CN114943471A - Low-carbon index system of power system and comprehensive evaluation method - Google Patents
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Abstract
The invention relates to a low-carbon index system of a power system and a comprehensive evaluation method, wherein the method comprises the following steps: a. establishing a low-carbon evaluation index system of the power system, and establishing an evaluation model; b. on the basis of a low-carbon evaluation index system of the power system, a quantitative evaluation method needs to be established, so that the low-carbon level of the power system is comprehensively evaluated in multiple dimensions on the basis of data, and the specific evaluation method comprises the following steps: (1) preprocessing data; (2) analyzing the correlation; (3) the indicators are weighted. The low-carbon development level of the power system plays an important role in realizing the double-carbon target, and a comprehensive evaluation index and an analysis and evaluation model are required to be designed to realize scientific analysis and evaluation of the low-carbon level of the power system. The low-carbon evaluation method designs a low-carbon evaluation index system covering all links of the power system, realizes the analysis and evaluation of the low-carbon level through data preprocessing, correlation analysis, index empowerment and the like, and verifies the feasibility of the method through example analysis.
Description
Technical Field
The invention relates to a low-carbon index system of a power system and a comprehensive evaluation method, and belongs to the technical field of power systems.
Background
The core for realizing the aim of 'double carbon' is to control carbon emission, energy combustion is a main carbon dioxide emission source in China, and accounts for about 87% of all carbon dioxide emission, and the emission of the power industry accounts for about 40% of the emission of the energy industry. The dual carbon target puts higher requirements on energy and power consumption total amount control, utilization efficiency improvement, structure optimization and the like. The power industry faces huge development pressure in low carbon wave tide and has great potential of emission reduction. In recent years, key units in the power industry actively respond to national emission reduction policies, low-carbon development plans and comprehensive working schemes are formulated, and remarkable low-carbon benefits are obtained. The development of low-carbon power relates to a plurality of factors, so that the low-carbon level of a power grid cannot be effectively mastered by only depending on a large amount of data statistics. In order to objectively reflect the electric-carbon correlation and show the carbon peak carbon neutralization process of the power system, a low-carbon evaluation index system and a comprehensive evaluation model need to be constructed urgently to realize scientific analysis and evaluation of the low-carbon level of the power system.
The low-carbon evaluation index system is a means and a tool for objectively and comprehensively evaluating the low-carbon development degree of the power system, and most of domestic and foreign scholars begin with the aspects of population, energy, economy, technology, policy and the like at present to analyze the low-carbon level of a city or a region. The research on the design and evaluation model of the index system specially aiming at the low-carbon evaluation of the power system has fewer documents. The Yandrong university plane is designed with low carbon power network evaluating index system and quantizing evaluating method. Aiming at the problem of power distribution network scheduling evaluation, the national grid company Li Chen designs a comprehensive evaluation model and method based on an analytic hierarchy process and an entropy weight method, thereby realizing scientific and objective evaluation of power distribution management services. The method is characterized in that the low-carbon operation fuzzy comprehensive evaluation method of the power distribution network is established by the university of south China, and the Wenyang research and the like around the aspects of low-carbon power supply, low-loss power grid, peak shifting and valley filling, terminal emission reduction and the like. In the literature, a low-carbon evaluation index system is mainly constructed by using an analytic hierarchy process, the established index system is mostly based on a theoretical angle, and the selected indexes are numerous and have large differences. At present, a power grid enterprise applies relevant carbon-electricity indexes such as a power carbon emission index system, a carbon effect code and the like, but only can reflect a local state of carbon-electricity cooperation from a single dimension, and cannot reflect a full-link state of the carbon-electricity cooperation. A reasonable and scientific carbon-electricity index system needs to be established urgently, carbon peak reaching conditions of the power industry in China are reflected timely and accurately, carbon emission states of the power industry are analyzed and early warned through index monitoring, and reference is provided for formulating carbon emission reduction strategies.
