A method for evaluating the clean generation of a power system

Abstract

The application provides a power system generation cleanness evaluation method, and the application establishes a comprehensive evaluation method from two aspects of a deterministic planning index and an uncertain operation index, considers the deterministic and uncertain characteristics of generation, and can establish a generation cleanness fuzzy membership evaluation matrix for new energy output uncertainty, combines a planning evaluation result, constructs a two-dimensional evaluation matrix of each to-be-evaluated region, and thus obtains an intuitive and accurate evaluation result, and solves the problem that the existing power system generation cleanness evaluation method does not consider the output uncertainty factor, and the accuracy of power system generation cleanness evaluation is low.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and more specifically, to a method for evaluating the cleanliness of power generation in a power system. Background Technology

[0002] Currently, my country is accelerating the construction of a new power system based on new energy sources, vigorously developing new energy, and gradually phasing out traditional energy sources based on the safe and reliable substitution of new energy, thus accelerating the decarbonization of electricity and promoting a clean energy transition. The introduction of new energy generating units such as wind and hydropower is beneficial for reducing carbon emissions from electricity supply and improving the cleanliness of energy supply. However, the output uncertainty of new energy units is often high, which poses a significant challenge to evaluating the cleanliness of power generation in the new power system. Current research on power grid evaluation index systems mainly includes research on single-perspective evaluation indicators and research on comprehensive power grid evaluation index systems. Research on single-perspective evaluation indicators refers to in-depth research on the decomposition and quantification of indicators in one aspect of power supply reliability, operational economy, or safety, and provides evaluation conclusions on the power grid in this aspect. Research on comprehensive power grid evaluation index systems refers to integrating the research results of various single-perspective evaluations, striving for a comprehensive evaluation of the power grid. However, both single-perspective and comprehensive evaluation systems lack consideration for the output uncertainty of new energy operation, making it difficult to reflect the randomized and complex characteristics of the new power system's operation mode and providing reliable evaluation references for unit installation planning. Summary of the Invention

[0003] To address the aforementioned shortcomings in existing technologies, this invention provides a method for evaluating the cleanliness of power generation in power systems. This method solves the problem that existing methods for evaluating the cleanliness of power generation in power systems do not consider output uncertainty factors, resulting in low accuracy in evaluating the cleanliness of power generation in power systems.

[0004] To achieve the above-mentioned objectives, the technical solution adopted by this invention is: a method for evaluating the cleanliness of power generation in a power system, comprising:

[0005] For the power system in the evaluation area, establish a deterministic planning indicator matrix and an uncertain operational indicator matrix;

[0006] Based on the deterministic programming index matrix, calculate the Kendall correlation coefficient of each row vector of deterministic programming indicators in the deterministic programming index matrix;

[0007] The evaluation weight of each row of deterministic programming indicator row vectors is obtained based on the Kendall correlation coefficient of each row.

[0008] Based on the evaluation weights, the row vectors of deterministic programming indicators are sorted to obtain the evaluation indicator sequence;

[0009] A radar chart is drawn based on the evaluation index sequence to obtain the planning evaluation results for each area to be evaluated.

[0010] The elements in each row of the uncertainty operation index matrix are summed to obtain the operation evaluation index for each period of each region to be evaluated.

[0011] The membership function is used to process the operation evaluation index for each cycle and construct a fuzzy membership evaluation matrix.

[0012] Based on the fuzzy membership evaluation matrix and the planning evaluation results, a two-dimensional evaluation matrix is ​​constructed for each region to be evaluated, and the power generation cleanliness of the power system in the region to be evaluated is evaluated based on the two-dimensional evaluation matrix.

[0013] Furthermore, the deterministic planning index matrix is ​​{Φ(m)}, where Φ(m) is the deterministic planning index column vector of the power system in the m-th region to be evaluated, and the value of m ranges from [1,M], where m is an integer and M is the number of regions to be evaluated.

