CN116402377A - Comprehensive evaluation method, system, equipment and medium for energy cabin - Google Patents
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Abstract
The invention provides a comprehensive evaluation method, a system, equipment and a medium of an energy cabin, which comprise the following steps: calculating an evaluation index value of a bottom layer evaluation index corresponding to each upper-level evaluation index in a preset energy cabin comprehensive evaluation index system; fitting the bottom layer evaluation index values corresponding to the bottom layer evaluation indexes based on the trapezoidal fuzzy membership function to obtain membership values of the bottom layer evaluation index values; calculating the weight of the bottom evaluation index; calculating the evaluation scores of the upper evaluation indexes according to the weights and membership values of the bottom evaluation indexes corresponding to the upper evaluation indexes, and comprehensively evaluating the energy cabin based on the evaluation scores; according to the method, the evaluation scores of the upper-level evaluation indexes are calculated, the energy bin is reasonably and effectively comprehensively evaluated according to the evaluation scores, the qualitative-to-quantitative conversion of the indexes in the comprehensive evaluation index system of the energy bin is realized, and the problem that the indexes cannot be solved due to uncertainty in the evaluation is solved.
Description
Technical Field
The invention belongs to the field of comprehensive evaluation of energy, and particularly relates to a comprehensive evaluation method, system, equipment and medium of an energy cabin.
Background
Because of more energy supply devices, small capacity and distributed layout in the small-sized park, the comprehensive energy utilization rate is low, and the multi-energy complementation of the comprehensive energy system can not be realized. The energy cabin is a plug-and-play small energy supply system which is flexibly configured according to the energy demand of users and integrates equipment such as energy supply, energy storage, energy conversion and the like in a modularized manner, and can provide economic, reliable, efficient, flexible and low-carbon integrated comprehensive energy service for the users through multi-energy complementary optimization and energy efficiency management of wind, light, natural gas and other energy sources, and meanwhile, the integrated collaborative energy supply of regional energy sources can be realized through flexible combination, so that the problems of poor energy mutual economy, concentrated energy load regional, low comprehensive energy utilization rate and the like are solved.
However, the current energy cabins are still in a research and construction stage, the research data are less, a reasonable and effective comprehensive evaluation method is lacked, the types of the energy cabins are various, the energy forms are different, and the problems of one-sided performance, subjectivity, index complexity, more qualitative indexes and the like exist in the evaluation process.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a comprehensive evaluation method of an energy cabin, which comprises the following steps:
Calculating an evaluation index value of a bottom layer evaluation index corresponding to each upper-level evaluation index based on operation data of the energy cabin in a preset energy cabin comprehensive evaluation index system; the comprehensive evaluation index system of the energy cabin is a multi-layer evaluation system, and the multi-layer evaluation system comprises a plurality of upper-level evaluation indexes and a plurality of bottom-layer evaluation indexes arranged under each upper-level evaluation index;
removing all the bottom layer evaluation indexes based on the evaluation index values of all the bottom layer evaluation indexes and preset correlation coefficients, classifying the rest of the bottom layer evaluation indexes, fitting the bottom layer evaluation index values corresponding to the rest of the bottom layer evaluation indexes based on a trapezoidal fuzzy membership function, and obtaining membership values of all the bottom layer evaluation index values;
calculating the weight of each bottom layer evaluation index based on the upper level evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index value corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
and calculating the evaluation score of each upper evaluation index according to the weight of the bottom evaluation index corresponding to each upper evaluation index and the membership value, and comprehensively evaluating the energy cabin based on the evaluation score of each upper evaluation index.
Preferably, the upper evaluation index includes: safety index, reliability index, economical index, environment-friendly index and technical index;
wherein, the bottom evaluation index corresponding to the security index comprises: a heating safety index and a gas supply safety index; the bottom layer evaluation index corresponding to the reliability index comprises: a power supply reliability index, a cooling/heating reliability index, and a gas supply reliability index; the bottom evaluation indexes corresponding to the economic indexes comprise: an initial investment cost index, a maintenance cost index, an operation cost index, an investment recovery period index, an internal yield index and a financial net present value index; the bottom evaluation indexes corresponding to the environment-friendly indexes comprise: each pollutant discharge amount index and waste comprehensive utilization rate index; the bottom evaluation indexes corresponding to the technical indexes comprise: a primary energy utilization efficiency index, an energy self-utilization index, a device utilization index and a device service life index.
Preferably, the removing the bottom layer evaluation indexes based on the evaluation index values of the bottom layer evaluation indexes and the preset correlation coefficients includes:
and calculating the correlation coefficient between every two bottom layer evaluation indexes under each upper layer evaluation index based on the evaluation index value of each bottom layer evaluation index, removing one bottom layer evaluation index of every two bottom layer evaluation indexes when the absolute value of the correlation coefficient is larger than or equal to a preset correlation coefficient absolute value threshold value, and confirming the rest bottom layer evaluation indexes.
Preferably, the classifying the remaining bottom layer evaluation indexes, fitting the bottom layer evaluation index values corresponding to the remaining bottom layer evaluation indexes based on a trapezoidal fuzzy membership function, and obtaining membership values of the bottom layer evaluation index values includes:
classifying the residual bottom layer evaluation indexes according to index properties to obtain a plurality of evaluation index categories, wherein each evaluation index category comprises a plurality of bottom layer evaluation indexes;
fitting the bottom layer evaluation index values corresponding to the bottom layer evaluation indexes in each evaluation index category according to the trapezoidal fuzzy membership function corresponding to each evaluation index category to obtain the fuzzy membership value corresponding to each bottom layer evaluation index value;
preferably, the evaluation index category includes one or more of the following: positive evaluation index, reverse evaluation index and moderate evaluation index; the trapezoidal fuzzy membership function comprises one or more of the following: a rising half-trapezoid distribution function, a falling half-trapezoid distribution function and a trapezoid distribution function; the bottom layer evaluation index value corresponding to the positive evaluation index corresponds to an ascending half-trapezoid distribution function, the bottom layer evaluation index value corresponding to the inverse evaluation index corresponds to a descending half-trapezoid distribution function, and the bottom layer evaluation index value corresponding to the moderate evaluation index corresponds to a trapezoid distribution function.
Preferably, the calculating the weight of each bottom layer evaluation index based on the top layer evaluation index, the remaining bottom layer evaluation index and the bottom layer evaluation index value corresponding to the remaining bottom layer evaluation index of the energy cabin comprehensive evaluation index system includes:
calculating subjective weight of the bottom layer evaluation index relative to the upper layer evaluation index by adopting a network analytic hierarchy process based on the upper layer evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
calculating objective weight of the bottom layer evaluation index relative to the upper layer evaluation index by adopting an entropy method based on the upper layer evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
and carrying out combined weighting on the subjective weight and the objective weight, and calculating the weight of the bottom layer evaluation index relative to the upper evaluation index.