Aiming at the problem of low carbon analysis and evaluation of the power system, the invention constructs a low carbon evaluation comprehensive index system covering all links of power production, transmission and distribution, consumption, scheduling transaction and the like and designs a comprehensive evaluation model comprising the steps of data preprocessing, correlation analysis, index empowerment and the like to realize the comprehensive evaluation of the low carbon index. The low-carbon evaluation index system and the evaluation model of the power system can reflect the current situation of carbon emission and the potential of carbon emission, realize monitoring and evaluation of carbon-electricity cooperative state, provide decision support for evaluating the potential of carbon emission reduction and making a carbon emission reduction strategy for power enterprises, further promote clean energy production, electrified energy consumption and high-efficiency energy utilization, and promote the power energy industry to reach the peak as early as possible.
Disclosure of Invention
The invention designs a low-carbon evaluation comprehensive index system for a power system, which not only covers all links of power production, transmission and distribution, consumption, scheduling transaction and the like, but also realizes comprehensive evaluation of low-carbon indexes by a comprehensive evaluation model comprising the steps of data preprocessing, correlation analysis, index empowerment and the like, and a comprehensive evaluation method.
The invention is realized by the following technical scheme: a low-carbon index system of a power system and a comprehensive evaluation method are disclosed, the method comprises the following steps:
a. establishing a low-carbon evaluation index system of the power system, and establishing an evaluation model;
b. on the basis of a low-carbon evaluation index system of the power system, a quantitative evaluation method needs to be established, so that the low-carbon level of the power system is comprehensively evaluated in multiple dimensions on the basis of data, and the specific evaluation method comprises the following steps:
(1) preprocessing data;
(2) analyzing the correlation;
(3) the indicators are weighted.
Preferably, the method comprises the following steps: the low-carbon evaluation index system of the power system in the step 1 consists of 5 parts, namely a macroscopic index, a power generation link, a power transmission and distribution link, an electric energy consumption link and a scheduling transaction link; the macroscopic index is used for evaluating the overall carbon emission characteristics of the power system, and comprises the carbon emission characteristics of the power system and the social overall carbon emission characteristics, wherein the carbon emission intensity of the power system is evaluated from two dimensions of the emission total amount and the emission intensity;
the low-carbon indexes of the system mainly comprise clean energy installation ratio, clean energy consumption ratio, wind-solar power generation installation ratio, new energy power prediction precision, coal-electricity flexibility modification level, power generation standard coal consumption of a thermal power plant and the like;
the power transmission and distribution link undertakes the functions of low-carbon power transmission and distribution, and low-carbon evaluation indexes of the power transmission and distribution link comprise trans-regional power transmission capacity, utilization efficiency of a power transmission channel, specific gravity of electric quantity of transmitted clean energy, line loss rate of a power transmission and distribution line, full-ring energy-saving level of power transmission and distribution and the like;
the electric energy consumption link is a driving force and an indirect source of carbon emission of the power system, and low-carbon development indexes of the electric energy consumption link are evaluated mainly from dimensions such as energy consumption control, energy efficiency level, electric energy substitution level and the like;
the dispatching and trading link is a technology and a market means for dispatching and regulating the low-carbon power, and mainly comprises indexes such as system regulating capacity, load side peak regulation capacity, accurate power load response capacity, carbon trading level, new energy dispatching risk level and the like.
Preferably, the method comprises the following steps: the energy consumption control aspect mainly comprises indexes such as total energy consumption, total power consumption of the whole society, electricity consumption of residents per capita, energy consumption intensity, fossil energy consumption proportion and the like, the energy efficiency level aspect mainly comprises indexes such as unit GDP energy consumption, unit GDP electricity consumption, unit GDP carbon emission, industrial ten-thousand-yuan added value energy consumption, high energy consumption industry energy efficiency level, energy processing conversion efficiency and the like, and the electric energy substitution level aspect mainly comprises indexes such as electric energy accounting for terminal energy consumption proportion, accumulated substitution electric quantity, annual substitution electric quantity, "coal-to-electricity" number of households, charging and changing station number, port shore electricity, point kilns, electric boilers and the like.
