Electricity-carbon coupling system operation efficiency optimization method based on DEA dynamic feedback

Abstract

The invention discloses an electricity-carbon coupling system operation efficiency optimization method based on DEA dynamic feedback. The method comprises the following steps: S1, inputting multiple input indexes and output indexes into a DEA energy efficiency evaluation model; s2, evaluating the effectiveness of decision units of the DEA energy efficiency evaluation model, and circularly adjusting the input index and output index proportion of the non-effective decision units until the DEA energy efficiency evaluation model of the optimal decision unit is output; s3, eliminating the influence of external environment variables on the operation efficiency of the electric-carbon coupling system by using an SFA regression model oriented by the input index relaxation value of the DEA energy efficiency evaluation model; and S4, optimizing the operation efficiency of the power-carbon coupling system by using the SFA regression model. By adopting the electricity-carbon coupling system operation efficiency optimization method based on DEA dynamic feedback, the influence of environment and random errors can be effectively eliminated, and the accuracy of low-carbon operation decision of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a method for optimizing the operating efficiency of an electric-carbon coupling system based on DEA dynamic feedback. Background Art

[0002] With the continuous increase in global energy demand and the enhancement of environmental protection awareness, the efficient operation and low carbonization of power systems have become the focus of research. The electric-carbon coupling system refers to a system that combines the power system with carbon emission management, which optimizes the operation of the power system, reduces carbon emissions, and achieves sustainable development. In view of this, traditional energy efficiency evaluation methods often ignore the influence of external environmental factors and random errors, resulting in inaccurate energy efficiency evaluation results and difficulty in guiding actual operations.

[0003] Specifically, 1. Influence of environmental factors: The operating efficiency of the electric-carbon coupling system is affected by a variety of external environmental factors, such as light intensity, ambient temperature, etc. Changes in these factors will cause fluctuations in the system's energy efficiency. Traditional energy efficiency evaluation methods are difficult to accurately reflect these influences;

[0004] 2. The existence of random errors: In actual operation, there are random errors in the data due to measurement errors, equipment failures, etc. These errors will affect the accuracy of energy efficiency evaluation and thus affect the effectiveness of the optimization strategy;

[0005] 3. Lack of dynamic feedback mechanism: Most existing optimization methods use static models and lack dynamic feedback mechanisms. Once the system operating conditions change, the optimization strategy may no longer be applicable and needs to be re-evaluated and adjusted. Summary of the invention

[0006] The purpose of the present invention is to provide a method for optimizing the operating efficiency of an electric-carbon coupling system based on DEA dynamic feedback to solve the above technical problems.

[0007] To achieve the above object, the present invention provides a method for optimizing the operating efficiency of an electric-carbon coupling system based on DEA dynamic feedback, comprising the following steps:

[0008] S1. Input multiple input indicators and output indicators into the DEA energy efficiency evaluation model, where the multiple input indicators include active power consumption, reactive power consumption and line loss rate input, and the output indicators include node carbon flow rate and carbon emissions of the electric-carbon coupling system; the decision unit of the DEA energy efficiency evaluation model is the corresponding operating state of the electric-carbon coupling system every hour;

[0009] S2, using the projection value analysis method, according to the output result of step S1, evaluate the effectiveness of the decision-making unit of the DEA energy efficiency evaluation model, and cyclically adjust the input index and output index ratio of the ineffective decision-making unit until the DEA energy efficiency evaluation model of the optimal decision-making unit is output;

[0010] S21. Calculate the projection value between the input and output of the decision unit:

[0011]

[0012] In the formula, [θ*,s +* ,s -* ] represents the optimal solution of the DEA energy efficiency evaluation model; represents the optimal decision-making unit; represents the j0th decision-making unit;

[0013] S22. Calculate the gap between the current decision-making unit and the optimal decision-making unit:

[0014]

[0015] In the formula, represents the j0th decision unit Optimal Decision Making Unit the gap;

[0016] S23. Adjust the ratio of the input index and the output index of the decision-making unit based on the calculation result of step S22:

[0017] when and When , it means that the i-th input indicator and the r-th output indicator are both valid, and the j0-th decision-making unit is determined The optimal decision unit

[0018] when When , it means that the i-th input indicator has more input Reduce the input amount of the i-th input indicator;

[0019] when When , it means that the rth output indicator has less output Increase the output of the rth output indicator;

[0020] when When , it means that the i-th input indicator is under-invested Increase the input amount of the i-th input indicator;

[0021] when When , it means that the rth output indicator has more output Reduce the output of the rth output indicator;

[0022] S24, re-inputting the input index and output index adjusted by step S23 into the DEA energy efficiency evaluation model, and looping steps S1 to S23 until the optimal decision unit is output;

[0023] S3. Use the SFA regression model guided by the relaxation value of the input indicators of the DEA energy efficiency evaluation model to eliminate the impact of external environmental variables on the operational efficiency of the electricity-carbon coupling system;

[0024] S4. Use the SFA regression model to optimize the operational efficiency of the electricity-carbon coupling system.