Preferably, the calculating, by using a network hierarchical analysis method, subjective weight of the bottom layer evaluation index relative to the upper layer evaluation index includes:
Constructing a control factor layer and a network index layer under each control factor by adopting a network analytic hierarchy process based on the upper evaluation index, the residual bottom evaluation index and the bottom evaluation index value corresponding to the residual bottom evaluation index of the energy cabin comprehensive evaluation index system;
constructing a super matrix and a weight matrix based on the control factor layer and the network index layer;
combining the super matrix with the weight matrix to obtain a normalized weighted super matrix;
and obtaining subjective weight of the bottom evaluation index relative to the upper evaluation index by calculating the normalized eigenvector of the weighted super matrix about the eigenvalue.
Preferably, the calculating the objective weight of the bottom layer evaluation index relative to the upper layer evaluation index by adopting an entropy method based on the upper layer evaluation index, the remaining bottom layer evaluation index and the bottom layer evaluation index value corresponding to the remaining bottom layer evaluation index of the energy cabin comprehensive evaluation index system comprises:
constructing a characteristic data matrix of the bottom layer evaluation index based on the upper level evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index value corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
Calculating the proportion value occupied by each characteristic value in the characteristic data matrix, and generating a proportion matrix; calculating entropy values of the bottom layer evaluation indexes based on the specific gravity values of the bottom layer evaluation indexes;
and calculating the difference coefficient of each bottom layer evaluation index based on the entropy value of each bottom layer evaluation index, and calculating the objective weight of the bottom layer evaluation index relative to the upper evaluation index based on the difference coefficient.
Preferably, the evaluation value is calculated as follows:
wherein b i An evaluation score r for the i-th upper evaluation index ij Fuzzy membership value, w, of evaluation index value of jth bottom layer evaluation index in ith upper-level evaluation index rij The weight of the jth bottom layer evaluation index in the ith upper evaluation index relative to the upper evaluation index is given, and f is the upper evaluation indexThe total number of the evaluation indexes of the middle-bottom layer;
based on the same inventive concept, the invention also provides a comprehensive evaluation system of the energy cabin, which comprises:
the system comprises an evaluation index value acquisition module, a membership value acquisition module, a weight acquisition module and a comprehensive evaluation module;
the evaluation index value acquisition module is used for calculating the evaluation index value of the bottom layer evaluation index corresponding to each upper-level evaluation index based on the operation data of the energy cabin in a preset energy cabin comprehensive evaluation index system; the comprehensive evaluation index system of the energy cabin is a multi-layer evaluation system, and the multi-layer evaluation system comprises a plurality of upper-level evaluation indexes and a plurality of bottom-layer evaluation indexes arranged under each upper-level evaluation index;
The membership value acquisition module is used for eliminating each bottom layer evaluation index based on the evaluation index value of each bottom layer evaluation index and a preset correlation coefficient, classifying the rest bottom layer evaluation indexes, fitting the bottom layer evaluation index value corresponding to the rest bottom layer evaluation index based on a trapezoidal fuzzy membership function, and acquiring the membership value of each bottom layer evaluation index value;
the weight acquisition module is used for calculating the weight of each bottom layer evaluation index based on the upper level evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index value corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
the comprehensive evaluation module is used for calculating the evaluation score of each upper evaluation index according to the weight of the bottom evaluation index corresponding to each upper evaluation index and the membership value, and comprehensively evaluating the energy cabin based on the evaluation score of each upper evaluation index.
Preferably, the upper evaluation index of the evaluation index value acquisition module includes: safety index, reliability index, economical index, environment-friendly index and technical index;
Wherein, the bottom evaluation index corresponding to the security index comprises: a heating safety index and a gas supply safety index; the bottom layer evaluation index corresponding to the reliability index comprises: a power supply reliability index, a cooling/heating reliability index, and a gas supply reliability index; the bottom evaluation indexes corresponding to the economic indexes comprise: an initial investment cost index, a maintenance cost index, an operation cost index, an investment recovery period index, an internal yield index and a financial net present value index; the bottom evaluation indexes corresponding to the environment-friendly indexes comprise: each pollutant discharge amount index and waste comprehensive utilization rate index; the bottom evaluation indexes corresponding to the technical indexes comprise: a primary energy utilization efficiency index, an energy self-utilization index, a device utilization index and a device service life index.
Preferably, the membership value obtaining module includes:
and calculating the correlation coefficient between every two bottom layer evaluation indexes under each upper layer evaluation index based on the evaluation index value of each bottom layer evaluation index, removing one bottom layer evaluation index of every two bottom layer evaluation indexes when the absolute value of the correlation coefficient is larger than or equal to a preset correlation coefficient absolute value threshold value, and confirming the rest bottom layer evaluation indexes.
Preferably, the membership value obtaining module includes:
classifying the residual bottom layer evaluation indexes according to index properties to obtain a plurality of evaluation index categories, wherein each evaluation index category comprises a plurality of bottom layer evaluation indexes;
fitting the bottom layer evaluation index values corresponding to the bottom layer evaluation indexes in each evaluation index category according to the trapezoidal fuzzy membership function corresponding to each evaluation index category to obtain the fuzzy membership value corresponding to each bottom layer evaluation index value.
Preferably, the membership value obtaining module includes:
the evaluation index category includes one or more of the following: positive evaluation index, reverse evaluation index and moderate evaluation index; the trapezoidal fuzzy membership function comprises one or more of the following: a rising half-trapezoid distribution function, a falling half-trapezoid distribution function and a trapezoid distribution function; the bottom layer evaluation index value corresponding to the positive evaluation index corresponds to an ascending half-trapezoid distribution function, the bottom layer evaluation index value corresponding to the inverse evaluation index corresponds to a descending half-trapezoid distribution function, and the bottom layer evaluation index value corresponding to the moderate evaluation index corresponds to a trapezoid distribution function.
Preferably, the weight obtaining module is specifically configured to:
Calculating subjective weight of the bottom layer evaluation index relative to the upper layer evaluation index by adopting a network analytic hierarchy process based on the upper layer evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
calculating objective weight of the bottom layer evaluation index relative to the upper layer evaluation index by adopting an entropy method based on the upper layer evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
and carrying out combined weighting on the subjective weight and the objective weight, and calculating the weight of the bottom layer evaluation index relative to the upper evaluation index.
