CN117933754A - Comprehensive energy microgrid evaluation generation system and method based on variable component sub-algorithm - Google Patents
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Abstract
The invention discloses a comprehensive energy microgrid evaluation generation system based on a variable component sub-algorithm. The comprehensive energy microgrid evaluation generation method based on the variable component sub-algorithm comprises the following steps: acquiring a first-level index and a second-level index corresponding to each first-level index, namely constructing an evaluation index system; determining index weights according to the variable component sub-algorithm; and obtaining the evaluation grade ordering value of each sample through a TOPSIS algorithm. According to the invention, on the basis of considering carbon emission, the index weights of mutual correlation and coupling are calculated by utilizing superposition and entanglement characteristics of quantum bits in a variable component sub-algorithm, so that the generated result is more accurate and more practical, and the method can be suitable for the change of different practical scenes of the renewable energy micro-grid.
Description
Technical Field
The invention relates to the technical field of renewable energy microgrid construction, in particular to a comprehensive energy microgrid evaluation generation system and method based on a variable component sub-algorithm.
Background
The existing renewable energy micro-grid construction comprehensive evaluation index generation process lacks consideration of carbon emission, and the carbon emission has double influences on economy and environment, so that the related index weights are in a mutually-influenced coupling state, the accuracy of the result output by the existing comprehensive evaluation generation system or method is low, namely, the difference from the actual situation is large, the reference of the result is poor, and the adaptability of comprehensively evaluating different renewable energy micro-grid scenes is poor.
In order to improve the accuracy and adaptability of the result of the renewable energy micro-grid construction comprehensive evaluation generating system or method, a person skilled in the art carries out various improvements, such as patent CN112862154B proposes a regional energy system planning optimization comprehensive evaluation system containing seven indexes from three aspects of economic benefit, operation benefit and environmental benefit. The economic benefit index mainly comprises annual values such as initial investment of the whole life cycle, running cost and total cost; the operation benefit index mainly comprises average efficiency and electric/heat network purchase electricity/heat curve leveling rate; the environmental benefit index mainly comprises the total carbon emission and the renewable energy utilization rate. The weight coefficient calculation method of the patent CN116452154B for each data index comprises the following steps: firstly, carrying out standardization processing on each data index, eliminating the influence of different units of the index, establishing a judgment matrix X under the condition of n data indexes, carrying out standardization processing on original data to obtain a standardization matrix Y, then calculating the information entropy of the data index, and finally calculating the weight of each data index according to the information entropy. The patent CN114418160A establishes a multi-objective function with the lowest running economy cost, the lowest carbon dioxide emission and the highest wind energy and light energy utilization rate of the multi-energy system, combines a hierarchical analysis method and an entropy weight method, establishes a combination weight for the multi-objective function, and optimizes a dispatching result. And establishing an evaluation index and evaluation quantitative classification aiming at each multi-objective function, determining an evaluation index weight by adopting a hierarchical analysis method, inputting the optimized scheduling result of the last step into an integrated energy system evaluation system, evaluating the multi-objective function according to the evaluation index weight and the evaluation index evaluation data by utilizing a fuzzy integrated evaluation method, meeting the evaluation quantitative classification requirement, and completing weight distribution. The patent CN108565899B designs a DG starting and running characteristic comprehensive evaluation index system, wherein the evaluation indexes comprise starting characteristic indexes and running characteristic indexes, the starting characteristic indexes and the running characteristic indexes are classified, objective weights of all the evaluation indexes are calculated by using a variation coefficient method, subjective weights of all the evaluation indexes are calculated by using a hierarchical analysis method, comprehensive weights of all the evaluation indexes are calculated by using a multiplication combination weighting method, relative sticking progress of all the evaluation indexes is calculated by using an approximate ideal solution ordering method, and comprehensive evaluation is carried out.
However, in the improvement, there is still room for improvement in accuracy of results and adaptability to different scenes.
Disclosure of Invention
In order to solve the technical problems, the invention provides a comprehensive energy microgrid evaluation generation system and method based on a variable component sub-algorithm.
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
The invention adopts the following technical scheme:
Providing a comprehensive energy microgrid evaluation generation system based on a variable component sub-algorithm, wherein index weights used by the evaluation generation system are obtained based on the variable component sub-algorithm;
The evaluation generation system includes:
The system construction module is used for acquiring the first-level index and the second-level index corresponding to each first-level index;
the weight distribution module is used for determining the optimal weights of the primary index and the secondary index according to a variable component sub-algorithm;
The comprehensive evaluation module is used for acquiring an evaluation level ranking value of each sample through a TOPSIS algorithm according to the optimal weight of each index;
The system construction module comprises:
The natural resource condition acquisition unit is used for acquiring natural resource condition data;
The equipment and technical condition acquisition unit is used for acquiring equipment and technical condition data;
The economic impact acquisition unit is used for acquiring economic impact data;
the social influence acquisition unit is used for acquiring social influence data;
the environment influence acquisition unit is used for acquiring environment influence data;
the carbon emission benefit acquisition unit is used for acquiring carbon emission benefit data;
The natural resource condition acquisition unit comprises: the energy distribution condition acquisition subunit is used for acquiring energy distribution condition data; the climate condition acquisition subunit is used for acquiring climate condition data; the geological condition acquisition subunit is used for acquiring geological condition data;
The equipment and technical condition acquisition unit comprises: the power generation equipment acquisition subunit is used for acquiring power generation equipment type data; the inverter acquisition subunit is used for acquiring inverter type data; the generating capacity acquisition subunit is used for acquiring generating capacity data; the power generation array group string number acquisition subunit is used for acquiring power generation array group string number data; the power generation array column spacing acquisition subunit is used for acquiring power generation array column spacing data;
the economic impact collection unit includes: the project investment acquisition subunit is used for acquiring project investment data; the project profit acquisition subunit is used for acquiring project profit data; the project compensation capability acquisition subunit is used for acquiring project compensation capability data;
the social influence collecting unit includes: the new energy duty ratio acquisition subunit is used for acquiring new energy duty ratio data; the local economic impact acquisition subunit is used for acquiring local economic impact data; the local employment influence acquisition subunit is used for acquiring local employment influence data;
The environmental impact collection unit includes: the noise pollution acquisition subunit is used for acquiring noise pollution data; the dust pollution acquisition subunit is used for acquiring dust pollution data;
the carbon emission benefit collection unit includes: the carbon emission reduction acquisition subunit is used for acquiring carbon emission reduction data; and the carbon transaction acquisition subunit is used for acquiring carbon transaction data.
