Wind power photovoltaic development bearing capacity coefficient evaluation method under multi-element constraint

Abstract

The invention provides a wind power photovoltaic development bearing capacity coefficient evaluation method under multi-element constraints. The method comprises the following steps: constructing an index system for evaluating the wind power photovoltaic development bearing capacity coefficient; determining an index value of a first-level index and a first-level index weight; and performing weighted summation on the index value of the first-level index and the first-level index weight of the evaluation area to obtain a wind power photovoltaic development bearing capacity coefficient of the evaluation area. According to the method, multi-element constraint influences such as the current situation of regional new energy development and utilization, the current situation of economic and social development, the power supply and demand tension degree and the land resource guarantee degree are considered, the wind power photovoltaic development bearing capacity coefficient of each region is evaluated based on the analytic hierarchy process and the geometric averaging method, and the accuracy and rationality of the evaluation result are effectively improved; in addition, the regional difference is considered, different types of regions are divided, algorithm construction is carried out in a subarea mode, and the efficiency of evaluating the wind power photovoltaic development bearing capacity coefficient of each region is improved.

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 carrying capacity of wind power and photovoltaic development is an important evaluation indicator of the development potential of regional wind power and photovoltaic, and has important reference value for the sustainable development of regional new energy. The existing land or resource carrying capacity evaluation methods are mainly based on economic development and resource endowment, and are used to serve national space planning or resource allocation. For the evaluation of the development potential and limits of regional wind power and photovoltaic and other new energy, the existing methods are mainly based on industry experience, regional economic development and policies, and are determined by subjective empirical values. The accuracy and rationality of the wind power and photovoltaic development carrying capacity coefficient values ​​obtained by the evaluation need to be improved, and an accurate, systematic and reasonable evaluation method is urgently needed. Summary of the invention

[0003] In view of the defects of 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, comprising 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 primary index layer and a secondary index layer;

[0007] Step S2, predetermining an indicator value calculation model for each secondary indicator in the secondary indicator layer;

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

[0009] Step S4, performing geometric averaging on the index values ​​of each secondary index corresponding to each primary index to obtain the index value of each primary index of the evaluation area;

[0010] Step S5, determining the weight of each primary indicator of the evaluation area, including two methods:

[0011] The first method is to pre-establish a regional type evaluation model, input the index values ​​of the corresponding primary indicators of the evaluation area into the regional type evaluation model, and obtain the regional type of the evaluation area;

[0012] Pre-establishing a mapping relationship table between the area type and the weight of each first-level indicator in the first-level indicator layer; searching the mapping relationship table according to the area type of the evaluation area to obtain the weight of each first-level indicator of the evaluation area;

[0013] The second method is to use a first-level indicator weight determination algorithm to determine the weight of each first-level indicator in the evaluation area;

[0014] Step S6, performing weighted summation on the index value and the weight of the primary index of the evaluation area to obtain the wind power and photovoltaic development carrying capacity coefficient of the evaluation area.

[0015] Preferably, the first-level indicator layer includes 7 first-level indicators, namely: first-level indicators of wind and solar resource development and utilization level , Energy transition pressure level one indicator 2. First-level indicators of power supply and demand tension , first-level indicators of social and economic development level , first-level indicators of land resource security , First-level indicators of renewable energy power consumption level and sociocultural preference level 1 indicators ;

[0016] The first-level indicator of the development and utilization level of wind and solar resources , including three secondary indicators, namely: secondary indicator of wind power and photovoltaic development ratio , Secondary indicators of wind power and photovoltaic installed capacity ratio Secondary indicators of wind power and photovoltaic power generation ratio ;

[0017] The first level indicator of energy transition pressure , including two secondary indicators, namely: thermal power installed capacity ratio secondary indicator Secondary indicators of thermal power generation ratio ;

[0018] The first-level indicator of the degree of tension in electricity supply and demand , including a secondary indicator, which is the secondary indicator of electricity self-sufficiency rate ;

[0019] The first-level indicator of social and economic development level , including two secondary indicators, namely: population concentration restriction coefficient secondary indicator Secondary indicators of GDP restriction coefficient per capita ;

[0020] The first-level indicator of land resource security , including a secondary indicator, which is the secondary indicator of land development intensity ;

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

[0022] The first-level index of sociocultural preference , including a secondary indicator, which is: sociological coefficient secondary indicator .