[0014] The column vector of deterministic programming indicators is: Φ(m)=[Φ ZJ,X ,Φ LY,X ,Φ JH,X ,Φ RL,X ],

[0015]

[0016]

[0017]

[0018]

[0019] Where Φ(m) is the column vector of deterministic planning indicators for the power system in the m-th region to be evaluated, Φ ZJ,X Φ represents the installed capacity ratio of new energy units of type X. LY,X Φ represents the utilization hours of the new energy unit of type X. JH,X For the seasonal imbalance of type X new energy units, Φ RL,X The capacity factor for type X of new energy units, where type X includes: wind turbines (WT), hydropower units (HP), and photovoltaic units (PV). Let N be the installed capacity of the i-th new energy unit in the X-th type of new energy unit. I N represents the number of new energy generating units in type X. X E represents the total number of types of new energy generating units. X，i，d Let N be the power generation of the i-th new energy unit in type X on day d. DTo count the number of days, The sum of the output of the new energy generating units of type X on day d. For the average output of the new energy unit of type X, P X，i，d S represents the active power output of the i-th new energy unit in type X on day d. X，i Let be the rated capacity of the i-th new energy unit in the X-th type of new energy unit.

[0020] Furthermore, the uncertainty operation index matrix is ​​as follows:

[0021]

[0022] Where Ω(m) is the uncertainty operation index matrix of the power system in the m-th region to be evaluated, Ω FD (1) represents the proportion of power generation in the first cycle, Ω FD (2) represents the power generation ratio for the second cycle, Ω FD (T) represents the proportion of power generation in the Tth cycle, Ω QF (1) represents the wind and solar curtailment rate for the first cycle, Ω QF (2) represents the wind and solar curtailment rate for the second cycle, Ω QF (T) represents the wind and solar curtailment rate in the Tth cycle, Ω MH (1) represents the coal consumption for power generation in the first cycle, Ω MH (2) represents the coal consumption for power generation in the second cycle, Ω MH (T) represents the coal consumption for power generation in the Tth cycle, Ω JZ (1) represents the seasonal maximum and minimum output for the first cycle, Ω JZ (2) represents the seasonal maximum and minimum output for the second cycle, Ω JZ (T) represents the seasonal maximum and minimum output for the Tth period.

[0023] Furthermore, the formula for calculating the power generation ratio is as follows:

[0024]

[0025] Among them, Ω FD E represents the proportion of electricity generated. X，i，d Let N be the power generation of the i-th new energy unit in type X on day d. D To count the number of days, N I N represents the number of new energy generating units in type X. X This represents the total number of types of new energy generating units. Type X of new energy generating units includes: wind turbine units (WT), hydropower units (HP), and photovoltaic units (PV).

[0026] The formula for calculating the wind and solar curtailment rate is as follows:

[0027]

[0028] Among them, Ω QF For wind and solar curtailment rates, Let WT be the predicted output value of the i-th wind turbine unit on day d, and P be the predicted output value of the i-th wind turbine unit on day d. WT，i，d Let WT be the actual output value of the i-th wind turbine unit on day d. Let P be the predicted power output of the i-th photovoltaic unit on day d. PV，i，d N represents the actual power output of the i-th photovoltaic unit on day d. D For counting days;

[0029] The formula for calculating the coal consumption for power generation is:

[0030]

[0031] Among them, Ω MH For coal consumption in power generation, δ X P represents the standard coal consumption for power generation of the Xth type of new energy unit. X，i，d Let i be the active power output of the i-th new energy unit in the X-type new energy unit on day d.

[0032] The formula for calculating the maximum and minimum output in the season is:

[0033] Ω JZ =[max{P X，d，i}, min{P X，d，i}], d = 1, 2, ..., N D

[0034] Among them, Ω JZ For the maximum and minimum output during the season, max{P X，d，i To find the maximum value of the sequence, min{P} X，d，i To find the minimum value of the sequence, P X，i，d Let be the active power output of the i-th new energy unit in the X-type new energy unit on day d.

[0035] Furthermore, the step of calculating the Kendall correlation coefficient of each row vector of deterministic programming indicators in the deterministic programming indicator matrix includes:

[0036] Take any two rows of deterministic planning index row vectors from the deterministic planning index matrix {Φ(m)}, and sort the elements in each row of deterministic planning index row vectors. Here, Φ(m) is the deterministic planning index column vector of the power system of the m-th region to be evaluated. The deterministic planning index matrix is ​​composed of multiple Φ(m), where the value of m is in the range of [1, M], m is an integer, and M is the number of regions to be evaluated.