Further, the weight distribution module includes:
the function definition unit is used for converting each secondary index into grade data for evaluating the quality degree, weighting each secondary index and establishing a linear weighting function;
an optimization variable determining unit for determining each weight coefficient in the linear weighting function as an optimization variable of the optimization problem;
the weight coefficient optimization model building unit is used for generating an objective function of an optimization problem and an objective function of a quantum circuit;
the optimizing unit is used for optimizing the weight coefficient of each secondary index through a variable component sub-algorithm to obtain the optimal weight of the secondary index and the optimal weight of the primary index corresponding to the secondary index.
The comprehensive energy microgrid evaluation generation method based on the variable component sub-algorithm comprises the following steps:
the system construction module is used for acquiring primary indexes and secondary indexes corresponding to the primary indexes;
The weight distribution module is used for determining the optimal weights of the primary index and the secondary index according to a variable component sub-algorithm;
the comprehensive evaluation module acquires the evaluation level ranking value of each sample through a TOPSIS algorithm according to the optimal weight of each index;
The primary index comprises: natural resource conditions, equipment and technical conditions, economic impact, social impact, environmental impact and carbon emission benefits;
The secondary indicators corresponding to the natural resource situation include: energy distribution conditions, climate conditions and geological conditions; the secondary indexes corresponding to the equipment and technical conditions comprise: the power generation device type, the inverter type, the power generation amount, the number of power generation array strings and the power generation array column spacing; the secondary indicators corresponding to the economic impact include: project investment, project profit, and project compensation capacity; the secondary indicators corresponding to the social impact include: new energy duty ratio, local economic impact and local employment impact; the secondary indicators corresponding to the environmental impact include: noise pollution and dust pollution; the secondary indicators corresponding to the carbon emission benefits include: carbon emissions reduction and carbon trade.
Further, the weight distribution module determines the optimal weights of the primary index and the secondary index according to a variable component sub-algorithm, and the process includes:
the function definition unit converts each secondary index into grade data for evaluating the quality degree, weights each secondary index and establishes a linear weighting function ：
；
In the above-mentioned method, the step of,The evaluation scores of the secondary indexes are sequentially presented,Is thatThe corresponding weight coefficient is used for the weight coefficient,Is the total score;
an optimization variable determination unit that determines each weight coefficient in the linear weighting function as an optimization variable of the optimization problem: Wherein In the variable component sub-algorithm, a classical circuit is utilized to solve a vector matrix in a classical solver mode;
the weight coefficient optimization model building unit generates an objective function of an optimization problem and an objective function of a quantum circuit: Wherein, the method comprises the steps of, wherein, As a function of the object to be processed,As a function of the cost,In order to obtain an evaluation score according to the existing evaluation method,Is according to the formula: the total score obtained is used to determine, Is the precision requirement;
And the optimizing unit optimizes the weight coefficient of each secondary index through a variable component sub-algorithm to obtain the optimal weight of the secondary index and the optimal weight of the primary index corresponding to the secondary index.
Further, the optimizing unit optimizes the weight coefficient of each secondary index through the variable component sub-algorithm, and the process comprises the following steps:
Let the number of qubits be The iterative calculation times are as followsThe maximum iteration number is；
S1: preparing initial qubits: order the，，The state vector of each qubit represents a set of potential solutions to the optimal value of the weight coefficient, the dimension of the vectorEqual to the number of weight coefficients, i.e.The initial state of 18 particles is randomly initialized:
；
Is the first The initial state of the individual qubits,Is the firstInitial states of the respective qubits in 18 dimensions;
S2: to make each qubit locally optimal And determining the global optimum of the qubit, i.e. the optimum solution of the weighting coefficients，；
S3: calculating the state of each qubit: Wherein, the method comprises the steps of, wherein, As a cost function;
S4: as the iterative calculation proceeds, the update is at the first New local optimum for each qubit at multiple iterations：
；
Wherein,，Is the firstThe quantum bit is at the firstThe locally optimal state at the time of the iteration,Is the firstThe number of iterations of the quantum bit isThe objective function corresponding to the local optimum state,As a function of the object to be processed,Is the firstThe number of iterations of the quantum bit isA time quantum bit state;
S5: based on the new local optimum of each qubit Update at the firstGlobal optimum for each qubit at multiple iterationsI.e. the optimum weighting factor is，；
S6: each qubit changes its own state according to the following equation:
；
；
；
In the above-mentioned method, the step of, To at the firstGlobal optimal state of each quantum bit in the next iteration; Is the first The quantum bit is at the firstLocal optimal state in the time of iteration; Is the first The quantum bit is at the firstThe first iterationMaintaining a local optimal state; Is the first Local optimum of individual qubitsAnd global optimum stateA random state in between; Is the first When each quantum bit of the iteration is in the global optimal state, the firstThe state in which the qubit of the dimension is located; Is the first Second iteration (a)The first quantum bitA dimension state; Is the first Step size parameter values of the secondary iteration; Is the first The average state vector of the current optimal state of all quantum bits in the next iteration;、 To be in the interval Random numbers obeying uniform distribution;
S7: judging Whether or not to be equal toAnd if the first index is equal to the second index, ending, and obtaining the optimal weight of the second index and the optimal weight of the first index corresponding to the second index, otherwise, returning to the step S3.