Preferably, the method comprises the following steps: the data preprocessing in the step 2 specifically comprises the following steps:
(1) index type reconciliation
The indexes in the low-carbon index system include three types: 1) positive type index: the larger the value is, the better the value is; 2) negative index: the smaller the value, the better; 3) moderate index: preferably, a proper intermediate value is selected, and the negative type index and the moderate type index need to be converted into a positive type index through a conforming treatment;
for the negative index, the formula (1) or (2) is used for conversion,
x * m-x, M being the maximum upper bound (1)
x * ＝1/x，x＞0 (2)
Wherein M is the maximum upper bound of the index x;
adopting formula (3) conversion for moderate index;
wherein [ q ] is 1 ,q 2 ]Is an optimal stable interval; n is a radical of 1 、N 2 Respectively x allowed upper and lower bounds,
by the above treatment, both the negative type index and the moderate type index can be converted into the positive type index.
(2) Dimensionless treatment of indexes
The non-dimensionalization of the index is realized by eliminating the influence of the original dimension of the index through mathematical change to standardize and standardize the index data by adopting a standardized processing method and an extreme value processing method,
1) standardized processing method
Wherein
Is a dimensionless index sample value,
s j the average value and the mean square error of the observation sample of the jth index are respectively, and the sample value obtained by standardization has a positive value and a negative value, so that the method is not suitable for occasions requiring data larger than 0;
2) extreme value processing method
Wherein N is 1j 、N 2j Are each x j Observing the maximum and minimum values of the sample, as known by the formula
Preferably, the method comprises the following steps: the correlation analysis in the step 2 specifically comprises the following steps:
based on the comprehensive principle, the low-carbon evaluation comprises a plurality of indexes, correlation and overlapping exist between different indexes, and a dimensionality reduction and simplification index system is needed, so that the calculation process is simplified, and the evaluation result is optimized;
the principal component analysis method comprises the following steps:
1) setting a certain level of indexes to have n second-level indexes, wherein each second-level index has m data samples, and a data sample matrix which can be obtained is as follows:
X＝(X ij ) m×n ，i＝1，2，…m；j＝1，2，…n (6)
wherein X ij The ith data of the jth index;
2) and solving a covariance matrix of the samples, wherein the covariance matrix is used for reflecting the correlation among index data:
3) finding a characteristic root λ i And arranged from large to small, thereby reflecting the influence of each principal component, principal component Z i The contribution rate of (A) is:
the cumulative contribution rate of the first i principal components is
And selecting the main component corresponding to the characteristic value with the accumulated contribution rate of 85-95%.
4) And obtaining sample data values corresponding to the principal components through the feature vectors corresponding to the feature values:
the system is more simplified by screening indexes, the correlation among the indexes is greatly weakened, and the evaluation process is simpler and clearer.
Preferably, the method comprises the following steps: the step 2 of assigning the weight by the index specifically comprises the following steps:
in order to reflect the contribution degree of different indexes to the evaluation result, each index needs to be endowed with a proper weight coefficient. The first-level index cannot obtain direct data support, and a subjective weighting method based on G-1 is adopted. In order to fully utilize the comparative information brought by the discrete degree of each sample data, a comprehensive weighting method combining G-1 and an entropy weight method is adopted to weight the second-level index and the third-level index.
(1) G-1 method
Sorting the indexes from large to small according to importance, and judging the importance degree of adjacent indexes:
θ k the importance degree of the kth evaluation index in the indexes is calculated, and then the weight of each index is calculated by the following formula:
(2) entropy weight method
The entropy weight method determines a weight coefficient from the information content of the index observation. If the entropy value is small, it indicates that the variation degree of the index data is large, and the weight coefficient of the index should be increased. The entropy weight method comprises the following calculation steps:
1) calculating the characteristic proportion of the ith data under the j index:
and calculating an entropy value:
2) calculating the difference coefficient:
g i ＝1-e j (16)
3) determining a weight coefficient:
(3) comprehensive empowerment
The integrated weighting method formed by summarizing the above two methods is shown in equation (18):
wherein k is 1 ，k 2 Is undetermined constant, satisfies k 1 >0，k 2 >0 and k 1 +k 2 ＝1。
The low-carbon index system and the comprehensive evaluation method of the power system designed by the invention have the following characteristics:
1) scientifically: each index can reflect the low-carbon development condition of the power system, and meanwhile, the constructed index can be analyzed and checked by a scientific method;
2) the comprehensiveness: covering all links of the power system, and considering new problems in a development mode;
3) the practicability is as follows: the method has operability and scalability, avoids the bulkiness and the complexity, does not consider the index without data source, and obtains a clear analysis conclusion;
4) simplicity: removing redundancy according to the correlation, and simplifying an index system;
5) subjective and objective combination: qualitative and quantitative combination (quantitative is dominant), subjective and objective combination (objective is dominant);
6) development property: the index system has a structure convenient for expansion and is suitable for the evolution and development in a new period.