[0037] Calculate the Kendall correlation coefficient between two rows of deterministic programming index row vectors based on the sorted deterministic programming index row vectors.

[0038] Based on the Kendall correlation coefficients of the two rows of deterministic programming index row vectors, the Kendall correlation coefficients of each row of deterministic programming index row vectors are obtained.

[0039] Furthermore, the formula for calculating the Kendall correlation coefficient of the two rows of deterministic programming index row vectors is as follows:

[0040]

[0041] Where, τ ij To select the Kendall correlation coefficient for the deterministic programming index row vectors in rows i and j, N cc For the ordered pairs in the deterministic programming index row vectors of the i-th and j-th rows after sorting, N dc For the out-of-order pairs in the deterministic programming index row vectors of the i-th and j-th rows after sorting, Let be the number of rows in the i-th row of deterministic programming indicators that have the same variable value before sorting. is the number of rows with the same variable value in the j-th row of deterministic programming indicators before sorting. A is a constant, A = M(M-1) / 2, where M is the number of column vectors of deterministic programming indicators or the number of regions to be evaluated, and m ranges from [1,M], where m is an integer.

[0042] Furthermore, the formula for calculating the Kendall correlation coefficient of each row vector of deterministic programming indicators is as follows:

[0043]

[0044] Where, τ i Let τ be the Kendall correlation coefficient of the row vector of deterministic programming indicators in the i-th row. ij To select the Kendall correlation coefficient for the deterministic programming index row vectors in rows i and j, N GH The number of row vectors for deterministic programming indicators.

[0045] Furthermore, the evaluation weight of each row vector of the deterministic programming index is: 1-τ i , where τ i Let be the Kendall correlation coefficient of the row vector of deterministic programming indicators in the i-th row;

[0046] The evaluation index sequence is as follows: Where u is the evaluation index sequence, u1 is the row vector of the first deterministic programming index in the evaluation index sequence, u kLet k be the row vector of the deterministic programming indicator in the evaluation indicator sequence. For the Nth index in the evaluation index sequence GH A row vector of deterministic planning indicators;

[0047] The row vector u of each deterministic programming indicator in the evaluation index sequence k The central angle of the sector in the radar image is θ. mk =2πu k , where θ mk The central angle of the sector of the row vector of the k-th deterministic programming indicator in the evaluation indicator sequence;

[0048] Take O m Draw a unit circle with O as the center. m For the set center, through the center O m Multiple rays O m P mk Where k takes values ​​in the range [1, N]. GH ], k is an integer, P mk Let P be the intersection point of the ray and the circle, satisfying: ∠P mk O m P m(k+1) =θ mk ,∠P mk O m P m(k+1) For ray O m P mk With ray O m P m(k+1) The included angle, starting from k=1 to k=N GH Construct ∠P in sequence. mk O m P m(k+1) The angle bisector of the k-th deterministic programming index row vector is proportionally converted to a distance from the center O. m length The row vector of each deterministic planning indicator is distanced from the center O of the circle. m length Mark on each ∠P mk O m P m(k+1) On the angle bisectors, mark the points on each angle bisector. Connect the marks in sequence to get the radar chart.

[0049] Furthermore, the calculation formula for the planning evaluation result of each area to be evaluated is as follows:

[0050]

[0051] Among them, V m This represents the planning evaluation result for the m-th region to be evaluated. Let u be the distance from the center of the circle on the radar chart to the row vector of the k-th deterministic programming index in the m-th region to be evaluated. k Let N be the row vector of the k-th deterministic programming indicator in the evaluation indicator sequence. GH The number of row vectors for deterministic programming indicators.

[0052] Furthermore, the fuzzy membership evaluation matrix is ​​as follows:

[0053]

[0054] Among them, A MH For the fuzzy membership evaluation matrix, Ω s (1) is the operational evaluation index for the first cycle, Ω s (2) Ω is the operational evaluation index for the second cycle. s (T) represents the operational evaluation index for the Tth period, f1(·) represents the membership function for a poor evaluation result, f2(·) represents the membership function for a good evaluation result, and f3(·) represents the membership function for a good evaluation result.

