An evaluation method for the developable carrying capacity coefficient of wind power and photovoltaic under multi-factor constraints

Abstract

The present invention provides a method for evaluating the bearing capacity coefficient of wind power and photovoltaic development under multi-factor constraints, including: constructing an index system for evaluating the bearing capacity coefficient of wind power and photovoltaic development; determining the index values of the primary indicators and the weights of the primary indicators; performing weighted summation on the index values of the primary indicators and the weights of the primary indicators in the evaluation area to obtain the bearing capacity coefficient of wind power and photovoltaic development in the evaluation area. The present invention takes into account the influence of multi-factor constraints such as the current situation of new energy development and utilization in the region, the current situation of economic and social development, the degree of power supply and demand tension, and the guarantee degree of land resources. Based on the analytic hierarchy process and the geometric mean method, the bearing capacity coefficient of wind power and photovoltaic development in each region is evaluated, effectively improving the accuracy and rationality of the evaluation results. In addition, the present invention takes into account regional differences, divides different types of regions, and constructs algorithms for each region, improving the efficiency of evaluating the bearing capacity coefficient of wind power and photovoltaic development in each region.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power and photovoltaic development and utilization, and specifically relates to a method for evaluating the bearing capacity coefficient of wind power and photovoltaic development under multi-factor constraints. Background Art

[0002] The bearing capacity of wind power and photovoltaic development is an important evaluation index for the development potential of regional wind power and photovoltaic, and has important reference value for the sustainable development of regional new energy. Currently, the existing land or resource bearing capacity evaluation methods mainly evaluate according to economic development and resource endowment, and are used to serve the national territorial space planning or resource allocation. For the evaluation of the development potential and limit of new energy such as regional wind power and photovoltaic, the existing methods mainly subjectively determine the empirical values based on factors such as industry experience, regional economic development and policies. The accuracy and rationality of the bearing capacity coefficient values of wind power and photovoltaic development obtained by evaluation need to be improved. There is an urgent need for an accurate, systematic and reasonable evaluation method. Summary of the Invention

[0003] In view of the defects existing in the prior art, the present invention provides a method for evaluating the bearing capacity coefficient of wind power and photovoltaic development under multi-factor constraints, which can effectively solve the above problems.

[0004] The technical solution adopted by the present invention is as follows:

[0005] The present invention provides a method for evaluating the bearing capacity coefficient of wind power and photovoltaic development under multi-factor constraints, including the following steps:

[0006] Step S1, constructing an index system for evaluating the bearing capacity coefficient of wind power and photovoltaic development; the index system includes a first-level index layer and a second-level index layer;

[0007] Step S2, pre-determining the index value calculation models of each second-level index in the second-level index layer;

[0008] Step S3, collecting the evaluation basic data of the evaluation area, inputting it into the index value calculation model of the second-level index, and obtaining the index values of each second-level index of the evaluation area;

[0009] Step S4, performing geometric mean on the index values of each second-level index corresponding to each first-level index to obtain the index value of each first-level index of the evaluation area;

[0010] Step S5, determining the weights of each first-level index of the evaluation area, including two methods:

[0011] The first one is to pre-establish a regional type evaluation model, input the index value of the corresponding first-level index of the evaluation area into the regional type evaluation model, and obtain the regional type of the evaluation area;

[0012] Pre - establish a mapping relation table between the regional type and the weights of each first - level index in the first - level index layer; according to the regional type of the evaluation area, search the mapping relation table to obtain the weights of each first - level index of the evaluation area.

[0013] Second, use the first - level index weight determination algorithm to determine the weights of each first - level index of the evaluation area.

[0014] Step S6, perform weighted summation on the index values and weights of the first - level indexes of the evaluation area to obtain the wind - power and photovoltaic development bearing capacity coefficient of the evaluation area.

[0015] Preferably, the first - level index layer includes 7 first - level indexes, namely: the first - level index of the development and utilization level of wind and light resources 、the first - level index of energy transformation pressure 、the first - level index of the degree of tension between power supply and demand 、the first - level index of the level of social and economic development 、the first - level index of the guarantee degree of land resources 、the first - level index of the consumption level of renewable energy power and the first - level index of social and cultural preferences ;

[0016] The first - level index of the development and utilization level of wind and light resources , includes three second - level indexes, namely: the second - level index of the development ratio of wind power and photovoltaic 、the second - level index of the installed capacity ratio of wind power and photovoltaic and the second - level index of the power generation ratio of wind power and photovoltaic ;

[0017] The first - level index of energy transformation pressure , includes two second - level indexes, namely: the second - level index of the installed capacity ratio of thermal power and the second - level index of the power generation ratio of thermal power ;

