CN112330112A - User energy evaluation method of new energy combined cooling heating and power system based on cloud model - Google Patents
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Abstract
The invention discloses a user energy evaluation method of a new energy combined cooling heating and power system based on a cloud model, which comprises the following steps: respectively acquiring the total power of power supply, heat supply and cold supply of the system and the required power data of corresponding loads; respectively calculating an electric load satisfaction rate, a heat load satisfaction rate and a cold load satisfaction rate according to the acquired data, and taking the electric load satisfaction rate, the heat load satisfaction rate and the cold load satisfaction rate as evaluation indexes; the evaluation indexes further comprise electricity utilization satisfaction, heat utilization satisfaction and cold utilization satisfaction; respectively solving the weight of each evaluation index by adopting an analytic hierarchy process and a variation coefficient process, and solving the comprehensive weight of each evaluation index by adopting a game theory; calculating the comprehensive certainty of each evaluation index; and determining the user energy evaluation level according to the level of the maximum comprehensive determination degree. The invention has the beneficial effects that: the cloud model is applied to the user energy evaluation process, randomness and fuzziness in the evaluation process are effectively described, and the evaluation result can be obtained visually and accurately.
Description
Technical Field
The invention relates to the technical field of user energy evaluation of a new energy combined cooling heating and power system, in particular to a user energy evaluation method and system of a new energy combined cooling heating and power system based on a cloud model.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The renewable energy source combined cooling heating and power system is a poly-generation system integrating cooling, heating and power generation based on an energy gradient utilization principle, is arranged near a user and supplies cold, heat, electricity and domestic hot water at a short distance, and realizes waste heat recovery and gradient utilization during power generation. Greatly improves the comprehensive utilization efficiency of energy sources and greatly reduces the emission of pollutants such as carbon dioxide, sulfur oxide and the like.
At present, the research on the design and the optimized operation of the combined cooling heating and power system is mature, the evaluation research on the system performance, the design, the energy supply, the energy consumption and the like is less, and various index systems are different. Further research has found that the conventional evaluation criteria are only from the viewpoint of energy supply systems, and particularly, the evaluation of the vicinity of the user side of the system is very small, and the evaluation of the energy consumption of the user side of the system is directly related to the development and popularization of the combined cooling, heating and power system. In addition, the evaluation of the user energy utilization is also a comprehensive embodiment of the design, operation and energy supply conditions of the combined cooling heating and power system to a certain extent.
Specifically, the evaluation of user performance mainly has the following two problems:
(1) the user energy evaluation criteria are not uniform, and the results obtained by transverse comparison to a great extent with the user energy evaluation non-uniformity are not so accurate.
(2) The evaluation method of the user energy is single, a reasonable evaluation method is one of the keys for evaluating the system, and the single method for determining the weight is easily influenced by the characteristics of the weight method, so that the evaluation result has great deviation.
In order to better evaluate the energy consumption of the user and correctly grasp the energy supply condition of the combined cooling heating and power system, it is necessary to evaluate and analyze the energy consumption of the user.
Disclosure of Invention
In view of the above, the invention provides a new energy combined cooling heating and power system user energy evaluation method and system based on a cloud model, which fully considers the characteristics of simultaneous cooling, heating and power supply of the combined cooling heating and power system, establishes user energy evaluation indexes, and can realize accurate evaluation of user energy.
In order to achieve the above purpose, in some embodiments, the following technical solutions are adopted:
a user energy evaluation method of a new energy combined cooling heating and power system based on a cloud model comprises the following steps:
respectively acquiring the total power of power supply, heat supply and cold supply of the system and the required power data of corresponding loads;
respectively calculating an electric load satisfaction rate, a heat load satisfaction rate and a cold load satisfaction rate according to the acquired data, and taking the electric load satisfaction rate, the heat load satisfaction rate and the cold load satisfaction rate as evaluation indexes; the evaluation indexes further comprise electricity utilization satisfaction, heat utilization satisfaction and cold utilization satisfaction;
respectively solving the weight of each evaluation index by adopting an analytic hierarchy process and a variation coefficient process, and solving the comprehensive weight of each evaluation index by adopting a game theory based on the weight of each evaluation index obtained by the two methods;
calculating the comprehensive certainty of each evaluation index;
and determining the user energy evaluation level according to the level of the maximum comprehensive determination degree.