[0055] The expression for the membership function f1(·) is:

[0056]

[0057] The expression for the membership function f2(·) is:

[0058]

[0059] The expression for the membership function f3(·) is:

[0060]

[0061] x is Ω s (1) to Ω s (T) is any of the operational evaluation indicators.

[0062] The technical solutions of the embodiments of the present invention have at least the following advantages and beneficial effects:

[0063] This invention establishes a comprehensive evaluation method based on both deterministic planning indicators and uncertain operational indicators, taking into account both the deterministic and uncertain characteristics of power generation. Simultaneously, it can establish a fuzzy membership evaluation matrix for the cleanliness of power generation in the face of uncertain renewable energy output. Combined with the planning evaluation results, a two-dimensional evaluation matrix is ​​constructed for each region to be evaluated, thereby obtaining intuitive and accurate evaluation results. This solves the problem that existing power system power generation cleanliness evaluation methods do not consider output uncertainty factors, resulting in low accuracy in power system power generation cleanliness evaluation. Attached Figure Description

[0064] Figure 1 This is a flowchart of a method for evaluating the cleanliness of power generation in a power system. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0066] like Figure 1 As shown, a method for evaluating the cleanliness of power generation in a power system includes the following steps:

[0067] S1. Establish a deterministic planning index matrix and an uncertain operation index matrix for the power system in the region to be evaluated;

[0068] In step S1, the deterministic planning index matrix is ​​{Φ(m)}, where Φ(m) is the deterministic planning index column vector of the power system in the m-th region to be evaluated, and the value of m ranges from [1,M], where m is an integer and M is the number of regions to be evaluated.

[0069] The column vector of deterministic programming indicators is: Φ(m)=[Φ ZJ,X ,Φ LY,X ,Φ JH,X ,Φ RL,X ],

[0070]

[0071]

[0072]

[0073]

[0074] Where Φ(m) is the column vector of deterministic planning indicators for the power system in the m-th region to be evaluated, Φ ZJ,X Φ represents the installed capacity ratio of new energy units of type X. LY,X Φ represents the utilization hours of the new energy unit of type X. JH,X For the seasonal imbalance of type X new energy units, Φ RL,X The capacity factor for type X of new energy units, where type X includes: wind turbines (WT), hydropower units (HP), and photovoltaic units (PV). Let N be the installed capacity of the i-th new energy unit in the X-th type of new energy unit. IN represents the number of new energy generating units in type X. X E represents the total number of types of new energy generating units. X,i,d Let N be the power generation of the i-th new energy unit in type X on day d. d To count the number of days, The sum of the output of the new energy generating units of type X on day d. For the average output of the new energy unit of type X, P X,i,d S represents the active power output of the i-th new energy unit in type X on day d. X,i Let be the rated capacity of the i-th new energy unit in the X-th type of new energy unit.

[0075] In step S1, the uncertainty operation index matrix is:

[0076]

[0077] Where Ω(m) is the uncertainty operation index matrix of the power system in the m-th region to be evaluated, Ω FD (1) represents the proportion of power generation in the first cycle, Ω FD (2) represents the power generation ratio for the second cycle, Ω FD (T) represents the proportion of power generation in the Tth cycle, Ω QF (1) represents the wind and solar curtailment rate for the first cycle, Ω QF (2) represents the wind and solar curtailment rate for the second cycle, Ω QF (T) represents the wind and solar curtailment rate in the Tth cycle, Ω MH (1) represents the coal consumption for power generation in the first cycle, Ω MH (2) represents the coal consumption for power generation in the second cycle, Ω MH (T) represents the coal consumption for power generation in the Tth cycle, Ω JZ (1) represents the seasonal maximum and minimum output for the first cycle, Ω JZ (2) represents the seasonal maximum and minimum output for the second cycle, Ω JZ (T) represents the seasonal maximum and minimum output for the Tth period.

[0078] The formula for calculating the power generation ratio is:

[0079]

[0080] Among them, Ω FD E represents the proportion of electricity generated. X，i，d Let N be the power generation of the i-th new energy unit in type X on day d. D To count the number of days, N IN represents the number of new energy generating units in type X. X This represents the total number of types of new energy generating units. Type X of new energy generating units includes: wind turbine units (WT), hydropower units (HP), and photovoltaic units (PV).