[0025] Preferably, the expression of the DEA energy efficiency evaluation model described in step S1 is as follows:

[0026]

[0027] In the formula, θ represents the operational efficiency value of the decision-making unit; λ represents the weight of the decision-making unit; e t represents a non-Archimedean infinitesimal; s - and + They represent the slack variables of input index and output index respectively; i represents the weight of the i-th input index of the k-th decision-making unit, and k=1,2,…,24, i=1,2,3 represent the input of active power consumption, non-active power consumption and line loss rate respectively; y rk represents the rth output indicator of the kth decision-making unit, and r=1, 2 represent the node carbon flow rate and the carbon emissions of the electric-carbon coupling system respectively; y 0k represents the initial output index of the kth decision-making unit; x ik represents the i-th input index of the k-th decision-making unit; x 0k represents the initial input index of the kth decision-making unit; k represents the weight of the kth decision-making unit; n represents the total number of decision-making units.

[0028] Preferably, step S3 specifically includes the following steps:

[0029] S31, taking environmental factors and random errors as independent variables and the slack value of input indicators as dependent variables, the SFA regression model expression is as follows:

[0030] s ik =f i (z k β p )+ν ik +u ik (4);

[0031] In the formula, s ik represents the relaxation value of the i-th input indicator of the k-th decision-making unit; f i (z k β p ) represents the expected relaxation value of the i-th input indicator of the k-th decision-making unit; z k Represents environment variables; β prepresents the pth environmental variable coefficient value; ν ik +u ik represents the mixed error term, where ν ik is a random interference term and obeys u ik To manage the inefficiency term and follow the normal distribution truncated at zero and All represents variance, μ represents mean;

[0032] S32: Using the SFA regression model constructed in step S31 to evaluate the input index x of the decision-making unit ik Adjustments are made to adjust all decision-making units to the same environmental conditions to identify the low-carbon operation efficiency of the electricity-carbon coupling system:

[0033]

[0034] In the formula, It represents the input index value of the i-th input index of the k-th decision-making unit after adjustment using the SFA regression model; It means placing all decision-making units in the same external environment. represents the parameters of the SFA regression model; It means that the random errors of all decision-making units are adjusted to the same state, so that each decision-making unit faces the same objective environment. Represents the efficiency value of the decision-making unit.

[0035] Preferably, the environmental variables described in step S31 include the external light intensity and average ambient temperature of the electric-carbon coupling system.

[0036] Therefore, the present invention adopts the above-mentioned method for optimizing the operation efficiency of the electric-carbon coupling system based on DEA dynamic feedback, which has the following beneficial effects:

[0037] 1. Comprehensive energy efficiency evaluation: By constructing the DEA energy efficiency evaluation model, a number of input indicators (such as the active power consumption, reactive power consumption and line loss rate of the node) and output indicators (such as the node carbon flow rate or carbon emissions of the electricity-carbon coupling) are comprehensively considered, which can comprehensively and accurately evaluate the operational efficiency of the electricity-carbon coupling system;

[0038] 2. Dynamic feedback mechanism: By cyclically adjusting the input and output ratios of ineffective decision-making units, the DEA energy efficiency evaluation model of the optimal decision-making unit is output. This dynamic feedback mechanism ensures that the system can be continuously optimized during operation and improves overall efficiency.

[0039] 3. Elimination of environmental variables: An SFA regression model guided by the relaxation value of the input indicators of the DEA energy efficiency evaluation model is constructed to eliminate the impact of external environmental variables (such as the external light intensity and average ambient temperature of the electric-carbon coupling system) on operational efficiency, making the evaluation results more fair and accurate, and not subject to interference from external uncontrollable factors;

[0040] 4. Correction of random errors: Use the SFA regression model to adjust the input indicators of the decision-making units, adjust all decision-making units to the same environmental conditions, identify the low-carbon operation efficiency of the electricity-carbon coupling system, and correct random errors so that each decision-making unit faces the same objective environment, thereby improving the scientificity and reliability of the evaluation;

[0041] 5. Optimize operational efficiency: The SFA regression model is used to optimize the operational efficiency of the electric-carbon coupling system, achieving dynamic and efficient operation of the system. This not only improves the energy efficiency of the power system, but also effectively reduces carbon emissions and promotes the sustainable development of the power system;

[0042] In summary, the present invention utilizes the system input-output operational efficiency cycle adjustment decision model with the DEA method as the core, which can provide accurate decision-making data for low-carbon energy demand response between enterprises or users, provide a basis for low-carbon operation optimization decision-making of photovoltaic power systems, and provide a theoretical application research demonstration for building a dynamic carbon metering management system for new power systems.