Drawings
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a line graph showing the comprehensive evaluation of each year;
fig. 3 is a radar map of low carbon development conditions in each horizontal year.
Detailed Description
The invention will be described in detail below with reference to the following drawings: as shown in fig. 1, a low-carbon index system of a power system and a comprehensive evaluation method thereof include the following steps:
a. establishing a low-carbon evaluation index system of the power system, and establishing an evaluation model;
b. on the basis of a low-carbon evaluation index system of the power system, a quantitative evaluation method needs to be established, so that the low-carbon level of the power system is comprehensively evaluated in multiple dimensions on the basis of data, and the specific evaluation method comprises the following steps:
(1) preprocessing data;
(2) analyzing the correlation;
(3) the index is weighted.
The low-carbon evaluation index system of the power system in the step 1 consists of 5 parts, namely a macroscopic index, a power generation link, a power transmission and distribution link, an electric energy consumption link and a scheduling transaction link; the macroscopic index is used for evaluating the overall carbon emission characteristics of the power system, and comprises the carbon emission characteristics of the power system and the social overall carbon emission characteristics, wherein the carbon emission intensity of the power system is evaluated from two dimensions of the emission total amount and the emission intensity;
the low-carbon indexes of the system mainly comprise clean energy installation ratio, clean energy consumption ratio, wind-solar power generation installation ratio, new energy power prediction precision, coal-electricity flexibility modification level, power generation standard coal consumption of a thermal power plant and the like;
the power transmission and distribution link undertakes the functions of low-carbon power transmission and distribution, and low-carbon evaluation indexes of the power transmission and distribution link comprise trans-regional power transmission capacity, utilization efficiency of a power transmission channel, specific gravity of electric quantity of transmitted clean energy, line loss rate of a power transmission and distribution line, full-ring energy-saving level of power transmission and distribution and the like;
the electric energy consumption link is a driving force and an indirect source of carbon emission of the power system, and low-carbon development indexes of the electric energy consumption link are evaluated mainly from dimensions such as energy consumption control, energy efficiency level, electric energy substitution level and the like;
the dispatching and trading link is a technology and a market means for dispatching and regulating the low-carbon power, and mainly comprises indexes such as system regulating capacity, load side peak regulation capacity, accurate power load response capacity, carbon trading level, new energy dispatching risk level and the like.
Preferably, the method comprises the following steps: the energy consumption control aspect mainly comprises indexes such as total energy consumption, total power consumption of the whole society, electricity consumption of residents per capita, energy consumption intensity, fossil energy consumption proportion and the like, the energy efficiency level aspect mainly comprises indexes such as unit GDP energy consumption, unit GDP electricity consumption, unit GDP carbon emission, industrial ten-thousand-yuan added value energy consumption, high energy consumption industry energy efficiency level, energy processing conversion efficiency and the like, and the electric energy substitution level aspect mainly comprises indexes such as electric energy accounting for terminal energy consumption proportion, accumulated substitution electric quantity, annual substitution electric quantity, "coal-to-electricity" number of households, charging and changing station number, port shore electricity, point kilns, electric boilers and the like.