Preferably, the weight obtaining module calculates subjective weight of the bottom layer evaluation index relative to the upper layer evaluation index by adopting a network analytic hierarchy process based on the upper layer evaluation index, the remaining bottom layer evaluation index and the bottom layer evaluation index value corresponding to the remaining bottom layer evaluation index of the energy cabin comprehensive evaluation index system, and the subjective weight comprises:
constructing a control factor layer and a network index layer under each control factor by adopting a network analytic hierarchy process based on the upper evaluation index, the residual bottom evaluation index and the bottom evaluation index value corresponding to the residual bottom evaluation index of the energy cabin comprehensive evaluation index system;
Constructing a super matrix and a weight matrix based on the control factor layer and the network index layer;
combining the super matrix with the weight matrix to obtain a normalized weighted super matrix;
and obtaining subjective weight of the bottom evaluation index relative to the upper evaluation index by calculating the normalized eigenvector of the weighted super matrix about the eigenvalue.
Preferably, the weight obtaining module calculates objective weight of the bottom layer evaluation index relative to the upper layer evaluation index by adopting an entropy method based on the upper layer evaluation index, the remaining bottom layer evaluation index and the bottom layer evaluation index value corresponding to the remaining bottom layer evaluation index of the energy cabin comprehensive evaluation index system, and the objective weight comprises:
constructing a characteristic data matrix of the bottom layer evaluation index based on the upper level evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index value corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
calculating the proportion value occupied by each characteristic value in the characteristic data matrix, and generating a proportion matrix; calculating entropy values of the bottom layer evaluation indexes based on the specific gravity values of the bottom layer evaluation indexes;
and calculating the difference coefficient of each bottom layer evaluation index based on the entropy value of each bottom layer evaluation index, and calculating the objective weight of the bottom layer evaluation index relative to the upper evaluation index based on the difference coefficient.
Preferably, the evaluation score of the comprehensive evaluation module is calculated as follows:
wherein b i An evaluation score r for the i-th upper evaluation index ij Fuzzy membership value, w, of evaluation index value of jth bottom layer evaluation index in ith upper-level evaluation index rij The weight of the jth bottom layer evaluation index in the ith upper evaluation index relative to the upper evaluation index is given, and f is the total number of the bottom layer evaluation indexes in the upper evaluation index;
based on the same inventive concept, the present invention also provides a computer device, comprising: one or more processors;
a memory for storing one or more programs;
when the one or more programs are executed by the one or more processors, a method for comprehensive evaluation of an energy compartment is implemented as described above.
Based on the same inventive concept, the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed, implements a comprehensive evaluation method of an energy cabin as described above.
Compared with the closest prior art, the invention has the following beneficial effects:
the invention provides a comprehensive evaluation method, a system, equipment and a medium of an energy cabin, which comprise the following steps: in a preset comprehensive evaluation index system of the energy cabin, calculating an evaluation index value of a bottom layer evaluation index corresponding to each upper-level evaluation index based on operation data of the energy cabin; the comprehensive evaluation index system of the energy cabin is a multi-layer evaluation system, and the multi-layer evaluation system comprises a plurality of upper-level evaluation indexes and a plurality of bottom-layer evaluation indexes arranged under each upper-level evaluation index; removing all the bottom layer evaluation indexes based on the evaluation index values of all the bottom layer evaluation indexes and preset correlation coefficients, classifying the rest of the bottom layer evaluation indexes, fitting the bottom layer evaluation index values corresponding to the rest of the bottom layer evaluation indexes based on a trapezoidal fuzzy membership function, and obtaining membership values of all the bottom layer evaluation index values; calculating the weight of each bottom layer evaluation index based on the upper level evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index value corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system; calculating the evaluation score of each upper evaluation index according to the weight of the bottom evaluation index corresponding to each upper evaluation index and the membership value, and comprehensively evaluating the energy cabin based on the evaluation score of each upper evaluation index; according to the invention, the weight of the bottom evaluation index and the membership value of the bottom evaluation index are obtained, the evaluation score of the upper evaluation index is calculated, and the energy bin is reasonably and effectively comprehensively evaluated according to the evaluation score, so that the index in the comprehensive evaluation index system of the energy bin is converted from qualitative to quantitative, and the problem that the index cannot be solved due to uncertainty in evaluation is solved.
Drawings
FIG. 1 is a schematic flow chart of a comprehensive evaluation method of an energy cabin provided by the invention;
FIG. 2 is a flow chart of comprehensive evaluation of an energy cabin provided by the invention;
FIG. 3 is a schematic diagram of an index fuzzy membership function provided by the invention;
FIG. 4 is a diagram of a fuzzy membership function of the inverse index according to the present invention;
FIG. 5 is a schematic diagram of a moderate index fuzzy membership function provided by the present invention;
FIG. 6 is a flow chart of the energy compartment performance evaluation index weighting implementation provided by the invention;
fig. 7 is a schematic diagram of a comprehensive evaluation system of an energy cabin provided by the invention.
Detailed Description
The following describes the embodiments of the present invention in further detail with reference to the drawings.
Example 1:
the comprehensive evaluation method of the energy cabin provided by the invention is shown in figure 1, and comprises the following steps:
step 1: in a preset comprehensive evaluation index system of the energy cabin, calculating an evaluation index value of a bottom layer evaluation index corresponding to each upper-level evaluation index based on operation data of the energy cabin; the comprehensive evaluation index system of the energy cabin is a multi-layer evaluation system, and the multi-layer evaluation system comprises a plurality of upper-level evaluation indexes and a plurality of bottom-layer evaluation indexes arranged under each upper-level evaluation index;
Step 2: removing all the bottom layer evaluation indexes based on the evaluation index values of all the bottom layer evaluation indexes and preset correlation coefficients, classifying the rest of the bottom layer evaluation indexes, fitting the bottom layer evaluation index values corresponding to the rest of the bottom layer evaluation indexes based on a trapezoidal fuzzy membership function, and obtaining membership values of all the bottom layer evaluation index values;
step 3: calculating the weight of each bottom layer evaluation index based on the upper level evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index value corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
step 4: and calculating the evaluation score of each upper evaluation index according to the weight of the bottom evaluation index corresponding to each upper evaluation index and the membership value, and comprehensively evaluating the energy cabin based on the evaluation score of each upper evaluation index.
