Renewable energy power consumption weight determination and decomposition method

Abstract

The invention provides a renewable energy power consumption weight determination and decomposition method, and the method comprises the steps: determining influence factors which affect the renewable energy power consumption, and the influence factors comprise a resource endowment factor, an economic development factor, a power consumption capability factor, an environmental protection constraint factor and an industry business environment factor; evaluating to obtain an index of each influence factor of each research sub-region, and calculating to obtain a comprehensive index of renewable energy power consumption; and the increment of the overall renewable energy power generation amount of the research region from the current year to the future year is allocated to each research sub-region according to the proportion of the renewable energy power consumption comprehensive index of each research sub-region, so that the renewable energy power generation amount needing to be consumed by each research sub-region in the future year is obtained. According to the method, the multi-index comprehensive evaluation system is constructed, normalization processing and weighted calculation are combined, the consumption responsibility weight is scientifically distributed, and the fairness and rationality of renewable energy power consumption distribution are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy power planning and optimization, and specifically relates to a method for determining and decomposing responsibility weights for renewable energy power consumption. Background Art

[0002] The renewable energy power consumption responsibility weight mechanism is a mechanism established to promote the proportional consumption of renewable energy power generation in various regions. With the rapid development of renewable energy, how to reasonably allocate the consumption of renewable energy power in various regions has become an important task.

[0003] At present, the method for determining the responsibility weight of renewable energy power consumption is to add the estimated annual renewable energy electricity produced and consumed in the region to the estimated annual net input renewable energy electricity, and divide it by the estimated annual total social electricity consumption in the region. The current mechanism for allocating the responsibility weight of renewable energy power consumption is mainly based on indicators such as wind and solar energy resources and grid absorption capacity, and has initially achieved the guidance of renewable energy absorption targets among regions. However, the existing technology has problems such as incomplete consideration of resource types, a single indicator system, and a relatively simple allocation method. In addition, the existing technology mainly relies on static resource quantity and absorption capacity data, which makes it difficult to achieve fair sharing and reasonable incentives among regions, resulting in some regions facing greater cost pressure or potential unfairness in actual implementation, affecting the efficient realization of the overall renewable energy absorption target in the country. Therefore, a dynamic optimization method that comprehensively considers multiple factors is needed to ensure the fairness and rationality of the allocation results. Summary of the invention

[0004] In view of the defects of the prior art, the present invention provides a method for determining and decomposing the responsibility weights of renewable energy power consumption, which can effectively solve the above problems.

[0005] The technical solution adopted by the present invention is as follows:

[0006] The present invention provides a method for determining and decomposing renewable energy power consumption weights, comprising the following steps:

[0007] Step S1, dividing the study area into N study sub-areas;

[0008] Step S2, determining the factors affecting the consumption of renewable energy power, including resource endowment factors, economic development factors, power consumption capacity factors, environmental protection constraints factors and industry business environment factors;

[0009] Step S3: Evaluate and obtain the index corresponding to each influencing factor of each research sub-region i in the current year, including the resource endowment index , Economic Development Index , Power Consumption Capacity Index , Environmental Constraint Index and industry business environment index ; Where i=1,2,...,N;

[0010] Step S4, evaluating and obtaining the weights of each index of each research sub-region i in the current year;

[0011] Step S5: using the weights of each index determined in step S4, calculate the resource endowment index of each research sub-region i. , Economic Development Index , Power Consumption Capacity Index , Environmental Constraint Index and industry business environment index Perform weighted summation to obtain the comprehensive index of renewable energy power consumption in each research sub-region i in the current year ;

[0012] Step S6: predict the total renewable energy generation in the study area in future years ; Using the formula = - , and obtain the increment of overall renewable energy power generation in the study area from the current year to the future year ;in, Represents the total renewable energy generation required in the study area in the current year;

[0013] The increase in overall renewable energy generation in the study area from the current year to the future year , according to the comprehensive index of renewable energy power consumption in each research sub-region i The ratio of the comprehensive index of renewable energy power consumption in the study area is allocated to each study sub-area i, so as to obtain the renewable energy power generation that needs to be consumed in each study sub-area i in future years.

[0014] Preferably, in step S3, for each influencing factor j, j=1,2,...,5, representing resource endowment factor, economic development factor, power consumption capacity factor, environmental protection constraint factor and industry business environment factor respectively; the influencing factor index of each research sub-region i in the current year is The evaluation method is:

[0015] Step S3.1, determine the factors that have a direct impact on influencing factor j. Impact factors are expressed as: ;

[0016] Step S3.2, obtain each impact factor of the research sub-region i in the current year The value of is expressed as ; where k=1,2,..., ;

[0017] Step S3.3: Use formula (1) to calculate the impact factor of each sub-region i in the current year. The value of After standardization, we can get the impact factor of each research sub-region i in the current year. The standardized value of :

[0018] (1)

[0019] in:

[0020] Represents the impact factors of N research sub-areas in the current year The minimum value of , that is: ;

[0021] Represents the impact factors of N research sub-areas in the current year The maximum value of is: ;

[0022] Step S3.4, determine each influencing factor The weight in the study sub-area i ;

[0023] Step S3.5: Use formula (2) to obtain the influencing factor index of the research sub-region i in the current year :

[0024] (2)

[0025] Thus, the influencing factor index of influencing factor j of research sub-region i in the current year is obtained .

