CN113507111A - Blind number theory-based planning target annual power profit and loss assessment method - Google Patents

Blind number theory-based planning target annual power profit and loss assessment method Download PDF

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CN113507111A
CN113507111A CN202110703597.6A CN202110703597A CN113507111A CN 113507111 A CN113507111 A CN 113507111A CN 202110703597 A CN202110703597 A CN 202110703597A CN 113507111 A CN113507111 A CN 113507111A
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power
blind
confidence
load
interval
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肖白
李克
张节潭
杨森林
苟晓侃
刘金山
王学斌
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Northeast Electric Power University
State Grid Qinghai Electric Power Co Ltd
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Northeast Dianli University
State Grid Qinghai Electric Power Co Ltd
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Abstract

The invention relates to a method for evaluating annual electric power profit and loss of a planning target based on a blind number theory, which is characterized in that the blind number theory is adopted to characterize and depict the uncertainty of a new energy power supply and an electric load, a blind number model of various power supplies and loads is established, and the model of annual electric power profit and loss of the planning target based on the blind number theory is further established, so that the problems of insufficient consideration of the capacity of the new energy power supply participating in electric power balance and unclear confidence coefficient of the capacity of the new energy power supply in the process of researching the electric power profit and loss of an electric system containing large-scale new energy power supply grid connection in the planning target year can be solved.

Description

Blind number theory-based planning target annual power profit and loss assessment method
Technical Field
The invention relates to the field of power supply planning in a power system, in particular to a planning target year power profit and loss evaluation method based on a blind number theory.
Background
The assessment of whether the standby condition of the power grid is sufficient and the profit and loss condition of the power supply in the current and future periods are main contents and important meanings for assessing the power balance of the power grid, and the assessment is an important basis for carrying out power system planning problems and system design schemes. Accurate evaluation of power balance of the power grid in the planning period is beneficial to checking the rationality of a system power supply and a power grid planning scheme, ensures the reliability of the system, is related to the safe and stable operation of the whole power system, and influences the development of industrial production and power utilities. Due to the volatility and uncertainty of the new energy wind-solar power supply and the power load, when the research and planning target year contains the problems of the profit and loss of the electric power system of large-scale new energy power supply grid connection, the problems of insufficient consideration of the capacity of the new energy power supply participating in electric power balance, unclear capacity confidence coefficient of the new energy power supply and the like exist. So far, the literature report and the practical use which are the same as the content of the invention are not seen.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and establish a planning target annual power profit and loss evaluation method based on the blind number theory, which can overcome the problems of insufficient capacity consideration of a new energy power source participating in power balance, unclear capacity confidence coefficient of the new energy power source and the like.
The technical scheme adopted for achieving the purpose of the invention is that a planning target year electric power profit and loss evaluation method based on a blind number theory is characterized by comprising the following contents:
1) establishing blind digital model of uncertain factors
The new energy power supply and the power load both have randomness and uncertainty, the values of the new energy power supply and the power load in a target year are not always fixed to a certain value but are frequently distributed in a plurality of value intervals in a fluctuating way, the confidence values in the intervals are not completely the same, the blind number theory can estimate the probability of the output power of the new energy power supply or the occurrence probability of the load in each interval in the range through the actual situation and establish a blind number model, so that the 'strong' uncertainty of each factor is converted into the 'weak' uncertainty of the confidence interval and the confidence coefficient,
establishing a blind number model of uncertainty information as follows:
dividing blind number interval of uncertainty variable
Let alpha be [0,1 ]]Wherein
Figure BDA0003131226590000011
Then call
Figure BDA0003131226590000012
Is a blind number, and the expression is formula (1):
Figure BDA0003131226590000013
wherein x is a variable, xiIs a confidence interval, aiIs a confidence interval xiConfidence of, α is
Figure BDA0003131226590000014
M is the total confidence of
Figure BDA0003131226590000021
The order of (a);
② constructing judgment matrix
