Electric power and electric quantity balance scheme comprehensive decision-making method considering thermal power emission reduction strategy configuration

Abstract

The invention relates to an electric power and electric quantity balance scheme comprehensive decision-making method considering thermal power emission reduction strategy configuration. The method comprises the following steps: establishing a thermal power emission reduction strategy configuration model; constructing an evaluation index system, and calculating through an analytic hierarchy process and an entropy weight method to obtain a combined weight of each evaluation index contained in the evaluation index system; and carrying out comprehensive evaluation by adopting an ideal point approaching method to obtain an optimal electric power and electric quantity balance scheme considering the thermal power emission reduction strategy. According to the invention, after the thermal power generating unit is flexibly transformed, the influence of different thermal power emission reduction strategies on the comprehensive decision-making of the electric power and electric quantity balance scheme is analyzed; from the perspective of subjective and objective combination, different characteristics of an analytic hierarchy process and an entropy weight method in a subjective and objective evaluation system are fully considered, and a combined weight calculation method combining the two methods is provided; according to the method, the electric power and electric quantity balance scheme is comprehensively evaluated and optimized by adopting a sorting method approaching an ideal point method, and the optimal electric power and electric quantity balance scheme considering the thermal power emission reduction strategy is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching power and quantity balance, and in particular to a comprehensive decision-making method for power and quantity balance scheme considering the configuration of thermal power emission reduction strategy. Background Art

[0002] In recent years, on the one hand, due to the increasingly prominent contradiction between coal and electricity and the large-scale access of intermittent energy such as new energy, the uncertainty and complexity of power balance have increased significantly, which has brought greater challenges to the power balance of the power grid. On the other hand, with the construction of ultra-high voltage power grids, cross-regional and cross-provincial power exchange has become an important part of power balance, which has put forward higher requirements for the overall consideration of the optimal allocation of resources across the entire network. The power balance objectives have become more diversified, and the factors that need to be considered in the power balance analysis have become more complicated.

[0003] At present, the evaluation and decision-making of power and electricity balance schemes are mostly based on a single simple economic indicator, relying on expert experience, and lacking a comprehensive and systematic evaluation and decision-making model. With the advancement of the dual carbon goals and the development of a new power system with dual high characteristics, the power and electricity balance analysis work can no longer simply take the power and electricity balance adequacy as a single goal, but also needs to comprehensively consider the optimization of the allocation of resources across the entire network, energy conservation and emission reduction, as well as economic and social benefits. Summary of the invention

[0004] In order to solve the problem that the existing technology lacks comprehensive consideration of power and electricity balance analysis, the purpose of the present invention is to provide a comprehensive decision-making method for power and electricity balance solutions that can coordinate the allocation of electric energy across regions and seasons, realize the overall optimal utilization of power generation resources in the entire network within a certain period, and thus realize the scientific scheduling of the power system and consider the configuration of thermal power emission reduction strategies.

[0005] To achieve the above object, the present invention adopts the following technical solution: a comprehensive decision-making method for power and electricity balance scheme considering the configuration of thermal power emission reduction strategy, the method comprising the following steps in order:

[0006] (1) Establish a thermal power emission reduction strategy configuration model;

[0007] (2) An evaluation index system is constructed based on the thermal power emission reduction strategy configuration model, and the combined weights of each evaluation index included in the evaluation index system are calculated by using the hierarchical analysis method and entropy weight method;

[0008] (3) Based on the combined weights of the evaluation indicators obtained in step (2), a comprehensive evaluation is performed using the approximate ideal point method to obtain the optimal power balance solution that takes into account the thermal power emission reduction strategy.