[0081] The formula for calculating the wind and solar curtailment rate is as follows:

[0082]

[0083] Among them, Ω QF For wind and solar curtailment rates, Let WT be the predicted output value of the i-th wind turbine unit on day d, and P be the predicted output value of the i-th wind turbine unit on day d. WT，i，d Let WT be the actual output value of the i-th wind turbine unit on day d. Let P be the predicted power output of the i-th photovoltaic unit on day d. PV，i，d N represents the actual power output of the i-th photovoltaic unit on day d. D For counting days;

[0084] The formula for calculating the coal consumption for power generation is:

[0085]

[0086] Among them, Ω MH For coal consumption in power generation, δ X P represents the standard coal consumption for power generation of the Xth type of new energy unit. X，i，d Let i be the active power output of the i-th new energy unit in the X-type new energy unit on day d.

[0087] The formula for calculating the maximum and minimum output in the season is:

[0088] Ω JZ =[max{P X，d，i}, min{P X，d，i}], d = 1, 2, ..., N D

[0089] Among them, Ω JZ For the maximum and minimum output during the season, max{P X，d，i To find the maximum value of the sequence, min{P} X，d，i To find the minimum value of the sequence, P X，i，d Let be the active power output of the i-th new energy unit in the X-type new energy unit on day d.

[0090] S2. Based on the deterministic programming index matrix, calculate the Kendall correlation coefficient of each row vector of deterministic programming indicators in the deterministic programming index matrix.

[0091] Step S2 specifically involves randomly selecting two rows of deterministic planning index row vectors from the deterministic planning index matrix {Φ(m)}, and sorting the elements in each row of deterministic planning index row vectors. Here, Φ(m) is the deterministic planning index column vector of the power system of the m-th region to be evaluated. The deterministic planning index matrix is ​​composed of multiple Φ(m), where m ranges from [1, M], m is an integer, and M is the number of regions to be evaluated.

[0092] Calculate the Kendall correlation coefficient between two rows of deterministic programming index row vectors based on the sorted deterministic programming index row vectors.

[0093] The formula for calculating the Kendall correlation coefficient of the row vectors of two deterministic programming indicators is as follows:

[0094]

[0095] Where, τ ij To select the Kendall correlation coefficient for the deterministic programming index row vectors in rows i and j, N cc For the ordered pairs in the deterministic programming index row vectors of the i-th and j-th rows after sorting, N dc For the out-of-order pairs in the deterministic programming index row vectors of the i-th and j-th rows after sorting, Let be the number of rows in the i-th row of deterministic programming indicators that have the same variable value before sorting. is the number of rows with the same variable value in the j-th row of deterministic programming indicators before sorting. A is a constant, A = M(M-1) / 2, where M is the number of column vectors of deterministic programming indicators or the number of regions to be evaluated, and m takes the value range [1, M], where m is an integer.

[0096] Based on the Kendall correlation coefficients of the two rows of deterministic programming index row vectors, the Kendall correlation coefficients of each row of deterministic programming index row vectors are obtained.

[0097] The formula for calculating the Kendall correlation coefficient of each row vector of deterministic programming indicators is as follows:

[0098]

[0099] Where, τ i Let τ be the Kendall correlation coefficient of the row vector of deterministic programming indicators in the i-th row. ij To select the Kendall correlation coefficient for the deterministic programming index row vectors in rows i and j, N GH The number of row vectors for deterministic programming indicators.

[0100] S3. Calculate the evaluation weight of each row of deterministic programming indicator row vectors based on the Kendall correlation coefficient of each row of deterministic programming indicator row vectors.

[0101] In step S3, a Kendall coefficient of 1 indicates that the indicator has a consistent rank correlation with other indicators; while a Kendall coefficient of 0 indicates that the indicator is independent of other indicators. Therefore, the evaluation weight of each row vector of deterministic programming indicators is: 1-τ i , where τ i Let be the Kendall correlation coefficient of the row vector of deterministic programming indicators in the i-th row.

[0102] S4. Sort the row vectors of deterministic programming indicators according to the evaluation weight to obtain the evaluation indicator sequence.