[0023] Preferably, the index value calculation model of each secondary index in the secondary index layer is:

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

[0025]

[0026] in:

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

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

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

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

[0031]

[0032] in:

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

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

[0035] ③ Secondary indicator of power self-sufficiency rate The calculation sub-model is shown in formula (6):

[0036] (6)

[0037] in:

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

[0039] ④ Secondary index of population concentration restriction coefficient Secondary indicators of GDP restriction coefficient per capita The calculation sub-models are shown in formulas (7) and (8) respectively:

[0040] (7)

[0041] (8)

[0042] in:

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

[0044] LCGDP is the restriction coefficient of GDP per unit area; ALGDP is GDP per unit area;

[0045] ⑤ Secondary indicators of land development intensity 2. Completion of the responsibility weight of renewable energy power consumption , Secondary indicators of expected target of renewable energy power consumption responsibility weight , sociological coefficient secondary index The calculation sub-model is: read the current relevant database and analyze it.

[0046] Preferably, in step S4, the index values ​​of the secondary indicators corresponding to each primary indicator are geometrically averaged to obtain the index value of each primary indicator of the evaluation area, specifically:

[0047] The first-level indicator layer includes First-level indicators, expressed as: ;

[0048] For each first-level indicator , ,have Secondary indicators are expressed as: ;

[0049] First level indicator The index value , calculated using formula (9):

[0050] (9)

[0051] in: , respectively represent the secondary indicators The indicator value.

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

[0053] ① Select representative first-level indicators from the first-level indicators of the first-level indicator layer: first-level indicator of energy transition pressure 2. First-level indicators of power supply and demand tension and first-level indicators of social and economic development level ;

[0054] ② Classify each representative first-level indicator:

[0055] Level 1 indicator for energy transition pressure , divided into two levels: high energy transition pressure and low energy transition pressure;

[0056] The first-level indicator of power supply and demand tension , divided into three levels: tight power supply, balanced power supply and demand, and sufficient power supply, which correspond to high, medium, and low tensions in power supply and demand;

[0057] For the first-level indicators of social and economic development level , divided into two levels: low level of social and economic development and high level of social and economic development;

[0058] ③ Establish the corresponding relationship between the regional type and the level of each representative primary indicator, and obtain the regional type evaluation model:

[0059] Category 1: areas with tight power supply and high pressure for energy transformation;

[0060] The second type of region: electricity supply is tight, but energy transformation pressure is low;

[0061] Category III: balanced electricity supply and demand, with high pressure for energy transformation; Category IV: balanced electricity supply and demand, with low pressure for energy transformation;

[0062] The fifth type of region: There is sufficient electricity supply, but the level of social and economic development is low; the sixth type of region: There is sufficient electricity supply, but the level of social and economic development is high.

[0063] Preferably, the range of each level indicator value of each representative first-level indicator is:

[0064] Level 1 indicator for energy transition pressure , its index value When the energy transition pressure is high, the indicator value When the pressure for energy transformation is low;

[0065] The first-level indicator of power supply and demand tension , its index value When the power supply is sufficient, the index value When the power supply and demand is balanced, its index value When the power supply is tight;

[0066] For the first-level indicators of social and economic development level , its index value When the social and economic development level is low, the index value At that time, the level of social and economic development was high.

[0067] Preferably, a first-level indicator weight determination algorithm is used to determine the weights of each first-level indicator in the evaluation area, specifically:

[0068] Step S5.1, determining a scoring rule for importance between two indicators in the first-level indicator layer;

[0069] Step S5.2, assigning values ​​to the importance matrix of the evaluation area according to the scoring rule, and obtaining the assigned importance matrix of the evaluation area, which is expressed as:

[0070] (10)

[0071] in: represents the importance matrix; Represents the number of first-level indicators in the first-level indicator layer; Represents the first-level indicator Compared with the first-level index The relative importance of , ;

[0072] The value of satisfies the following relationship:

[0073] (11)

[0074] Step S5.3, using formula (11), obtain the first-level index Weight :

[0075] (12)

[0076] in: Represents the first-level indicator the relative importance of .