The judgment matrix analysis method is used for judging and determining the weight coefficient, a decision analysis method combining expert experience and quantitative analysis is applied to a blind number theory, a judgment matrix is obtained by comparing change intervals of all factors pairwise, the weight coefficient of each factor is calculated, and the confidence value of each interval is obtained, compared with a 9-scale method, the relative weight value obtained by adopting an 9/9-9/1 scale method is good in difference, and the judgment of a person is more accurate;
determining confidence value of blind number interval
The expert compares every two intervals by using a 9/9-9/1 scaling method, determines the element value of a judgment matrix according to the confidence value of each interval where the load is located, then calculates the maximum eigenvalue of the judgment matrix and the corresponding eigenvector thereof, carries out consistency check on the judgment matrix, considers that the consistency of the judgment matrix can be accepted if the consistency index CR <0.1, and then carries out normalization on the eigenvector to obtain the weight coefficient of the solved element, namely the confidence value of each blind number interval in the blind number model;
2) establishing a blind number model for planning annual electric power profit and loss of target based on blind number theory
Establishing blind number models of various power supplies and loads according to a blind number theory:
establishing a load blind number model:
because the load change has randomness, the annual average increase rate of the power demand is determined according to three increase modes of high, medium and low speed to determine the load demand, namely the high load acceleration is 10.5%, the medium load acceleration is 9%, the low load acceleration is 7.5%, a load three-order blind number model is established, the confidence coefficient of each interval is obtained by a judgment matrix method, a 9/9-9/1 scaling method is adopted to replace a 9 scaling method to obtain a judgment matrix, the confidence value of each change interval of the load is obtained according to the knowledge of matrix theory, and thus the three-order blind number model of the load is obtained as an expression (2) and is used for obtaining the judgment matrix J of the confidence coefficient of each intervalpIs represented by the formula (3):
Figure BDA0003131226590000022
Figure BDA0003131226590000023
in the formula, P0The load maximum value of the current year; alpha is alphal.1-αl.3The confidence value corresponding to each change interval of the load is obtained; x is the number oflAs load confidence interval, JpA judgment matrix of load blind numbers;
second, establishing wind power blind digital model
Wind power has randomness and intermittent uncertainty, and long-term fluctuation characteristics research according to output power of a new energy power supply shows that the maximum output power value of a single wind power plant can reach the rated installed capacity value, but the wind power plants with wider coverage area and more gradually participate in polymerization grid connection, so that the concurrency rate of large-scale wind power is gradually lower than 100% along with the increase of the installed capacity, namely the total power value of large-scale wind power generation is lower than the total installed capacity value, therefore, a load blind number model is analogized, the total fluctuation variation range of the wind power is determined according to a wind power continuous power curve value, power intervals are divided equally according to quartering, and the ratio of the length of corresponding continuous time in each interval to the total time is used as the confidence coefficient of each interval, so that a wind power fourth-order blind number model is established as formula (4) and formula (5):
Figure BDA0003131226590000031
PΔ＝PW.max-PW.min (5)
in the formula, PW.max、PW.minMaximum and minimum power, x, for wind power generationwFor wind power confidence interval, PΔFor the total fluctuation range of wind power, alphaw.1,αw.2,...,αw.4The confidence coefficient corresponding to each change interval of the wind power is obtained;
establishing photovoltaic blind digital model
Similar to wind power generation, the photovoltaic power generation power also has the characteristics of randomness and intermittence, the maximum power generation power of a single photovoltaic power station can reach the rated capacity of the single photovoltaic power station, the maximum output power value of a large-scale photovoltaic power station is smaller than the total installed capacity after the large-scale photovoltaic power station is connected into a power grid, and the minimum output power value of the photovoltaic power station is 0 because the photovoltaic power station does not generate power at night, so that an output power long-term fluctuation characteristic curve is obtained by predicting based on a set photovoltaic planning construction capacity new energy power generation power long-term fluctuation characteristic prediction method in a similar way to a wind power blind number model, each change interval and a confidence value are obtained, and the photovoltaic four-order blind number model is established as the formula (6):
Figure BDA0003131226590000032
in the formula, PPV.max、PPV.minThe maximum value and the minimum value of the photovoltaic power generation power are obtained; x is the number ofwIs a wind power confidence interval; alpha pv.1, alpha pv.2, and alpha pv.4 are confidence degrees corresponding to each photovoltaic power generation interval;
establishing blind digital model of thermal power, water and electricity