[0018] The first - level index of the degree of tension between power supply and demand , includes a second - level index, which is the second - level index of the power self - sufficiency rate ;

[0019] The first - level index of the level of social and economic development , includes two second - level indexes, namely: the second - level index of the population aggregation degree limit coefficient and the second - level index of the land - average GDP limit coefficient ;

[0020] The first - level index of the guarantee degree of land resources , includes a second - level index, which is the second - level index of the intensity of land development ;

[0021] The first-level indicator of the renewable energy power consumption level , including two second-level indicators, namely: the second-level indicator of the completion of the renewable energy power consumption responsibility weight and the second-level indicator of the expected target of the renewable energy power consumption responsibility weight ;

[0022] The first-level indicator of social and cultural preferences , including one second-level indicator, namely: the second-level indicator of the sociology coefficient .

[0023] Preferably, the calculation models of the indicator values of the second-level indicators in the second-level indicator layer are as follows:

[0024] ① The calculation sub-models of the second-level indicators of the wind power and photovoltaic development ratio , the second-level indicator of the wind power and photovoltaic installed capacity ratio and the second-level indicator of the wind power and photovoltaic power generation ratio are shown in formulas (1), (2), and (3) respectively:

[0025]

[0026] Where:

[0027] p w&r,d is the wind power and photovoltaic development ratio; A w&r,d is the developed and utilized area of wind power and photovoltaic; A u is the land area available for wind power and photovoltaic development;

[0028] p w&r,cap is the wind power and photovoltaic installed capacity ratio; CAP w&r is the wind power and photovoltaic installed capacity; CAP is the total installed capacity of power sources;

[0029] p w&r,pow is the wind power and photovoltaic power generation ratio; POW w&r is the wind power and photovoltaic power generation; POW is the total power generation of the whole society;

[0030] ② The calculation sub-models of the second-level indicators of the thermal power installed capacity ratio and the second-level indicator of the thermal power generation ratio are shown in formulas (4) and (5) respectively:

[0031]

[0032] Where:

[0033] p c,cap is the thermal power installed capacity ratio; CAP cThe installed capacity of thermal power is; CAP is the total installed capacity of power sources;

[0034] p c,pow is the proportion of thermal power generation; POW c is the thermal power generation; POW is the total power generation of the whole society;

[0035] ③ The calculation sub-model of the secondary index of the power self-sufficiency rate is as shown in formula (6):

[0036] (6)

[0037] Where:

[0038] EC is the total electricity consumption of the whole society;

[0039] ④ The calculation sub-models of the secondary index of the population agglomeration degree limit coefficient and the secondary index of the land average GDP limit coefficient are as shown in formulas (7) and (8) respectively: and the secondary index of the land average GDP limit coefficient

[0040] (7)

[0041] (8)

[0042] Where:

[0043] LCPD is the population agglomeration degree limit coefficient; PD is the population density;

[0044] LCGDP is the land average GDP limit coefficient; ALGDP is the land average GDP;

[0045] ⑤ The calculation sub-models of the secondary indexes of the land development intensity, the completion situation of the renewable energy power consumption responsibility weight, the expected target of the renewable energy power consumption responsibility weight, and the sociology coefficient are: obtained by reading and analyzing the current relevant database. and the secondary index of the completion situation of the renewable energy power consumption responsibility weight and the secondary index of the expected target of the renewable energy power consumption responsibility weight and the secondary index of the sociology coefficient

[0046] Preferably, in step S4, the geometric mean of the index values of each secondary index corresponding to each primary index is calculated to obtain the index value of each primary index of the evaluation area, specifically:

[0047] Assume that the primary index layer includes primary indexes, denoted as: primary index ;

[0048] For each primary index , , having ​​​Secondary indicators, expressed as: ;

[0049] Primary indicator indicator value , calculated using formula (9):

[0050] (9)

[0051] Wherein: , respectively representing the indicator values of the secondary indicators .