[0026] Preferably, in step S3.4, if the influencing factor j is a resource endowment factor, then each of its influencing factors The weight in the study sub-area i , determined by:

[0027] A1, using formula (3), various influencing factors of the influencing factor j of the sub-region i in the current year are studied The value of Sum and get the impact factor value and :

[0028] (3)

[0029] A2, using formula (4), obtain each impact factor The weight in the study sub-area i :

[0030] (4)

[0031] Thus, the influencing factors of resource endowment factors are determined The weight in the study sub-area i .

[0032] Preferably, if the influencing factor j is economic development factor, power consumption capacity factor, environmental protection constraint factor and industry business environment factor, then for each influencing factor j, its influencing factor The weight in the study sub-area i , determined by:

[0033] The renewable energy generation in sub-region i is taken as the dependent variable to study the influencing factors under influencing factor j in sub-region i. As the independent variable, the influencing factors of the research sub-region i were fitted by the multivariate linear regression method. The relationship between the renewable energy generation in the research sub-region i is used to determine the influencing factors The weight in the study sub-area i .

[0034] Preferably, the renewable energy generation in the research sub-region i is used as the dependent variable to study the influencing factors under the influencing factor j in the research sub-region i. As the independent variable, the influencing factors of the research sub-region i were fitted by the multivariate linear regression method. The relationship between the renewable energy generation in the research sub-region i is used to determine the influencing factors The weight in the study sub-area i , specifically:

[0035] B1, get the nearest The renewable energy generation series R i ={R i,1 ,R i,2 ,..., R i,l} and each impact factor under impact factor j The numerical sequence of ; Among them, R i,1 ,R i,2 ,..., R i,l , respectively, the research sub-area i from the first year to the Annual renewable energy generation; , respectively, are the impact factors of the research sub-region i In the first year to The value of the year; among them, the 1st year to the The years are arranged chronologically;

[0036] B2, for the renewable energy generation sequence R i ={R i,1 ,R i,2 ,..., R i,l Each element in} is normalized using formula (5) to obtain the normalized renewable energy power generation sequence R i,norm ={R i,1,norm ,R i,2,norm ,...,R i,l,norm}:

[0037] R i,norm ={ , ,..., } (5)

[0038] in: and , representing R i,1 ,R i,2 ,..., R i,l The maximum and minimum values ​​in ;

[0039] Impact Factor The numerical sequence of Each element in is normalized using formula (6) to obtain the normalized impact factor The numerical sequence of

[0040] :

[0041] (6)

[0042] in: and , representing The maximum and minimum values ​​in ;

[0043] B3, establish the multivariate linear regression equation:

[0044] R i,norm = + + +…+ +…+ + (7)

[0045] in: represents the intercept; represents the error term; , ,…, ,…, All are regression coefficients;

[0046] B4, through fitting, we get the regression coefficient , ,…, ,…, The value of

[0047] B5, using formula (8), we get the impact factor The weight in the study sub-area i :

[0048] (8)

[0049] Thus, the influencing factors under influencing factor j are obtained The weight in the study sub-area i .

[0050] Preferably, for the resource endowment factor, its influencing factors include wind power installed capacity, solar power installed capacity, hydropower installed capacity, biomass power installed capacity and geothermal power installed capacity;

[0051] For economic development factors, the influencing factors include total social electricity consumption and per capita electricity consumption;

[0052] For the power consumption capacity factor, its influencing factors include the scale of regulating power supply, demand-side response capability, renewable energy penetration rate and renewable energy utilization rate;

[0053] For environmental protection constraints, the influencing factors include the total carbon emission control target, carbon emission intensity and the percentage of ecological red line constraint area;

[0054] For industry business environment factors, the influencing factors include the proportion of market-based trading electricity, the scale of grid investment, the scale of green certificate trading and the proportion of non-technical costs of renewable energy power generation.

[0055] Preferably, in step S4, the weights of each index of each research sub-region i in the current year are evaluated and obtained, specifically:

[0056] Using formula (9), we can get the weights of each index of the research sub-region i in the current year:

[0057]

[0058]

[0059] (9)

[0060]

[0061]

[0062] in: , , , , , respectively, resource endowment index , Economic Development Index , Power Consumption Capacity Index , Environmental Constraint Index , and industry business environment index The weight of .

[0063] Preferably, in step S5, formula (10) is used to obtain the comprehensive index of renewable energy power consumption in the research sub-region i in the current year: :

[0064] (10)

[0065] This step is finished.

[0066] Preferably, in step S6, the renewable energy power generation that needs to be absorbed in each study sub-region i in future years is determined by formula (11):

[0067] = + (11)

[0068] in: represents the renewable energy generation in the study sub-region i in the current year; Study the renewable energy generation in sub-region i for future years.