[0009] The step (1) specifically comprises the following steps:

[0010] (1a) The objective function of the thermal power emission reduction strategy configuration model is to minimize the total cost, which includes the total investment cost C1 of the flexibility transformation of thermal power units, the total power generation cost C2 during the dispatch period, and the cost of abandoned wind and solar power C3;

[0011] min(C1+C2+C3) (1)

[0012]

[0013]

[0014]

[0015] Where: C1 is the total investment cost of the flexibility transformation of thermal power units; C2 is the total power generation cost during the dispatch period; C3 is the cost of abandoned wind and solar power; Ω is the set of thermal power units j; T is the set of time periods t in short-term dispatch, each time period t represents 1 hour; S is the set of time periods s in long-term dispatch, each time period s represents 1 month; I j The annual investment cost for flexibility transformation of thermal power unit j; D s is the number of days in the sth month; c b is the unit abandoned wind and solar cost coefficient; c w is the unit power generation cost of wind and solar; is the output of thermal power unit j in the tth period of the sth month; The new energy output for the tth period of the sth month; is the abandoned wind and solar power in the tth period of the sth month; j A variable that is 0 or 1, which is 1 if and only if the thermal power unit undergoes flexibility transformation, otherwise it is 0; f j is the power generation cost function of thermal power unit j;

[0016] (1b) The constraints of the thermal power emission reduction strategy configuration model include load balance constraints, carbon emission constraints, wind and solar power curtailment constraints, ramp constraints and thermal power unit output range constraints;

[0017] The load balance constraint means that the sum of the output of thermal power units plus the output of new energy sources in each time period is equal to the current load, and the formula is:

[0018]

[0019] Where: L s.t is the load in the tth period of the sth month;

[0020] The carbon emission constraint means that the carbon emissions of thermal power units in each time period are less than the current carbon emission limit:

[0021]

[0022] Where: c e is the carbon emission coefficient, K is the carbon emission limit for that period;

[0023] The wind and solar power abandonment constraint means that the wind abandonment in each period, i.e., the sum of the new energy output and the abandoned wind and solar power, is equal to the maximum wind and solar power generation available in the current period, that is:

[0024]

[0025] Where: is the maximum wind and solar power generation in the tth period of the sth month;

[0026] The ramp constraint means that the difference in output of the thermal power unit in adjacent time periods is less than or equal to the maximum ramp rate.

[0027]

[0028] Where: R j is the ramp rate of thermal power unit j without flexibility modification; is the ramp rate of thermal power unit j after flexibility transformation;

[0029] The output range constraint of the thermal power unit refers to the output of the thermal power unit in each period. At minimum steady-fire load With rated capacity between:

[0030]

[0031] Where: is the maximum output of thermal power unit j; is the minimum stable combustion load of thermal power unit j before flexibility transformation; It is the minimum stable combustion load of thermal power unit j before flexibility transformation.

[0032] The step (2) specifically comprises the following steps:

[0033] (2a) In combination with the goal of power balance analysis, the resource optimization allocation level, energy conservation and emission reduction, economic benefits and social benefits are used as evaluation indicators. Based on the key performance indicator analysis method, key evaluation indicators are selected from the evaluation indicators, and an evaluation indicator system is constructed based on the key evaluation indicators;

[0034] (2b) The decision-making objectives and considerations required for the power and electricity balance plan are divided into target layer and indicator layer by using the hierarchical analysis method. Let the number of evaluation objectives be m and the number of evaluation indicators be n. The normalized evaluation matrix Y is constructed by using the hierarchical analysis method with the target layer as the row and the indicator layer as the column.m×n , specifically including the following steps:

[0035] (2b1) A judgment matrix A is established based on the elements of each layer, and the values ​​of the judgment matrix A are assigned the importance of the elements of each layer using a scale of 1 to 9;

[0036] (2b2) ​​Solve the weights of the indicators at each level in the hierarchical analysis method. According to the judgment matrix A, use the eigenvector method to calculate the weights of each factor under the same standard; first find the maximum eigenvalue λ of the judgment matrix A max and the corresponding maximum eigenvalue λ max The eigenvector of is normalized to obtain the weight vector ω j =[ω1,ω2,…,ω n ], where j = 1, 2, ..., n;

[0037] (2b3) After obtaining the weight vector of the judgment matrix A, a consistency check is performed to ensure the validity of the judgment matrix; if the consistency check fails, the judgment matrix A is modified;

[0038] (2b4) Construct the evaluation matrix X m×n : Find ω j Then, according to the actual survey sampling value or expert rating list, an evaluation matrix X is constructed with the target layer as the row and the evaluation index as the column. m×n ;