[0052] Preferably, the regional type evaluation model is:

[0053] ① Among the primary indicators in the primary indicator layer, select representative primary indicators: the primary indicator of energy transformation pressure , the primary indicator of power supply-demand tightness and the primary indicator of social and economic development level ;

[0054] ② Classify each representative primary indicator:

[0055] For the primary indicator of energy transformation pressure , it is divided into two grades: high energy transformation pressure and low energy transformation pressure;

[0056] For the primary indicator of power supply-demand tightness , it is divided into three grades: tight power supply, balanced power supply-demand, and abundant power supply, corresponding to representing high, medium, and low power supply-demand tightness;

[0057] For the primary indicator of social and economic development level , it is divided into two grades: low social and economic development level and high social and economic development level;

[0058] ③ Establish the correspondence between the regional type and the grades of each representative primary indicator to obtain the regional type evaluation model:

[0059] The first type of region: tight power supply, and at the same time, high energy transformation pressure;

[0060] The second type of region: tight power supply, and at the same time, low energy transformation pressure;

[0061] The third type of region: balanced power supply-demand, and at the same time, high energy transformation pressure; The fourth type of region: balanced power supply-demand, and at the same time, low energy transformation pressure;

[0062] The fifth type of region: abundant power supply, and at the same time, low social and economic development level; The sixth type of region: abundant power supply, and at the same time, high social and economic development level.

[0063] Preferably, the value ranges of the grade indexes of each representative first-level index are as follows:

[0064] For the first-level index of energy transition pressure , when its index value , it indicates a high energy transition pressure; when its index value , it indicates a low energy transition pressure;

[0065] For the first-level index of the tightness of power supply and demand , when its index value , it indicates an abundant power supply; when its index value , it indicates a balanced power supply and demand; when its index value , it indicates a tight power supply;

[0066] For the first-level index of the level of social and economic development , when its index value , it indicates a low level of social and economic development; when its index value , it indicates a high level of social and economic development.

[0067] Preferably, the weight determination algorithm for the first-level indexes is used to determine the weights of the first-level indexes of the evaluation area, specifically:

[0068] Step S5.1: Determine the scoring rules for the importance between two indexes in the first-level index layer;

[0069] Step S5.2: According to the scoring rules, assign values to the importance matrix of the evaluation area to obtain the importance matrix of the evaluation area after assignment, which is expressed as:

[0070] (10)

[0071] Where: represents the importance matrix; represents the number of first-level indexes in the first-level index layer; represents the first-level index relative to the first-level index relative importance degree, where, , ;

[0072] The value of satisfies the following relationship:

[0073] (11)

[0074] Step S5.3: Using formula (11), obtain the weight of the first-level index :

[0075] (12)

[0076] Wherein: represents the relative importance of the first-level indicators .

[0077] Preferably, the scoring rules for the importance between any two indicators in the first-level indicator layer are shown in the following table:

[0078] Table: Scoring Rules for the Importance between Any Two Indicators

[0079]

[0080] It is the 1-9 scale method.

[0081] Preferably, step S6 is specifically as follows:

[0082] Using formula (13), obtain the wind power and photovoltaic development carrying capacity coefficient of the evaluation area:

[0083] (13)

[0084] Wherein: is the index value of the first-level indicator . is the wind power and photovoltaic development carrying capacity coefficient.

[0085] A method for evaluating the wind power and photovoltaic development carrying capacity coefficient under multi-factor constraints provided by the present invention has the following advantages:

[0086] The present invention provides a method for evaluating the wind power and photovoltaic development carrying capacity coefficient under multi-factor constraints. This method takes into account the influence of multi-factor constraints such as the current situation of new energy development and utilization in the region, the current situation of economic and social development, the degree of power supply and demand tension, and the land resource guarantee degree. Based on the analytic hierarchy process and the geometric mean method, it evaluates the wind power and photovoltaic development carrying capacity coefficient of each region. It is a quantitative method for evaluating the wind power and photovoltaic development carrying capacity coefficient, which effectively improves the accuracy and rationality of the evaluation results; in addition, the present invention considers regional differences, divides different types of regions, and constructs algorithms for each region, thereby improving the efficiency of evaluating the wind power and photovoltaic development carrying capacity coefficient of each region. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 is a flowchart of a method for evaluating the wind power and photovoltaic development carrying capacity coefficient under multi-factor constraints provided by the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0088] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention clearer and more understandable, the present invention will be 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.

[0089] The present invention proposes an evaluation method for the developable bearing capacity coefficient of wind power and photovoltaic power under multi-factor constraints. This method takes into account the influence of multi-factor constraints such as the current situation of new energy development and utilization in the region, the current situation of economic and social development, the degree of power supply and demand tension, and the guarantee degree of land resources. Based on the analytic hierarchy process and the geometric mean method, the developable bearing capacity coefficient of wind power and photovoltaic power in each region is evaluated, which is a quantitative evaluation method for the developable bearing capacity coefficient of wind power and photovoltaic power, effectively improving the accuracy and rationality of the evaluation results; in addition, the present invention considers regional differences, divides different types of regions, and constructs algorithms for each region, thereby improving the efficiency of evaluating the developable bearing capacity coefficient of wind power and photovoltaic power in each region; as Figure 1 shown, the specific steps are as follows:

[0090] Step S1, construct an index system for evaluating the developable bearing capacity coefficient of wind power and photovoltaic power; the index system includes a first-level index layer and a second-level index layer;

[0091] As a preferred implementation manner, in the present invention, the index system is:

[0092] The first-level index layer includes 7 first-level indexes, namely: the first-level index of the development and utilization level of wind and light resources , the first-level index of the energy transformation pressure , the first-level index of the degree of power supply and demand tension , the first-level index of the social and economic development level , the first-level index of the guarantee degree of land resources , the first-level index of the consumption level of renewable energy power and the first-level index of social and cultural preferences ;

[0093] The first-level index of the development and utilization level of wind and light resources , includes three second-level indexes, namely: the second-level index of the development ratio of wind power and photovoltaic power , the second-level index of the installed capacity ratio of wind power and photovoltaic power and the second-level index of the power generation ratio of wind power and photovoltaic power ;

[0094] The first-level index of the energy transformation pressure , includes two second-level indexes, namely: the second-level index of the installed capacity ratio of thermal power and the second-level index of the power generation ratio of thermal power ;

[0095] The first-level index of the power supply-demand tightness , including a second-level index, which is the second-level index of the power self-sufficiency rate ;

[0096] The first-level index of the social and economic development level , including two second-level indexes, namely: the second-level index of the population agglomeration degree limit coefficient and the second-level index of the GDP per unit land area limit coefficient ;

[0097] The first-level index of the land resource guarantee degree , including a second-level index, which is the second-level index of the land development intensity ;

[0098] The first-level index of the renewable energy power consumption level , including two second-level indexes, namely: the second-level index of the completion situation of the renewable energy power consumption responsibility weight and the second-level index of the expected target of the renewable energy power consumption responsibility weight ;

[0099] The first-level index of the social and cultural preference , including a second-level index, which is: the second-level index of the sociology coefficient .

[0100] The index system, the general symbols of each index, and the symbols used in the subsequent calculation model are shown in Table 1:

[0101] Table 1 Index system for evaluating the bearing capacity coefficient of wind and photovoltaic development

[0102]

[0103] Step S2, pre-determine the index value calculation model of each second-level index in the second-level index layer;

[0104] As a preferred implementation method, the index value calculation models of each second-level index are as follows:

[0105] ① The calculation sub-models of the second-level index of the wind and photovoltaic development ratio , the second-level index of the wind and photovoltaic installed capacity ratio and the second-level index of the wind and photovoltaic power generation ratio are shown in formulas (1), (2) and (3) respectively:

[0106]

[0107] Among them:

[0108] p w&r,dis the development ratio of wind power and photovoltaic power; A w&r,d is the developed and utilized area of wind power and photovoltaic power, with the unit of km 2 ; A u is the land area available for the development of wind power and photovoltaic power, with the unit of km 2 ;

[0109] p w&r,cap is the installed capacity ratio of wind power and photovoltaic power; CAP w&r is the installed capacity of wind power and photovoltaic power, with the unit of 100 million kilowatts; CAP is the total installed capacity of power sources, with the unit of 100 million kilowatts;

[0110] p w&r,pow is the electricity generation ratio of wind power and photovoltaic power; POW w&r is the electricity generation of wind power and photovoltaic power, with the unit of 100 million kWh; POW is the total electricity generation of the whole society, with the unit of 100 million kWh;

[0111] ② Sub - calculation models of the secondary indicators of the installed capacity ratio of thermal power and the secondary indicator of the electricity generation ratio of thermal power are shown in formulas (4) and (5) respectively:

[0112]

[0113] Among them:

[0114] p c,cap is the installed capacity ratio of thermal power; CAP c is the installed capacity of thermal power, with the unit of 100 million kilowatts; CAP is the total installed capacity of power sources, with the unit of 100 million kilowatts;

[0115] p c,pow is the electricity generation ratio of thermal power; POW c is the electricity generation of thermal power, with the unit of 100 million kWh; POW is the total electricity generation of the whole society, with the unit of 100 million kWh;

[0116] ③ Sub - calculation model of the secondary indicator of the power self - sufficiency rate is shown in formula (6):

[0117] (6)

[0118] Among them:

[0119] EC is the total electricity consumption of the whole society, with the unit of 100 million kWh;

[0120] ④ Sub - calculation models of the secondary indicator of the population aggregation degree limit coefficient and the secondary indicator of the land - average GDP limit coefficient are shown in formulas (7) and (8) respectively:

[0121] (7)

[0122] (8)

[0123] Wherein:

[0124] LCPD is the population agglomeration degree limit coefficient; PD is the population density, with the unit of person / km²;

[0125] LCGDP is the land average GDP limit coefficient; ALGDP is the land average GDP, with the unit of 10,000 yuan / km 2 , where the land average GDP refers to the land average gross domestic product of the evaluation area;

[0126] ⑤ Secondary indicators of land development intensity , completion situation of the responsibility weight for renewable energy power consumption secondary indicator , expected target of the responsibility weight for renewable energy power consumption secondary indicator , secondary indicator of sociology coefficient The calculation sub-model is: read the current relevant database and analyze to obtain.