The low-carbon index system comprises maximum, minimum and intermediate indexes, and the optimized value direction of the optimized structure needs to be determined through the consistency processing; the unit and the magnitude of each index are different, and data needs to be subjected to dimensionless processing. The consistency processing and the dimensionless processing are collectively called as a data preprocessing stage, and the unified data format is preprocessed through the data to prepare for subsequent analysis and evaluation. The data preprocessing in the step 2 specifically comprises the following steps:
(1) index type reconciliation
The indexes in the low-carbon index system include three types: 1) positive type index: the larger the value, the better; 2) negative index: the smaller the value, the better; 3) moderate index: preferably, a proper intermediate value is selected, and the negative type index and the moderate type index need to be converted into a positive type index through a conforming treatment;
for the negative index, the formula (1) or (2) is used for conversion,
x * m-x, M being the maximum upper bound (1)
x * ＝1/x，x＞0 (2)
Wherein M is the maximum upper bound of the index x;
for the moderate index, the formula (3) is adopted for conversion;
wherein [ q ] is 1 ,q 2 ]The optimal stable interval is set; n is a radical of hydrogen 1 、N 2 Respectively x allowed upper and lower bounds,
by the above treatment, both the negative type index and the moderate type index can be converted into the positive type index.
(2) Dimensionless treatment of indexes
The non-dimensionalization of the index is realized by eliminating the influence of the original dimension of the index through mathematical change to standardize and standardize the index data by adopting a standardized processing method and an extreme value processing method,
1) standardized processing method
Wherein
Is a dimensionless index sample value,
s j the average value and the mean square error of the observation sample of the jth index are respectively, and the sample value obtained by standardization has a positive value and a negative value, so that the method is not suitable for occasions requiring data larger than 0;
2) extreme value processing method
Wherein N is 1j 、N 2j Are respectively x j Observing the maximum and minimum values of the sample, known as formula
Preferably, the method comprises the following steps: the correlation analysis in the step 2 specifically comprises the following steps:
based on the comprehensive principle, the low-carbon evaluation comprises a plurality of indexes, correlation and overlapping exist between different indexes, and a dimensionality reduction and simplification index system is needed, so that the calculation process is simplified, and the evaluation result is optimized;
the principal component analysis method comprises the following steps:
1) setting a certain level of indexes to have n second-level indexes, wherein each second-level index has m data samples, and a data sample matrix which can be obtained is as follows:
X＝(X ij ) m×n ，i＝1，2，…m；j＝1，2，…n (6)
wherein X ij The ith data of the jth index;
2) and solving a covariance matrix of the samples, wherein the covariance matrix is used for reflecting the correlation among index data:
3) finding a characteristic root λ i And arranged from large to small, thereby reflecting the influence of each principal component, principal component Z i The contribution rate of (A) is:
the cumulative contribution rate of the first i principal components is
And selecting the main component corresponding to the characteristic value with the accumulated contribution rate of 85-95%.
4) And obtaining sample data values corresponding to the principal components through the feature vectors corresponding to the feature values:
the system is more simplified by screening indexes, the correlation among the indexes is greatly weakened, and the evaluation process is simpler and clearer.
Preferably, the method comprises the following steps: the step 2 of assigning the weight by the index specifically comprises the following steps:
in order to reflect the contribution degree of different indexes to the evaluation result, each index needs to be endowed with a proper weight coefficient. The first-level index cannot obtain direct data support, and a subjective weighting method based on G-1 is adopted. In order to fully utilize the comparative information brought by the discrete degree of each sample data, a comprehensive weighting method combining G-1 and an entropy weight method is adopted to weight the second-level index and the third-level index.
(1) G-1 method
Sorting the indexes according to importance from large to small, and judging the importance degree of adjacent indexes:
θ k the importance degree of the kth evaluation index in the indexes is calculated, and then the weight of each index is calculated by the following formula:
(2) entropy weight method
The entropy weight method determines a weight coefficient from the information content of the index observation. If the entropy value is small, the index data has a large variation degree, and the weight coefficient of the index should be increased. The entropy weight method comprises the following calculation steps:
1) calculating the characteristic proportion of the ith data under the j index:
and calculating an entropy value:
2) calculating the difference coefficient:
g i ＝1-e j (16)
3) determining a weight coefficient:
(3) comprehensive empowerment
The integrated weighting method formed by summarizing the above two methods is shown in equation (18):
wherein k is 1 ，k 2 Is undetermined constant, satisfies k 1 >0，k 2 >0 and k 1 +k 2 ＝1。
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
As shown in fig. 1, the low-carbon evaluation index system of the power system is constructed from dimensions such as macroscopic index, power generation, power transmission and distribution, electric energy consumption, scheduling transaction and the like, wherein each module has the following functions:
(1) macroscopic index
The macroscopic indexes are used for evaluating the overall carbon emission characteristics of the power system, and comprise the carbon emission characteristics of the power system and the social overall carbon emission characteristics, wherein the carbon emission intensity of the power system is evaluated from two dimensions of the emission total amount and the emission intensity.