[0069] The method for determining and decomposing the responsibility weight of renewable energy power consumption provided by the present invention has the following advantages:

[0070] The present invention provides a method for determining and decomposing the responsibility weight of renewable energy power consumption, which is a mechanism for determining and decomposing the responsibility weight of renewable energy power consumption that comprehensively considers multi-dimensional factors, including multiple types of renewable energy resources, economic development level, environmental constraints, power grid consumption capacity and industry business environment. By constructing a multi-indicator comprehensive evaluation system, combined with normalization processing and weighted calculation, the scientific allocation of consumption responsibility weights ensures the fairness and rationality of renewable energy power consumption allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A flow chart of a method for determining and decomposing the responsibility weights of renewable energy power consumption provided by the present invention. DETAILED DESCRIPTION

[0072] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0073] The present invention provides a method for determining and decomposing the responsibility weight of renewable energy power consumption, which is a mechanism for determining and decomposing the responsibility weight of renewable energy power consumption that comprehensively considers multi-dimensional factors. The multi-dimensional factors include multiple types of renewable energy resources (including wind energy, solar energy, biomass energy, geothermal energy and hydropower), economic development level, environmental constraints, power grid consumption capacity and industry business environment. By constructing a multi-indicator comprehensive evaluation system, combined with normalization processing and weighted calculation, the consumption responsibility weight is scientifically allocated to ensure the fairness and rationality of renewable energy power consumption allocation.

[0074] See also Figure 1 The present invention provides a method for determining and decomposing renewable energy power consumption weights, comprising the following steps:

[0075] Step S1, dividing the study area into N study sub-areas;

[0076] Step S2, determining the factors affecting the consumption of renewable energy power, including resource endowment factors, economic development factors, power consumption capacity factors, environmental protection constraints factors and industry business environment factors;

[0077] Step S3: Evaluate and obtain the index corresponding to each influencing factor of each research sub-region i in the current year, including the resource endowment index , Economic Development Index , Power Consumption Capacity Index , Environmental Constraint Index and industry business environment index ; Where i=1,2,...,N;

[0078] In this step, for each influencing factor j, j=1,2,...,5, representing resource endowment factors, economic development factors, power consumption capacity factors, environmental protection constraints and industry business environment factors respectively; the influencing factor index of each research sub-region i in the current year is The evaluation method is:

[0079] Step S3.1, determine the factors that have a direct impact on influencing factor j. Impact factors are expressed as: ;

[0080] As an embodiment, the influencing factors of each influencing factor are:

[0081] For resource endowment factors, the influencing factors include wind power installed capacity, solar power installed capacity, hydropower installed capacity, biomass power installed capacity and geothermal power installed capacity;

[0082] For economic development factors, the influencing factors include total social electricity consumption and per capita electricity consumption;

[0083] For the power consumption capacity factor, its influencing factors include the scale of regulating power supply, demand-side response capability, renewable energy penetration rate and renewable energy utilization rate;

[0084] For environmental protection constraints, the influencing factors include the total carbon emission control target, carbon emission intensity and the percentage of ecological red line constraint area;

[0085] For industry business environment factors, the influencing factors include the proportion of market-based trading electricity, the scale of grid investment, the scale of green certificate trading and the proportion of non-technical costs of renewable energy power generation.

[0086] Step S3.2, obtain each impact factor of the research sub-region i in the current year The value of is expressed as ; where k=1,2,..., ;

[0087] Step S3.3: Use formula (1) to calculate the impact factor of each sub-region i in the current year. The value of After standardization, we can get each impact factor of the research sub-region i in the current year. The standardized value of :

[0088] (1)

[0089] in:

[0090] Represents the impact factors of N research sub-areas in the current year The minimum value of , that is: ;

[0091] Represents the impact factors of N research sub-areas in the current year The maximum value of is: ;

[0092] Step S3.4, determine each influencing factor The weight in the study sub-area i ;

[0093] Step S3.5: Use formula (2) to obtain the influencing factor index of the research sub-region i in the current year :

[0094] (2)

[0095] Thus, the influencing factor index of influencing factor j of sub-region i in the current year is obtained .

[0096] In the present invention, according to the type of influencing factors, the weight of each influencing factor in the research sub-area i is There are two ways to determine :

[0097] If the influencing factor j is a resource endowment factor, then its influencing factors The weight in the study sub-area i , determined by:

[0098] A1, using formula (3), the various influencing factors of the influencing factor j of the sub-region i in the current year are studied The value of Sum and get the impact factor value and :

[0099] (3)

[0100] A2, using formula (4), obtain each impact factor The weight in the study sub-area i :

[0101] (4)

[0102] Thus, the influencing factors of resource endowment factors are determined The weight in the study sub-area i .

[0103] If the influencing factor j is the economic development factor, the power consumption capacity factor, the environmental protection constraint factor and the industry business environment factor, then for each influencing factor j, its influencing factor The weight in the study sub-area i , determined by:

[0104] The renewable energy generation in sub-region i is taken as the dependent variable to study the influencing factors under influencing factor j in sub-region i. As the independent variable, the influencing factors of the research sub-region i were fitted by the multivariate linear regression method. The relationship between the renewable energy generation in the research sub-region i is used to determine the influencing factors The weight in the study sub-area i .