[0127] Step S3, collect the evaluation basic data of the evaluation area, input it into the index value calculation model of the secondary indicators, and obtain the index values of each secondary indicator of the evaluation area;

[0128] Step S4, perform geometric mean on the index values of each secondary indicator corresponding to each primary indicator to obtain the index value of each primary indicator of the evaluation area;

[0129] Specifically, assume that the primary indicator layer includes primary indicators, denoted as: primary indicator ;

[0130] For each primary indicator , , has secondary indicators, denoted as: ;

[0131] The index value of the primary indicator is calculated using formula (9):

[0132] (9)

[0133] Wherein: , respectively represent the index values of the secondary indicators .

[0134] Step S5, determine the weights of each primary indicator of the evaluation area, including two methods:

[0135] First, pre - establish a regional type evaluation model, input the index values of the corresponding first - level indicators of the evaluation region into the regional type evaluation model, and obtain the regional type of the evaluation region.

[0136] Pre - establish a mapping relationship table between the regional type and the weights of each first - level indicator in the first - level indicator layer; according to the regional type of the evaluation region, search the mapping relationship table to obtain the weights of each first - level indicator of the evaluation region.

[0137] Second, use a first - level indicator weight determination algorithm to determine the weights of each first - level indicator of the evaluation region.

[0138] The following is a detailed introduction to these two situations respectively:

[0139] (1) Determine the weights of each first - level indicator of the evaluation region according to the regional type

[0140] The regional type evaluation model is as follows:

[0141] ① Among the first - level indicators in the first - level indicator layer, select representative first - level indicators: the first - level indicator of energy transformation pressure , the first - level indicator of power supply - demand tightness and the first - level indicator of social and economic development level .

[0142] ② Classify each representative first - level indicator:

[0143] For the first - level indicator of energy transformation pressure , it is divided into two grades: high energy transformation pressure and low energy transformation pressure; the preferred method is: for the first - level indicator of energy transformation pressure , when its index value , it is high energy transformation pressure; when its index value , it is low energy transformation pressure.

[0144] For the first - level indicator of power supply - demand tightness , it is divided into three grades: tight power supply, balanced power supply - demand, and abundant power supply, corresponding to representing high, medium, and low power supply - demand tightness; the preferred method is: for the first - level indicator of power supply - demand tightness , when its index value , it is abundant power supply; when its index value , it is balanced power supply - demand; when its index value , it is tight power supply.

[0145] For the first - level indicator of social and economic development level , it is divided into two grades: low social and economic development level and high social and economic development level; the preferred method is: for the first - level indicator of social and economic development level , when its index value , it indicates a low level of social and economic development; when its index value , it indicates a high level of social and economic development.

[0146] ③ Establish the corresponding relationship between the regional types and the grades of each representative first-level index to obtain the regional type evaluation model:

[0147] The first type of region: Tight power supply, and at the same time, high pressure for energy transformation;

[0148] The second type of region: Tight power supply, and at the same time, low pressure for energy transformation;

[0149] The third type of region: Balanced power supply and demand, and at the same time, high pressure for energy transformation; The fourth type of region: Balanced power supply and demand, and at the same time, low pressure for energy transformation;

[0150] The fifth type of region: Abundant power supply, and at the same time, low level of social and economic development; The sixth type of region: Abundant power supply, and at the same time, high level of social and economic development.

[0151] One way of using the above regional type evaluation model is as follows:

[0152] For the evaluated region, when calculating the index values of its first-level index of energy transformation pressure , the degree of power supply and demand tension

[0153] first-level index and the first-level index of social and economic development level , first, according to the index value of the first-level index of the degree of power supply and demand tension , determine its corresponding grade;

[0154] If it is the grade of tight power supply or the grade of balanced power supply and demand, then further determine its grade of energy transformation pressure, and then determine the regional type of the scoring region, which is one of the first type of region to the fourth type of region;

[0155] If it is the grade of abundant power supply, then further determine its level of social and economic development, and then determine the regional type of the scoring region, which is the fifth type of region or the sixth type of region.