Specifically, step 1 includes:
as shown in fig. 2, the preset comprehensive evaluation index system of the energy cabin is a multi-layer evaluation system, including a plurality of upper evaluation indexes and a plurality of lower evaluation indexes set under each upper evaluation index, where the upper evaluation indexes include: safety index, reliability index, economical index, environment-friendly index and technical index;
The bottom evaluation index corresponding to the safety index at least comprises: the heat supply safety index and the air supply safety index are evaluated to evaluate whether the design value of each operation parameter is exceeded in the operation process of the energy cabin, wherein the calculation formula of the heat supply safety index is as follows:
wherein f h An evaluation index value which is a heat supply safety index;
the evaluation index value of the air supply safety index is:
wherein f g An evaluation index value which is an air supply safety index;
the bottom layer evaluation indexes corresponding to the reliability indexes at least comprise: the power supply reliability index, the cold/heat supply reliability index and the air supply reliability index, and further comprise a power supply reliability rate, wherein an evaluation index value of the power supply reliability is the power supply reliability rate, namely, the percentage of the ratio of the total number of hours of effective power supply time to the user during the statistics period, based on the operation data of the energy cabin, the calculation formula of the power supply reliability rate is as follows:
wherein R is e For the power supply reliability, T cons To average power failure time T all Is the statistical period time;
the evaluation index value of the cooling/heating reliability is the cooling/heating reliability, namely, the ratio of the effective heating/cooling time to the statistical time of the user in the statistical period, and the calculation formula of the cooling/heating reliability is as follows:
Wherein R is hc T for cooling/heating reliability hc To cool/warm the temperature is higher/lower than the user's demand for hours, T all Counting the number of hours during the period;
the evaluation index value of the air supply reliability is the air supply reliability, namely the ratio of the effective air supply time to the user in the statistical period.
Wherein R is g For the reliability of air supply, T g For the number of hours for the air supply to meet the user's requirements, T all Counting the number of hours during the period;
the energy supply reliability is the average value of the sum of three network reliability of power supply reliability, cold/heat supply reliability and air supply reliability in an energy supply network, and the calculation formula is as follows:
wherein R is power The energy supply reliability is realized;
the bottom evaluation indexes corresponding to the economic indexes at least comprise: an initial investment cost index, a maintenance cost index, an operation cost index, an investment recovery period index, an internal yield index and a financial net present value index; the evaluation index value of the initial investment cost index is the purchase construction cost of the energy cabin, the evaluation index value of the maintenance cost index is the operation maintenance cost and the overhaul cost of the energy cabin, and the evaluation index value of the operation cost index is the cost of energy cabin external purchase energy and energy conversion;
the investment recovery period index is the time required for recovering the investment according to the set reference profit margin under the condition of considering the value of the fund time, and the evaluation index value of the investment recovery period index is calculated according to the following formula:
Wherein CI is the annual benefit, CO is the annual cost, t is the year, t=0 indicates the time the project has been in progress, t=1 indicates the project has been in progress for one year, P D For dynamic investment recovery period, i 0 Is the reference yield;
the evaluation index value of the internal yield index is the internal yield, which refers to the discount rate of the project with zero net present value of each year in the calculation period, and the calculation formula of the internal yield is as follows:
IRR is the internal yield, and q is the operation age of the energy cabin;
the judgment criterion is IRR calculated and i of the project 0 In comparison, when IRR is greater than or equal to i 0 When the internal yield is not lower than the reference yield level, the method is feasible; otherwise, the infeasible internal yield can be used as a dynamic evaluation index for measuring comprehensive investment benefits of the energy cabin;
the evaluation index value of the financial net present value index is a financial net present value ((FNPV), which is the sum of present values for converting the financial net cash flow rate of each year in the project calculation period to the development activity starting point, and when FNPV >0, it is indicated that the scheme can obtain excessive profits in addition to the profits meeting the reference profitability requirement, when FNPV=0, it is indicated that the scheme can meet the profitability level of the reference profitability requirement, it is financially viable, and when FNPV <0, it is indicated that the scheme cannot meet the profitability requirement of the reference profitability requirement, it is not viable, and it is indicated that the financial net present value is calculated by the following formula:
Wherein FNPV is a financial net present value, n is a project calculation period, NC t To calculate the new cash flow, i, for the t-th year of the cycle c Is the reference yield;
the bottom evaluation indexes corresponding to the environmental protection indexes at least comprise: the pollutant emission indexes and the waste comprehensive utilization index, wherein the pollutant emission at least comprises SO2 emission, NOX emission, CO2 emission, CO emission and ash emission, after the emission of various pollutants is calculated, the actual loss caused by environmental pollution and the annual pollution discharge charge ratio are calculated according to the current pollution discharge charge standard, the environmental value standard of pollutant emission reduction in the power industry is estimated, the environmental loss of the pollutant emission reduction in the same capacity of electric energy production by relative coal-fired power generation is measured, and the environmental loss calculation formula of the pollutant emission reduction in the same capacity of electric energy production by relative coal-fired power generation is as follows:
wherein B is env Environmental loss of pollutant reduced by producing equal capacity electric energy relative to coal-fired power generation, V i The environmental value of emission reduction for the ith pollutant, y is the type of the pollutant, Q i.c The emission quantity of the ith pollutant of the coal-fired generator set, Q i.dg For the emission of the ith pollutant of the energy cabin, Q DG Annual energy production of the energy compartment;
the evaluation index value of the waste comprehensive utilization index is the waste comprehensive utilization rate, the waste comprehensive utilization rate is obtained by converting sewage, sludge, industrial garbage, kitchen garbage and other wastes in a multi-energy system into new available energy forms such as fuel gas and the like through biomass energy, the purification and reuse proportion is improved, and the calculation formula of the waste comprehensive utilization rate is as follows:
wherein P is ga G is the comprehensive utilization rate of waste in G for comprehensive utilization of waste amount all Is an exemplary zone total amount of waste;
the bottom evaluation indexes corresponding to the technical indexes at least comprise: the evaluation index value of the primary energy utilization efficiency index is the primary energy utilization efficiency, which refers to the degree to which the energy contained in the energy is effectively utilized, and the calculation formula is as follows:
wherein v is the utilization efficiency of primary energy, W is the net output electric quantity, Q 1 To supply heat to the annual effective waste heat, Q 2 The total annual effective waste heat and cold supply quantity, B is the annual total fuel gas consumption quantity, Q L The low-level heating value of the fuel gas is obtained;
the evaluation index value of the energy source self-utilization index is the energy source self-utilization rate, the energy source self-utilization rate is the ratio of the power consumption of the energy source cabin to the total power generated by the power supply, the spontaneous self-utilization degree of the energy source cabin is reflected, and the calculation formula of the energy source self-utilization rate is as follows:
Wherein, gamma mdemcr P is the energy source self-utilization rate i (t) is the power output of the energy cabin, P g (t) is the electric power of the energy cabin;
the evaluation index value of the equipment utilization index is equipment utilization rate, the equipment utilization rate is the ratio of the actual working time to the planned working time of the equipment in a period of time, and the calculation formula of the equipment utilization rate is as follows:
wherein eta e T is the utilization rate of equipment in the multi-energy network 0 Is the unit plan working time length, T n The actual working time of the nth equipment in unit time is N, which is the number of energy link equipment in the regional system;
the service life index of the equipment refers to the service life of the energy cabin from the time of being put into the system to the time of being unable to maintain the normal working state in the system through maintenance; the comprehensive evaluation index system of the energy cabin constructed by the invention covers five dimensions of safety, reliability, economy, environmental protection, technical performance and the like, comprehensively and reasonably reflects the comprehensive benefits of the energy cabin, provides support for planning and running of the energy cabin system, quantifies the bottom evaluation index by calculating the evaluation index value of the bottom evaluation index in the comprehensive evaluation index system of the energy cabin, enables each bottom evaluation index to be imaged, and facilitates the calculation of the subsequent evaluation scores.