(2) Power generation link
The power generation link is a direct source of carbon emission of the power system, and low-carbon indexes of the power system mainly comprise clean energy installation proportion, clean energy consumption proportion, wind-solar power generation installation proportion, new energy power prediction precision, coal-electricity flexibility improvement level, power generation standard coal consumption of a thermal power plant and the like.
(3) Power transmission and distribution link
The power transmission and distribution link undertakes the functions of low-carbon power transmission and distribution, and low-carbon evaluation indexes of the low-carbon power transmission and distribution link comprise trans-regional power transmission capacity, utilization efficiency of power transmission channels, specific gravity of electric quantity of clean energy transmission, line loss rate of power transmission and distribution lines, full-ring energy-saving level of power transmission and distribution and the like.
(4) Electric energy consumption link
The electric energy consumption link is a driving force and an indirect source of carbon emission of the electric power system, and low-carbon development indexes of the electric energy consumption link are evaluated mainly from dimensions such as energy consumption control, energy efficiency level, electric energy substitution level and the like.
1) The energy consumption control aspect mainly comprises indexes of total energy consumption, total electricity consumption of the whole society, electricity consumption of residents per capita, energy consumption intensity, fossil energy consumption proportion and the like.
2) And the energy efficiency level mainly comprises indexes such as unit GDP energy consumption, unit GDP power consumption, unit GDP carbon emission, industrial ten-thousand-yuan added value energy consumption, high-energy-consumption industrial energy efficiency level, energy processing conversion efficiency and the like.
3) The electric energy substitution level mainly comprises indexes such as electric energy consumption proportion of terminal energy consumption, accumulated substitution electric quantity, annual substitution electric quantity, number of 'coal-to-electricity' households, number of charging and exchanging stations, and number of port shore power, point kilns, electric boilers and the like.
(5) Scheduling transaction links
The dispatching and trading link is a technology and a market means for dispatching and regulating the low-carbon power, and mainly comprises indexes such as system regulating capacity, load side peak regulation capacity, accurate power load response capacity, carbon trading level, new energy dispatching risk level and the like.
As shown in table 1 below, the low carbon development level in 5 years was evaluated based on the operating data of a certain power saving system.
TABLE 1 5 years of operating data of electric power system in a certain province
Each index is first pre-processed to convert all indices into positive indices so that the larger the expectation, the better. Since the number of indexes is small, the correlation is weak, and correlation processing is not performed. And carrying out subjective weighting and objective weighting on each index through a G-1 method and an entropy weight method to obtain each index weight coefficient.
The results of the comprehensive evaluation of the low carbon evolution status of the power system in each year can be obtained as shown in table 2 below, along with the weight coefficients of the indicators.
TABLE 2 evaluation index weight coefficient of low carbon power grid
And (3) designating the maximum value of each index in each horizontal year as a reference, extending each index value to 0-1, and showing the low carbon condition in each year according to the result.
As can be seen from fig. 2, the low carbon level of the power system in the year of 5 in province generally increases, and the increase is large. As can be seen from fig. 3, the installed proportion of the low-carbon power supply increases year by year in each horizontal year, and the power level outside the receiving area increases rapidly. The comprehensive grid loss rate level of each horizontal year is stable, and the fluctuation of the comprehensive grid loss rate level obviously influences the power grid evaluation result due to the large weight coefficient of the comprehensive grid loss rate level. The method has the advantages that the accepting force of clean energy resources is continuously increased, the power transmission and distribution network and the electric energy scheduling strategy are optimized, the loss rate and the capacity-to-load ratio of the network are improved, and the low-carbon development level of the electric power system is continuously improved.