[0105] The specific multiple linear regression method is as follows:

[0106] B1, get the nearest The renewable energy generation series R i ={R i,1 ,R i,2 ,..., R i,l} and each impact factor under impact factor j The numerical sequence of ; Among them, R i,1 ,R i,2 ,..., R i,l , respectively, the research sub-area i from the first year to the Annual renewable energy generation; , respectively, are the impact factors of the research sub-region i In the first year to The value of the year; among them, the 1st year to the The years are arranged chronologically;

[0107] B2, for the renewable energy generation sequence R i ={R i,1 ,R i,2 ,..., R i,l Each element in} is normalized using formula (5) to obtain the normalized renewable energy power generation sequence R i,norm ={R i,1,norm ,R i,2,norm ,...,R i,l,norm}:

[0108] R i,norm ={ , ,..., } (5)

[0109] in: and , representing R i,1 ,R i,2 ,..., R i,l The maximum and minimum values ​​in ;

[0110] Impact Factor The numerical sequence of Each element in is normalized using formula (6) to obtain the normalized impact factor The numerical sequence of

[0111] :

[0112] (6)

[0113] in: and , representing The maximum and minimum values ​​in ;

[0114] B3, establish the multivariate linear regression equation:

[0115] R i,norm = + + +…+ +…+ + (7)

[0116] in: represents the intercept; represents the error term; , ,…, ,…, All are regression coefficients;

[0117] B4, through fitting, we get the regression coefficient , ,…, ,…, The value of

[0118] B5, using formula (8), we get the impact factor The weight in the study sub-area i :

[0119] (8)

[0120] Thus, the influencing factors under influencing factor j are obtained The weight in the study sub-area i .

[0121] Step S4, evaluating and obtaining the weights of each index of each research sub-region i in the current year;

[0122] Using formula (9), we can get the weights of each index of the research sub-region i in the current year:

[0123]

[0124]

[0125] (9)

[0126]

[0127]

[0128] in: , , , , , respectively, resource endowment index , Economic Development Index , Power Consumption Capacity Index , Environmental Constraint Index , and industry business environment index The weight of

[0129] Step S5: using the weights of each index determined in step S4, calculate the resource endowment index of each research sub-region i. , Economic Development Index , Power Consumption Capacity Index , Environmental Constraint Index and industry business environment index Perform weighted summation to obtain the comprehensive index of renewable energy power consumption in each research sub-region i in the current year ;

[0130] Specifically, formula (10) is used to obtain the comprehensive index of renewable energy power consumption in the research sub-region i in the current year: :

[0131] (10)

[0132] This step is finished.

[0133] Step S6: predict the total renewable energy generation in the study area in future years ; Using the formula = - , and obtain the increment of overall renewable energy power generation in the study area from the current year to the future year ;in, Represents the total renewable energy generation required in the study area in the current year;

[0134] The increase in overall renewable energy generation in the study area from the current year to the future year , according to the comprehensive index of renewable energy power consumption in each research sub-region i The ratio of the comprehensive index of renewable energy power consumption in the study area is allocated to each study sub-area i, so as to obtain the renewable energy power generation that needs to be consumed in each study sub-area i in future years.

[0135] Specifically, the renewable energy generation capacity that needs to be absorbed by each research sub-region i in future years is determined by formula (11):

[0136] = + (11)

[0137] in: represents the renewable energy generation in the study sub-region i in the current year; Study the renewable energy generation in sub-region i for future years.

[0138] Example:

[0139] In this embodiment, the factors affecting the consumption of renewable energy power include five types of factors: resource endowment factors, economic development factors, power consumption capacity factors, environmental protection constraints factors and industry business environment factors;

[0140] The influencing factors of each influencing factor are:

[0141] For resource endowment factors, the influencing factors include wind power installed capacity, solar power installed capacity, hydropower installed capacity, biomass power installed capacity and geothermal power installed capacity;

[0142] For economic development factors, the influencing factors include total social electricity consumption and per capita electricity consumption;

[0143] For the power consumption capacity factor, its influencing factors include the scale of regulating power supply, demand-side response capability, renewable energy penetration rate and renewable energy utilization rate;

[0144] For environmental protection constraints, the influencing factors include the total carbon emission control target, carbon emission intensity and the percentage of ecological red line constraint area;

[0145] For industry business environment factors, the influencing factors include the proportion of market-based trading electricity, the scale of grid investment, the scale of green certificate trading and the proportion of non-technical costs of renewable energy power generation.

[0146] In this embodiment, a method for determining and decomposing renewable energy power consumption weights is provided. The research area has N research sub-areas, each of which is a province, and includes the following steps:

[0147] 1. Calculation of resource endowment index

[0148] 1. Data acquisition:

[0149] Obtain the installed capacity supported by the technically exploitable renewable energy resources such as wind power, solar power, hydropower, biomass power, geothermal power, etc. in the province, denoted as W i ,S i ,H i ,B i ,G i ; Where i is the i-th province. Assuming there are N provinces in total, then i={1,2,...,N}, and the meanings are the same in the following text;

[0150] Calculate the maximum installed capacity of wind power, solar power, hydropower, biomass power, and geothermal power in all provinces in the country, denoted as W max ,S max ,H max ,B max ,G max , the minimum value is recorded as W min ,S min ,H min ,B min ,G min, in , , , , ; , , , , .

[0151] 2. Standardization:

[0152] Calculate the standardized values ​​of the province's installed capacity of wind power, solar power, hydropower, biomass power, and geothermal power respectively:

[0153] , , , , .

[0154] 3. Weight calculation:

[0155] Calculate the total installed capacity of different renewable energy sources in the province, denoted as T i , T i= W i +S i +H i +B i +G i ; Calculate the proportion of the province's different renewable energy installed capacity to the province's total renewable energy power generation installed capacity , , , , ,in .