In other embodiments, the following technical solutions are adopted:
a user energy evaluation system of a new energy combined cooling heating and power system based on a cloud model comprises:
the device is used for respectively acquiring the total power of power supply, heat supply and cold supply of the system and the required power data of corresponding loads;
means for calculating an electric load satisfaction rate, a heat load satisfaction rate, and a cold load satisfaction rate, respectively, according to the acquired data, and taking the calculated results as evaluation indexes; the evaluation indexes further comprise electricity utilization satisfaction, heat utilization satisfaction and cold utilization satisfaction;
the device is used for solving the weight of each evaluation index by adopting an analytic hierarchy process and a variation coefficient method respectively, and solving the comprehensive weight of each evaluation index by adopting a game theory based on the weight of each evaluation index solved by the two methods;
means for calculating a comprehensive degree of certainty of each evaluation index;
and the device is used for determining the grade of the user evaluation according to the grade of the maximum comprehensive determination degree.
In other embodiments, the following technical solutions are adopted:
a terminal device comprising a processor and a computer-readable storage medium, the processor being configured to implement instructions; the computer-readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the user energy evaluation method of the cloud model-based new energy combined cooling heating and power system.
In other embodiments, the following technical solutions are adopted:
a computer-readable storage medium, wherein a plurality of instructions are stored, and the instructions are suitable for being loaded by a processor of a terminal device and executing the user energy evaluation method of the cloud model-based new energy combined cooling heating and power system.
Compared with the prior art, the invention has the beneficial effects that:
the method optimizes the weight result, and is more objective and reasonable in weight solving and more accurate in weight result.
The cloud model is applied to the user energy evaluation process, randomness and fuzziness in the evaluation process are effectively described, and the evaluation result can be obtained visually and accurately.
The invention can play a guiding role in the optimization of the system and the later upgrading of the strategy adjustment by analyzing the energy consumption of the user.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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FIG. 1 is a flow chart of a method for evaluating user energy of a new energy combined cooling heating and power system in an embodiment of the invention;
FIG. 2 is a schematic diagram of a forward cloud generator according to an embodiment of the present invention;
FIG. 3 is a cloud model for evaluation according to an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
In one or more embodiments, a user energy evaluation method for a new energy combined cooling heating and power system based on a cloud model is disclosed, and with reference to fig. 1, the method includes:
(1) and respectively acquiring the total power of power supply, heat supply and cold supply of the system and the required power data of corresponding loads.
(2) Respectively calculating indexes of an electric load satisfaction rate, a heat load satisfaction rate and a cold load satisfaction rate according to the acquired data, and taking the indexes as evaluation indexes; the evaluation index also includes electricity utilization satisfaction, heat utilization satisfaction and cold utilization satisfaction.
Specifically, the electricity utilization satisfaction, the heat utilization satisfaction, the cold utilization satisfaction, the electricity load satisfaction, the heat load satisfaction and the cold load satisfaction are determined as evaluation indexes;
wherein the electricity consumption satisfaction, heat consumption satisfaction and cold consumption satisfaction data are obtained according to a scoring questionnaire; the load satisfaction rate index data is obtained by calculating the total energy supply power of the system and the required power of the corresponding load, such as: the electric load satisfaction rate is the ratio of the required power of the electric load to the total power supply power of the system; the heat load satisfaction rate and the cold load satisfaction rate index data are also calculated in this way.
Obtaining an evaluation score value of the combined cooling heating and power system according to the 6 index data; and establishing an index evaluation grade table as shown in the table 1.
TABLE 1 index evaluation grade Table
(3) And respectively solving the weight of each evaluation index by adopting an analytic hierarchy process and a variation coefficient process, and solving the comprehensive weight of each evaluation index by adopting a game theory based on the weight of each evaluation index obtained by the two methods.
In particular, the amount of the solvent to be used,
1) solving for the weight Q by analytic hierarchy process1。
An Analytic Hierarchy Process (AHP) is a method for determining subjective weight, and the weight value of an index is solved by a column writing judgment matrix method. The method comprises the following specific steps:
the judgment matrix is written according to the column of the table 2.