[0077] Preferably, the scoring rules for the importance of each pair of indicators in the first-level indicator layer are as follows:

[0078] Table: Scoring rules for importance between two indicators

[0079] It is a 1~9 scale.

[0080] Preferably, step S6 specifically comprises:

[0081] Using formula (13), the wind power and photovoltaic development carrying capacity coefficient of the evaluation area is obtained:

[0082] (13)

[0083] in: First-level indicator The indicator value of Develop carrying capacity coefficient for wind and photovoltaic power.

[0084] The method for evaluating the bearing capacity coefficient of wind power and photovoltaic development under multi-factor constraints provided by the present invention has the following advantages:

[0085] The present invention proposes a method for evaluating the carrying capacity coefficient of wind power and photovoltaic development under multi-factor constraints. The method takes into account the impact of multi-factor constraints such as the current status of regional new energy development and utilization, the current status of economic and social development, the degree of tension in electricity supply and demand, and the degree of land resource security. Based on the hierarchical analysis method and the geometric mean method, the carrying capacity coefficient of wind power and photovoltaic development in various regions is evaluated. This is a quantitative method for evaluating the carrying capacity coefficient of wind power and photovoltaic development, which effectively improves 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 in different regions, thereby improving the efficiency of evaluating the carrying capacity coefficient of wind power and photovoltaic development in various regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 A flow chart of a method for evaluating the bearing capacity coefficient of wind power and photovoltaic development under multi-factor constraints provided by the present invention. DETAILED DESCRIPTION

[0087] 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.

[0088] The present invention proposes a method for evaluating the carrying capacity coefficient of wind power and photovoltaic development under multi-factor constraints. The method takes into account the impact of multi-factor constraints such as the current status of regional new energy development and utilization, the current status of economic and social development, the degree of tension in electricity supply and demand, and the degree of land resource security. Based on the hierarchical analysis method and the geometric mean method, the carrying capacity coefficient of wind power and photovoltaic development in various regions is evaluated. It is a quantitative method for evaluating the carrying capacity coefficient of wind power and photovoltaic development, which effectively improves 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 in different regions, thereby improving the efficiency of evaluating the carrying capacity coefficient of wind power and photovoltaic development in various regions. Figure 1 As shown, the specific steps include:

[0089] Step S1, constructing 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;

[0090] As a preferred embodiment, in the present invention, the index system is:

[0091] The first-level indicator layer includes 7 first-level indicators, namely: first-level indicators of wind and solar resource development and utilization level , Energy transition pressure level one indicator 2. First-level indicators of power supply and demand tension , first-level indicators of social and economic development level , first-level indicators of land resource security , First-level indicators of renewable energy power consumption level and sociocultural preference level 1 indicators ;

[0092] The first-level indicator of the development and utilization level of wind and solar resources , including three secondary indicators, namely: secondary indicator of wind power and photovoltaic development ratio , Secondary indicators of wind power and photovoltaic installed capacity ratio Secondary indicators of wind power and photovoltaic power generation ratio ;

[0093] The first level indicator of energy transition pressure , including two secondary indicators, namely: thermal power installed capacity ratio secondary indicator Secondary indicators of thermal power generation ratio ;

[0094] The first-level indicator of the degree of tension in electricity supply and demand , including a secondary indicator, which is the secondary indicator of electricity self-sufficiency rate ;

[0095] The first-level indicator of social and economic development level , including two secondary indicators, namely: population concentration restriction coefficient secondary indicator Secondary indicators of GDP restriction coefficient per capita ;

[0096] The first-level indicator of land resource security , including a secondary indicator, which is the secondary indicator of land development intensity ;

[0097] The first-level indicator of renewable energy power consumption level , including two secondary indicators, namely: the secondary indicator of the completion of the responsibility weight of renewable energy power consumption Secondary indicators of expected target of renewable energy power consumption responsibility weight ;

[0098] The first-level index of sociocultural preference , including a secondary indicator, which is: sociological coefficient secondary indicator .