[0103] In step S4, the evaluation index sequence is as follows: Where u is the evaluation index sequence, u1 is the row vector of the first deterministic programming index in the evaluation index sequence, u k Let k be the row vector of the deterministic programming indicator in the evaluation indicator sequence. For the Nth index in the evaluation index sequence GH A row vector of deterministic planning indicators;

[0104] S5. Draw a radar chart based on the evaluation index sequence to obtain the planning evaluation results for each area to be evaluated;

[0105] In step S5, the row vector u of each deterministic programming indicator in the evaluation indicator sequence k The central angle of the sector in the radar image is θ. mk =2πu k , where θ mk The central angle of the sector of the row vector of the k-th deterministic programming indicator in the evaluation indicator sequence;

[0106] Take O m Draw a unit circle with O as the center. m For the set center, through the center O m Multiple rays O m P mk Where k takes values ​​in the range [1, N]. GH ], k is an integer, P mk Let P be the intersection point of the ray and the circle, satisfying: ∠P mk O m P m(k+1) =θ mk ,∠P mk O m P m(k+1) For ray O m P mk With ray O m P m(k+1) The included angle, starting from k=1 to k=N GH Construct ∠P in sequence.mk O m P m(k+1) The angle bisector of the k-th deterministic programming index row vector is proportionally converted to a distance from the center O. m length The row vector of each deterministic planning indicator is distanced from the center O of the circle. m length Mark on each ∠P mk O m P m(k+1) On the angle bisectors, mark the points on each angle bisector. Connect the marks in sequence to get the radar chart.

[0107] The formula for calculating the planning evaluation result of each area to be evaluated is as follows:

[0108]

[0109] Among them, V m This represents the planning evaluation result for the m-th region to be evaluated. Let u be the distance from the center of the circle on the radar chart to the row vector of the k-th deterministic programming index in the m-th region to be evaluated. k Let N be the row vector of the k-th deterministic programming indicator in the evaluation indicator sequence. GH The number of row vectors for deterministic programming indicators.

[0110] S6. Sum the elements of each row in the uncertainty operation index matrix to obtain the operation evaluation index for each period of each region to be evaluated.

[0111] S7. Using membership functions, the operational evaluation indicators for each cycle are processed to construct a fuzzy membership evaluation matrix.

[0112] The fuzzy membership evaluation matrix is ​​as follows:

[0113]

[0114] Among them, A MH For the fuzzy membership evaluation matrix, Ω s (1) is the operational evaluation index for the first cycle, Ω s (2) Ω is the operational evaluation index for the second cycle. s (T) represents the operational evaluation index for the Tth period, f1(·) represents the membership function for a poor evaluation result, f2(·) represents the membership function for a good evaluation result, and f3(·) represents the membership function for a good evaluation result.

[0115] The expression for the membership function f1(·) is:

[0116]

[0117] The expression for the membership function f2(·) is:

[0118]

[0119] The expression for the membership function f3(·) is:

[0120]

[0121] x is Ω s (1) to Ω s (T) is any of the operational evaluation indicators.

[0122] S8. Based on the fuzzy membership evaluation matrix and the planning evaluation results, construct a two-dimensional evaluation matrix for each region to be evaluated, and evaluate the power generation cleanliness of the power system in the region to be evaluated based on the two-dimensional evaluation matrix.

[0123] Step S8 specifically involves summing the columns of the fuzzy membership evaluation matrix to obtain three evaluation results. The operational evaluation level corresponding to the column in the fuzzy membership evaluation matrix containing the maximum value of the three results is taken as the specific operational evaluation result. The planning evaluation is divided into three levels: poor, good, and excellent. When using this method, the planning evaluation result V is used as the basis for the evaluation. m The level at which the planning evaluation is conducted determines the specific results.

[0124] Since the three columns in the fuzzy membership evaluation matrix correspond to three operational evaluation levels (poor, good, and excellent), and the planning evaluation also corresponds to three evaluation levels (poor, good, and excellent), the two-dimensional evaluation matrix is ​​as follows:

[0125]

[0126] Among them, a 11 ~a 13 a 21 ~a 23 a 31 ~a 33 These represent (good planning, good operation), (good planning, good operation), (good planning, poor operation), (good planning, good operation), (good planning, good operation), (good planning, poor operation), (poor planning, good operation), (poor planning, good operation), (poor planning, good operation), (poor planning, poor operation).