[0156] The principle of the regional type evaluation model provided by the present invention is:

[0157] For regions with tight power supply and balanced power supply and demand, power is consumed locally and there is no power transmission outside.

[0158] Therefore, according to the pressure of energy transformation, it is further divided into four types of regions, namely: one of the first type of region to the fourth type of region;

[0159] For regions with abundant power supply, in addition to local consumption, power can be transmitted out. Therefore, they are further divided into the fifth category of regions and the sixth category of regions according to the level of social and economic development.

[0160] (2) Use the first-level index weight determination algorithm to determine the weights of each first-level index in the evaluation region

[0161] The specific method is as follows:

[0162] Step S5.1, determine the scoring rules for the importance between two indicators in the first-level index layer; as shown in Table 2, it is the scoring rules table for the importance between two indicators in the first-level index layer:

[0163] Table 2: Scoring rules for the importance between two indicators

[0164]

[0165] It is the 1-9 scale method.

[0166] Step S5.2, according to the scoring rules, assign values to the importance matrix of the evaluation region to obtain the importance matrix of the evaluation region after assignment, expressed as:

[0167] (10)

[0168] Where: represents the importance matrix; represents the number of first-level indicators in the first-level index layer; represents the first-level indicator relative to the first-level indicator of the relative importance degree, where, , ;

[0169] The value of

[0170] (11)

[0171] For the importance matrix , when establishing the index system for evaluating the bearing capacity coefficient of wind power and photovoltaic development in Table 1, its first-level indicators have 7, namely: the first-level indicator of the development and utilization level of wind and light resources , the first-level indicator of the pressure of energy transformation , the first-level indicator of the degree of tension between power supply and demand , the first-level indicator of the level of social and economic development , the first-level indicator of the guarantee degree of land resources , the first-level indicator of the consumption level of renewable energy power The first-level indicator of social and cultural preferences A weight matrix as shown in Table 3 can be correspondingly established :

[0172] Table 3 Weight matrix

[0173]

[0174] When assigning values to the weight matrix in the evaluation area of Table 3 according to the scoring rules in Table 2, the expert scoring method is used for assignment

[0175] Step S5.3, using formula (11), obtain the weight of the first-level indicator of :

[0176] (12)

[0177] Wherein: represents the relative importance of the first-level indicator of

[0178] Step S6, perform weighted summation on the indicator values of the first-level indicators and the first-level indicator weights in the evaluation area to obtain the wind power and photovoltaic development carrying capacity coefficient of the evaluation area

[0179] Specifically, using formula (13), obtain the wind power and photovoltaic development carrying capacity coefficient of the evaluation area:

[0180] (13)

[0181] Wherein: is the indicator value of the first-level indicator of is the wind power and photovoltaic development carrying capacity coefficient

[0182] The present invention proposes a method for evaluating the wind power and photovoltaic development carrying capacity coefficient under multi-factor constraints, which has the following advantages:

[0183] (1) Comprehensively consider the impacts of factors such as the current situation of new energy development and utilization in the region, the current situation of economic and social development, the degree of power supply and demand tension, and the guarantee degree of land resources on wind power and photovoltaic development, provide a basis for the new energy development land planning and related decisions in the region, improve the rationality and accuracy of the evaluation results of the exploitable amount of wind power and photovoltaic resources in the region, serve the new energy resource census, energy planning and industrial layout optimization in the region, and promote the sustainable development of new energy in the region

[0184] (2)Considering the differential distribution characteristics such as regional power supply and demand, energy structure, resource endowment, and economic development, different types of regions are divided according to the energy transformation pressure, the tension degree of power supply and demand, and the social and economic development level. The evaluation of the carrying capacity coefficient for wind power and photovoltaic development is carried out through regional modeling, which can effectively improve the efficiency of evaluating the carrying capacity coefficient for wind power and photovoltaic development in a large number of different regions, and support the optimization and healthy and orderly development of the new energy industry layout in the region.

[0185] (3)The present invention has made great improvements in the systematicness and rationality of the index system, and has important policy support and reference value in engineering applications.