[0039] (2b5) Find the normalized evaluation matrix Y m×n :Since the dimensions of each indicator are different, the evaluation indicators need to be normalized. Let i = 1, 2, ..., m; j = 1, 2, ..., n, and use the vector normalization method to obtain the normalized evaluation matrix Y m×n :

[0040]

[0041] Where: x ij is the evaluation matrix X m×n Each element in y ij is the normalized evaluation matrix Y m×n Each element in

[0042] (2c) The entropy weight method is used to determine the weight of different indicators based on the amount of information contained in the entropy value of the indicator:

[0043] Normalize the data;

[0044]

[0045] In the formula, p ij is the normalized evaluation matrix X m×nThe elements in

[0046] Calculation of entropy values ​​of each indicator;

[0047]

[0048] In the formula, e j is the entropy value of the jth indicator. The smaller the entropy value of the indicator, the greater its entropy weight, which reflects that the indicator is more important and provides more useful information.

[0049] Calculation of entropy weight: Assume is the entropy weight of the jth evaluation index, and the entropy weight of the evaluation index is calculated using the following formula:

[0050]

[0051] (2d) Combining the entropy weight method with the hierarchical analysis method, the combined weight calculated by the hierarchical analysis method and the entropy weight method is:

[0052]

[0053] In the formula, 0≤α≤1, the value of α depends on ω j and The correlation is determined by the Kendall correlation coefficient; the combined weight is ψ j =[ψ1,ψ2,…,ψ n ], where j = 1, 2,…, n.

[0054] The step (3) specifically comprises the following steps:

[0055] (3a) Solve the weighted normalized decision matrix Z m×n : Evaluate the matrix Y by normalization m×n and the combined weight ψ j Solve the weighted normalized decision matrix Z m×n , where z ij =ψ j ·y ij , z ij is the weighted normalized decision matrix Z m×n Each element in y ij is the normalized evaluation matrix Y m×n Each element in

[0056] (3b) Determine the positive ideal solution and the negative ideal solution. The positive ideal solution refers to the weighted normalized decision matrix Z m×n The maximum value in each column is set The negative ideal solution refers to the weighted normalized decision matrix Z m×n The minimum value in each column is set

[0057] (3c) Calculate the weighted normalized decision matrix Z for each evaluation object m×n Each element in the positive ideal solution Distance

[0058]

[0059] Calculate the weighted normalized decision matrix Z for each evaluation object m×n The distance from each element to the negative ideal solution for:

[0060]

[0061] (3d) Solve the relative closeness and determine the evaluation results, and calculate the relative closeness H of each evaluation object and the optimal solution i :

[0062]

[0063] In the formula, H i The larger the value, the closer it is to the optimal solution. i The solution corresponding to the maximum value is taken as the optimal power balance solution considering the thermal power emission reduction strategy.

[0064] It can be seen from the above technical scheme that the beneficial effects of the present invention are: first, the present invention studies the influence of different thermal power emission reduction strategies on the comprehensive decision-making of the power and electricity balance plan after the flexibility transformation of the thermal power unit; second, the present invention configures the thermal power emission reduction strategy, takes multiple goals such as power and electricity abundance, energy conservation, environmental protection and economy as evaluation indicators, optimizes multiple evaluation indicators based on the key performance indicator analysis method, and constructs an evaluation indicator system; third, from the perspective of combining subjective and objective factors, the present invention fully considers the different characteristics of the hierarchical analysis method and the entropy weight method in the subjective and objective evaluation system, and proposes a combined weight calculation method that combines the two methods; fourth, the present invention adopts the sorting method of the approximate ideal point method to comprehensively evaluate and optimize the power and electricity balance plan, and obtains the optimal power and electricity balance plan considering the thermal power emission reduction strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0066] like Figure 1 As shown, a comprehensive decision-making method for power balance scheme considering the configuration of thermal power emission reduction strategy includes the following steps in order:

[0067] (1) Establish a thermal power emission reduction strategy configuration model;

[0068] (2) An evaluation index system is constructed based on the thermal power emission reduction strategy configuration model, and the combined weights of each evaluation index included in the evaluation index system are calculated by using the hierarchical analysis method and entropy weight method;

[0069] (3) Based on the combined weights of the evaluation indicators obtained in step (2), a comprehensive evaluation is performed using the approximate ideal point method to obtain the optimal power balance solution that takes into account the thermal power emission reduction strategy.