Further, the process of obtaining the evaluation level ranking value of each sample by the comprehensive evaluation module through the TOPSIS algorithm according to the optimal weight of each index comprises the following steps:
The weighted index matrix is obtained according to the following steps ：
Wherein,Representing different comprehensive evaluation results; Is the number of the secondary indexes, ；First, theIndex weights of the secondary indexes; Is the first The first comprehensive evaluation resultEvaluation scores of the secondary indexes;
Matrix of weighted indexes Maximum value set of (a) as a positive ideal solutionMinimum set as negative ideal solution：
The Euclidean distance calculation is performed according to the following formula:
Is the Euclidean distance of ideal solution; Euclidean distance for negative ideal solution;
calculating the ranking value of each evaluation grade ：
。
There is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the variable component sub-algorithm-based comprehensive energy microgrid evaluation generation method.
The electronic equipment comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the comprehensive energy micro-grid evaluation generation method based on the variable component sub-algorithm when executing the program.
The invention has the beneficial effects that: on the basis of considering carbon emission, the method utilizes superposition and entanglement characteristics of quantum bits in a variable component sub-algorithm to process and calculate the index weights of mutual correlation and coupling, so that the generated result is more accurate and more practical, and can adapt to the changes of different practical scenes of the renewable energy micro-grid.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a comprehensive energy microgrid evaluation generation method based on a variable component sub-algorithm;
FIG. 2 is a schematic diagram of the primary index and the secondary index corresponding to each primary index according to the present invention;
FIG. 3 is a matrix diagram of index evaluation levels;
FIG. 4 is a schematic diagram of weighted index rating and its positive and negative ideal solutions;
Fig. 5 is a graph comparing weight calculation results of the carbon emissions right trade without consideration and the carbon emissions right trade with consideration.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the described embodiments are merely some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1-2, a comprehensive energy microgrid evaluation generation system based on a variable component sub-algorithm is provided, and index weights used by the evaluation generation system are obtained based on the variable component sub-algorithm.
The evaluation generation system includes: the system comprises a system construction module, a weight distribution module and a comprehensive evaluation module.
The system construction module is used for acquiring the first-level index and the second-level index corresponding to each first-level index.
And the weight distribution module is used for determining the optimal weights of the primary index and the secondary index according to the variable component sub-algorithm.
And the comprehensive evaluation module is used for acquiring the evaluation level ranking value of each sample through a TOPSIS algorithm according to the optimal weight of each index.
A system building module comprising: natural resource condition acquisition unit, equipment and technical condition acquisition unit, economic impact acquisition unit, social impact acquisition unit, environmental impact acquisition unit and carbon emission benefit acquisition unit.
The natural resource condition acquisition unit is used for acquiring natural resource condition data. The natural resource condition acquisition unit comprises: the energy distribution condition acquisition subunit is used for acquiring energy distribution condition data; the climate condition acquisition subunit is used for acquiring climate condition data; and the geological condition acquisition subunit is used for acquiring geological condition data. The energy distribution condition takes distributed photovoltaic as an example, and mainly considers solar radiation energy, wherein the solar radiation energy consists of two points of solar radiation quantity in a month and sunshine hours in the month. The amount of solar radiation on the month is typically obtained by entering data of the location of the project in Meteonorm or PVSYST procedures, while the number of hours of solar radiation on the month is typically obtained by collecting data from the regional weather bureau. The climate condition data can obtain parameters such as the temperature extreme value of the place, the number of days of the place in the annual average cloudy days, the number of days of the place in the annual average sunny days, the number of days of the special climate, the number of days of the place in the annual average rainfall and the like through a local weather observation station and Meteonorm database according to the geographic position of the place of the project. The geological condition data comprises data such as basic topography, geological structure, ground hydrologic conditions, negative geological influence and the like, wherein the ground hydrologic conditions comprise data such as groundwater distribution, flow conditions, chemical components and the like.
The equipment and technical condition acquisition unit is used for acquiring equipment and technical condition data. The equipment and technical condition acquisition unit comprises: the power generation equipment acquisition subunit is used for acquiring power generation equipment type data; the inverter acquisition subunit is used for acquiring inverter type data; the generating capacity acquisition subunit is used for acquiring generating capacity data; the power generation array group string number acquisition subunit is used for acquiring power generation array group string number data; and the power generation array column spacing acquisition subunit is used for acquiring power generation array column spacing data. The renewable energy micro-grid core power generation equipment mainly comprises a solar panel and a wind motor, and the type data of the power generation equipment comprise parameters such as photoelectric conversion efficiency, short-circuit current, open-circuit voltage, power generation power and the like. The inverter type data comprises upper limit and lower limit of direct current voltage at the input side of the inverter, stability of output power quality of the inverter, MPPT function of the inverter, performance parameters such as capacity and efficiency of the inverter, and parameters such as protection logic of the inverter. The operation mode of the generator set array directly influences the absorption of distributed energy and finally influences the power generation efficiency of the power station, and mainly comprises two aspects of array group string number calculation and array column spacing calculation.
And the economic impact acquisition unit is used for acquiring economic impact data. The economic impact collection unit includes: the project investment acquisition subunit is used for acquiring project investment data; the project profit acquisition subunit is used for acquiring project profit data; and the project compensation capability acquisition subunit is used for acquiring project compensation capability data. Project investment data includes project design costs, equipment and material costs, construction management costs, and labor cost costs. Project profit data is the profit obtained by selling the electrical energy generated by a photovoltaic project at a local price, and requires calculation of the total generation revenue of the project and the net profit after the cost has been cut off. Project clearance data is data obtained through the net cash flow of the project and the recovery period of the investment.