The hydroelectric power is a conventional adjustable power supply, the output power of the hydroelectric power is influenced by seasonality, the hydroelectric power has a dry season, a flat season and a rich season, and the thermal power has a heating period and a non-heating period, so that the condition of the output power of the hydroelectric power is judged according to the condition of the month of year maximum load according to data statistics, and the power generation models representing the hydroelectric power and the thermal power according to a blind number form are respectively formula (7) and formula (8):
Figure BDA0003131226590000041
Figure BDA0003131226590000042
in the formula, xth、xtdRespectively are thermal power and water confidence intervals; alpha is alphath、αtdRespectively determining the utilization rates of thermal power and hydropower according to the actual conditions of the thermal power and the hydropower in the month of the maximum load; p is a radical ofth、ptdRespectively outputting power for thermal power and hydropower;
fifthly, establishing an expression of the power profit and loss balance blind number model
And (3) carrying out blind number operation based on the blind number models of the various power supplies, wherein the expression of the electric power profit-loss balance blind number model is shown as an expression (9):
Figure BDA0003131226590000043
wherein, r represents the spare capacity,
Figure BDA0003131226590000044
respectively represent the blind number model of the wind, light, fire and water power source energy utilization capacity.
The invention relates to a method for evaluating annual electric power profit and loss of a planning target based on a blind number theory, which adopts the blind number theory to characterize and depict the uncertainty of a new energy power supply and an electric load, establishes a blind number model of various power supplies and loads, and compared with the method for evaluating annual electric power profit and loss of the planning target of a traditional form method, the method can more finely quantize the new energy power supply and the electric load, reduce and eliminate the influence of the uncertainty of the new energy power supply and the electric load on the evaluation of profit and loss of electric power, is favorable for determining a reasonable power supply planning scheme and a reasonable power grid planning scheme of an electric power system, and has the advantages of science, reasonability, high application value and good effect.
Drawings
FIG. 1 is a diagram of possible value intervals of blind numbers and their corresponding confidence relationships;
FIG. 2 is a graph of blind power results of new energy power generation;
FIG. 3 is a diagram of a blind digital model of total power output of a region according to an embodiment;
FIG. 4 is a model diagram of a target year power profit and loss blind model;
FIG. 5 is a model diagram of a blind model of the profit and loss of electricity when the hydroelectric and thermal power installation is not changed;
fig. 6 is a diagram showing the influence of the speed increase of the hydro-thermoelectric device on the electric power profit and loss in the target year.
Detailed Description
The invention will be further illustrated with reference to figures 1-6 and examples.
The invention relates to a method for evaluating annual electric power profit and loss of a planning target based on a blind number theory, which adopts the blind number theory to characterize and characterize the uncertainty of a new energy power supply and an electric load, and establishes blind number models of various power supplies and loads, wherein the specific contents are as follows:
1) establishing blind digital model of uncertain factors
The blind information is an information form containing multiple uncertain information such as random information, fuzzy information and the like, and is usually expressed by blind numbers. In actual engineering, since knowledge about something is not comprehensive, for a random variable x, its value cannot be determined to fall at a certain point, but around the true value of x. The blind number idea is to use the interval number to represent the distribution of the variable x and use the confidence coefficient alpha to represent the possibility in the interval;
the new energy power source (such as wind, light and the like) and the power load have randomness and uncertainty, the value of the new energy power source in a target year is not always fixed to a certain value, but is distributed in a plurality of value intervals in a fluctuating way, and the confidence values in the intervals are not completely the same. The blind number theory can estimate the possibility of the output power or the load of the new energy power supply in each interval in the range through the actual situation, and establish a blind number model, so that the 'strong' uncertainty of each factor is converted into the 'weak' uncertainty of a credible interval and confidence coefficient;
establishing a blind number model of uncertainty information as follows:
dividing blind number interval of uncertainty variable
Let alpha be [0,1 ]]Wherein
Figure BDA0003131226590000051
Then call
Figure BDA0003131226590000052
For blind numbers, the expression can be shown as formula (1):
Figure BDA0003131226590000053
in the formula, xiIs a confidence interval, aiIs a confidence interval xiConfidence of, α is
Figure BDA0003131226590000054
M is the total confidence of
Figure BDA0003131226590000055
The order of (a). Such as xi＝[ai,bi]1,2, 1, m, then the number of blindness is counted
Figure BDA0003131226590000056
Is shown visually in figure 1.