[0099] The indicator system, the common symbols of each indicator and the symbols used in the subsequent calculation model are shown in Table 1:

[0100] Table 1 Index system for evaluating the load-bearing capacity coefficient of wind and photovoltaic development

[0101]

[0102] Step S2, predetermining an indicator value calculation model for each secondary indicator in the secondary indicator layer;

[0103] As a preferred implementation, the index value calculation model of each secondary index is as follows:

[0104] ① Secondary indicators of wind power and photovoltaic development ratio , Secondary indicators of wind power and photovoltaic installed capacity ratio Secondary indicators of wind power and photovoltaic power generation ratio The calculation sub-models are shown in formulas (1), (2) and (3) respectively:

[0105]

[0106] in:

[0107] p w&r,dA is the development ratio of wind power and photovoltaic power; w&r,d The developed and utilized area of ​​wind power and photovoltaic power, unit: km 2 ; A u is the land area available for wind and photovoltaic development, in km 2 ;

[0108] p w&r,cap is the proportion of wind power and photovoltaic installed capacity; CAP w&r is the installed capacity of wind power and photovoltaic power, in billion kilowatts; CAP is the total installed capacity of power sources, in billion kilowatts;

[0109] p w&r,pow POW is the proportion of wind and photovoltaic power generation; w&r is the power generation of wind power and photovoltaic power, in billion kWh; POW is the power generation of the whole society, in billion kWh;

[0110] ② Secondary indicators of thermal power installed capacity Secondary indicators of thermal power generation ratio The calculation sub-models are shown in formulas (4) and (5) respectively:

[0111]

[0112] in:

[0113] p c,cap is the proportion of thermal power installed capacity; CAP c is the installed capacity of thermal power, in billion kilowatts; CAP is the total installed capacity of power sources, in billion kilowatts;

[0114] p c,pow POW is the proportion of thermal power generation; c is the thermal power generation, in billion kWh; POW is the total social power generation, in billion kWh;

[0115] ③ Secondary indicator of power self-sufficiency rate The calculation sub-model is shown in formula (6):

[0116] (6)

[0117] in:

[0118] EC is the total electricity consumption of the society, in billion kWh;

[0119] ④ Secondary index of population concentration restriction coefficient Secondary indicators of GDP restriction coefficient per capita The calculation sub-models are shown in formulas (7) and (8) respectively:

[0120] (7)

[0121] (8)

[0122] in:

[0123] LCPD is the population concentration limit coefficient; PD is the population density, in person / km2;

[0124] LCGDP is the restriction coefficient of GDP per square kilometer; ALGDP is GDP per square kilometer, unit is 10,000 yuan / km 2 , where GDP per capita refers to the gross domestic product per capita in the evaluation area;

[0125] ⑤ Secondary indicators of land development intensity 2. Completion of the responsibility weight of renewable energy power consumption , Secondary indicators of expected target of renewable energy power consumption responsibility weight , sociological coefficient secondary index The calculation sub-model is: read the current relevant database and analyze it.

[0126] Step S3, collecting basic evaluation data of the evaluation area, inputting it into the index value calculation model of the secondary index, and obtaining the index value of each secondary index of the evaluation area;

[0127] Step S4, performing geometric averaging on the index values ​​of each secondary index corresponding to each primary index to obtain the index value of each primary index of the evaluation area;

[0128] Specifically, the first-level indicator layer includes First-level indicators, expressed as: ;

[0129] For each first-level indicator , ,have Secondary indicators are expressed as: ;

[0130] First level indicator The index value , calculated using formula (9):

[0131] (9)

[0132] in: , respectively represent the secondary indicators The indicator value.