[0186] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art in the technical field, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for evaluating the carrying capacity coefficient of wind power and photovoltaic development under multi-factor constraints, characterized in that, It includes the following steps: Step S1, construct an index system for evaluating the bearing capacity coefficient of wind power and photovoltaic development; the index system includes a primary index layer and a secondary index layer; Step S2, pre-determine the index value calculation models of each secondary index in the secondary index layer; Step S3, collect the evaluation basic data of the evaluation area and input it into the index value calculation model of the secondary index to obtain the index values of each secondary index in the evaluation area; Step S4, perform geometric mean on the index values of each secondary index corresponding to each primary index to obtain the index value of each primary index in the evaluation area; Step S5, determine the weights of each primary index in the evaluation area, including two methods: The first one, pre-establish a regional type evaluation model, input the index value of the corresponding primary index in the evaluation area into the regional type evaluation model to obtain the regional type of the evaluation area; Pre-establish a mapping relationship table between the regional type and the weights of each primary index in the primary index layer; according to the regional type of the evaluation area, search the mapping relationship table to obtain the weights of each primary index in the evaluation area; The second one, adopt a primary index weight determination algorithm to determine the weights of each primary index in the evaluation area; Step S6, perform weighted summation on the index value and the primary index weight of the primary index in the evaluation area to obtain the bearing capacity coefficient of wind power and photovoltaic development in the evaluation area; The primary index layer includes 7 primary indexes, namely: the primary index I1 of the development and utilization level of wind and light resources, the primary index I2 of the energy transformation pressure, the primary index I3 of the power supply and demand tension degree, the primary index I4 of the social and economic development level, the primary index I5 of the land resource guarantee degree, the primary index I6 of the renewable energy power consumption level, and the primary index I7 of the social and cultural preference; The first-level index I1 of wind and solar resource development and utilization level includes three second-level indexes: wind power and photovoltaic development ratio second-level index I2 11 , Wind power and photovoltaic installed capacity ratio secondary index I 12 Secondary index I 13 ; The first-level indicator I2 of the energy transformation pressure includes two second-level indicators, namely: the second-level indicator I of the thermal power installed capacity ratio 21 and the second-level indicator I of the thermal power generation ratio 22 ; The first-level index I3 of the power supply-demand tightness includes a second-level index, which is the second-level index I of the power self-sufficiency rate 31 ; The first-level indicator I4 of the social and economic development level includes two second-level indicators, namely: the second-level indicator I of the population agglomeration degree limit coefficient 41 and the second-level indicator I of the land average GDP limit coefficient 42 ; The first-level indicator I5 of the land resource guarantee degree includes a second-level indicator, which is the second-level indicator I of the land development intensity 51 ; The first-level indicator I6 for the consumption level of renewable energy power includes two second-level indicators, namely: the second-level indicator I for the completion of the renewable energy power consumption responsibility weight 61 and the second-level indicator I for the expected target of the renewable energy power consumption responsibility weight 62 ; The first-level social and cultural preference indicator I7 includes a second-level indicator, which is: the second-level sociology coefficient indicator I 71 ; The index value calculation models of each secondary index in the secondary index layer are: ① Secondary Index I of Wind and Photovoltaic Development Ratio 11 、 Secondary Index I of Wind and Photovoltaic Installed Capacity Ratio 12 and Secondary Index I of Wind and Photovoltaic Power Generation Ratio 13 The calculation sub-models are shown in formulas (1), (2) and (3) respectively: Where: p w&r,d is the development ratio of wind power and photovoltaic; A w&r,d is the developed and utilized area of wind power and photovoltaic; A u is the land area available for the development of wind power and photovoltaic; p w&r,cap is the proportion of wind and photovoltaic installed capacity; CAP w&r is the installed capacity of wind and photovoltaic; CAP is the total installed capacity of power sources; p w&r,pow is the proportion of wind power and photovoltaic power generation; POW w&r is the wind power and photovoltaic power generation; POW is the total power generation of the whole society; ② Sub - index I of the proportion of thermal power installed capacity 21 and Sub - index I of the proportion of thermal power generation 22 The calculation sub - models are shown in formulas (4) and (5) respectively: Where: p c,cap is the proportion of thermal power installed capacity; CAP c is the thermal power installed capacity; CAP is the total installed capacity of power sources; p c,pow is the proportion of thermal power generation; POW c is the thermal power generation; POW is the total power generation of the whole society; ③ Secondary indicator I of power self-sufficiency rate 31 The calculation sub-model is shown in formula (6) as follows: Where: EC is the total electricity consumption of the whole society; ④Secondary Index I of Population Agglomeration Degree Limitation Coefficient 41 and Secondary Index I of GDP per Unit Land Area Limitation Coefficient 42 The calculation sub-models are shown in Formulas (7) and (8) respectively: Where: LCPD is the population aggregation degree limit coefficient; PD is the population density; LCGDP is the land average GDP limit coefficient; ALGDP is the land average GDP; ⑤ Secondary Index I of Land Development Intensity 51 、 Secondary Index I of the Completion of the Renewable Energy Power Consumption Responsibility Weight 61 、 Secondary Index I of the Expected Target of the Renewable Energy Power Consumption Responsibility Weight 62 、 Secondary Index I of the Sociology Coefficient 71 The calculation sub-model is: Read the current relevant database and analyze to obtain it.