The social influence acquisition unit is used for acquiring social influence data. The social influence acquisition unit includes: the new energy duty ratio acquisition subunit is used for acquiring new energy duty ratio data; the local economic impact acquisition subunit is used for acquiring local economic impact data; the local employment effect acquisition subunit is used for acquiring the local employment effect data. The construction of the renewable energy micro-grid improves the solar energy utilization rate of the region where the project is located, improves the power supply duty ratio of the new energy, and further optimizes the local energy structure; the construction of renewable energy micro-grid projects is beneficial to the social and economic development of the whole area, and has a radiation driving effect in local social production and life, so that other renewable energy micro-grid projects of the area are driven, and the development of local economy can be promoted; in addition, the construction of the renewable energy microgrid not only requires high-level scientific and technological talents, but also requires a large number of professional workers, and the employment rate of the region is improved.
The environment influence acquisition unit is used for acquiring environment influence data. The environmental impact collection unit includes: the noise pollution acquisition subunit is used for acquiring noise pollution data; and the dust pollution acquisition subunit is used for acquiring dust pollution data. In the construction process of the distributed photovoltaic project, equipment such as a crane and a percussion drill are required, the installation of a component bracket of the project and the inclination angle adjustment of the component are required to be finished on the ground so as to generate a large amount of dust, and particularly, the noise and dust pollution of the project are required to be fully considered when the distributed photovoltaic project is constructed by a thermal power station in an urban area so as to improve the overall evaluation level of the project.
And the carbon emission benefit acquisition unit is used for acquiring carbon emission benefit data. The carbon emission benefit collection unit includes: the carbon emission reduction acquisition subunit is used for acquiring carbon emission reduction data; and the carbon transaction acquisition subunit is used for acquiring carbon transaction data. The renewable energy microgrid reduces the power generation requirement on a thermal power plant, finally reduces the carbon dioxide emission of a region, and can obtain benefits by participating in a carbon market.
A weight distribution module comprising: the system comprises a function definition unit, an optimization variable determination unit, a weight coefficient optimization model establishment unit and an optimization unit.
And the function definition unit is used for converting each secondary index into grade data for evaluating the quality degree, weighting each secondary index and establishing a linear weighting function.
And the optimization variable determining unit is used for determining each weight coefficient in the linear weighting function as an optimization variable of the optimization problem.
And the weight coefficient optimization model building unit is used for generating an objective function of the optimization problem and an objective function of the quantum circuit.
The optimizing unit is used for optimizing the weight coefficient of each secondary index through a variable component sub-algorithm to obtain the optimal weight of the secondary index and the optimal weight of the primary index corresponding to the secondary index.
The comprehensive energy microgrid evaluation generation method based on the variable component sub-algorithm comprises the following steps:
101: the system construction module is used for acquiring a first-level index and a second-level index corresponding to each first-level index, namely, constructing an evaluation index system;
102: the weight distribution module is used for determining the optimal weights of the primary index and the secondary index according to the variable component sub-algorithm;
103: and the comprehensive evaluation module acquires the evaluation level ranking value of each sample through a TOPSIS algorithm according to the optimal weight of each index.
Wherein, the first-level index includes: natural resource conditions, equipment and technical conditions, economic impact, social impact, environmental impact, and carbon emission benefits. The secondary indicators corresponding to the natural resource situation include: energy distribution conditions, climate conditions and geological conditions; the secondary indexes corresponding to the equipment and technical conditions comprise: the power generation device type, the inverter type, the power generation amount, the number of power generation array strings and the power generation array column spacing; the secondary indicators corresponding to economic impact include: project investment, project profit, and project compensation capacity; secondary metrics corresponding to social impact include: new energy duty ratio, local economic impact and local employment impact; secondary indicators corresponding to environmental impact include: noise pollution and dust pollution; the secondary indicators corresponding to the carbon emission benefits include: carbon emissions reduction and carbon trade.
The method has 18 secondary indexes in total, and the secondary indexes are obtained, so that the output result of the method has the characteristics of being comprehensive and strong in operability, the accuracy of the conclusion of the comprehensive evaluation index system can be improved, and higher references can be provided for the whole project.
The energy distribution condition takes distributed photovoltaic as an example, and mainly considers solar radiation energy, wherein the solar radiation energy consists of two points of solar radiation quantity in a month and sunshine hours in the month. The amount of solar radiation on the month is typically obtained by entering data of the location of the project in Meteonorm or PVSYST procedures, while the number of hours of solar radiation on the month is typically obtained by collecting data from the regional weather bureau. The climate condition data can obtain parameters such as the temperature extreme value of the place, the number of days of the place in the annual average cloudy days, the number of days of the place in the annual average sunny days, the number of days of the special climate, the number of days of the place in the annual average rainfall and the like through a local weather observation station and Meteonorm database according to the geographic position of the place of the project. The geological condition data comprises data such as basic topography, geological structure, ground hydrologic conditions, negative geological influence and the like, wherein the ground hydrologic conditions comprise data such as groundwater distribution, flow conditions, chemical components and the like.
The renewable energy micro-grid core power generation equipment mainly comprises a solar panel and a wind motor, and the type data of the power generation equipment comprise parameters such as photoelectric conversion efficiency, short-circuit current, open-circuit voltage, power generation power and the like. The inverter type data comprises upper limit and lower limit of direct current voltage at the input side of the inverter, stability of output power quality of the inverter, MPPT function of the inverter, performance parameters such as capacity and efficiency of the inverter, and parameters such as protection logic of the inverter. The operation mode of the generator set array directly influences the absorption of distributed energy and finally influences the power generation efficiency of the power station, and mainly comprises two aspects of array group string number calculation and array column spacing calculation.
