first 4 years peak load p
1, p
2, p
3, p
4;
Step 5-8: according to this power distribution station when the year before last to during first 4 years peak load p0, p1, p2, p3, p4, parameter L, b and c of matching logtis curvilinear function y=L/ (1+b × e-c × x);
Step 5-9: in the situation of L, b and c of acquisition, according to y=L/ (1+b × e-c × x), calculate current year next year to target year peak load p (1) ..., p (i) ..., p (n).
5. a kind of power distribution station as claimed in claim 1 peak load Forecasting Methodology, is characterized in that: the concrete grammar of described step 5-8 is: p
1=p
0/ (1+v
1), p
2=p0/ ((1+v
1) (1+v
2)), p
3=p0/ ((1+v
1) (1+v
2) (1+v
3)), p
4=p
0/ ((1+v
1) (1+v
2) (1+v
3) (1+v
4)).
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| CN201510199762.3A CN104751255A (en) | 2015-04-23 | 2015-04-23 | Distribution unit-area maximum load forecasting method |
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| CN201510199762.3A CN104751255A (en) | 2015-04-23 | 2015-04-23 | Distribution unit-area maximum load forecasting method |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105894137A (en) * | 2016-05-30 | 2016-08-24 | 中国南方电网有限责任公司电网技术研究中心 | Residential electricity demand forecasting method and system |
| CN109242132A (en) * | 2018-06-05 | 2019-01-18 | 国网江苏省电力有限公司南通供电分公司 | Subregion peak load prediction technique based on MapReduce frame |
| CN110263995A (en) * | 2019-06-18 | 2019-09-20 | 广西电网有限责任公司电力科学研究院 | Consider the distribution transforming heavy-overload prediction technique of load growth rate and user power utilization characteristic |
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| CN102682198A (en) * | 2012-04-26 | 2012-09-19 | 上海市电力公司 | Method for forecasting annual maximum electrical load |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105894137A (en) * | 2016-05-30 | 2016-08-24 | 中国南方电网有限责任公司电网技术研究中心 | Residential electricity demand forecasting method and system |
| CN109242132A (en) * | 2018-06-05 | 2019-01-18 | 国网江苏省电力有限公司南通供电分公司 | Subregion peak load prediction technique based on MapReduce frame |
| CN110263995A (en) * | 2019-06-18 | 2019-09-20 | 广西电网有限责任公司电力科学研究院 | Consider the distribution transforming heavy-overload prediction technique of load growth rate and user power utilization characteristic |
| CN110263995B (en) * | 2019-06-18 | 2022-03-22 | 广西电网有限责任公司电力科学研究院 | Distribution transformer overload prediction method considering load increase rate and user power utilization characteristics |
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Application publication date: 20150701 |