[0127] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the cleanliness of power generation in a power system, characterized in that, include: For the power system in the evaluation area, establish a deterministic planning indicator matrix and an uncertain operational indicator matrix; The deterministic programming index matrix is ​​as follows: , For the first A column vector of deterministic planning indicators for the power system of the region to be evaluated. The range of values ​​is , Take the integer. The number of areas to be evaluated; The column vector of deterministic programming indicators is: , in, For the first A column vector of deterministic planning indicators for the power system of the region to be evaluated. For the first The installed capacity ratio of new energy units of this type For the first Utilization hours of the type of new energy unit For the first Seasonal imbalance of different types of new energy generating units For the first The capacity factor of different types of new energy generating units, and the types of new energy generating units. Including: wind turbine units Hydropower units and photovoltaic units , For the first The first type of new energy unit The installed capacity of each new energy unit For the first The number of new energy units in the new energy unit type This represents the total number of types of new energy generating units. For the first The first type of new energy unit The first new energy unit was in the first Daily electricity generation To count the number of days, For the first Among the new energy units of this type, the first The sum of the efforts of the heavens, For the first The average output of this type of new energy unit is... For the first The first type of new energy unit The first new energy unit was in the first Heaven's merits and efforts For the first The first type of new energy unit The rated capacity of each new energy unit; The uncertainty operation index matrix is ​​as follows: in, For the first Uncertainty operation index matrix of the power system in the region to be evaluated. This represents the proportion of electricity generated in the first cycle. This represents the proportion of power generation in the second cycle. For the first The proportion of power generation per cycle This represents the wind and solar curtailment rate for the first cycle. This refers to the wind and solar curtailment rate for the second cycle. For the first The curtailment rate of wind and solar power in each cycle, This represents the coal consumption for power generation in the first cycle. This represents the coal consumption for power generation in the second cycle. For the first Coal consumption for power generation per cycle For the seasonal maximum and minimum output of the first cycle, For the seasonal maximum and minimum output of the second cycle, For the first The maximum and minimum seasonal output for each cycle; The formula for calculating the power generation ratio is: in, The proportion of electricity generated. For the first The first type of new energy unit The first new energy unit was in the first Daily electricity generation To count the number of days, For the first The number of new energy units in the new energy unit type The total number of types of new energy generating units, and the types of new energy generating units. Including: wind turbine units Hydropower units and photovoltaic units ; The formula for calculating the wind and solar curtailment rate is as follows: in, For wind and solar curtailment rates, For the first Each fan unit In the The predicted output value for the day, For the first Each fan unit In the The actual output value of the day, For the first One photovoltaic unit In the The predicted output value for the day, For the first One photovoltaic unit In the The actual output value of the day, For counting days; The formula for calculating the coal consumption for power generation is: in, Coal consumption for power generation For the first Standard coal consumption for power generation of this type of new energy unit For the first The first type of new energy unit The first new energy unit was in the first Heaven has contributed its efforts; The formula for calculating the maximum and minimum output in the season is: in, For the maximum and minimum output of the season, To find the maximum value of the sequence, To find the minimum value of the sequence, For the first The first type of new energy unit The first new energy unit was in the first Heaven has contributed its efforts; Based on the deterministic programming index matrix, calculate the Kendall correlation coefficient of each row vector of deterministic programming indicators in the deterministic programming index matrix; The evaluation weight of each row of deterministic programming indicator row vectors is obtained based on the Kendall correlation coefficient of each row; the evaluation weight of each row of deterministic programming indicator row vectors is: 1- ,in, For the first Kendall correlation coefficient of row vectors of deterministic programming indicators; Based on the evaluation weights, the row vectors of deterministic programming indicators are sorted to obtain the evaluation indicator sequence; the evaluation indicator sequence is as follows: ,in, For the evaluation index series, Let the row vector of the first deterministic programming indicator in the evaluation indicator sequence be denoted as . For the evaluation index sequence of the th A row vector of deterministic planning indicators. For the evaluation index sequence of the th A row vector of deterministic planning indicators; The row vector of each deterministic programming indicator in the evaluation indicator sequence The central angle of the sector in the radar image is... ,in, For the evaluation index sequence of the th The central angle of the sector of a row vector of deterministic programming indicators; by Draw a unit circle with center at the center. With the center of the circle as defined, pass through the center of the circle Multiple rays ,in, The range of values ​​is , Take the integer. Let be the intersection of the ray and the circle, satisfying: , For rays With rays The angle formed, from Take 1 from Pick Do in sequence The angle bisector of the first angle will be the first angle bisector of the second angle bisector. Each deterministic programming indicator row vector is proportionally converted to a distance from the center of the circle. length And the distance of each deterministic planning indicator row vector from the center of the circle. length Marked on each On the angle bisector, mark the points on each angle bisector. Connect the marks in sequence to get the radar chart. A radar chart is drawn based on the evaluation index sequence to obtain the planning evaluation result for each area to be evaluated; the calculation formula for the planning evaluation result of each area to be evaluated is as follows: in, For the first The planning evaluation results for the areas to be evaluated For the first The first area to be evaluated The length of the row vector of a deterministic programming index from the center of the circle on the radar chart. For the evaluation index sequence of the th A row vector of deterministic planning indicators. The number of row vectors for deterministic programming indicators; The elements in each row of the uncertainty operation index matrix are summed to obtain the operation evaluation index for each period of each region to be evaluated. The membership function is used to process the operational evaluation indicators for each cycle, and a fuzzy membership evaluation matrix is ​​constructed; the fuzzy membership evaluation matrix is ​​as follows: in, For fuzzy membership evaluation matrix, The performance evaluation indicators for the first cycle are as follows: The performance evaluation indicators for the second cycle are as follows: For the first Operational evaluation indicators for each cycle, For the membership function with a poor evaluation result, For the membership function that evaluates the result to be good, The membership function that evaluates the result as excellent; Membership function The expression is: Membership function The expression is: Membership function The expression is: for to Any one of the operational evaluation indicators; Based on the fuzzy membership evaluation matrix and the planning evaluation results, a two-dimensional evaluation matrix is ​​constructed for each region to be evaluated, and the power generation cleanliness of the power system in the region to be evaluated is evaluated based on the two-dimensional evaluation matrix.