Project investment data includes project design costs, equipment and material costs, construction management costs, and labor cost costs. Project profit data is the profit obtained by selling the electrical energy generated by a photovoltaic project at a local price, and requires calculation of the total generation revenue of the project and the net profit after the cost has been cut off. Project clearance data is data obtained through the net cash flow of the project and the recovery period of the investment.
The construction of the renewable energy micro-grid improves the solar energy utilization rate of the region where the project is located, improves the power supply duty ratio of the new energy, and further optimizes the local energy structure; the construction of renewable energy micro-grid projects is beneficial to the social and economic development of the whole area, and has a radiation driving effect in local social production and life, so that other renewable energy micro-grid projects of the area are driven, and the development of local economy can be promoted; in addition, the construction of the renewable energy microgrid not only requires high-level scientific and technological talents, but also requires a large number of professional workers, and the employment rate of the region is improved.
In the construction process of the distributed photovoltaic project, equipment such as a crane and a percussion drill are required, the installation of a component bracket of the project and the inclination angle adjustment of the component are required to be finished on the ground so as to generate a large amount of dust, and particularly, the noise and dust pollution of the project are required to be fully considered when the distributed photovoltaic project is constructed by a thermal power station in an urban area so as to improve the overall evaluation level of the project.
The renewable energy microgrid reduces the power generation requirement on a thermal power plant, finally reduces the carbon dioxide emission of a region, and can obtain benefits by participating in a carbon market.
The process of determining the optimal weights of the primary index and the secondary index by the weight distribution module according to the variable component sub-algorithm comprises the following steps:
Firstly, a function definition unit converts each secondary index into grade data for evaluating the quality degree, weights each secondary index, and establishes a linear weighting function ：
；
In the above-mentioned method, the step of,The evaluation scores of the secondary indexes are sequentially presented,Is thatThe corresponding weight coefficient is used for the weight coefficient,Is the total score.
The secondary indexes are provided with qualitative indexes and quantitative indexes, and the dimensions of the quantitative indexes are different, so that the function definition unit carries out corresponding quantization treatment on the secondary indexes, namely, the quantitative indexes are unified in dimension and the qualitative indexes are quantified, and each secondary index is converted into grade data for evaluating the quality degree. The quality degree can be evaluated by obtaining the scores, the corresponding scores are given after the actual conditions of the indexes of the renewable energy microgrid project are analyzed, the scores are in a percentage system and respectively have four grades of good grade, medium grade and bad grade, and the score quasi-sides are as follows:
Then, the optimization variable determining unit determines each weight coefficient in the linear weighting function as an optimization variable of the optimization problem: Wherein In the variable component sub-algorithm, a classical circuit is utilized, and a vector matrix is solved by a classical solver mode.
Then, the weight coefficient optimization model building unit generates an objective function of the optimization problem and an objective function of the quantum circuit:；
wherein, As a function of the object to be processed,As a function of the cost,In order to obtain an evaluation score according to the existing evaluation method,Is according to the formula: the total score obtained is used to determine, Is a precision requirement. As a cost functionAnd when the convergence result meets the precision requirement, the obtained weight coefficients can be used as the optimal solution of the weight coefficient optimizing problem.
And finally, optimizing the weight coefficient of each secondary index through a variable component sub-algorithm to obtain the optimal weight of the secondary index and the optimal weight of the primary index corresponding to the secondary index.
The optimizing unit optimizes the weight coefficient of each secondary index through a variable component sub-algorithm, and the process comprises the following steps: let the number of qubits beThe iterative calculation times are as followsThe maximum iteration number is；
S1: preparing initial qubits: order the，，The state vector of each qubit represents a set of potential solutions to the optimal value of the weight coefficient, the dimension of the vectorEqual to the number of weight coefficients, i.e.The initial state of 18 particles is randomly initialized:
；
Is the first The initial state of the individual qubits,Is the firstInitial states of the respective qubits in 18 dimensions;
S2: to make each qubit locally optimal And determining the global optimum of the qubit, i.e. the optimum solution of the weighting coefficients，；
S3: calculating the state of each qubit: Wherein, the method comprises the steps of, wherein, As a cost function;
S4: as the iterative calculation proceeds, the update is at the first New local optimum for each qubit at multiple iterations：
；
Wherein,，Is the firstThe quantum bit is at the firstThe locally optimal state at the time of the iteration,Is the firstThe number of iterations of the quantum bit isThe objective function corresponding to the local optimum state,As a function of the object to be processed,Is the firstThe number of iterations of the quantum bit isA time quantum bit state;
S5: based on the new local optimum of each qubit Update at the firstGlobal optimum for each qubit at multiple iterationsI.e. the optimum weighting factor is，；
S6: each qubit changes its own state according to the following equation:
；
；
；
In the above-mentioned method, the step of, To at the firstGlobal optimal state of each quantum bit in the next iteration; Is the first The quantum bit is at the firstLocal optimal state in the time of iteration; Is the first The quantum bit is at the firstThe first iterationMaintaining a local optimal state; Is the first Local optimum of individual qubitsAnd global optimum stateA random state in between; Is the first When each quantum bit of the iteration is in the global optimal state, the firstThe state in which the qubit of the dimension is located; Is the first Second iteration (a)The first quantum bitA dimension state; Is the first Step size parameter values of the secondary iteration; Is the first The average state vector of the current optimal state of all quantum bits in the next iteration;、 To be in the interval Random numbers obeying uniform distribution;
S7: judging Whether or not to be equal toAnd if the first index is equal to the second index, ending, and obtaining the optimal weight of the second index and the optimal weight of the first index corresponding to the second index, otherwise, returning to the step S3.