[0070] The step (1) specifically comprises the following steps:

[0071] (1a) The objective function of the thermal power emission reduction strategy configuration model is to minimize the total cost, which includes the total investment cost C1 of the flexibility transformation of thermal power units, the total power generation cost C2 during the dispatch period, and the cost of abandoned wind and solar power C3;

[0072] min(C1+C2+C3) (1)

[0073]

[0074]

[0075]

[0076] Where: C1 is the total investment cost of the flexibility transformation of thermal power units; C2 is the total power generation cost during the dispatch period; C3 is the cost of abandoned wind and solar power; Ω is the set of thermal power units j; T is the set of time periods t in short-term dispatch, each time period t represents 1 hour; S is the set of time periods s in long-term dispatch, each time period s represents 1 month; I j The annual investment cost for flexibility transformation of thermal power unit j; D s is the number of days in the sth month; c b is the unit abandoned wind and solar cost coefficient; c w is the unit power generation cost of wind and solar; is the output of thermal power unit j in the tth period of the sth month; The new energy output for the tth period of the sth month; is the abandoned wind and solar power in the tth period of the sth month; x j A variable that is 0 or 1, which is 1 if and only if the thermal power unit undergoes flexibility transformation, otherwise it is 0; f j is the power generation cost function of thermal power unit j;

[0077] (1b) The constraints of the thermal power emission reduction strategy configuration model include load balance constraints, carbon emission constraints, wind and solar power curtailment constraints, ramp constraints and thermal power unit output range constraints;

[0078] The load balance constraint means that the sum of the output of thermal power units plus the output of new energy sources in each time period is equal to the current load, and the formula is:

[0079]

[0080] Where: L s.t is the load in the tth period of the sth month;

[0081] The carbon emission constraint means that the carbon emissions of thermal power units in each time period are less than the current carbon emission limit:

[0082]

[0083] Where: c e is the carbon emission coefficient, K is the carbon emission limit for that period;

[0084] The wind and solar power abandonment constraint means that the wind abandonment in each period, i.e., the sum of the new energy output and the abandoned wind and solar power, is equal to the maximum wind and solar power generation available in the current period, that is:

[0085]

[0086] Where: is the maximum wind and solar power generation in the tth period of the sth month;

[0087] The ramp constraint means that the difference in output of the thermal power unit in adjacent time periods is less than or equal to the maximum ramp rate.

[0088]

[0089] Where: R j is the ramp rate of thermal power unit j without flexibility modification; is the ramp rate of thermal power unit j after flexibility transformation; flexibility transformation will increase the ramp rate of the unit;

[0090] The output range constraint of the thermal power unit refers to the output of the thermal power unit in each period. At minimum steady-fire load With rated capacity between:

[0091]

[0092] Where: is the maximum output of thermal power unit j; is the minimum stable combustion load of thermal power unit j before flexibility transformation; is the minimum stable combustion load of thermal power unit j before flexibility transformation. The minimum stable combustion load will also decrease due to flexibility transformation.

[0093] The step (2) specifically comprises the following steps:

[0094] (2a) In combination with the goal of power balance analysis, the resource optimization allocation level, energy conservation and emission reduction, economic benefits and social benefits are used as evaluation indicators. Based on the key performance indicator analysis method, key evaluation indicators are selected from the evaluation indicators, and an evaluation indicator system is constructed based on the key evaluation indicators;

[0095] (2b) The decision-making objectives and considerations required for the power and electricity balance plan are divided into target layer and indicator layer by using the hierarchical analysis method. Let the number of evaluation objectives be m and the number of evaluation indicators be n. The normalized evaluation matrix Y is constructed by using the hierarchical analysis method with the target layer as the row and the indicator layer as the column. m×n , specifically including the following steps:

[0096] (2b1) A judgment matrix A is established based on the elements of each layer. The value of the judgment matrix A is assigned the importance of each layer element using a scale of 1 to 9 according to the relative importance of each element;