[0156] 4. Calculation of resource endowment index:

[0157] Calculate the resource endowment index of the province:

[0158] 2. Calculation of Economic Development Index

[0159] 1. Data acquisition:

[0160] Get the total social electricity consumption and per capita electricity consumption of the province, denoted as E i and P i Calculate the maximum value of the total social electricity consumption and per capita electricity consumption of all provinces in the country and record it as E max ,P max , the minimum value is recorded as E min ,P min .in , ; , .

[0161] 2. Standardization:

[0162] The following formula is used to normalize the total social electricity consumption and per capita electricity consumption of the province:

[0163] , .

[0164] 3. Weight calculation:

[0165] Taking the province's renewable energy power generation as the dependent variable and the province's total social power consumption and per capita power consumption as the independent variables, the multivariate linear regression method is used to fit the relationship between the province's total social power consumption and per capita power consumption and the province's renewable energy power generation, thereby determining the corresponding weights of the total social power consumption and per capita power consumption indicators. The specific calculation process is carried out according to the following steps:

[0166] (1) Select the most recent Historical data of independent and dependent variables for the year

[0167] Get the province's nearest The renewable energy generation series R i ={R i,1 ,R i,2 ,..., R i,l}, for the whole society

[0168] Power Series E i ={E i,1 ,E i,2 ,...,E i,l} and the per capita electricity consumption series P i ={P i,1 ,P i,2 ,...,P i,l}

[0169] (2) Normalizing independent and dependent variables

[0170] Recent renewable energy generation Year data R i ={R i,1 ,R i,2 ,..., R i,l} to normalize and obtain R i

[0171] The maximum value R i，max and the minimum value R i,min , then the normalized renewable energy generation sequence is R i,norm ={ , ,..., }; Similarly, the total electricity consumption sequence E of the province can be obtained i and the per capita electricity consumption series P i The normalized data sequences are:

[0172] E i,norm ={ , ,..., }

[0173] P i,norm ={ , ,..., }.

[0174] (3) Multiple linear regression fitting

[0175]

[0176] : Normalized renewable energy electricity series;

[0177] , : Normalized independent variable data sequence;

[0178] : represents the intercept;

[0179] : error term;

[0180] , : regression coefficient;

[0181] Calculated by fitting , Regression coefficients.

[0182] (4) Calculate the weight of the impact of total electricity consumption and per capita electricity consumption on the province’s renewable energy generation

[0183] The weight of each indicator is obtained according to the regression coefficient:

[0184] ,

[0185] 4. Calculation of economic development index:

[0186]

[0187] (III) Calculation of power consumption capacity index

[0188] 1. Data acquisition:

[0189] Get the values ​​of the province’s power supply scale, demand-side response capability, renewable energy penetration rate, and renewable energy utilization rate, denoted as J i ,D i ,N i ,U i The maximum value of the power supply scale, demand-side response capacity, renewable energy penetration rate and renewable energy utilization rate of all provinces in the country is calculated and recorded as J max ,D max ,N max ,U max , the minimum value is recorded as Jmin ,D min ,N min ,U min .in , , , ; , , , .

[0190] 2. Standardization:

[0191] The province's power supply scale, demand-side response capability, renewable energy penetration rate, and renewable energy utilization rate are normalized numerically in turn:

[0192] , , , .

[0193] 3. Weight calculation:

[0194] Taking the province's renewable energy generation as the dependent variable and the province's regulated power scale, demand-side response capability, renewable energy penetration rate and renewable energy utilization rate as the independent variables, the multivariate linear regression method is used to fit the relationship between the province's regulated power scale, demand-side response capability, renewable energy penetration rate, renewable energy utilization rate and the province's renewable energy generation, thereby determining the corresponding weights of the regulated power scale, demand-side response capability, renewable energy penetration rate and renewable energy utilization rate indicators. The specific calculation process is carried out in the following steps:

[0195] (1) Select the most recent Historical data of independent and dependent variables for the year

[0196] Get the province's nearest The renewable energy generation series R i ={R i,1 ,R i,2 ,..., R i,l}, adjust the power supply

[0197] Scale Series J i ={J i,1 ,J i,2 ,...,J i, l}、Demand side response capability sequence D i ={D i,1 ,D i,2 ,...,D i, l}, Renewable energy penetration sequence N i ={N i,1 ,Ni,2 ,...,N i,l}, Renewable energy utilization rate series U i ={U i,1 ,U i,2 ,...,U i, l}

[0198] (2) Normalizing independent and dependent variables

[0199] Recent renewable energy generation The data of the year are normalized and the above economic development index is calculated

[0200] The normalization process mentioned in is the same;

[0201] Get the province's regulated power scale J i , demand-side response capability D i , renewable energy penetration rate N i , Renewable energy utilization rate U i The normalized data sequences are:

[0202] J i,norm ={ , ,..., }

[0203] D i,norm ={ , ,..., }

[0204] N i,norm ={ , ,..., }

[0205] U i,norm ={ , ,..., }.

[0206] (3) Multiple linear regression fitting

[0207]

[0208] : Normalized renewable energy electricity;

[0209] : Normalized independent variable data;

[0210] : represents the intercept;

[0211] : error term;

[0212] , , , : regression coefficient;

[0213] Calculated by fitting , , , Regression coefficients.