The low carbon development of the power system plays a crucial role in achieving the dual carbon goal. The method constructs a low-carbon evaluation index system covering the whole ring of power system transmission and distribution, dispatching and trading and the like, designs a comprehensive evaluation model comprising the steps of data preprocessing, correlation analysis, index empowerment and the like to realize the comprehensive evaluation of the low-carbon index, and verifies the feasibility of the index system and the evaluation method by taking data of a certain province of 5 years as an example. The low-carbon evaluation index system and the evaluation model of the power system provide support for evaluating carbon emission reduction potential and making carbon emission reduction strategies for power enterprises, and provide references for performance assessment, development planning, government decision and the like of the low-carbon power system.
While the invention has been described with reference to specific preferred embodiments, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (6)
1. A low-carbon index system of a power system and a comprehensive evaluation method are characterized in that: the method comprises the following steps:
a. establishing a low-carbon evaluation index system of the power system, and establishing an evaluation model;
b. on the basis of a low-carbon evaluation index system of the power system, a quantitative evaluation method needs to be established, so that the low-carbon level of the power system is comprehensively evaluated in multiple dimensions on the basis of data, and the specific evaluation method comprises the following steps:
(1) preprocessing data;
(2) analyzing the correlation;
(3) the indicators are weighted.
2. The power system low-carbon index system and the comprehensive evaluation method according to claim 1 are characterized in that: the low-carbon evaluation index system of the power system in the step 1 consists of 5 parts, namely a macroscopic index, a power generation link, a power transmission and distribution link, an electric energy consumption link and a scheduling transaction link; the macroscopic index is used for evaluating the overall carbon emission characteristics of the power system, and comprises the carbon emission characteristics of the power system and the social overall carbon emission characteristics, wherein the carbon emission intensity of the power system is evaluated from two dimensions of the emission total amount and the emission intensity;
the low-carbon indexes of the system mainly comprise a clean energy installation ratio, a clean energy consumption ratio, a wind-solar power generation installation ratio, new energy power prediction precision, coal-electricity flexibility improvement level and power generation standard coal consumption of a thermal power plant;
the power transmission and distribution link undertakes the functions of low-carbon power transmission and distribution, and low-carbon evaluation indexes of the low-carbon power transmission and distribution link comprise trans-regional power transmission capacity, utilization efficiency of a power transmission channel, specific gravity of electric quantity of transmitted clean energy, line loss rate of a power transmission and distribution line and full-ring energy-saving level of power transmission and distribution;
the electric energy consumption link is a driving force and an indirect source of the carbon emission of the power system, and low-carbon development indexes of the electric energy consumption link are evaluated mainly from energy consumption control, energy efficiency level and electric energy substitution level dimension;
the scheduling transaction link is a technology and a market means for scheduling and adjusting the low-carbon power, and mainly comprises system adjusting capacity, load side peak load adjusting capacity, accurate power load response capacity, carbon transaction level and new energy scheduling risk level indexes.
3. The power system low-carbon index system and the comprehensive evaluation method according to claim 2, wherein the evaluation method comprises the following steps: in the aspect of energy consumption control, the energy consumption control mainly comprises total energy consumption, total power consumption of the whole society, electricity consumption of residents per capita, energy consumption intensity, fossil energy consumption proportion indexes, and in the aspect of energy efficiency level, the energy consumption of unit GDP, unit GDP electricity consumption, unit GDP carbon emission, industrial ten thousand yuan added value energy consumption, high energy consumption industry energy efficiency level, energy processing conversion efficiency indexes, and in the aspect of electric energy substitution level, the energy consumption control mainly comprises quantity indexes of electric energy accounting for terminal energy consumption proportion, accumulated substitution electric quantity, annual substitution electric quantity, coal-to-electricity house number, charging and exchanging station number, and harbor shore electricity, kiln ignition and electric boilers.