[0097] (2b2) ​​Solve the weights of the indicators at each level in the hierarchical analysis method. According to the judgment matrix A, use the eigenvector method to calculate the weights of each factor under the same standard; first find the maximum eigenvalue λ of the judgment matrix A max and the corresponding maximum eigenvalue λ max The eigenvector of is normalized to obtain the weight vector ω j =[ω1,ω2,…,ω n ], where j = 1, 2, ..., n;

[0098] (2b3) After obtaining the weight vector of the judgment matrix A, a consistency check is performed to ensure the validity of the judgment matrix; if the consistency check fails, the judgment matrix A is modified;

[0099] (2b4) Construct the evaluation matrix X m×n : Find ω j Then, according to the actual survey sampling value or expert rating list, an evaluation matrix X is constructed with the target layer as the row and the evaluation index as the column. m×n ;

[0100] (2b5) Find the normalized evaluation matrix Y m×n :Since the dimensions of each indicator are different, the evaluation indicators need to be normalized. Let i = 1, 2, ..., m; j = 1, 2, ..., n, and use the vector normalization method to obtain the normalized evaluation matrix Y m×n :

[0101]

[0102] Where: x ij is the evaluation matrix X m×n Each element in y ij is the normalized evaluation matrix Y m×n Each element in

[0103] (2c) The entropy weight method is used to determine the weight of different indicators based on the amount of information contained in the entropy value of the indicator:

[0104] Normalize the data;

[0105]

[0106] In the formula, p ij is the normalized evaluation matrix X m×n The elements in

[0107] Calculation of entropy values ​​of each indicator;

[0108]

[0109] In the formula, e j is the entropy value of the jth indicator. The smaller the entropy value of the indicator, the greater its entropy weight, which reflects that the indicator is more important and provides more useful information.

[0110] Calculation of entropy weight: Assume is the entropy weight of the jth evaluation index, and the entropy weight of the evaluation index is calculated using the following formula:

[0111]

[0112] (2d) The entropy weight method is combined with the hierarchical analysis method so that the weight not only contains the data information itself, but also reflects the subjective judgment of the experts. The combined weight calculated by the hierarchical analysis method and the entropy weight method is:

[0113]

[0114] In the formula, 0≤α≤1, the value of α depends on ω j and The correlation is determined by the Kendall correlation coefficient; the combined weight is ψ j =[ψ1,ψ2,…,ψ n ], where j = 1, 2,…, n.

[0115] The step (3) specifically comprises the following steps:

[0116] (3a) Solve the weighted normalized decision matrix Z m×n : Evaluate the matrix Y by normalization m×n and the combined weight ψ j Solve the weighted normalized decision matrix Zm×n , where z ij =ψ j ·y ij , z ij is the weighted normalized decision matrix Z m×n Each element in y ij is the normalized evaluation matrix Y m×n Each element in

[0117] (3b) Determine the positive ideal solution and the negative ideal solution. The positive ideal solution refers to the weighted normalized decision matrix Z m×n The maximum value in each column is set The negative ideal solution refers to the weighted normalized decision matrix Z m×n The minimum value in each column is set

[0118] (3c) Calculate the weighted normalized decision matrix Z for each evaluation object m×n Each element in the positive ideal solution Distance

[0119]

[0120] Calculate the weighted normalized decision matrix Z for each evaluation object m×n The distance between each element in and the negative ideal solution for:

[0121]

[0122] (3d) Solve the relative closeness and determine the evaluation results, and calculate the relative closeness H of each evaluation object and the optimal solution i :

[0123]

[0124] In the formula, H i The larger the value, the closer it is to the optimal solution. i The scheme corresponding to the maximum value is taken as the optimal power balance scheme considering the thermal power emission reduction strategy.