② constructing judgment matrix
The judgment matrix analysis method is used for judging and determining the weight coefficient, a decision analysis method combining expert experience and quantitative analysis is applied to a blind number theory, a judgment matrix is obtained by comparing change intervals of all factors pairwise, the weight coefficient of each factor is calculated, the confidence value of each interval is obtained, and compared with a 9-scale method, the relative weight value obtained by adopting an 9/9-9/1 scale method is good in difference, and the judgment of a person is more accurate.
Table 1 shows the comparison of the judgment values in two scaling methods
Figure BDA0003131226590000057
Fourthly, determining confidence value of blind number interval
The expert compares every two intervals by using a 9/9-9/1 scaling method, determines the element value of the judgment matrix according to the relative degree of confidence of each interval where the load is positioned, then calculates the maximum eigenvalue of the judgment matrix and the corresponding eigenvector thereof, and performs consistency check on the judgment matrix, and if the consistency index CR <0.1, the consistency of the judgment matrix is considered to be acceptable. And then, normalizing the feature vector to obtain a weight coefficient of the solved element, namely a confidence value of each blind number interval in the blind number model.
2) Establishing a blind number model for planning annual electric power profit and loss of target based on blind number theory
Establishing blind number models of various power supplies and loads according to a blind number theory:
establishing a load blind number model:
because the load change has randomness, and the annual average increase rate of the power demand in a certain region during five years is calculated, three different increase modes of high speed, medium speed and low speed exist. Three development options for the load demand are thus defined, namely a high option load acceleration of 10.5%, a medium option load acceleration of 9% and a low option load acceleration of 7.5%. Therefore, a three-order blind number model of the load is established, the confidence coefficient of each interval is obtained by a judgment matrix method, a 9/9-9/1 scale method is adopted to replace a 9 scale method to obtain a judgment matrix, the confidence value of each change interval of the load is obtained according to matrix theory knowledge, and the three-order blind number model of the load is obtained according to the formula (2) and is used for obtaining the judgment matrix J of the confidence coefficient of each intervalpIs represented by formula (3).
Figure BDA0003131226590000061
Figure BDA0003131226590000062
In the formula, P0The load maximum value of the current year; alpha is alphal.1-αl.3The confidence value corresponding to each change interval of the load is obtained; x is the number oflAs load confidence interval, JpIs a judgment matrix of load blind numbers.
Second, establishing wind power blind digital model
Wind power has randomness and intermittent uncertainty, and long-term fluctuation characteristics research according to output power of a new energy power supply shows that the maximum output power value of a single wind power plant can reach the rated installed capacity value, but the wind power plants with wider coverage area and more gradually participate in polymerization grid connection, so that the concurrency rate of large-scale wind power is gradually lower than 100% along with the increase of the installed capacity, namely the total power value of large-scale wind power generation is lower than the total installed capacity value, therefore, a load blind number model is analogized, the total fluctuation variation range of the wind power is determined according to a wind power continuous power curve value, power intervals are divided equally according to quartering, and the ratio of the length of corresponding continuous time in each interval to the total time is used as the confidence coefficient of each interval, so that a wind power fourth-order blind number model is established as formula (4) and formula (5):
Figure BDA0003131226590000071
PΔ＝PW.max-PW.min (5)
in the formula, PW.max、PW.minMaximum and minimum power, x, for wind power generationwFor wind power confidence interval, PΔFor the total fluctuation range of wind power, alphaw.1,αw.2,...,αw.4The confidence coefficient corresponding to each change interval of the wind power is obtained;
establishing photovoltaic blind digital model
Similar to wind power generation, the photovoltaic power generation power also has the characteristics of randomness and intermittence, the maximum power generation power of a single photovoltaic power station can reach the rated capacity of the single photovoltaic power station, the maximum output power value of a large-scale photovoltaic power station is smaller than the total installed capacity after the large-scale photovoltaic power station is connected into a power grid, and the minimum output power value of the photovoltaic power station is 0 because the photovoltaic power station does not generate power at night, so that an output power long-term fluctuation characteristic curve is obtained by predicting based on a set photovoltaic planning construction capacity new energy power generation power long-term fluctuation characteristic prediction method in a similar way to a wind power blind number model, each change interval and a confidence value are obtained, and the photovoltaic four-order blind number model is established as the formula (6):
Figure BDA0003131226590000072
in the formula, PPV.max、PPV.minThe maximum value and the minimum value of the photovoltaic power generation power are obtained; x is the number ofwIs a wind power confidence interval; alpha pv.1, alpha pv.2, and alpha pv.4 are photovoltaic power generatorsAnd the confidence corresponding to the interval.