After obtaining the weights of the indexes according to the method, the comprehensive evaluation module obtains the evaluation level ranking value of each sample through the TOPSIS algorithm according to the optimal weights of the indexes, wherein the process comprises the following steps:
First, a weighted index matrix is obtained according to the following formula ：
In the above-mentioned method, the step of,Representing different comprehensive evaluation results; Is the number of the secondary indexes, ；First, theIndex weights of the secondary indexes; Is the first The first comprehensive evaluation resultEvaluation scores of the secondary indexes;
Then, the weighted index matrix Maximum value set of (a) as a positive ideal solutionMinimum set as negative ideal solution：
Then, the Euclidean distance calculation is performed according to the following formula:
Is the Euclidean distance of ideal solution; The Euclidean distance of the negative ideal solution.
Finally, the ranking value of each evaluation grade is calculated：
。
Through the above process, an index matrix can be obtainedIn the method, the distances between each item evaluation level and the positive ideal solution and the negative ideal solution can be expressed by Euclidean distances, so that the closeness between an evaluation sample and an ideal condition, namely the sorting value of the index is mapped, and the optimal sample can be obtained through analysis by comparing the sorting value of the index and the closeness of each sample to the ideal solution.
The existing renewable energy micro-grid construction comprehensive evaluation generation system or method is less and lacks consideration on carbon emission, so that the output result has poor referenceability. The double influences of carbon emission on economy and environment lead to a coupling state of mutual influence among related index weights, and the effective quantification of the index weights is difficult to be clear in the prior art. The invention aims to effectively quantify the related index weight with complex coupling relation on the basis of considering carbon emission, and constructs a more accurate renewable energy micro-grid evaluation generation method and system. The invention provides clear and reasonable judgment basis for the comprehensive benefit of constructing the renewable energy microgrid, processes and calculates the index weights related and coupled by utilizing the superposition and entanglement characteristics of the quantum bits in the variable component sub-algorithm, has clear effect and is more practical, and the invention can adapt to the change of the actual scene of the renewable energy microgrid and promote the adaptability.
The following description will take a newly built distributed photovoltaic project of a certain thermal power plant as an example.
Firstly, a comprehensive evaluation index system is constructed on the basis of determining the influence of carbon transaction on a distributed photovoltaic project, and specific data corresponding to indexes are obtained. And secondly, utilizing superposition and entanglement characteristics of the quantum bits to process coupling association relations among the secondary indexes, and determining the weights of the indexes. Then, the whole evaluation calculation was performed by using the TOPSIS method. To more accurately reflect the comprehensive performance of the distributed photovoltaic project in a context of accounting for carbon transactions.
The hardware aspect of the distributed photovoltaic project involves a total of 5 parts, including photovoltaic modules, combiner boxes, inverters, distribution segment switches, and ac loads. The specific power generation flow is as follows: the photovoltaic module receives solar radiation energy and converts the solar radiation energy into electric energy, the generated direct current is converged by the converging box, then the direct current is inverted into alternating current by the inverter, and finally the alternating current is transmitted to an alternating current load through the station power distribution section switch.
The monthly solar radiation quantity and the average daily irradiation hours of each month of the power plant are obtained through Meteonorm programs, the annual average solar radiation quantity of the power plant is 1385kWh/m 2, the total generating capacity of the whole life cycle of the project is 3066.41 kilowatt hours, the annual average generating capacity of the project is 122.70 kilowatt hours, the average annual equivalent utilization hours of the solar cell is 1213.76 hours, the annual reduction of carbon dioxide emission is about 476.72 tons, and the emission reduction income is about 2.8 kiloyuan.
And converting each secondary index into grade data for evaluating the quality degree, dividing the grading table into four grades of excellent, good, medium and poor, and finally generating an index evaluation grade matrix, as shown in figure 3. Each element in the index rating matrix represents the rating ratio corresponding to different rating levels of a certain index. For example, the energy distribution condition index, the data with the excellent evaluation grade is 0.6, and the data represents that 12 experts in 20 specialities give scores of more than 85. In fig. 3, B1-B6 represent natural resource conditions, equipment and technical conditions, economic impact, social impact, environmental impact, and carbon emission benefits in order. And C1-C18 sequentially represent energy distribution conditions, climate conditions, geological conditions, power generation equipment types, power generation array string numbers, inverter types, power generation array row spacing, power generation capacity, project investment, project profit, project compensation capacity, new energy duty ratio, local economic influence, local employment influence, noise pollution, dust pollution, carbon emission reduction and carbon transaction.
After the index evaluation grade matrix is obtained, the corresponding index evaluation grade matrix is obtainedAnd then, taking the weight coefficient corresponding to each secondary index as an optimization variable, and carrying out optimization solution on the weight by using a variable component sub-algorithm. The global optimal fitness change curve obtained by observing the iterative optimization of each secondary index is subjected to three hundred iterative computations, and the cost function is obtainedRapidly decreasing to 0.8031 and achieving convergence. At this timeIt is explained that the optimization result obtained after 300 iterations can meet the optimization requirement. The optimization results of the two-level index weight coefficients are shown in the following table:
and comprehensively evaluating the weight coefficient obtained according to the table through a TOPSIS algorithm.
The weighted index evaluation level and the positive and negative ideal solutions thereof are shown in fig. 4.
The results of calculating the euclidean distances between the four evaluation grades of good, medium, and bad and the positive ideal solution (optimal solution) and the negative ideal solution (worst solution) are as follows:
By comparing the Euclidean distance between each evaluation grade and the optimal and inferior solutions, the result with the optimal evaluation grade has more advantages, and the evaluation grade sorting value is optimal, so that the conclusion is integrated that the distributed photovoltaic project of a certain thermal power station has the optimal evaluation grade, and the distributed photovoltaic project belongs to a high-quality project.