4. The power system low-carbon index system and the comprehensive evaluation method according to claim 2, characterized in that: the data preprocessing in the step 2 specifically comprises the following steps:
(1) index type reconciliation
The indexes in the low-carbon index system include three types: 1) positive type index: the larger the value is, the better the value is; 2) negative index: the smaller the value, the better; 3) moderate type index: preferably, a proper intermediate value is selected, and the negative type index and the moderate type index need to be converted into a positive type index through a conforming treatment;
for the negative index, the formula (1) or (2) is used for conversion,
x * m-x, M being the maximum upper bound (1)
x * ＝1/x，x＞0 (2)
Wherein M is the maximum upper bound of the index x;
adopting formula (3) conversion for moderate index;
wherein [ q ] is 1 ，q 2 ]The optimal stable interval is set; n is a radical of 1 、N 2 Respectively x allowed upper and lower bounds,
by the above treatment, both the negative type index and the moderate type index can be converted into the positive type index.
(2) Dimensionless treatment of indexes
The non-dimensionalization of the index is realized by eliminating the influence of the original dimension of the index through mathematical change to standardize and standardize the index data by adopting a standardized processing method and an extreme value processing method,
1) standardization treatment method
Wherein
For dimensionless index sample values，
s j The mean value and the mean square error of the observation sample of the j index are respectively,
the sample value obtained by standardization has positive and negative values, and is not suitable for the occasion that the data is required to be more than 0;
2) extreme value processing method
Wherein N is 1j 、N 2j Are respectively x j Observing the maximum and minimum values of the sample, as known by the formula
5. The power system low-carbon index system and the comprehensive evaluation method according to claim 1 are characterized in that: the correlation analysis in the step 2 specifically comprises the following steps:
based on the comprehensive principle, the low-carbon evaluation comprises a plurality of indexes, correlation and overlapping exist between different indexes, and a dimensionality reduction and simplification index system is needed, so that the calculation process is simplified, and the evaluation result is optimized;
the principal component analysis method comprises the following steps:
1) setting a certain level of indexes to have n second-level indexes, wherein each second-level index has m data samples, and a data sample matrix which can be obtained is as follows:
X＝(X ij ) m×n ，i＝1，2，…m；j＝1，2，…n (6)
wherein X ij The ith data of the jth index;
2) and solving a covariance matrix of the samples, wherein the covariance matrix is used for reflecting the correlation among index data:
3) finding a characteristic root λ i And arranged from large to small, thereby reflecting the influence of each principal component, principal component Z i The contribution rate of (A) is:
the cumulative contribution rate of the first i principal components is
And selecting the main component corresponding to the characteristic value with the accumulated contribution rate of 85-95%.
4) And obtaining sample data values corresponding to the principal components through the feature vectors corresponding to the feature values:
the system is more simplified by screening indexes, the correlation among the indexes is greatly weakened, and the evaluation process is simpler and clearer.
6. The power system low-carbon index system and the comprehensive evaluation method according to claim 1, characterized in that: the step 2 of assigning the weight by the index specifically comprises the following steps:
in order to reflect the contribution degree of different indexes to the evaluation result, each index needs to be endowed with a proper weight coefficient. The first-level index cannot obtain direct data support, and a subjective weighting method based on G-1 is adopted. In order to fully utilize the comparative information brought by the discrete degree of each sample data, a comprehensive weighting method combining G-1 and an entropy weight method is adopted to weight the second-level index and the third-level index.
(1) G-1 method
Sorting the indexes from large to small according to importance, and judging the importance degree of adjacent indexes:
θ k the importance degree of the k-th evaluation index in the index is calculated, and then the weight of each index is calculated by the following formula:
(2) entropy weight method
The entropy weight method determines a weight coefficient from the information content of the index observation. If the entropy value is small, the index data has a large variation degree, and the weight coefficient of the index should be increased. The entropy weight method comprises the following calculation steps:
1) calculating the characteristic proportion of the ith data under the j index:
and calculating an entropy value:
2) calculating the difference coefficient:
g j ＝1-e j (16)
3) determining a weight coefficient:
(3) comprehensive empowerment
The integrated weighting method formed by summarizing the above two methods is shown in equation (18):
wherein k is 1 ，k 2 Is undetermined constant, satisfies k 1 ＞0，k 2 > 0 and k 1 +k 2 ＝1。
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