Establishing blind digital model of thermal power, water and electricity
The hydroelectric power is a conventional adjustable power supply, the output power of the hydroelectric power is influenced by seasonality, the hydroelectric power has a dry season, a flat season and a rich season, and the thermal power has a heating period and a non-heating period, so that the condition of the output power of the hydroelectric power is judged according to the condition of the month of year maximum load according to data statistics, and the power generation models representing the hydroelectric power and the thermal power according to a blind number form are respectively formula (7) and formula (8):
Figure BDA0003131226590000073
Figure BDA0003131226590000081
in the formula, xth、xtdRespectively are thermal power and water confidence intervals; alpha is alphath、αtdRespectively determining the utilization rates of thermal power and hydropower according to the actual conditions of the thermal power and the hydropower in the month of the maximum load; p is a radical ofth、ptdRespectively thermal power and hydroelectric output power.
Fifthly, establishing an expression of the power profit and loss balance blind number model
And (3) carrying out blind number operation based on the blind number models of the various power supplies, wherein the expression of the electric power profit-loss balance blind number model is shown as an expression (9):
Figure BDA0003131226590000082
wherein, r represents the spare capacity,
Figure BDA0003131226590000083
respectively represent the blind number model of the wind, light, fire and water power source energy utilization capacity.
Specific examples are as follows: the method of the invention is utilized to carry out optimized operation on solar photovoltaic-photothermal combined power generation:
taking 2019 as the current year and 2021 as the planning target year, the planning construction scheme of each power supply installation in the planning target year is assumed as follows. The installed capacity of wind power is increased by 30% year by year, the installed capacity of a photovoltaic power supply is increased by 15% year by year, and the installed capacities of thermal power and hydropower are increased by 5% year by year. And analyzing the power profit and loss of the power supply planning scheme of a certain region in 2021 by using the built blind digital model. Considering that the system spare capacity coefficient is 20% in 2021, the autumn with the largest load in a certain area is selected for power balance, and when the thermal power availability is 95% and the hydropower availability is 90%, the following results are calculated.
And (3) obtaining a confidence value of the load in each change interval by a judgment matrix method based on the possible change interval of the planned target annual load, so that a three-order blind number model of the load is obtained and is shown in an expression (10).
Figure BDA0003131226590000084
And on the basis of the existing new energy power supply installed and power generation data, performing predictive modeling according to the planned target year wind power installed capacity and a new energy power generation long-term fluctuation characteristic prediction method, and predicting to obtain a planned target year wind power continuous power curve under the capacity. And establishing a wind power fourth-order blind digital model according to formulas (4) and (5) based on the prediction curve, wherein the wind power fourth-order blind digital model is shown as a formula (11).
Figure BDA0003131226590000085
As shown in formula (11), under the condition of target wind power installation in a certain region, the minimum generated power of the predicted wind power is 7.6273MW, and therefore the wind power has a certain electric power supporting effect in a target year.