For reference, the calculation results of the data such as the index weights of each level, the euclidean distance, the ranking value and the like which do not consider the trading gain of the carbon emission rights are compared with the calculation results which consider the trading of the carbon emission rights, and the data comparison results of the index weights of the two are shown in fig. 5, namely, the euclidean distance and the ranking value thereof are shown in the following table:
as can be clearly seen from fig. 5 and the table above: the addition of the carbon emission trading profit index enables the weight distribution of the comprehensive evaluation index system to be more reasonable, reduces the gap between the dominant index weight value and the inferior index weight value, enables the original natural resource condition, equipment and technical conditions, economic influence, social influence, environmental influence and other index weights to show a descending trend, and the addition of the carbon emission benefit index does not change the weight ordering of the original index and can reflect the weight occupation ratio of each index more truly. Because the project power is 1.01Mwp, the carbon dioxide emission reduction amount is less, and annual carbon emission right trading income is lower, the carbon emission right index weight is lower, which belongs to a small-sized distributed photovoltaic facility. Therefore, the thermal power enterprises should enlarge the construction scale of the distributed photovoltaic projects, improve the carbon dioxide emission reduction amount and the carbon emission right trading income created by the distributed photovoltaic, and enable the carbon emission right index to obtain higher weight value.
And the addition of the carbon emission benefit index enables the Euclidean distance between the grade of the distributed photovoltaic project of a certain thermal power station to evaluate to be excellent and the optimal solution to be closer, the retraction percentage is about 4.17 percent, and the comprehensive ranking value of the result with the evaluation grade of the distributed photovoltaic project of the thermal power station to be higher, which indicates that the addition of the carbon emission benefit index can improve the comprehensive evaluation result of the distributed photovoltaic project of the thermal power station.
The computer readable storage medium is provided, and is used for storing a computer program, and the computer program is executed by a processor to realize the comprehensive energy micro-grid evaluation generation method based on the variable component sub-algorithm.
There is also provided an electronic device comprising at least a memory having a computer program stored thereon, and a processor, when executing the computer program on the memory, implementing the method provided by any of the embodiments of the disclosure.
It should be noted that the storage medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any storage medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).
Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.
While various embodiments of the present disclosure have been described in detail, the present disclosure is not limited to these specific embodiments, and various modifications and embodiments can be made by those skilled in the art on the basis of the concepts of the present disclosure, which modifications and modifications should fall within the scope of the claims of the present disclosure.
Claims (8)
1. The comprehensive energy microgrid evaluation generation system based on the variable component sub-algorithm is characterized in that index weights used by the evaluation generation system are obtained based on the variable component sub-algorithm;
The evaluation generation system includes:
The system construction module is used for acquiring the first-level index and the second-level index corresponding to each first-level index;
the weight distribution module is used for determining the optimal weights of the primary index and the secondary index according to a variable component sub-algorithm;
The comprehensive evaluation module is used for acquiring an evaluation level ranking value of each sample through a TOPSIS algorithm according to the optimal weight of each index;
The system construction module comprises:
The natural resource condition acquisition unit is used for acquiring natural resource condition data;
The equipment and technical condition acquisition unit is used for acquiring equipment and technical condition data;
The economic impact acquisition unit is used for acquiring economic impact data;
the social influence acquisition unit is used for acquiring social influence data;
the environment influence acquisition unit is used for acquiring environment influence data;
the carbon emission benefit acquisition unit is used for acquiring carbon emission benefit data;
The natural resource condition acquisition unit comprises: the energy distribution condition acquisition subunit is used for acquiring energy distribution condition data; the climate condition acquisition subunit is used for acquiring climate condition data; the geological condition acquisition subunit is used for acquiring geological condition data;
The equipment and technical condition acquisition unit comprises: the power generation equipment acquisition subunit is used for acquiring power generation equipment type data; the inverter acquisition subunit is used for acquiring inverter type data; the generating capacity acquisition subunit is used for acquiring generating capacity data; the power generation array group string number acquisition subunit is used for acquiring power generation array group string number data; the power generation array column spacing acquisition subunit is used for acquiring power generation array column spacing data;
the economic impact collection unit includes: the project investment acquisition subunit is used for acquiring project investment data; the project profit acquisition subunit is used for acquiring project profit data; the project compensation capability acquisition subunit is used for acquiring project compensation capability data;
the social influence collecting unit includes: the new energy duty ratio acquisition subunit is used for acquiring new energy duty ratio data; the local economic impact acquisition subunit is used for acquiring local economic impact data; the local employment influence acquisition subunit is used for acquiring local employment influence data;
The environmental impact collection unit includes: the noise pollution acquisition subunit is used for acquiring noise pollution data; the dust pollution acquisition subunit is used for acquiring dust pollution data;
the carbon emission benefit collection unit includes: the carbon emission reduction acquisition subunit is used for acquiring carbon emission reduction data; and the carbon transaction acquisition subunit is used for acquiring carbon transaction data.
2. The variable component sub-algorithm-based comprehensive energy microgrid evaluation generation system according to claim 1, wherein the weight distribution module comprises:
the function definition unit is used for converting each secondary index into grade data for evaluating the quality degree, weighting each secondary index and establishing a linear weighting function;
an optimization variable determining unit for determining each weight coefficient in the linear weighting function as an optimization variable of the optimization problem;
the weight coefficient optimization model building unit is used for generating an objective function of an optimization problem and an objective function of a quantum circuit;
the optimizing unit is used for optimizing the weight coefficient of each secondary index through a variable component sub-algorithm to obtain the optimal weight of the secondary index and the optimal weight of the primary index corresponding to the secondary index.