[0214] (4) Calculate the weights of the impact of the scale of regulating power sources, demand-side response capability, renewable energy penetration rate and renewable energy utilization rate on the province’s renewable energy power generation:

[0215] Specifically, the weight of each indicator is obtained according to each regression coefficient:

[0216]

[0217]

[0218]

[0219]

[0220] 4. Calculation of power consumption capacity index:

[0221] Calculate the province's electricity consumption capacity index:

[0222]

[0223] (IV) Calculation of Environmental Constraint Index

[0224] 1. Data acquisition:

[0225] Obtain the total carbon emission control target, carbon emission intensity and ecological red line constraint area ratio of the province, denoted as C i ,M i ,L i The maximum value of the total carbon emission control target, carbon emission intensity and ecological red line constraint area ratio of all provinces in the country is recorded as C max ,M max ,L max , the minimum value is recorded as C min ,M min ,L min .in , , ; , , .

[0226] 2. Standardization:

[0227] The province’s total carbon emission control target, carbon emission intensity, and ecological red line constraint area ratio are normalized numerically in turn:

[0228] , , .

[0229] 3. Weight calculation:

[0230] Taking the province's renewable energy power generation as the dependent variable, and the province's total carbon emission control target, carbon emission intensity, and ecological red line constraint area ratio as independent variables, the multivariate linear regression method is used to fit the relationship between the province's total carbon emission control target, carbon emission intensity, and ecological red line constraint area ratio and the province's renewable energy power generation, thereby determining the weights corresponding to the carbon emission control target, carbon emission intensity, and ecological red line constraint area ratio indicators. The specific calculation process is carried out according to the following steps:

[0231] (1) Select the most recent Historical data of independent and dependent variables for the year

[0232] Get the province's nearest The renewable energy generation series R i ={R i,1 ,R i,2 ,..., R i,l}, Carbon emission total control target sequence C i ={C i,1 ,C i,2 ,...,C i,l}、Carbon emission intensity sequence M i ={M i,1 ,M i,2 ,...,M i,l}、Ecological red line constraint area ratio sequence L i ={L i,1 ,L i,2 ,...,L i, l}.

[0233] (2) Normalizing independent and dependent variables

[0234] Recent renewable energy generation The data of the year are normalized and the above economic development index is calculated

[0235] The normalization process mentioned in is the same;

[0236] Get the total carbon emission control target C of the province i 、Carbon emission intensity Mi , Ecological red line constraint area ratio L i The normalized data sequences are:

[0237] C i,norm ={ , ,..., }

[0238] M i,norm ={ , ,..., }

[0239] L i,norm ={ , ,..., }.

[0240] (3) Multiple linear regression fitting

[0241]

[0242] : Normalized renewable energy electricity;

[0243] : Normalized independent variable data;

[0244] : represents the intercept;

[0245] : error term;

[0246] , , : regression coefficient;

[0247] Calculated by fitting , , Regression coefficients.

[0248] (4) Calculate the impact weights of the total carbon emission control target, carbon emission intensity, and ecological red line constraint area ratio on the province’s renewable energy power generation

[0249] The weight of each indicator is obtained according to the regression coefficient:

[0250] , ,

[0251] 4. Calculation of environmental constraint index:

[0252]

[0253] (V) Calculation of industry business environment index

[0254] 1. Data acquisition:

[0255] Get the proportion of market-based electricity transactions, grid investment scale, green certificate transaction scale and non-technical cost ratio of renewable energy power generation in the province, denoted as K i ,V i ,F i ,Q i The maximum value of the market-based electricity trading ratio, grid investment scale, green certificate trading scale and non-technical cost ratio of renewable energy power generation in all provinces in the country is calculated and recorded as K max ,V max ,F max ,Q max , the minimum value is recorded as K min ,V min ,F min ,Q min .in , , , .

[0256] 2. Standardization:

[0257] The proportion of market-based electricity transactions, the scale of grid investment, the scale of green certificate transactions, and the proportion of non-technical costs of renewable energy power generation in the province are processed in turn using normalized numerical values:

[0258] , , ,

[0259] 3. Weight calculation:

[0260] Taking the province's renewable energy power generation as the dependent variable, and the province's market-based trading power share, grid investment scale, green certificate trading scale, and renewable energy power generation non-technical cost ratio as independent variables, the multivariate linear regression method is used to fit the relationship between the province's market-based trading power share, grid investment scale, green certificate trading scale, and renewable energy power generation non-technical cost ratio and the province's renewable energy power generation, thereby determining the corresponding weights of the market-based trading power share, grid investment scale, green certificate trading scale, and renewable energy power generation non-technical cost ratio indicators. The specific calculation process is carried out in the following steps:

[0261] (1) Select the most recent Historical data of independent and dependent variables for the year

[0262] Get the province's nearest The renewable energy generation series R i ={R i,1 ,R i,2 ,..., R i,l}, market-oriented

[0263] Easy electricity proportion sequence K i ={K i,1 ,K i,2 ,...,K i, l}、Grid investment scale sequence V i ={V i,1 ,V i,2 ,...,V i, l}、Green certificate transaction scale sequence F i ={F i,1 ,F i,2 ,...,F i,l}、Ratio sequence of non-technical cost of renewable energy generation Q i ={Q i,1 ,Q i,2 ,...,Q i, l}.

[0264] (2) Normalizing independent and dependent variables

[0265] Recent renewable energy generation The data of the year are normalized and used in the calculation of the above economic development index.