Similar to the establishment process of a wind power blind digital model, a target year photovoltaic power supply continuous power curve is obtained through prediction according to a planned target year photovoltaic power supply installed capacity and a new energy power generation power long-term fluctuation characteristic prediction method. And establishing a photovoltaic fourth-order blind digital model according to the formula (6) based on the prediction curve, wherein the photovoltaic fourth-order blind digital model is shown as the formula (12).
Figure BDA0003131226590000091
And establishing blind digital models of the thermal power and the hydropower according to thermal power and hydropower planning capacity of a planning target year and the availability of respective power supplies by combining the formulas (7) and (8) as shown in the formulas (13) and (14).
Figure BDA0003131226590000092
Figure BDA0003131226590000093
Based on various power supply blind number models and load blind number models in the planning target year, the following results can be obtained through blind number operation in combination with the formula (4-9).
1) And the total generated power of the new energy is a blind result in the target year.
The blind number result of the total power generation of the new energy in the planning target year obtained by blind number addition operation based on the formulas (11) and (12) is shown in fig. 2 and table 2.
TABLE 2 wind/solar Total Power Blind number results distribution
Figure BDA0003131226590000094
Figure BDA0003131226590000101
From fig. 2 and table 2, it can be seen that the total power generation power of the new energy is integrally distributed in 22 power intervals between 7.63MW and 8445.2MW, and the uncertainty of the power generation of the new energy power supply is reflected due to different confidence levels in each interval. When the total power generation power of the new energy is between 1769.5MW and 2112.8MW, the confidence coefficient of the new energy is 0.136 at most; the confidence value that the total power of the new energy is lower than 351MW is less than 7%, and the confidence values that the total output of the new energy exceeds 5980MW are less than 2%.
2) And (4) blind result of total power output power of a certain region in the target year.
The blind number result of the total power output of a certain area of the planned target year obtained by the blind number addition operation based on the equations (11) to (14) is shown in fig. 3.
As can be seen from FIG. 3, the total output power of various power supplies in a certain region in 2021 is distributed between 15158MW and 23595MW, and the shape of the blind number model is basically consistent with that of the new energy wind and light power supply blind number model. Wherein the confidence of the total output power between 16205MW and 17263MW exceeds 12%.
3) And planning a target annual electric power profit and loss blind number result in a certain area.
Considering the system 20% standby and various power supply installation speed increases of the target year, the blind number calculation is performed based on the formulas (9) - (14), and the result of the power profit and loss of a certain region of the target year under the planning scheme is obtained as shown in fig. 4 and table 3.
TABLE 4-3 target annual electric power profit and loss blind number results
Figure BDA0003131226590000102
Figure BDA0003131226590000111
Figure BDA0003131226590000121
Fig. 4 and table 3 show confidence values of each interval and each interval of power profit and loss of a certain region in 2021 under the planning scheme, the overall distribution is between 2027.9MW and 11740MW, and power surplus exists. Under the condition that the new energy power generation power and the hydroelectric power reach the set power generation capacity, the confidence coefficient of the system power profit and loss between 4146.56MW and 4353.89MW is high and can reach 4.12% at most.
TABLE 4 planning target year electric balance Meter (MW)
Figure BDA0003131226590000122
Figure BDA0003131226590000131
The planned target annual power balance table adopting the conventional table method is shown in table 4, and shows the planned target annual load development condition, the standby condition, the required installed capacity, the possible installed capacity, the new energy power generation condition, the power profit and loss and the like. The new energy power supply is divided into four conditions of not participating in power balance, and 10%, 30% and 50% of generated power participate in power balance. As can be seen from comparison between fig. 4 and table 4, compared with a table method in which a new energy power supply is considered to be added to target annual power balance in a certain proportion, the method for estimating the profit and loss of the planned target annual power based on the blind number theory can fully consider the uncertainty of the output power of the new energy power supply and the power load, and after various power supply output power intervals and confidence degrees of the intervals are analyzed and calculated, a value range of the profit and loss of the power and the confidence degrees of the intervals are obtained, the contained information is more detailed, more information can be provided for planners to perform planning design of related power systems, and the method is helpful for arranging inter-provincial power electric quantity trading plans, adjusting power supply structures and the like.