Specifically, step 2 includes:
all bottom evaluation indexes of the comprehensive evaluation index system of the energy cabin are expressed as:
wherein D is it The method comprises the steps of setting a first bottom layer evaluation index corresponding to an i upper level rating index;
and respectively calculating correlation coefficients between every two bottom evaluation indexes under each upper evaluation index, wherein the calculation formula of the correlation coefficients is as follows:
wherein s is ij Is the correlation coefficient of two bottom evaluation index values in the same upper-level rating index, x i One of the two bottom layer evaluation index values,
is the average value of one bottom layer evaluation index value in every two bottom layer evaluation index values, y i For another bottom layer evaluation index value of the two bottom layer evaluation index values, & lt/EN & gt>
The average value of the other bottom layer evaluation index values in the two bottom layer evaluation index values is l, and the number of the bottom layer evaluation index value sample data is l;
wherein, the liquid crystal display device comprises a liquid crystal display device,
and->
The formula of (2) is as follows:
obtaining the correlation coefficient of each bottom layer evaluation index under each upper layer evaluation index, taking the absolute value of the correlation coefficient, and if the value is closer to 1, indicating that the correlation of the two bottom layer evaluation indexes is stronger, the correlation discrimination principle can be as shown in the following table 1:
TABLE 1 discriminant principle for correlation test
| Sequence number | Correlation coefficient | Correlation of | |
| 1 | |s ij |＝0 | Completely uncorrelated | |
| 2 | 0＜|s ij |＜0.3 | Is not substantially correlated | |
| 3 | 0.3≤|s ij |＜0.5 | Low degree of correlation | |
| 4 | 0.5≤|s ij |＜0.8 | Significant correlation | |
| 5 | 0.8≤|s ij |＜1 | Highly correlated | |
| 6 | |s ij |＝1 | Complete correlation |
When the absolute value of the correlation coefficient is larger than or equal to a preset correlation coefficient (such as 0.8), removing any one of two bottom layer evaluation indexes corresponding to the absolute value of the correlation coefficient, and confirming the rest bottom layer evaluation indexes;
according to the properties of the bottom layer evaluation index, the bottom layer evaluation index is divided into three types of positive indexes, reverse indexes and moderate indexes, wherein the positive indexes refer to indexes with larger values and better values, such as power supply reliability indexes; the reverse index means an index which is as good as the smaller the value, such as an index of the discharge amount of each pollutant; the moderate index is an index which has positive effect on the system development when the numerical value is within a certain range, and the excessive or the insufficient numerical value is unfavorable for the development;
fitting the bottom evaluation index value corresponding to each type of bottom evaluation index through a trapezoidal fuzzy membership function corresponding to the evaluation index type to obtain a corresponding fuzzy membership value, wherein the trapezoidal fuzzy membership function comprises an ascending half trapezoidal distribution function, a descending half trapezoidal distribution function and a trapezoidal distribution function;
As shown in fig. 3, the bottom evaluation index value corresponding to the positive evaluation index corresponds to a half-ascending trapezoidal distribution function, which is shown in the following formula:
wherein A is 1 (x) As a ascending half trapezoid distribution function, a 1 A is the first end value of the independent variable interval of the distribution function 2 A second end value of the independent variable interval of the distribution function, wherein x is an independent variable;
as shown in fig. 4, the bottom layer evaluation index value corresponding to the evaluation index corresponds to a halfpace-down distribution function, and the halfpace-down distribution function is shown as follows:
wherein A is 2 (x) To reduce the half trapezoid distribution function, a 3 A is the first end value of the independent variable interval of the distribution function 4 A second end value of the distribution function argument interval;
as shown in fig. 5, the bottom evaluation index value corresponding to the moderate evaluation index corresponds to a trapezoidal distribution function, and the trapezoidal distribution function is shown as follows:
wherein A is 3 (x) The method comprises the steps that a is a trapezoidal distribution function, a is a first end value of a distribution function independent variable interval, b is a second end value of the distribution function independent variable interval, c is a third end value of the distribution function independent variable interval, and d is a fourth end value of the distribution function independent variable interval;
determining interval boundaries in the trapezoidal fuzzy membership functions corresponding to various bottom layer evaluation indexes, and obtaining fuzzy membership values of the bottom layer evaluation index values from the corresponding trapezoidal fuzzy membership functions; according to the invention, the bottom layer evaluation index with higher correlation is removed, so that the bottom layer evaluation index is reduced, and the rationality of comprehensive evaluation of the energy cabin is enhanced.