TABLE 2
| Quantized value | Factor ratio factor |
| 1 | Of equal importance |
| 3 | Of slight importance |
| 5 | Of greater importance |
| 7 | Of strong importance |
| 9 | Of extreme importance |
| 2,4,6,8 | Intermediate values of two adjacent judgments |
In this embodiment, the determination matrix Y-a is as follows:
solving the maximum eigenvalue lambda and the maximum eigenvector alpha of the judgment matrix.
Firstly, multiplying indexes by each row:
and then, solving the geometric mean value of each row:
then, normalization processing is performed to obtain a weight coefficient of the jth index:
obtaining the maximum characteristic root of the judgment matrix:
in the formula, alpha is the maximum eigenvector; c is a constructed judgment matrix; n is the order of the decision matrix.
And thirdly, judging the consistency of the matrix.
And calculating a check coefficient CR, comparing the magnitude of CR with that of 0.1, if CR is less than 0.1, passing consistency check, and otherwise, correcting the matrix.
Wherein CR is a test coefficient, CI is a consistency index, and RI is a random consistency index; the RI is related to the dimension of the determination matrix, and generally, the larger the dimension is, the higher the probability of occurrence of random deviation of consistency is. The RI values are shown in Table 3.
TABLE 3
| Dimension number | 1 | 2 | 3 | 4 | 5 | 6 | ... | ... |
| |
0 | 0 | 0.58 | 0.89 | 1.12 | 1.26 | ... | ... |
After the consistency inspection is finished, if the inspection is passed, the maximum feature vector is a weighted value; if the check fails, the decision matrix needs to be re-written.
2) Solving the weight Q by a coefficient of variation method2。
Firstly, obtaining the original data of each index.
And (2) carrying out standardization processing on the original data.
In the formula, alphaij、αmax、αminRespectively representing the jth index value of the ith sample, the jth index maximum value of the ith sample and the jth index minimum value of the ith sample.
Calculating weight Q2。
In the formula (I), the compound is shown in the specification,
is the average of the j-th index; sigmajIs the standard deviation of the j-th index; vJIs the coefficient of variation of j indexes, namely the coefficient of standard deviation; w is ajIs the weight of the j index.
3) And solving the comprehensive weight of the index by adopting a game theory.
(ii) constructing a basic weight set.
Constructing a basic weight set: u. ofk＝{uk1 uk2 ... ukm}，k＝1,2,...,L
Any linear combination of the L weight vectors is:
in the formula, alphakIs a weight coefficient; u is a comprehensive weight vector in the L weight sets.
And (2) optimizing the L weight vectors.
Let u and each ukThe dispersion of (a) is minimized. Namely, the following conditions are satisfied:
from the differential nature of the matrix, the optimized first derivative translates into:
carrying out normalization treatment on the obtained product to obtain:
the obtained comprehensive weight is:
(4) and obtaining the certainty factor of each evaluation index based on the cloud model.
Calculating the comprehensive certainty factor of each evaluation index based on the comprehensive weight and the certainty factor of each evaluation index;
specifically, the cloud model is a mathematical model based on normal distribution and bell-shaped membership functions, is used for realizing uncertainty conversion between qualitative and quantitative determination of certain things and phenomena in the objective world, and has good applicability to the randomness problem in the evaluation process of the new energy combined cooling heating and power system.
The cloud model represents the overall characteristics of the cloud model through three parameters of expected Ex, entropy En and super entropy He, wherein Ex represents the expectation of cloud drop in the discourse domain space distribution and can represent the point of a qualitative concept; en reveals a correlation between ambiguity and randomness for measuring uncertainty and ambiguity of qualitative concepts; he is the uncertainty used to measure entropy.
The cloud model generator comprises a forward cloud generator and a reverse cloud generator and is used for realizing conversion of numerical values and uncertainties. In this embodiment, a forward generator is used, which is a mapping from qualitative to quantitative, and the input quantities are Ex, En, He and N, and the output quantity is a quantitative value of N cloud droplets, as shown in fig. 2.