[0043] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The present invention is a flow chart of a method for optimizing the operational efficiency of an electric-carbon coupling system based on DEA dynamic feedback. DETAILED DESCRIPTION

[0045] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the invented product is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of the present invention, it should also be noted that, unless otherwise clearly specified and limited, the terms "setting", "installation", and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0046] The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0047] like Figure 1 As shown, a method for optimizing the operation efficiency of an electric-carbon coupling system based on DEA dynamic feedback includes the following steps:

[0048] S1. Input multiple input indicators and output indicators into the DEA energy efficiency evaluation model, where the multiple input indicators include active power consumption, reactive power consumption and line loss rate input, and the output indicators include node carbon flow rate and carbon emissions of the electric-carbon coupling system; the decision unit of the DEA energy efficiency evaluation model is the corresponding operating state of the electric-carbon coupling system every hour;

[0049] Preferably, the expression of the DEA energy efficiency evaluation model described in step S1 is as follows:

[0050]

[0051] In the formula, θ represents the operational efficiency value of the decision-making unit; λ represents the weight of the decision-making unit; e t represents a non-Archimedean infinitesimal; s - and + They represent the slack variables of input index and output index respectively; i represents the weight of the i-th input index of the k-th decision-making unit, and k=1,2,…,24, i=1,2,3 represent the input of active power consumption, non-active power consumption and line loss rate respectively; y rk represents the rth output indicator of the kth decision-making unit, and r=1, 2 represent the node carbon flow rate and the carbon emissions of the electric-carbon coupling system respectively; y 0k represents the initial output index of the kth decision-making unit; x ik represents the i-th input index of the k-th decision-making unit; x0k represents the initial input index of the kth decision-making unit; k represents the weight of the kth decision-making unit; n represents the total number of decision-making units.

[0052] S2, using the projection value analysis method, according to the output result of step S1, evaluate the effectiveness of the decision-making unit of the DEA energy efficiency evaluation model, and cyclically adjust the input index and output index ratio of the ineffective decision-making unit until the DEA energy efficiency evaluation model of the optimal decision-making unit is output;

[0053] S21. Calculate the projection value between the input and output of the decision unit:

[0054]

[0055] In the formula, [θ*,s +* ,s -* ] represents the optimal solution of the DEA energy efficiency evaluation model; represents the optimal decision-making unit; represents the j0th decision-making unit;

[0056] S22. Calculate the gap between the current decision-making unit and the optimal decision-making unit:

[0057]

[0058] In the formula, represents the j0th decision unit Optimal Decision Making Unit the gap;

[0059] S23. Adjust the ratio of the input index and the output index of the decision-making unit based on the calculation result of step S22:

[0060] when and When , it means that the i-th input indicator and the r-th output indicator are both valid, and the j0-th decision-making unit is determined The optimal decision unit

[0061] when When , it means that the i-th input indicator has more input Reduce the input amount of the i-th input indicator;

[0062] when When , it means that the rth output indicator has less output Increase the output of the rth output indicator;

[0063] when When , it means that the i-th input indicator has less investment Increase the input amount of the i-th input indicator;

[0064] when When , it means that the rth output indicator has more output Reduce the output of the rth output indicator;

[0065] S24, re-input the input index and output index adjusted by step S23 into the DEA energy efficiency evaluation model, and loop step S1-step S23 until the optimal decision unit is output.

[0066] In this embodiment, it is assumed that there is an electric-carbon coupling system, which runs for 24 hours and each hour is used as a decision unit. The output results of the DEA energy efficiency evaluation model are as follows:

[0067] Table 1 Output results of DEA energy efficiency evaluation model (efficiency score)

[0068]

[0069]

[0070] As shown in Table 1, the efficiency score is 0.85 for hour 1, indicating that there is room for improvement in the operation status of this hour; input redundancy: the active power consumption of hour 1 can be reduced by 10%, the non-active power consumption can be reduced by 5%, and the line loss rate can be reduced by 2%; insufficient output: the node carbon flow rate of hour 1 can be increased by 15%, and the carbon emissions of the electric-carbon coupling system can be reduced by 10%. By analogy, the operation status of the electric-carbon coupling system every hour can be optimized, and the overall energy efficiency and environmental performance of the system can be improved by reducing redundant inputs and increasing insufficient outputs.