Specifically, step 3 includes:
as shown in fig. 6, a network analytic hierarchy process is adopted to construct a control factor layer and a network index layer under each control factor, the upper evaluation index corresponds to the control factor in the network analytic hierarchy process, the lower evaluation index corresponds to the network index, and the control factor layer is established according to the upper evaluation index of the comprehensive evaluation index system of the energy cabin for controlThe factor of the system is security C 1 Reliability C 2 Economical efficiency C 3 Environmental protection C 4 Technical C 5 ；
Establishing a network layer under a control factor layer according to the bottom evaluation indexes corresponding to the upper evaluation indexes, wherein the bottom evaluation indexes of the network layer are also called network indexes, and the number of control factors is m, and the network index set under the ith control factor layer uses y i1 ，y i2 ，…，y in To express that n is the number of network indexes under the control factor layer, aiming at the ith control factor C i Sequentially obtaining the influence degree comparison of other n-1 network indexes by taking the jth network index as a criterion to obtain a sequencing vector W ij Further obtaining a judgment matrix under the ith control factor, and similarly obtaining an influence judgment matrix W of the ith control factor on other control factors i1 ，W i2 ，…,W im Thereby constructing a supermatrix under various criteria as shown in the following formula:
Wherein W is a supermatrix, W mm Comparing the influence degree of the mth network index as a criterion on the mth control factor to other mth-1 control factors;
for control factor C i And respectively comparing the normalized feature vectors with the importance of other control elements, judging the importance, and solving the normalized feature vectors (sequencing vectors) according to a square root method to construct a weight matrix, wherein the weight matrix is shown in the following formula:
wherein A is a weight matrix, a mm The importance comparison of the m-1 control factors with the m-th network index as a criterion is carried out on the m-th control factors;
combining the weighting matrix A and the super matrix W to obtain normalized weighted super matrixMatrix W G The weighted super matrix is as follows:
wherein W is G Is a weighted super matrix;
obtaining subjective weight w of the bottom evaluation index relative to the upper evaluation index by calculating normalized eigenvector of the weighted supermatrix on eigenvalue j1 ；
When objective weights of the bottom layer evaluation indexes relative to the upper layer evaluation indexes are calculated through weighting by an entropy method, a characteristic value matrix of the bottom layer evaluation indexes is constructed based on the upper layer evaluation indexes and the rest bottom layer evaluation indexes of the energy cabin comprehensive evaluation index system, wherein the characteristic value matrix is shown in the following formula:
Wherein X is a characteristic value matrix, X ph The characteristic value of the h bottom layer evaluation index in the p-th upper evaluation index is obtained;
calculating the characteristic value x of the jth bottom layer evaluation index in the ith upper evaluation index ij The occupied proportion, namely, the characteristic value of the bottom evaluation index is normalized, and the calculation formula is as follows:
wherein p is ij Is x ij The specific gravity, x ij The characteristic value of the j-th bottom layer evaluation index in the i-th upper layer evaluation index is represented by p, the total number of the upper layer evaluation indexes is represented by n, and the total number of the bottom layer evaluation indexes is represented by n;
p ij also is x ij Normalized eigenvalues, thereby obtaining a normalized eigenvalue specific gravity matrix:
wherein R is normalized eigenvalue gravity matrix, p ph Is x ph Normalized characteristic values;
and calculating the entropy value of the j-th bottom evaluation index, wherein the calculation formula is as follows:
wherein e j Entropy value of the j-th bottom layer evaluation index;
the entropy value of the relative importance of the characterization index j is calculated as follows:
wherein E is j To characterize the entropy of the relative importance of index j, when all the bottom-layer evaluation indices p ij When equal, the entropy is the largest, lnp, due to the entropy E j The smaller the variation degree of the bottom layer evaluation index is, the larger the variation degree is, otherwise, the entropy value E is j The larger the variation degree of the bottom layer evaluation index is, the smaller the variation degree of the bottom layer evaluation index is, and the difference coefficient of the j-th bottom layer evaluation index is calculated according to the following calculation formula:
g j ＝1-E j
Wherein g j The difference coefficient of the bottom evaluation index is the j-th item;
based on the difference coefficient of the jth bottom layer evaluation index, calculating the objective weight w of the jth bottom layer evaluation index j2 The formula is as follows:
wherein w is j2 Objective weight of the j-th bottom evaluation index, j e {1, 2., h };
calculating objective weights of the bottom evaluation indexes relative to the upper evaluation indexes by the method;
a simple arithmetic average algorithm is adopted as a weighting method to carry out combined weighting on subjective weights and objective weights, and the weight of a bottom evaluation index relative to an upper evaluation index is calculated, wherein the calculation formula is as follows:
wherein w is r The weight of the bottom layer evaluation index relative to the upper evaluation index, w j1 Subjective weight of the j-th bottom evaluation index;
according to the invention, the subjective weight and the objective weight of the bottom evaluation index are combined and weighted, so that the influence of subjective factors on the weight is reduced, and the problem of weight calculation misalignment caused by the mutual influence between indexes is solved.
Specifically, step 4 includes:
and respectively carrying out fuzzy synthesis by adopting a common multiplication and addition algorithm according to the weight of the bottom evaluation index corresponding to each upper evaluation index and the membership value, and calculating the evaluation score of each upper evaluation index, wherein the calculation formula is as follows:
Wherein b i An evaluation score r for the i-th upper evaluation index ij Fuzzy membership value, w, of evaluation index value of jth bottom layer evaluation index in ith upper-level evaluation index rij The weight of the jth bottom layer evaluation index in the ith upper evaluation index relative to the upper evaluation index is given, and f is the total number of the bottom layer evaluation indexes in the upper evaluation index;
adding the obtained evaluation scores of each upper evaluation index to obtain a total evaluation score of the energy cabin, and comprehensively evaluating the energy cabin according to the total evaluation score; according to the invention, reasonable and effective comprehensive evaluation is carried out on the energy bin according to the total evaluation score, the qualitative to quantitative conversion of the indexes in the comprehensive evaluation index system of the energy bin is realized, and the problem that the indexes cannot be solved due to uncertainty in the evaluation is solved.
Example 2:
based on the same inventive concept, the invention also provides a comprehensive evaluation system of the energy cabin, as shown in fig. 7, comprising:
the system comprises an evaluation index value acquisition module, a membership value acquisition module, a weight acquisition module and a comprehensive evaluation module;
the evaluation index value acquisition module is used for calculating the evaluation index value of the bottom layer evaluation index corresponding to each upper-level evaluation index based on the operation data of the energy cabin in a preset energy cabin comprehensive evaluation index system; the comprehensive evaluation index system of the energy cabin is a multi-layer evaluation system, and the multi-layer evaluation system comprises a plurality of upper-level evaluation indexes and a plurality of bottom-layer evaluation indexes arranged under each upper-level evaluation index;
The membership value acquisition module is used for eliminating each bottom layer evaluation index based on the evaluation index value of each bottom layer evaluation index and a preset correlation coefficient, classifying the rest bottom layer evaluation indexes, fitting the bottom layer evaluation index value corresponding to the rest bottom layer evaluation index based on a trapezoidal fuzzy membership function, and acquiring the membership value of each bottom layer evaluation index value;
the weight acquisition module is used for calculating the weight of each bottom layer evaluation index based on the upper level evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index value corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
the comprehensive evaluation module is used for calculating the evaluation score of each upper evaluation index according to the weight of the bottom evaluation index corresponding to each upper evaluation index and the membership value, and comprehensively evaluating the energy cabin based on the evaluation score of each upper evaluation index.
Preferably, the upper evaluation index of the evaluation index value acquisition module includes: safety index, reliability index, economical index, environment-friendly index and technical index;
Wherein, the bottom evaluation index corresponding to the security index comprises: a heating safety index and a gas supply safety index; the bottom layer evaluation index corresponding to the reliability index comprises: a power supply reliability index, a cooling/heating reliability index, and a gas supply reliability index; the bottom evaluation indexes corresponding to the economic indexes comprise: an initial investment cost index, a maintenance cost index, an operation cost index, an investment recovery period index, an internal yield index and a financial net present value index; the bottom evaluation indexes corresponding to the environment-friendly indexes comprise: each pollutant discharge amount index and waste comprehensive utilization rate index; the bottom evaluation indexes corresponding to the technical indexes comprise: a primary energy utilization efficiency index, an energy self-utilization index, a device utilization index and a device service life index.
Preferably, the membership value obtaining module is specifically configured to:
and calculating the correlation coefficient between every two bottom layer evaluation indexes under each upper layer evaluation index based on the evaluation index value of each bottom layer evaluation index, removing one bottom layer evaluation index of every two bottom layer evaluation indexes when the absolute value of the correlation coefficient is larger than or equal to a preset correlation coefficient absolute value threshold value, and confirming the rest bottom layer evaluation indexes.