[0125] In summary, the present invention studies the influence of different thermal power emission reduction strategies on the comprehensive decision-making of the power and electricity balance plan after the flexibility transformation of the thermal power units; the present invention configures the thermal power emission reduction strategy, takes multiple objectives such as power and electricity abundance, energy conservation, environmental protection and economy as evaluation indicators, optimizes multiple evaluation indicators based on the key performance indicator analysis method, and constructs an evaluation indicator system; from the perspective of combining subjective and objective, the present invention fully considers the different characteristics of the hierarchical analysis method and the entropy weight method in the subjective and objective evaluation system, and proposes a combined weight calculation method that combines the two methods; the present invention adopts the sorting method of the approximate ideal point method to comprehensively evaluate and optimize the power and electricity balance plan, and obtains the optimal power and electricity balance plan considering the thermal power emission reduction strategy.

Claims

1. A comprehensive decision-making method for power balance scheme considering the configuration of thermal power emission reduction strategy, characterized by: The method comprises the following steps in order: (1) Establish a thermal power emission reduction strategy configuration model; (2) An evaluation index system is constructed based on the thermal power emission reduction strategy configuration model, and the combined weights of each evaluation index included in the evaluation index system are calculated by using the hierarchical analysis method and entropy weight method; (3) Based on the combined weights of the evaluation indicators obtained in step (2), a comprehensive evaluation is performed using the approximate ideal point method to obtain the optimal power balance solution that takes into account the thermal power emission reduction strategy.

2. The comprehensive decision-making method for electric power balance scheme considering the configuration of thermal power emission reduction strategy according to claim 1 is characterized by: The step (1) specifically comprises the following steps: (1a) The objective function of the thermal power emission reduction strategy configuration model is to minimize the total cost, which includes the total investment cost C1 of the flexibility transformation of thermal power units, the total power generation cost C2 during the dispatch period, and the cost of abandoned wind and solar power C3; min(C1+C2+C3) (1) Where: C1 is the total investment cost of the flexibility transformation of thermal power units; C2 is the total power generation cost during the dispatch period; C3 is the cost of abandoned wind and solar power; Ω is the set of thermal power units j; T is the set of time periods t in short-term dispatch, each time period t represents 1 hour; S is the set of time periods s in long-term dispatch, each time period s represents 1 month; I j The annual investment cost for flexibility transformation of thermal power unit j; D s is the number of days in the sth month; c b is the unit abandoned wind and solar cost coefficient; c w is the unit power generation cost of wind and solar; is the output of thermal power unit j in the tth period of the sth month; The new energy output for the tth period of the sth month; is the abandoned wind and solar power in the tth period of the sth month; j A variable that is 0 or 1, which is 1 if and only if the thermal power unit undergoes flexibility transformation, otherwise it is 0; f j is the power generation cost function of thermal power unit j; (1b) The constraints of the thermal power emission reduction strategy configuration model include load balance constraints, carbon emission constraints, wind and solar power curtailment constraints, ramp constraints and thermal power unit output range constraints; The load balance constraint means that the sum of the output of thermal power units plus the output of new energy sources in each time period is equal to the current load, and the formula is: Where: L s.t is the load in the tth period of the sth month; The carbon emission constraint means that the carbon emissions of thermal power units in each time period are less than the current carbon emission limit: Where: c e is the carbon emission coefficient, K is the carbon emission limit for that period; The wind and solar power abandonment constraint means that the wind abandonment in each period, i.e., the sum of the new energy output and the abandoned wind and solar power, is equal to the maximum wind and solar power generation available in the current period, that is: Where: is the maximum wind and solar power generation in the tth period of the sth month; The ramp constraint means that the difference in output of the thermal power unit in adjacent time periods is less than or equal to the maximum ramp rate. Where: R j is the ramp rate of thermal power unit j without flexibility modification; is the ramp rate of thermal power unit j after flexibility transformation; The output range constraint of the thermal power unit refers to the output of the thermal power unit in each period. At minimum steady-fire load With rated capacity between: Where: is the maximum output of thermal power unit j; is the minimum stable combustion load of thermal power unit j before flexibility transformation; It is the minimum stable combustion load of thermal power unit j before flexibility transformation.