The selection of the cloud model parameters is specifically as follows:
He＝k
wherein, Imin、ImaxThe left and right boundaries of each grade scoring interval of the evaluation index are shown, and k is a constant. Calculating the cloud characteristic values Ex, En and He corresponding to different levels by using the forward cloud generator principleAnd drawing the cloud model diagrams corresponding to different grades by Matlab software.
In particular, it is possible to use,
1) generating normal random numbers En ', En' -N (En, He) with En as expected value and He as standard deviation2)；
2) Generating a normal random number x, x-N (Ex, En ') with Ex as expected and En' as standard deviation2)；
3) Computing
Mu (x) is the certainty of the evaluation index, and x is 1 cloud drop in the number domain;
4) repeating steps 1) -3) until N cloud droplets are generated;
5) an evaluation cloud model is generated through the above steps, as shown in fig. 3.
According to the certainty of each evaluation index, the comprehensive certainty K can be calculated:
in the formula: u. of*The weight of each evaluation index; mu.siThe degree of certainty of each evaluation index.
(5) And determining the user energy evaluation level according to the level of the maximum comprehensive determination degree.
Specifically, the grade of the evaluation index is determined according to the cloud model graphs of different grades, so that the user energy evaluation grade is determined.
Example two
In one or more embodiments, a user energy evaluation system for a new energy combined cooling heating and power system based on a cloud model is disclosed, which includes:
the device is used for respectively acquiring the total power of power supply, heat supply and cold supply of the system and the required power data of corresponding loads;
means for calculating an electric load satisfaction rate, a heat load satisfaction rate, and a cold load satisfaction rate, respectively, according to the acquired data, and taking the calculated results as evaluation indexes; the evaluation indexes further comprise electricity utilization satisfaction, heat utilization satisfaction and cold utilization satisfaction;
the device is used for solving the weight of each evaluation index by adopting an analytic hierarchy process and a variation coefficient method respectively, and solving the comprehensive weight of each evaluation index by adopting a game theory based on the weight of each evaluation index solved by the two methods;
means for calculating a comprehensive degree of certainty of each evaluation index;
and the device is used for determining the grade of the user evaluation according to the grade of the maximum comprehensive determination degree.
It should be noted that the specific implementation manner of each device is implemented by using the method in the first embodiment, and is not described again.
EXAMPLE III
In one or more embodiments, a terminal device is disclosed, which includes a server, where the server includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the new energy combined cooling heating and power method based on a cloud model in the first embodiment. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A user energy evaluation method of a new energy combined cooling heating and power system based on a cloud model is characterized by comprising the following steps:
respectively acquiring the total power of power supply, heat supply and cold supply of the system and the required power data of corresponding loads;
respectively calculating an electric load satisfaction rate, a heat load satisfaction rate and a cold load satisfaction rate according to the acquired data, and taking the electric load satisfaction rate, the heat load satisfaction rate and the cold load satisfaction rate as evaluation indexes; the evaluation indexes further comprise electricity utilization satisfaction, heat utilization satisfaction and cold utilization satisfaction;
respectively solving the weight of each evaluation index by adopting an analytic hierarchy process and a variation coefficient process, and solving the comprehensive weight of each evaluation index by adopting a game theory based on the weight of each evaluation index obtained by the two methods;
calculating the comprehensive certainty of each evaluation index;
and determining the user energy evaluation level according to the level of the maximum comprehensive determination degree.
2. The user energy evaluation method of the new energy combined cooling heating and power system based on the cloud model as claimed in claim 1, wherein the step of solving the weight of each evaluation index by using an analytic hierarchy process specifically comprises:
determining a judgment matrix according to the original data of the user satisfaction degree;
solving the maximum eigenvalue lambda and the maximum eigenvector alpha of the judgment matrix;
carrying out consistency check on the judgment matrix; if the check is passed, the maximum feature vector alpha is used as a weight vector, and if the check is not passed, the re-column writing judgment matrix is returned.