If the situation that the target annual thermal power and electric power installation is not changed is considered, a result of planning the target annual electric power profit and loss based on the blind number theory is shown in fig. 5. Fig. 6 shows the influence of the power profit and loss on the power supply planning scheme in the target year when the speed of the hydroelectric power is different in the current year without considering the power generation of the new energy power supply.
As can be seen from fig. 5, under the condition that the hydroelectric power installation is unchanged in 2019, the surplus power of the system is larger than zero and is distributed between 619MW and 10332 MW; it can be known from fig. 6 that when the new energy power generation capacity is not considered, the water, power and thermal power capacity increase rate and the target annual power profit and loss are basically in a proportional relationship, and the power surplus of the regional target annual power supply planning scheme is increased along with the increase of the speed increase of the water, power and thermal power installation. Compared with a new energy power supply, the conventional hydroelectric and thermal power supply which is controllable and relatively stable in power generation still dominates in the calculation of the profit and loss of the power in the planned target year in order to maintain the reliability of system power generation, and if the confidence capacity of the new energy power supply is effectively and reasonably utilized, the starting of hydroelectric and thermal power can be reduced.
While the present invention has been described in detail and with reference to specific embodiments thereof, it will be apparent to one skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope thereof as defined in the appended claims.

Claims (1)

1. A planning target annual power profit and loss assessment method based on a blind number theory is characterized by comprising the following contents:
1) establishing blind digital model of uncertain factors
The new energy power supply and the power load both have randomness and uncertainty, the values of the new energy power supply and the power load in a target year are not always fixed to a certain value but are frequently distributed in a plurality of value intervals in a fluctuating way, the confidence values in the intervals are not completely the same, the blind number theory can estimate the probability of the output power of the new energy power supply or the occurrence probability of the load in each interval in the range through the actual situation and establish a blind number model, so that the 'strong' uncertainty of each factor is converted into the 'weak' uncertainty of the confidence interval and the confidence coefficient,
establishing a blind number model of uncertainty information as follows:
dividing blind number interval of uncertainty variable
Let alpha be [0,1 ]]Wherein
Figure FDA0003131226580000011
Then call
Figure FDA0003131226580000012
Is a blind number, and the expression is formula (1):
Figure FDA0003131226580000013
wherein x is a variable, xiIs a confidence interval, aiIs a confidence interval xiConfidence of, α is
Figure FDA0003131226580000014
M is the total confidence of
Figure FDA0003131226580000015
The order of (a);
② constructing judgment matrix
The judgment matrix analysis method is used for judging and determining the weight coefficient, a decision analysis method combining expert experience and quantitative analysis is applied to a blind number theory, a judgment matrix is obtained by comparing change intervals of all factors pairwise, the weight coefficient of each factor is calculated, and the confidence value of each interval is obtained, compared with a 9-scale method, the relative weight value obtained by adopting an 9/9-9/1 scale method is good in difference, and the judgment of a person is more accurate;
determining confidence value of blind number interval
The expert compares every two intervals by using a 9/9-9/1 scaling method, determines the element value of a judgment matrix according to the confidence value of each interval where the load is located, then calculates the maximum eigenvalue of the judgment matrix and the corresponding eigenvector thereof, carries out consistency check on the judgment matrix, considers that the consistency of the judgment matrix can be accepted if the consistency index CR <0.1, and then carries out normalization on the eigenvector to obtain the weight coefficient of the solved element, namely the confidence value of each blind number interval in the blind number model;
2) establishing a blind number model for planning annual electric power profit and loss of target based on blind number theory
Establishing blind number models of various power supplies and loads according to a blind number theory:
establishing a load blind number model:
because the load change has randomness, the annual average increase rate of the power demand is determined according to three increase modes of high speed, medium speed and low speed, namely the high load is increased by 10.5 percent, the medium load is increased by 9 percent and the low load is increased by 9 percentThe load acceleration is 7.5%, a three-order blind number model of the load is established, the confidence coefficient of each interval is obtained by a judgment matrix method, a 9/9-9/1 scale method is adopted to replace a 9 scale method to obtain a judgment matrix, the confidence value of each change interval of the load is obtained according to the knowledge of matrix theory, and the three-order blind number model of the load is obtained as formula (2) and is used for obtaining a judgment matrix J of the confidence coefficient of each intervalpIs represented by the formula (3):