2. The method for evaluating the cleanliness of power generation in a power system according to claim 1, characterized in that, The step of calculating the Kendall correlation coefficient of each row vector of deterministic programming indicators in the deterministic programming indicator matrix includes: From the deterministic programming indicator matrix Take any two rows of deterministic programming index row vectors and sort the elements in each row. For the first The deterministic planning index column vector of the power system in the region to be evaluated, and the deterministic planning index matrix consists of multiple... constitute, The range of values ​​is , Take the integer. The number of areas to be evaluated; Calculate the Kendall correlation coefficient between two rows of deterministic programming index row vectors based on the sorted deterministic programming index row vectors. Based on the Kendall correlation coefficients of the two rows of deterministic programming index row vectors, the Kendall correlation coefficients of each row of deterministic programming index row vectors are obtained.

3. The method for evaluating the cleanliness of power generation in a power system according to claim 2, characterized in that, The formula for calculating the Kendall correlation coefficient of the row vectors of two deterministic programming indicators is as follows: in, To select the first row and number Kendall correlation coefficient of row vectors for deterministic programming indicators. For the sorted number row and number The same pairs in the row vector of deterministic programming indicators. For the sorted number row and number Disordered pairs in the row vector of deterministic programming indices The first one before sorting The deterministic programming index is the number of row vectors with identical variable values. The first one before sorting The number of rows with identical variable values ​​in a row vector of a deterministic programming index. It is a constant. , This refers to the number of column vectors for deterministic planning indicators or the number of areas to be evaluated. The range of values ​​is , Take the integer part.

4. The method for evaluating the cleanliness of power generation in a power system according to claim 2, characterized in that, The formula for calculating the Kendall correlation coefficient of each row vector of deterministic programming indicators is as follows: in, For the first Kendall correlation coefficient of row vectors for deterministic programming indicators. To select the first row and number Kendall correlation coefficient of row vectors for deterministic programming indicators. The number of row vectors for deterministic programming indicators.

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