2. The evaluation method for the wind power and photovoltaic development bearing capacity coefficient under multi-factor constraints according to claim 1, wherein, Step S4, perform geometric mean on the index values of each secondary index corresponding to each primary index to obtain the index value of each primary index in the evaluation area. Specifically: Let the first-level index layer include n first-level indices, denoted as: First-level index I1, I2,..., I n ; For each first-level indicator I i , where i = 1, 2,..., n, there are m i second-level indicators, denoted as: First-level indicator I i The index value x i , is calculated using formula (9): Wherein: respectively represent the secondary indicators indicator values.

3. The assessment method for the wind power and photovoltaic development carrying capacity coefficient under multi-factor constraints according to claim 2, characterized in that, The regional type evaluation model is: ① Among the primary indexes in the primary index layer, select representative primary indexes: the primary index I2 of the energy transformation pressure, the primary index I3 of the power supply and demand tension degree, and the primary index I4 of the social and economic development level; ② Classify each representative primary index: For the primary index I2 of the energy transformation pressure, it is divided into two levels: high energy transformation pressure and low energy transformation pressure; For the primary index I3 of the power supply and demand tension degree, it is divided into three levels: tight power supply, balanced power supply and demand, and abundant power supply, corresponding to representing high, medium and low power supply and demand tension degrees; For the primary index I4 of the social and economic development level, it is divided into two levels: low social and economic development level and high social and economic development level; ③Establish the corresponding relationship between the regional types and the levels of each representative first-level indicator to obtain the regional type evaluation model: The first type of region: The power supply is tight, and at the same time, the pressure of energy transformation is high; The second type of region: The power supply is tight, and at the same time, the pressure of energy transformation is low; The third type of region: The power supply and demand are balanced, and at the same time, the pressure of energy transformation is high; The fourth type of region: The power supply and demand are balanced, and at the same time, the pressure of energy transformation is low; The fifth type of region: The power supply is abundant, and at the same time, the level of social and economic development is low; The sixth type of region: The power supply is abundant, and at the same time, the level of social and economic development is high.

4. The method for evaluating the wind power and photovoltaic development bearing capacity coefficient under multi-factor constraints according to claim 3, wherein The range of the index values of each level of each representative first-level indicator is as follows: For the first-level indicator I2 of the energy transformation pressure, when its index value x2≥60%, the energy transformation pressure is high; when its index value x2<60%, the energy transformation pressure is low; For the first-level indicator I3 of the degree of power supply and demand tension, when its index value x3<0, the power supply is abundant; when its index value 0≤x3≤40%, the power supply and demand are balanced; when its index value x3>40%, the power supply is tight; For the first-level indicator I4 of the level of social and economic development, when its index value x4≥0.8, the level of social and economic development is low; when its index value x4<0.8, the level of social and economic development is high.

5. The evaluation method for the wind power and photovoltaic development carrying capacity coefficient under multi-factor constraints according to claim 2, wherein, Adopt the first-level indicator weight determination algorithm to determine the weights of the first-level indicators of the evaluation region, specifically: Step S5.1, determine the scoring rules for the importance between two indicators in the first-level indicator layer; Step S5.2, according to the scoring rules, assign values to the importance matrix of the evaluation region to obtain the importance matrix of the evaluation region after assignment, expressed as: Among them: W represents the importance matrix; n represents the number of first-level indicators in the first-level indicator layer; ω ij represents the first-level indicator I i The relative importance with respect to the first-level indicator I j where i = 1, 2,..., n, j = 1, 2,..., n; ω ij The value of satisfies the following relationship: Step S5.3, using formula (11), obtain the weight ω i of the first-level indicator I i : Among them: ξ i represents the relative importance of the first-level indicator I i .

6. The evaluation method for the wind power and photovoltaic development carrying capacity coefficient under multi-factor constraints according to claim 5, characterized in that, The scoring rules for the importance between two indicators in the first-level indicator layer are shown in the following table: Table: Scoring rules for the importance between two indicators It is the 1-9 scale method.

7. The evaluation method of the wind power and photovoltaic development bearing capacity coefficient under multi-factor constraints according to claim 5, wherein Step S6 is specifically: Adopt formula (13) to obtain the wind power and photovoltaic development bearing capacity coefficient of the evaluation region: where: x i is the index value of the first-level indicator I i ; η is the wind power and photovoltaic development carrying capacity coefficient.

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