[0266] The proposed normalization process is the same;

[0267] Get the proportion of market-based electricity transactions in the province K i , Grid investment scale V i 、Green Certificate Trading Scale F i , can be

[0268] Non-technical cost ratio of renewable energy generation Q i The normalized data sequences are:

[0269] K i,norm ={ , ,..., }

[0270] V i,norm ={ , ,..., }

[0271] F i,norm ={ , ,..., }

[0272] Q i,norm ={ , ,..., }.

[0273] (3) Multiple linear regression fitting.

[0274]

[0275] : Normalized renewable energy electricity;

[0276] : Normalized independent variable data;

[0277] : represents the intercept;

[0278] : error term;

[0279] , , , : regression coefficient.

[0280] Calculated by fitting , , , Regression coefficients.

[0281] (4) Calculate the weight of the impact of the scale of regulating power supply, demand-side response capability, renewable energy penetration rate and renewable energy utilization rate on the province’s renewable energy power generation

[0282] The weight of each indicator is obtained according to the regression coefficient:

[0283]

[0284]

[0285]

[0286]

[0287] 4. Calculation of industry business environment index:

[0288] Calculate the province's industry business environment index:

[0289] .

[0290] (VI) Calculation method of comprehensive index of consumption responsibility weight:

[0291] Comprehensive resource endowment index , Economic Development Index , Power Consumption Capacity Index , Environmental Constraint Index , Industry Business Environment Index , calculate the comprehensive index of renewable energy power consumption in the province , the calculation process is as follows:

[0292]

[0293] in: , , , , It is the ratio of the absolute value of each index in the province to the sum of the absolute values ​​of the corresponding indexes in all provinces in the country.

[0294] (i=1,2,...,N)

[0295] (i=1,2,...,N)

[0296] (i=1,2,...,N)

[0297] (i=1,2,...,N)

[0298] (i=1,2,...,N)

[0299] Repeat steps 1 to 6 above to calculate the comprehensive index of consumption responsibility weight for each province in the country, that is, , , ..., , .

[0300] (VII) Calculation of renewable energy electricity distribution in the province:

[0301] Calculate the increase in the amount of renewable energy electricity required in the country in future years compared to the current national renewable energy power generation , then the renewable energy power generation capacity that province i should bear is for:

[0302] (i=1,2,...,N)

[0303] in: Represents the renewable energy power generation in the study sub-region i in the current year.

[0304] The method for determining and decomposing the responsibility weight of renewable energy power consumption provided by the present invention has the following advantages:

[0305] (1) A more scientific indicator system: Through a multi-dimensional comprehensive evaluation system, factors such as resource endowment, economic development, grid absorption capacity, environmental constraints and industry business environment are introduced to make the distribution of responsibility weights more comprehensive and reasonable.

[0306] (2) Improving the fairness and objectivity of inter-regional comparisons: By introducing normalization processing, the impact of differences in the absolute values ​​of resource endowments in different regions on the comprehensive evaluation is eliminated, making the resource endowment index between regions more comparable.

[0307] (3) Improved incentive mechanism: Strengthen policy guidance, scientifically incentivize regions with great resource potential to accelerate the development of renewable energy, and promote the efficient achievement of the country's overall new energy consumption goals.

[0308] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be considered as the scope of protection of the present invention.

Claims

1. A method for determining and decomposing renewable energy power consumption weights, characterized in that: The following steps are involved: Step S1, dividing the study area into N study sub-areas; Step S2, determining the factors affecting the consumption of renewable energy power, including resource endowment factors, economic development factors, power consumption capacity factors, environmental protection constraints factors and industry business environment factors; Step S3: Evaluate and obtain the index corresponding to each influencing factor of each research sub-region i in the current year, including the resource endowment index , Economic Development Index , Power Consumption Capacity Index , Environmental Constraint Index and industry business environment index ; Where i=1,2,...,N; Step S4, evaluating and obtaining the weights of each index of each research sub-region i in the current year; Step S5: using the weights of each index determined in step S4, calculate the resource endowment index of each research sub-region i. , Economic Development Index , Power Consumption Capacity Index , Environmental Constraint Index and industry business environment index Perform weighted summation to obtain the comprehensive index of renewable energy power consumption in each research sub-region i in the current year ; Step S6: predict the total renewable energy generation in the study area in future years ; Using the formula = - , and obtain the increment of overall renewable energy power generation in the study area from the current year to the future year ;in, Represents the total renewable energy generation required in the study area in the current year; The increase in overall renewable energy generation in the study area from the current year to the future year , according to the comprehensive index of renewable energy power consumption in each research sub-region i The ratio of the comprehensive index of renewable energy power consumption in the study area is allocated to each study sub-area i, so as to obtain the renewable energy power generation that needs to be consumed in each study sub-area i in future years.

2. A method for determining and decomposing renewable energy power consumption weights according to claim 1, characterized in that: In step S3, for each influencing factor j, j=1,2,...,5, representing resource endowment factors, economic development factors, power consumption capacity factors, environmental protection constraints and industry business environment factors respectively; the influencing factor index of each research sub-region i in the current year is The evaluation method is: Step S3.1, determine the factors that have a direct impact on influencing factor j. Impact factors are expressed as: ; Step S3.2, obtain each impact factor of the research sub-region i in the current year The value of is expressed as ; where k=1,2,..., ; Step S3.3: Use formula (1) to calculate the impact factor of each sub-region i in the current year. The value of After standardization, we can get each impact factor of the research sub-region i in the current year. The standardized value of : （1） in: Represents the impact factors of N research sub-areas in the current year The minimum value of , that is: ; Represents the impact factors of N research sub-areas in the current year The maximum value of is: ; Step S3.4, determine each influencing factor The weight in the study sub-area i ; Step S3.5: Use formula (2) to obtain the influencing factor index of the research sub-region i in the current year : （2） Thus, the influencing factor index of influencing factor j of sub-region i in the current year is obtained .