[0071] S3. Since the operation efficiency of the electric-carbon coupling system is not only affected by the decision-making unit, but also by external environmental factors such as light intensity, ambient temperature, and random errors in system input, the SFA regression model (the stochastic frontier analysis regression model, based on the SFA regression analysis, can correct the input value of each decision-making unit under the same external environment so that it can be compared under the same external conditions) guided by the relaxation value of the input index of the DEA energy efficiency evaluation model is used to eliminate the influence of external environmental variables on the operation efficiency of the electric-carbon coupling system;

[0072] Step S3 specifically includes the following steps:

[0073] S31, taking environmental factors and random errors as independent variables and the slack value of input indicators as dependent variables, the SFA regression model expression is as follows:

[0074] s ik =f i (z k β p )+ν ik +uik (4);

[0075] In the formula, s ik represents the relaxation value of the i-th input indicator of the k-th decision-making unit; f i (z k β p ) represents the expected relaxation value of the i-th input indicator of the k-th decision-making unit; z k Represents environment variables; β p represents the pth environmental variable coefficient value; ν ik +u ik represents the mixed error term, where ν ik is a random interference term and obeys u ik To manage the inefficiency term and follow the normal distribution truncated at zero and All represents variance, μ represents mean;

[0076] The environmental variables described in step S31 include the external light intensity and average ambient temperature of the electric-carbon coupling system.

[0077] S32: Using the SFA regression model constructed in step S31 to evaluate the input index x of the decision-making unit ik Adjustments are made to adjust all decision-making units to the same environmental conditions to identify the low-carbon operation efficiency of the electricity-carbon coupling system:

[0078]

[0079] In the formula, It represents the input index value of the i-th input index of the k-th decision-making unit after adjustment using the SFA regression model; It means placing all decision-making units in the same external environment. represents the parameters of the SFA regression model; It means that the random errors of all decision-making units are adjusted to the same state, so that each decision-making unit faces the same objective environment. Represents the efficiency value of the decision-making unit.

[0080] S4. Use the SFA regression model to optimize the operational efficiency of the electricity-carbon coupling system.

[0081] Verification example:

[0082] The t-test method is used to test whether there are significant differences in the energy efficiency evaluation results between the traditional DEA model and the SFA regression model guided by the relaxation value of the input indicators of the DEA energy efficiency evaluation model, and to verify the necessity of establishing the SFA regression model to eliminate environmental factors and random errors.

[0083] 1. Data collection: Collect the energy efficiency evaluation results of the traditional DEA model and the SFA regression model, which are recorded as x1, x2,…, x n and y1,y2,…,y m ;

[0084] 2. Calculate the mean of the traditional DEA model and the mean of the SFA regression model

[0085]

[0086]

[0087] 3. Calculate the standard deviation s of the traditional DEA model x and the standard deviation s of the SFA regression model y :

[0088]

[0089]

[0090] 4. Calculate the t value:

[0091]

[0092] The combined standard deviation s p The calculation formula is as follows:

[0093]

[0094] 5. Determine the degrees of freedom df = n + m-2, and find the critical value based on the degrees of freedom and significance level in the t distribution table. If the absolute value of the calculated t value is greater than the critical value, it is considered that there is a significant difference in the means of the two groups of data; otherwise, it is considered that there is no significant difference.