3. The comprehensive decision-making method for electric power balance scheme considering the configuration of thermal power emission reduction strategy according to claim 1 is characterized by: The step (2) specifically comprises the following steps: (2a) In combination with the goal of power balance analysis, the resource optimization allocation level, energy conservation and emission reduction, economic benefits and social benefits are used as evaluation indicators. Based on the key performance indicator analysis method, key evaluation indicators are selected from the evaluation indicators, and an evaluation indicator system is constructed based on the key evaluation indicators; (2b) Using the analytic hierarchy process, the decision-making objectives and considerations required for the power balance plan are divided into target layer and indicator layer. Let the number of evaluation objectives be m and the number of evaluation indicators be n. Use the analytic hierarchy process to construct a normalized evaluation matrix Y with the target layer as the row and the indicator layer as the column. m×n , specifically including the following steps: (2b1) A judgment matrix A is established based on the elements of each layer, and the values ​​of the judgment matrix A are assigned the importance of the elements of each layer using a scale of 1 to 9; (2b2) ​​Solve the weights of the indicators at each level in the hierarchical analysis method. According to the judgment matrix A, use the eigenvector method to calculate the weights of each factor under the same standard; first find the maximum eigenvalue λ of the judgment matrix A max and the corresponding maximum eigenvalue λ max The eigenvector of is normalized to obtain the weight vector ω j =[ω1,ω2,…,ω n ], where j = 1, 2, ..., n; (2b3) After obtaining the weight vector of the judgment matrix A, a consistency check is performed to ensure the validity of the judgment matrix; if the consistency check fails, the judgment matrix A is modified; (2b4) Construct the evaluation matrix X m×n : Find ω j Then, construct an evaluation matrix X with the target layer as the row and the evaluation index as the column m×n ; (2b5) Find the normalized evaluation matrix Y m×n :Since the dimensions of each indicator are different, the evaluation indicators need to be normalized. Let i = 1, 2, ..., m; j = 1, 2, ..., n, and use the vector normalization method to obtain the normalized evaluation matrix Y m×n : Where: x ij is the evaluation matrix X m×n Each element in y ij is the normalized evaluation matrix Y m×n Each element in (2c) The entropy weight method is used to determine the weight of different indicators based on the amount of information contained in the entropy value of the indicator: Normalize the data; In the formula, p ij is the normalized evaluation matrix X m×n The elements in Calculation of entropy values ​​of each indicator; In the formula, e j is the entropy value of the jth indicator. The smaller the entropy value of the indicator, the greater its entropy weight, which reflects that the indicator is more important and provides more useful information. Calculation of entropy weight: Assume is the entropy weight of the jth evaluation index, and the entropy weight of the evaluation index is calculated using the following formula: (2d) Combining the entropy weight method with the hierarchical analysis method, the combined weight calculated by the hierarchical analysis method and the entropy weight method is: In the formula, 0≤α≤1, the value of α depends on ω j and The correlation is determined by the Kendall correlation coefficient; the combined weight is ψ j =[ψ1,ψ2,…,ψ n ], where j = 1, 2,…, n.

4. The comprehensive decision-making method for electric power balance scheme considering the configuration of thermal power emission reduction strategy according to claim 1 is characterized by: The step (3) specifically comprises the following steps: (3a) Solve the weighted normalized decision matrix Z m×n : Evaluate the matrix Y by normalization m×n and the combined weight ψ j Solve the weighted normalized decision matrix Z m×n , where z ij =ψ j ·y ij , z ij is the weighted normalized decision matrix Z m×n Each element in y ij is the normalized evaluation matrix Y m×n Each element in (3b) Determine the positive ideal solution and the negative ideal solution. The positive ideal solution refers to the weighted normalized decision matrix Z m×n The maximum value in each column is set The negative ideal solution refers to the weighted normalized decision matrix Z m×n The minimum value in each column is set (3c) Calculate the weighted normalized decision matrix Z for each evaluation object m×n Each element in the positive ideal solution Distance Calculate the weighted normalized decision matrix Z for each evaluation object m×n The distance D of each element to the negative ideal solution i - for: (3d) Solve the relative closeness and determine the evaluation results, and calculate the relative closeness H of each evaluation object and the optimal solution i : In the formula, H i The larger the value, the closer it is to the optimal solution. i The solution corresponding to the maximum value is taken as the optimal power balance solution considering the thermal power emission reduction strategy.

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