[0133] Step S5, determining the weight of each primary indicator of the evaluation area, including two methods:

[0134] The first method is to pre-establish a regional type evaluation model, input the index values ​​of the corresponding primary indicators of the evaluation area into the regional type evaluation model, and obtain the regional type of the evaluation area;

[0135] Pre-establishing a mapping relationship table between the area type and the weight of each first-level indicator in the first-level indicator layer; searching the mapping relationship table according to the area type of the evaluation area to obtain the weight of each first-level indicator of the evaluation area;

[0136] The second method is to use a first-level indicator weight determination algorithm to determine the weights of each first-level indicator in the evaluation area.

[0137] The following describes these two situations in detail:

[0138] 1. Determine the weight of each primary indicator in the evaluation area according to the regional type

[0139] The regional type evaluation model is:

[0140] ① Select representative first-level indicators from the first-level indicators of the first-level indicator layer: first-level indicator of energy transition pressure 2. First-level indicators of power supply and demand tension and first-level indicators of social and economic development level ;

[0141] ② Classify each representative first-level indicator:

[0142] Level 1 indicator for energy transition pressure , divided into two levels: high energy transition pressure and low energy transition pressure; the preferred method is: for the first-level indicator of energy transition pressure , its index value When the energy transition pressure is high, the indicator value When the pressure for energy transformation is low;

[0143] The first-level indicator of power supply and demand tension , divided into three levels: tight power supply, balanced power supply and demand, and sufficient power supply, corresponding to high, medium, and low power supply and demand tensions; the preferred method is: for the first-level index of power supply and demand tension , its index value When the power supply is sufficient, the index value When the power supply and demand is balanced, its index value When the power supply is tight;

[0144] For the first-level indicators of social and economic development level , divided into two levels: low level of social and economic development and high level of social and economic development; the optimization method is: for the first-level indicators of social and economic development level , its index value When the social and economic development level is low, the index value At that time, the level of social and economic development was high.

[0145] ③ Establish the corresponding relationship between the regional type and the level of each representative primary indicator, and obtain the regional type evaluation model:

[0146] Category 1: areas with tight power supply and high pressure for energy transformation;

[0147] The second type of region: electricity supply is tight, but energy transformation pressure is low;

[0148] Category III: balanced electricity supply and demand, with high pressure for energy transformation; Category IV: balanced electricity supply and demand, with low pressure for energy transformation;

[0149] The fifth type of region: There is sufficient electricity supply, but the level of social and economic development is low; the sixth type of region: There is sufficient electricity supply, but the level of social and economic development is high.

[0150] One way to use the above regional type assessment model is:

[0151] For the evaluation area, when the first-level index of energy transformation pressure is calculated , the degree of tension in electricity supply and demand

[0152] First level indicator and first-level indicators of social and economic development level When determining the index value, firstly, according to the first-level index of power supply and demand tension The indicator value is used to determine its corresponding level;

[0153] If it is a power supply tension level or a power supply and demand balance level, its energy transformation pressure level is further determined, and then the regional type of the scoring area is determined, which is one of the first to fourth categories of areas;

[0154] If the electricity supply is sufficient, the level of socio-economic development will be further determined, and then the regional type of the scoring area will be determined as the fifth or sixth category area.

[0155] The regional type evaluation model provided by the present invention has the following principles:

[0156] For areas with tight power supply and balanced power supply and demand, power is consumed locally and no power is transmitted.

[0157] Therefore, the regions are further divided into four categories according to the pressure of energy transformation, namely: one of the first to fourth categories of regions;

[0158] For areas with sufficient electricity supply, electricity can be transmitted in addition to local consumption. Therefore, they are further divided into fifth and sixth category areas based on the level of social and economic development.

[0159] (ii) Adopt the first-level indicator weight determination algorithm to determine the weight of each first-level indicator in the evaluation area

[0160] The specific method is as follows:

[0161] Step S5.1, determine the scoring rules for the importance between two indicators in the first-level indicator layer; as shown in Table 2, it is a scoring rule table for the importance between two indicators in the first-level indicator layer:

[0162] Table 2: Scoring rules for the importance of two indicators

[0163] It is a 1~9 scale.