3. The comprehensive energy microgrid evaluation generation method based on the variable component sub-algorithm is characterized by comprising the following steps of:
the system construction module is used for acquiring primary indexes and secondary indexes corresponding to the primary indexes;
The weight distribution module is used for determining the optimal weights of the primary index and the secondary index according to a variable component sub-algorithm;
the comprehensive evaluation module acquires the evaluation level ranking value of each sample through a TOPSIS algorithm according to the optimal weight of each index;
The primary index comprises: natural resource conditions, equipment and technical conditions, economic impact, social impact, environmental impact and carbon emission benefits;
The secondary indicators corresponding to the natural resource situation include: energy distribution conditions, climate conditions and geological conditions; the secondary indexes corresponding to the equipment and technical conditions comprise: the power generation device type, the inverter type, the power generation amount, the number of power generation array strings and the power generation array column spacing; the secondary indicators corresponding to the economic impact include: project investment, project profit, and project compensation capacity; the secondary indicators corresponding to the social impact include: new energy duty ratio, local economic impact and local employment impact; the secondary indicators corresponding to the environmental impact include: noise pollution and dust pollution; the secondary indicators corresponding to the carbon emission benefits include: carbon emissions reduction and carbon trade.
4. The variable component sub-algorithm-based comprehensive energy microgrid evaluation generation method according to claim 3, wherein the process of determining the optimal weights of the primary index and the secondary index by the weight distribution module according to the variable component sub-algorithm comprises the following steps:
the function definition unit converts each secondary index into grade data for evaluating the quality degree, weights each secondary index and establishes a linear weighting function ：
；
In the above-mentioned method, the step of,The evaluation scores of the secondary indexes are sequentially shown as the/>For/>The corresponding weight coefficient is used for the weight coefficient,Is the total score;
an optimization variable determination unit that determines each weight coefficient in the linear weighting function as an optimization variable of the optimization problem: wherein/> In the variable component sub-algorithm, a classical circuit is utilized to solve a vector matrix in a classical solver mode;
the weight coefficient optimization model building unit generates an objective function of an optimization problem and an objective function of a quantum circuit: Wherein/> As an objective function,/>As a cost function,/>For the evaluation score obtained according to the existing evaluation method,/>Is according to the formula: /(I)The total score obtained,/>Is the precision requirement;
And the optimizing unit optimizes the weight coefficient of each secondary index through a variable component sub-algorithm to obtain the optimal weight of the secondary index and the optimal weight of the primary index corresponding to the secondary index.
5. The variable component sub-algorithm-based comprehensive energy microgrid evaluation generation method according to claim 4, wherein the optimizing unit optimizes the weight coefficient of each secondary index through the variable component sub-algorithm comprises:
Let the number of qubits be The iterative calculation times are/>The maximum number of iterations is/>；
S1: preparing initial qubits: order the，/>，/>The state vector of each qubit represents a set of potential solutions to the optimal value of the weight coefficient, the dimension/>, of the vectorEqual to the number of weight coefficients, i.e./>The initial state of 18 particles is randomly initialized:
；
For/> Initial state of individual qubits,/>For/>Initial states of the respective qubits in 18 dimensions;
S2: to make each qubit locally optimal And determining the global optimal state of the qubit, i.e. the optimal solution/>, of the weight coefficient，/>；
S3: calculating the state of each qubit: Wherein/> As a cost function;
S4: as the iterative calculation proceeds, the update is at the first New local optimum state/>, for each qubit at multiple iterations：
；
Wherein,，/>For/>The number of qubits is at/>The locally optimal state at the time of the iteration,For/>The number of iterations of each qubit is/>Target function corresponding to time local optimal state,/>As an objective function,/>For/>The number of iterations of each qubit is/>A time quantum bit state;
S5: based on the new local optimum of each qubit Update at/>Global optimal state/>, of each qubit at a second iterationI.e. the optimal weight coefficient is/>，/>；
S6: each qubit changes its own state according to the following equation:
；
；
；
In the above-mentioned method, the step of, To at/>Global optimal state of each quantum bit in the next iteration; /(I)For/>The quantum bit is at the firstLocal optimal state in the time of iteration; /(I)For/>The number of qubits is at/>First time of iteration/>Maintaining a local optimal state; For/> Locally optimal state of the individual qubits/>And global optimal state/>A random state in between; /(I)Is the firstWhen each quantum bit of the secondary iteration is in a global optimal state, the/>The state in which the qubit of the dimension is located; /(I)For/>Iteration number/>First/>, of the individual qubitsA dimension state; /(I)For/>Step size parameter values of the secondary iteration; /(I)For/>The average state vector of the current optimal state of all quantum bits in the next iteration; /(I)、/>To be in interval/>Random numbers obeying uniform distribution;
S7: judging Whether or not to equal/>And if the first index is equal to the second index, ending, and obtaining the optimal weight of the second index and the optimal weight of the first index corresponding to the second index, otherwise, returning to the step S3.
6. The comprehensive energy micro-grid evaluation generation method based on the variable component sub-algorithm according to claim 5, wherein the comprehensive evaluation module obtains the evaluation rank ordering value of each sample through the TOPSIS algorithm according to the optimal weight of each index comprises the following steps:
The weighted index matrix is obtained according to the following steps ：
Wherein,Representing different comprehensive evaluation results; /(I)Is the number of secondary indexes,/>；/>First/>Index weights of the secondary indexes; /(I)For/>The/>, in the comprehensive evaluation resultsEvaluation scores of the secondary indexes;
Matrix of weighted indexes Maximum value set of (3) as positive ideal solution/>The minimum set is taken as the negative ideal solution/>：
/>
The Euclidean distance calculation is performed according to the following formula:
;
is the Euclidean distance of ideal solution; /(I) Euclidean distance for negative ideal solution;
calculating the ranking value of each evaluation grade ：
。
7. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements the integrated energy microgrid evaluation generation method based on the variable component sub-algorithm according to any one of claims 3 to 6.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the variable component sub-algorithm based integrated energy microgrid evaluation generation method according to any one of claims 3-6 when executing the program.
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