[0095] In this embodiment, the energy efficiency evaluation result of the traditional DEA model is (10, 12, 10, 18, 15), and the energy efficiency evaluation result of the SFA regression model described in the present invention is (20, 15, 15, 19, 21). The t value obtained by the above calculation is -2.5, and the critical value obtained by looking up the table is 2.306. It can be seen that the absolute value of the t value is greater than the critical value, so it is determined that there is a significant difference between the energy efficiency evaluation results of the traditional DEA model and the SFA regression model, thereby verifying the necessity of the SFA regression model described in the present invention.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for optimizing the operational efficiency of an electric-carbon coupling system based on DEA dynamic feedback, characterized in that: The following steps are involved: S1. Input multiple input indicators and output indicators into the DEA energy efficiency evaluation model, where the multiple input indicators include active power consumption, reactive power consumption and line loss rate input, and the output indicators include node carbon flow rate and carbon emissions of the electric-carbon coupling system; the decision unit of the DEA energy efficiency evaluation model is the corresponding operating state of the electric-carbon coupling system every hour; S2, using the projection value analysis method, according to the output result of step S1, evaluate the effectiveness of the decision-making unit of the DEA energy efficiency evaluation model, and cyclically adjust the input index and output index ratio of the ineffective decision-making unit until the DEA energy efficiency evaluation model of the optimal decision-making unit is output; S21. Calculate the projection value between the input and output of the decision unit: In the formula, [θ*,s +* ,s -* ] represents the optimal solution of the DEA energy efficiency evaluation model; represents the optimal decision-making unit; represents the j0th decision-making unit; S22. Calculate the gap between the current decision-making unit and the optimal decision-making unit: In the formula, represents the j0th decision unit Optimal Decision Making Unit the gap; S23. Adjust the ratio of the input index and the output index of the decision-making unit based on the calculation result of step S22: when and When , it means that the i-th input indicator and the r-th output indicator are both valid, and the j0-th decision-making unit is determined The optimal decision unit When Δx j0 > 0, it means that the i-th input indicator has an additional input of Δx j0 , reduce the input amount of the i-th input indicator; When Δy rj0 > 0, it means that the rth output indicator produces less Δy j0 , increase the output of the rth output indicator; When Δx j0 When <0, it means that the i-th input indicator is under-invested by Δx j0 , increase the input amount of the i-th input indicator; When Δy rj0 When <0, it means that the rth output indicator produces more than Δy j0 , reduce the output of the rth output indicator; S24, re-inputting the input index and output index adjusted by step S23 into the DEA energy efficiency evaluation model, and looping steps S1 to S23 until the optimal decision unit is output; S3. Use the SFA regression model guided by the relaxation value of the input indicators of the DEA energy efficiency evaluation model to eliminate the impact of external environmental variables on the operational efficiency of the electricity-carbon coupling system; S4. Use the SFA regression model to optimize the operational efficiency of the electricity-carbon coupling system.

2. The method for optimizing the operation efficiency of the electric-carbon coupling system based on DEA dynamic feedback according to claim 1 is characterized in that: The expression of the DEA energy efficiency evaluation model described in step S1 is as follows: In the formula, θ represents the operational efficiency value of the decision-making unit; λ represents the weight of the decision-making unit; e t represents a non-Archimedean infinitesimal; s - and + They represent the slack variables of input index and output index respectively; i represents the weight of the i-th input index of the k-th decision-making unit, and k=1,2,…,24, i=1,2,3 represent the input of active power consumption, non-active power consumption and line loss rate respectively; y rk represents the rth output indicator of the kth decision-making unit, and r=1, 2 represent the node carbon flow rate and the carbon emissions of the electricity-carbon coupling system respectively; y 0k represents the initial output index of the kth decision-making unit; x ik represents the i-th input index of the k-th decision-making unit; x 0k represents the initial input index of the kth decision-making unit; k represents the weight of the kth decision-making unit; n represents the total number of decision-making units.

3. The method for optimizing the operation efficiency of the electric-carbon coupling system based on DEA dynamic feedback according to claim 2 is characterized in that: Step S3 specifically The following steps are involved: S31, taking environmental factors and random errors as independent variables and the slack value of input indicators as dependent variables, the SFA regression model expression is as follows: s ik =f i (z k ;b p )+n ik +u ik (4) In the formula, s ik represents the relaxation value of the i-th input indicator of the k-th decision-making unit; f i (z k β p ) represents the expected relaxation value of the i-th input indicator of the k-th decision-making unit; z k Represents environment variables; β p represents the coefficient value of the pth environmental variable; ν ik +u ik represents the mixed error term, where ν ik is a random interference term and obeys u ik To manage the inefficiency term and follow the normal distribution truncated at zero and All represents variance, μ represents mean; S32: Using the SFA regression model constructed in step S31 to evaluate the input index x of the decision-making unit ik Adjustments are made to adjust all decision-making units to the same environmental conditions to identify the low-carbon operation efficiency of the electricity-carbon coupling system: In the formula, It represents the input index value of the i-th input index of the k-th decision-making unit after adjustment using the SFA regression model; It means placing all decision-making units in the same external environment. represents the parameters of the SFA regression model; It means that the random errors of all decision-making units are adjusted to the same state, so that each decision-making unit faces the same objective environment. Represents the efficiency value of the decision-making unit.

4. The method for optimizing the operation efficiency of the electric-carbon coupling system based on DEA dynamic feedback according to claim 3 is characterized in that: The environmental variables described in step S31 include the external light intensity and average ambient temperature of the electric-carbon coupling system.

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