[0164] Step S5.2, assigning values ​​to the importance matrix of the evaluation area according to the scoring rule, and obtaining the assigned importance matrix of the evaluation area, which is expressed as:

[0165] (10)

[0166] in: represents the importance matrix; Represents the number of first-level indicators in the first-level indicator layer; Represents the first-level indicator Compared with the first-level index The relative importance of , ;

[0167] The value of satisfies the following relationship:

[0168] (11)

[0169] For the importance matrix When the index system for evaluating the carrying capacity coefficient of wind power and photovoltaic development in Table 1 is established, there are 7 first-level indicators, namely: the first-level index of wind and photovoltaic resource development and utilization level , Energy transition pressure level one indicator 2. First-level indicators of power supply and demand tension , first-level indicators of social and economic development level , first-level indicators of land resource security , First-level indicators of renewable energy power consumption level and sociocultural preference level 1 indicators , the importance matrix shown in Table 3 can be established accordingly :

[0170] Table 3 Importance matrix

[0171]

[0172] When assigning values ​​to the importance matrix of the evaluation area in Table 3 according to the scoring rules in Table 2, the expert scoring method is used.

[0173] Step S5.3, using formula (11), obtain the first-level index Weight :

[0174] (12)

[0175] in: Represents the first-level indicator the relative importance of .

[0176] Step S6, performing weighted summation on the index value and the weight of the primary index of the evaluation area to obtain the wind power and photovoltaic development carrying capacity coefficient of the evaluation area.

[0177] Specifically, formula (13) is used to obtain the wind power and photovoltaic development carrying capacity coefficient of the evaluation area:

[0178] (13)

[0179] in: First-level indicator The indicator value of Develop carrying capacity coefficient for wind and photovoltaic power.

[0180] The present invention proposes a method for evaluating the bearing capacity coefficient of wind power and photovoltaic development under multi-factor constraints, which has the following advantages:

[0181] (1) The impact of factors such as the current status of regional new energy development and utilization, economic and social development, the degree of tension in electricity supply and demand, and the degree of land resource security on wind power and photovoltaic development is comprehensively considered to provide a basis for regional new energy development land planning and related decision-making, improve the rationality and accuracy of the assessment results of the exploitable amount of regional wind power and photovoltaic resources, serve the regional new energy resource census, energy planning and industrial layout optimization, and promote the sustainable development of regional new energy.

[0182] (2) Taking into account the differentiated distribution characteristics of regional electricity supply and demand, energy structure, resource endowment, economic development, etc., different types of regions are divided according to the pressure of energy transformation, the degree of tension in electricity supply and demand, and the level of social and economic development. Regional modeling is used to evaluate the carrying capacity coefficient of wind power and photovoltaic development. This can effectively improve the efficiency of evaluating the carrying capacity coefficient of wind power and photovoltaic development in a large number of different regions, and support the optimization of regional new energy industry layout and healthy and orderly development.

[0183] (3) The present invention has made significant improvements in the systematicness and rationality of the indicator system, and has important policy support and reference value in engineering applications.

[0184] 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 evaluating the carrying capacity coefficient of wind power and photovoltaic development under multi-factor constraints, characterized in that: The following steps are involved: Step S1, constructing 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, predetermining an indicator value calculation model for each secondary indicator in the secondary indicator layer; Step S3, collecting basic evaluation data of the evaluation area, inputting it into the index value calculation model of the secondary index, and obtaining the index value of each secondary index of the evaluation area; Step S4, performing geometric averaging on the index values ​​of each secondary index corresponding to each primary index to obtain the index value of each primary index of the evaluation area; Step S5, determining the weight of each primary indicator of the evaluation area, including two methods: The first method is to pre-establish a regional type evaluation model, input the index values ​​of the corresponding primary indicators of the evaluation area into the regional type evaluation model, and obtain the regional type of the evaluation area; Pre-establishing a mapping relationship table between the area type and the weight of each first-level indicator in the first-level indicator layer; searching the mapping relationship table according to the area type of the evaluation area to obtain the weight of each first-level indicator of the evaluation area; The second method is to use a first-level indicator weight determination algorithm to determine the weight of each first-level indicator in the evaluation area; Step S6, performing weighted summation on the index value and the weight of the primary index of the evaluation area to obtain the wind power and photovoltaic development carrying capacity coefficient of the evaluation area.