Figure FDA0003131226580000021
Figure FDA0003131226580000022
in the formula, P0The load maximum value of the current year; alpha is alphal.1-αl.3The confidence value corresponding to each change interval of the load is obtained; x is the number oflAs load confidence interval, JpA judgment matrix of load blind numbers;
second, establishing wind power blind digital model
Wind power has randomness and intermittent uncertainty, and long-term fluctuation characteristics research according to output power of a new energy power supply shows that the maximum output power value of a single wind power plant can reach the rated installed capacity value, but the wind power plants with wider coverage area and more gradually participate in polymerization grid connection, so that the concurrency rate of large-scale wind power is gradually lower than 100% along with the increase of the installed capacity, namely the total power value of large-scale wind power generation is lower than the total installed capacity value, therefore, a load blind number model is analogized, the total fluctuation variation range of the wind power is determined according to a wind power continuous power curve value, power intervals are divided equally according to quartering, and the ratio of the length of corresponding continuous time in each interval to the total time is used as the confidence coefficient of each interval, so that a wind power fourth-order blind number model is established as formula (4) and formula (5):
Figure FDA0003131226580000023
PΔ＝PW.max-PW.min (5)
in the formula, PW.max、PW.minMaximum and minimum power, x, for wind power generationwFor wind power confidence interval, PΔFor the total fluctuation range of wind power, alphaw.1,αw.2,...,αw.4The confidence coefficient corresponding to each change interval of the wind power is obtained;
establishing photovoltaic blind digital model
Similar to wind power generation, the photovoltaic power generation power also has the characteristics of randomness and intermittence, the maximum power generation power of a single photovoltaic power station can reach the rated capacity of the single photovoltaic power station, the maximum output power value of a large-scale photovoltaic power station is smaller than the total installed capacity after the large-scale photovoltaic power station is connected into a power grid, and the minimum output power value of the photovoltaic power station is 0 because the photovoltaic power station does not generate power at night, so that an output power long-term fluctuation characteristic curve is obtained by predicting based on a set photovoltaic planning construction capacity new energy power generation power long-term fluctuation characteristic prediction method in a similar way to a wind power blind number model, each change interval and a confidence value are obtained, and the photovoltaic four-order blind number model is established as the formula (6):
Figure FDA0003131226580000031
in the formula, PPV.max、PPV.minThe maximum value and the minimum value of the photovoltaic power generation power are obtained; x is the number ofwIs a wind power confidence interval; alpha pv.1, alpha pv.2, and alpha pv.4 are confidence degrees corresponding to each photovoltaic power generation interval;
establishing blind digital model of thermal power, water and electricity
The hydroelectric power is a conventional adjustable power supply, the output power of the hydroelectric power is influenced by seasonality, the hydroelectric power has a dry season, a flat season and a rich season, and the thermal power has a heating period and a non-heating period, so that the condition of the output power of the hydroelectric power is judged according to the condition of the month of year maximum load according to data statistics, and the power generation models representing the hydroelectric power and the thermal power according to a blind number form are respectively formula (7) and formula (8):
Figure FDA0003131226580000032
Figure FDA0003131226580000033
in the formula, xth、xtdRespectively are thermal power and water confidence intervals; alpha is alphath、αtdRespectively determining the utilization rates of thermal power and hydropower according to the actual conditions of the thermal power and the hydropower in the month of the maximum load; p is a radical ofth、ptdRespectively outputting power for thermal power and hydropower;
fifthly, establishing an expression of the power profit and loss balance blind number model
And (3) carrying out blind number operation based on the blind number models of the various power supplies, wherein the expression of the electric power profit-loss balance blind number model is shown as an expression (9):
Figure FDA0003131226580000034
wherein, r represents the spare capacity,
Figure FDA0003131226580000035
respectively represent the blind number model of the wind, light, fire and water power source energy utilization capacity.
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