3. A method for determining and decomposing renewable energy power consumption weights according to claim 2, characterized in that: In step S3.4, if the influencing factor j is a resource endowment factor, then each of its influencing factors The weight in the study sub-area i , determined by: A1, using formula (3), the various influencing factors of the influencing factor j of the sub-region i in the current year are studied The value of Sum and get the impact factor value and : （3） A2, using formula (4), obtain each impact factor The weight in the study sub-area i : （4） Thus, the influencing factors of resource endowment factors are determined The weight in the study sub-area i .

4. A method for determining and decomposing renewable energy power consumption weights according to claim 2, characterized in that: If the influencing factor j is the economic development factor, the power consumption capacity factor, the environmental protection constraint factor and the industry business environment factor, then for each influencing factor j, its influencing factor The weight in the study sub-area i , determined by: The renewable energy generation in sub-region i is taken as the dependent variable to study the influencing factors under influencing factor j in sub-region i. As the independent variable, the influencing factors of the research sub-region i were fitted by the multivariate linear regression method. The relationship between the renewable energy generation in the research sub-region i is used to determine the influencing factors The weight in the study sub-area i .

5. A method for determining and decomposing renewable energy power consumption weights according to claim 4, characterized in that: The renewable energy generation in sub-region i is taken as the dependent variable to study the influencing factors under influencing factor j in sub-region i. As the independent variable, the influencing factors of the research sub-region i were fitted by the multivariate linear regression method. The relationship between the renewable energy generation in the research sub-region i is used to determine the influencing factors The weight in the study sub-area i , specifically: B1, get the nearest The renewable energy generation series R i ={R i,1 ,R i,2 ,..., R i,l } and each impact factor under impact factor j The numerical sequence of ; Among them, R i,1 ,R i,2 ,..., R i,l , respectively, the research sub-area i from the first year to the Annual renewable energy generation; , respectively, are the impact factors of the research sub-region i In the first to The values ​​of the year; among them, the 1st year to the The years are arranged chronologically; B2, for the renewable energy generation sequence R i ={R i,1 ,R i,2 ,..., R i,l Each element in} is normalized using formula (5) to obtain the normalized renewable energy power generation sequence R i,norm ={R i,1,norm ,R i,2,norm ,..., R i,l,norm }: R i,norm ={ , ,..., } （5） in: and , representing R i,1 ,R i,2 ,..., R i,l The maximum and minimum values ​​in ; Impact Factor The numerical sequence of Each element in is normalized using formula (6) to obtain the normalized impact factor The numerical sequence of ： （6） in: and , representing The maximum and minimum values ​​in ; B3, establish the multivariate linear regression equation: R i,norm = + + +…+ +…+ + （7） in: represents the intercept; represents the error term; , ,…, ,…, All are regression coefficients; B4, through fitting, we get the regression coefficient , ,…, ,…, The value of B5, using formula (8), we get the impact factor The weight in the study sub-area i : （8） Thus, the influencing factors under influencing factor j are obtained The weight in the study sub-area i .

6. A method for determining and decomposing renewable energy power consumption weights according to claim 2, characterized in that: For resource endowment factors, the influencing factors include wind power installed capacity, solar power installed capacity, hydropower installed capacity, biomass power installed capacity and geothermal power installed capacity; For economic development factors, the influencing factors include total social electricity consumption and per capita electricity consumption; For the power consumption capacity factor, its influencing factors include the scale of regulating power supply, demand-side response capability, renewable energy penetration rate and renewable energy utilization rate; For environmental protection constraints, the influencing factors include the total carbon emission control target, carbon emission intensity and the percentage of ecological red line constraint area; For industry business environment factors, the influencing factors include the proportion of market-based trading electricity, the scale of grid investment, the scale of green certificate trading and the proportion of non-technical costs of renewable energy power generation.

7. A method for determining and decomposing renewable energy power consumption weights according to claim 1, characterized in that: Step S4, evaluate and obtain the weights of each index of each research sub-region i in the current year, specifically: Using formula (9), we can get the weights of each index of the research sub-region i in the current year: ； ； ； （9） ； ； in: , , , , , respectively, resource endowment index , Economic Development Index , Power Consumption Capacity Index , Environmental Constraint Index , and industry business environment index The weight of .

8. A method for determining and decomposing renewable energy power consumption weights according to claim 7, characterized in that: In step S5, formula (10) is used to obtain the comprehensive index of renewable energy power consumption in the research sub-region i in the current year: : （10） This step is finished.

9. A method for determining and decomposing renewable energy power consumption weights according to claim 1, characterized in that: In step S6, the renewable energy generation capacity that needs to be absorbed by each study sub-region i in future years is determined by formula (11): = + （11） in: represents the renewable energy generation in the study sub-region i in the current year; Study the renewable energy generation in sub-region i for future years.

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