3. The user energy evaluation method of the new energy combined cooling heating and power system based on the cloud model as claimed in claim 1, wherein the step of solving the weight of each evaluation index by using a variation coefficient method specifically comprises:
acquiring original data of each evaluation index;
carrying out standardization processing on the original data;
calculating a weight Q according to the standard deviation coefficient of each index2。
4. The user energy evaluation method of the new energy combined cooling heating and power system based on the cloud model as claimed in claim 1, wherein the comprehensive weight of each evaluation index is solved by adopting a game theory based on the weight of each evaluation index obtained by the two methods, and the specific process comprises:
constructing a basic weight set uk＝{uk1 uk2 ... ukm}，k＝1,2,...,L；
Any integrated weight vector in the L weight sets is:
αkis a weight coefficient;
optimizing L weight vectors u to make u and each ukThe dispersion of (2) is minimized;
normalizing the weight coefficient to obtain a comprehensive weight as follows:
5. the user energy evaluation method of the new energy combined cooling heating and power system based on the cloud model as claimed in claim 1, wherein calculating the comprehensive certainty of each evaluation index specifically includes:
obtaining the certainty factor of each evaluation index based on the cloud model;
and calculating the comprehensive certainty factor of each evaluation index based on the comprehensive weight and the certainty factor of each evaluation index.
6. The user energy evaluation method for the new energy combined cooling heating and power system based on the cloud model as claimed in claim 5, wherein the obtaining of the degree of certainty of each evaluation index based on the cloud model specifically includes:
determining cloud characteristic values Ex, En and He of each evaluation index;
generating a normal random number En' with En as an expected value and He as a standard deviation;
generating a normal random number x with Ex as an expected value and En' as a standard deviation;
the degree of certainty of each evaluation index is calculated based on the normal random number En' and the normal random number x.
7. The user energy evaluation method for the new energy combined cooling heating and power system based on the cloud model as claimed in claim 1, wherein the comprehensive degree of certainty of each evaluation index is calculated based on the comprehensive weight and degree of certainty of each evaluation index, and specifically comprises:
wherein u is*The weight of each evaluation index; mu.siThe degree of certainty of each evaluation index.
8. A new energy combined cooling heating and power system user energy evaluation system based on cloud model is characterized by comprising:
the device is used for respectively acquiring the total power of power supply, heat supply and cold supply of the system and the required power data of corresponding loads;
means for calculating an electric load satisfaction rate, a heat load satisfaction rate, and a cold load satisfaction rate, respectively, according to the acquired data, and taking the calculated results as evaluation indexes; the evaluation indexes further comprise electricity utilization satisfaction, heat utilization satisfaction and cold utilization satisfaction;
the device is used for solving the weight of each evaluation index by adopting an analytic hierarchy process and a variation coefficient method respectively, and solving the comprehensive weight of each evaluation index by adopting a game theory based on the weight of each evaluation index solved by the two methods;
means for calculating a comprehensive degree of certainty of each evaluation index;
and the device is used for determining the grade of the user evaluation according to the grade of the maximum comprehensive determination degree.
9. A terminal device comprising a processor and a computer-readable storage medium, the processor being configured to implement instructions; the computer-readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the user energy evaluation method of the cloud model-based new energy combined cooling heating and power system according to any one of claims 1 to 7.
10. A computer-readable storage medium, in which a plurality of instructions are stored, wherein the instructions are adapted to be loaded by a processor of a terminal device and execute the method for evaluating the user energy of the cloud model-based new energy combined cooling heating and power system according to any one of claims 1 to 7.
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| CN113537820A (en) * | 2021-07-29 | 2021-10-22 | 山东普赛通信科技股份有限公司 | Two-network balance comprehensive evaluation method and system for heat supply system |
| CN114118786A (en) * | 2021-11-24 | 2022-03-01 | 国网山东省电力公司枣庄供电公司 | Comprehensive energy system energy efficiency evaluation method and device and terminal equipment |
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| CN113537820A (en) * | 2021-07-29 | 2021-10-22 | 山东普赛通信科技股份有限公司 | Two-network balance comprehensive evaluation method and system for heat supply system |
| CN113537820B (en) * | 2021-07-29 | 2023-11-14 | 山东普赛通信科技股份有限公司 | Comprehensive evaluation method and system for two-network balance of heating system |
| CN114118786A (en) * | 2021-11-24 | 2022-03-01 | 国网山东省电力公司枣庄供电公司 | Comprehensive energy system energy efficiency evaluation method and device and terminal equipment |
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