Preferably, the membership value obtaining module includes:
classifying the residual bottom layer evaluation indexes according to index properties to obtain a plurality of evaluation index categories, wherein each evaluation index category comprises a plurality of bottom layer evaluation indexes;
fitting the bottom layer evaluation index values corresponding to the bottom layer evaluation indexes in each evaluation index category according to the trapezoidal fuzzy membership function corresponding to each evaluation index category to obtain the fuzzy membership value corresponding to each bottom layer evaluation index value.
Preferably, the membership value obtaining module includes:
the evaluation index type includes one or more of the following: positive evaluation index, reverse evaluation index and moderate evaluation index; the trapezoidal fuzzy membership function comprises one or more of the following: a rising half-trapezoid distribution function, a falling half-trapezoid distribution function and a trapezoid distribution function; the bottom layer evaluation index value corresponding to the positive evaluation index corresponds to an ascending half-trapezoid distribution function, the bottom layer evaluation index value corresponding to the inverse evaluation index corresponds to a descending half-trapezoid distribution function, and the bottom layer evaluation index value corresponding to the moderate evaluation index corresponds to a trapezoid distribution function.
Preferably, the weight obtaining module is specifically configured to:
Calculating subjective weight of the bottom layer evaluation index relative to the upper layer evaluation index by adopting a network analytic hierarchy process based on the upper layer evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
calculating objective weight of the bottom layer evaluation index relative to the upper layer evaluation index by adopting an entropy method based on the upper layer evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
and carrying out combined weighting on the subjective weight and the objective weight, and calculating the weight of the bottom layer evaluation index relative to the upper evaluation index.
Preferably, the weight obtaining module calculates subjective weight of the bottom layer evaluation index relative to the upper layer evaluation index by adopting a network analytic hierarchy process based on the upper layer evaluation index, the remaining bottom layer evaluation index and the bottom layer evaluation index value corresponding to the remaining bottom layer evaluation index of the energy cabin comprehensive evaluation index system, and the subjective weight comprises:
constructing a control factor layer and a network index layer under each control factor by adopting a network analytic hierarchy process based on the upper evaluation index, the residual bottom evaluation index and the bottom evaluation index value corresponding to the residual bottom evaluation index of the energy cabin comprehensive evaluation index system;
Constructing a super matrix and a weight matrix based on the control factor layer and the network index layer;
combining the super matrix with the weight matrix to obtain a normalized weighted super matrix;
and obtaining subjective weight of the bottom evaluation index relative to the upper evaluation index by calculating the normalized eigenvector of the weighted super matrix about the eigenvalue.
Preferably, the weight obtaining module calculates objective weight of the bottom layer evaluation index relative to the upper layer evaluation index by adopting an entropy method based on the upper layer evaluation index, the remaining bottom layer evaluation index and the bottom layer evaluation index value corresponding to the remaining bottom layer evaluation index of the energy cabin comprehensive evaluation index system, and the objective weight comprises:
constructing a characteristic data matrix of the bottom layer evaluation index based on the upper level evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index value corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
calculating the proportion value occupied by each characteristic value in the characteristic data matrix, and generating a proportion matrix; calculating entropy values of the bottom layer evaluation indexes based on the specific gravity values of the bottom layer evaluation indexes;
and calculating the difference coefficient of each bottom layer evaluation index based on the entropy value of each bottom layer evaluation index, and calculating the objective weight of the bottom layer evaluation index relative to the upper evaluation index based on the difference coefficient.
Preferably, the evaluation score of the comprehensive evaluation module is calculated as follows:
wherein b i An evaluation score r for the i-th upper evaluation index ij Fuzzy membership value, w, of evaluation index value of jth bottom layer evaluation index in ith upper-level evaluation index rij The weight of the jth bottom layer evaluation index relative to the upper layer evaluation index in the ith upper layer evaluation index is given, and f is the total number of the bottom layer evaluation indexes in the upper layer evaluation indexes.
Example 3:
based on the same inventive concept, the invention also provides a computer device comprising a processor and a memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application SpecificIntegrated Circuit, ASIC), off-the-shelf Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular to load and execute one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding functions, to implement the steps of a comprehensive assessment method for an energy compartment in the above embodiments.
Example 4:
based on the same inventive concept, the present invention also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a computer device, for storing programs and data. It is understood that the computer readable storage medium herein may include both built-in storage media in a computer device and extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the steps of a method for comprehensive evaluation of an energy compartment in the above embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the scope of protection thereof, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that various changes, modifications or equivalents may be made to the specific embodiments of the application after reading the present invention, and these changes, modifications or equivalents are within the scope of protection of the claims appended hereto.
Claims (12)
1. The comprehensive evaluation method of the energy cabin is characterized by comprising the following steps of:
in a preset comprehensive evaluation index system of the energy cabin, calculating an evaluation index value of a bottom layer evaluation index corresponding to each upper-level evaluation index based on operation data of the energy cabin; the comprehensive evaluation index system of the energy cabin is a multi-layer evaluation system, and the multi-layer evaluation system comprises a plurality of upper-level evaluation indexes and a plurality of bottom-layer evaluation indexes arranged under each upper-level evaluation index;
removing all the bottom layer evaluation indexes based on the evaluation index values of all the bottom layer evaluation indexes and preset correlation coefficients, classifying the rest of the bottom layer evaluation indexes, fitting the bottom layer evaluation index values corresponding to the rest of the bottom layer evaluation indexes based on a trapezoidal fuzzy membership function, and obtaining membership values of all the bottom layer evaluation index values;
calculating the weight of each bottom layer evaluation index based on the upper level evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index value corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
and calculating the evaluation score of each upper evaluation index according to the weight of the bottom evaluation index corresponding to each upper evaluation index and the membership value, and comprehensively evaluating the energy cabin based on the evaluation score of each upper evaluation index.
2. The method of claim 1, wherein the superior evaluation index comprises: safety index, reliability index, economical index, environment-friendly index and technical index;
wherein, the bottom evaluation index corresponding to the security index comprises: a heating safety index and a gas supply safety index;
the bottom layer evaluation index corresponding to the reliability index comprises: a power supply reliability index, a cooling/heating reliability index, and a gas supply reliability index;
the bottom evaluation indexes corresponding to the economic indexes comprise: an initial investment cost index, a maintenance cost index, an operation cost index, an investment recovery period index, an internal yield index and a financial net present value index;
the bottom evaluation indexes corresponding to the environment-friendly indexes comprise: each pollutant discharge amount index and waste comprehensive utilization rate index;
the bottom evaluation indexes corresponding to the technical indexes comprise: a primary energy utilization efficiency index, an energy self-utilization index, a device utilization index and a device service life index.
3. The method of claim 2, wherein the removing each bottom layer evaluation index based on the evaluation index value of each bottom layer evaluation index and a preset correlation coefficient comprises:
And calculating the correlation coefficient between every two bottom layer evaluation indexes under each upper layer evaluation index based on the evaluation index value of each bottom layer evaluation index, removing one bottom layer evaluation index of every two bottom layer evaluation indexes when the absolute value of the correlation coefficient is larger than or equal to a preset correlation coefficient absolute value threshold value, and confirming the rest bottom layer evaluation indexes.
4. The method of claim 3, wherein classifying the remaining bottom layer evaluation indexes and fitting bottom layer evaluation index values corresponding to the remaining bottom layer evaluation indexes based on a trapezoidal fuzzy membership function to obtain membership values of the bottom layer evaluation index values, comprises:
classifying the residual bottom layer evaluation indexes according to index properties to obtain a plurality of evaluation index categories, wherein each evaluation index category comprises a plurality of bottom layer evaluation indexes;
fitting the bottom layer evaluation index values corresponding to the bottom layer evaluation indexes in each evaluation index category according to the trapezoidal fuzzy membership function corresponding to each evaluation index category to obtain the fuzzy membership value corresponding to each bottom layer evaluation index value.
5. The method of claim 4, wherein the assessment index categories include one or more of: positive evaluation index, reverse evaluation index and moderate evaluation index; the trapezoidal fuzzy membership function comprises one or more of the following: a rising half-trapezoid distribution function, a falling half-trapezoid distribution function and a trapezoid distribution function; the bottom layer evaluation index value corresponding to the positive evaluation index corresponds to an ascending half-trapezoid distribution function, the bottom layer evaluation index value corresponding to the inverse evaluation index corresponds to a descending half-trapezoid distribution function, and the bottom layer evaluation index value corresponding to the moderate evaluation index corresponds to a trapezoid distribution function.
6. The method of claim 3, wherein calculating the weight of each of the bottom layer evaluation indexes based on the top layer evaluation index, the remaining bottom layer evaluation index, and the bottom layer evaluation index value corresponding to the remaining bottom layer evaluation index of the energy compartment comprehensive evaluation index system comprises:
calculating subjective weight of the bottom layer evaluation index relative to the upper layer evaluation index by adopting a network analytic hierarchy process based on the upper layer evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
calculating objective weight of the bottom layer evaluation index relative to the upper layer evaluation index by adopting an entropy method based on the upper layer evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
and carrying out combined weighting on the subjective weight and the objective weight, and calculating the weight of the bottom layer evaluation index relative to the upper evaluation index.
7. The method of claim 6, wherein the calculating the subjective weight of the bottom layer evaluation index relative to the upper layer evaluation index by using the network hierarchical analysis method based on the upper layer evaluation index, the remaining bottom layer evaluation index and the bottom layer evaluation index value corresponding to the remaining bottom layer evaluation index of the energy compartment comprehensive evaluation index system comprises:
Constructing a control factor layer and a network index layer under each control factor by adopting a network analytic hierarchy process based on the upper evaluation index, the residual bottom evaluation index and the bottom evaluation index value corresponding to the residual bottom evaluation index of the energy cabin comprehensive evaluation index system;
constructing a super matrix and a weight matrix based on the control factor layer and the network index layer;
combining the super matrix with the weight matrix to obtain a normalized weighted super matrix;
and obtaining subjective weight of the bottom evaluation index relative to the upper evaluation index by calculating the normalized eigenvector of the weighted super matrix about the eigenvalue.
8. The method of claim 6, wherein calculating the objective weight of the bottom layer evaluation index relative to the upper level evaluation index by using an entropy method based on the upper level evaluation index, the remaining bottom layer evaluation index, and the bottom layer evaluation index value corresponding to the remaining bottom layer evaluation index of the energy compartment comprehensive evaluation index system comprises:
constructing a characteristic data matrix of the bottom layer evaluation index based on the upper level evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index value corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
Calculating the proportion value occupied by each characteristic value in the characteristic data matrix, and generating a proportion matrix; calculating entropy values of the bottom layer evaluation indexes based on the specific gravity values of the bottom layer evaluation indexes;
and calculating the difference coefficient of each bottom layer evaluation index based on the entropy value of each bottom layer evaluation index, and calculating the objective weight of the bottom layer evaluation index relative to the upper evaluation index based on the difference coefficient.
9. The method of claim 1, wherein the scoring value is calculated as follows:
wherein b i An evaluation score r for the i-th upper evaluation index ij Fuzzy membership value, w, of evaluation index value of jth bottom layer evaluation index in ith upper-level evaluation index rij The weight of the jth bottom layer evaluation index relative to the upper layer evaluation index in the ith upper layer evaluation index is given, and f is the total number of the bottom layer evaluation indexes in the upper layer evaluation indexes.
10. An integrated evaluation system for an energy compartment, comprising:
the system comprises an evaluation index value acquisition module, a membership value acquisition module, a weight acquisition module and a comprehensive evaluation module;
the evaluation index value acquisition module is used for calculating the evaluation index value of the bottom layer evaluation index corresponding to each upper-level evaluation index based on the operation data of the energy cabin in a preset energy cabin comprehensive evaluation index system; the comprehensive evaluation index system of the energy cabin is a multi-layer evaluation system, and the multi-layer evaluation system comprises a plurality of upper-level evaluation indexes and a plurality of bottom-layer evaluation indexes arranged under each upper-level evaluation index;
The membership value acquisition module is used for eliminating each bottom layer evaluation index based on the evaluation index value of each bottom layer evaluation index and a preset correlation coefficient, classifying the rest bottom layer evaluation indexes, fitting the bottom layer evaluation index value corresponding to the rest bottom layer evaluation index based on a trapezoidal fuzzy membership function, and acquiring the membership value of each bottom layer evaluation index value;
the weight acquisition module is used for calculating the weight of each bottom layer evaluation index based on the upper level evaluation index, the residual bottom layer evaluation index and the bottom layer evaluation index value corresponding to the residual bottom layer evaluation index of the energy cabin comprehensive evaluation index system;
the comprehensive evaluation module is used for calculating the evaluation score of each upper evaluation index according to the weight of the bottom evaluation index corresponding to each upper evaluation index and the membership value, and comprehensively evaluating the energy cabin based on the evaluation score of each upper evaluation index.
11. A computer device, comprising: one or more processors;
a memory for storing one or more programs;
a comprehensive assessment method of an energy compartment according to any one of claims 1 to 7 is achieved when the one or more programs are executed by the one or more processors.
12. A computer-readable storage medium, on which a computer program is stored, which computer program, when executed, implements a method for comprehensive evaluation of an energy compartment according to any one of claims 1 to 7.
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