2. The method for evaluating the carrying capacity coefficient of wind power and photovoltaic development under multi-factor constraints according to claim 1 is characterized in that: The first-level indicator layer includes 7 first-level indicators, namely: first-level indicators of wind and solar resource development and utilization level , Energy transition pressure level 1 indicator 2. First-level indicators of power supply and demand tension , first-level indicators of social and economic development level , first-level indicators of land resource security , First-level indicators of renewable energy power consumption level and sociocultural preference level 1 indicators ; The first-level indicator of the development and utilization level of wind and solar resources , including three secondary indicators, namely: secondary indicator of wind power and photovoltaic development ratio , Secondary indicators of wind power and photovoltaic installed capacity Secondary indicators of wind power and photovoltaic power generation ; The first level indicator of energy transition pressure , including two secondary indicators, namely: thermal power installed capacity ratio secondary indicator Secondary indicators of thermal power generation ratio ; The first-level indicator of the degree of tension in electricity supply and demand , including a secondary indicator, which is the secondary indicator of electricity self-sufficiency rate ; The first-level indicator of social and economic development level , including two secondary indicators, namely: population concentration restriction coefficient secondary indicator Secondary indicators of GDP restriction coefficient per capita ; The first-level indicator of land resource security , including a secondary indicator, which is the secondary indicator of land development intensity ; The first-level indicator of renewable energy power consumption level , including two secondary indicators, namely: the secondary indicator of the completion of the responsibility weight of renewable energy power consumption Secondary indicators of expected target of renewable energy power consumption responsibility weight ; The first-level index of sociocultural preference , including a secondary indicator, which is: sociological coefficient secondary indicator .

3. The method for evaluating the carrying capacity coefficient of wind power and photovoltaic development under multi-factor constraints according to claim 2 is characterized in that: The index value calculation model of each secondary index in the secondary index layer is: ① Secondary indicators of wind power and photovoltaic development ratio , Secondary indicators of wind power and photovoltaic installed capacity ratio Secondary indicators of wind power and photovoltaic power generation ratio The calculation sub-models are shown in formulas (1), (2) and (3) respectively: ； in: p w&r,d A is the development ratio of wind power and photovoltaic power; w&r,d A is the developed and utilized area of ​​wind power and photovoltaic power; u The land area available for wind and photovoltaic development; p w&r,cap is the proportion of wind power and photovoltaic installed capacity; CAP w&r is the installed capacity of wind and photovoltaic power; CAP is the total installed capacity of power sources; p w&r,pow POW is the proportion of wind power and photovoltaic power generation; w&r is the power generation of wind and photovoltaic power; POW is the power generation of the whole society; ② Secondary indicators of thermal power installed capacity Secondary indicators of thermal power generation ratio The calculation sub-models are shown in formulas (4) and (5) respectively: ； in: p c,cap is the proportion of thermal power installed capacity; CAP c is the installed capacity of thermal power; CAP is the total installed capacity of power sources; p c,pow POW is the proportion of thermal power generation; c is the thermal power generation; POW is the total social power generation; ③ Secondary indicator of power self-sufficiency rate The calculation sub-model is shown in formula (6): （6） in: EC is the total electricity consumption of the society; ④ Secondary index of population concentration restriction coefficient Secondary indicators of GDP restriction coefficient per capita The calculation sub-models are shown in formulas (7) and (8) respectively: （7） （8） in: LCPD is the population concentration limit coefficient; PD is the population density; LCGDP is the restriction coefficient of GDP per unit area; ALGDP is GDP per unit area; ⑤ Secondary indicators of land development intensity 2. Completion of the responsibility weight of renewable energy power consumption , Secondary indicators of expected target of renewable energy power consumption responsibility weight , sociological coefficient secondary index The calculation sub-model is: read the current relevant database and analyze it.

4. The method for evaluating the carrying capacity coefficient of wind power and photovoltaic development under multi-factor constraints according to claim 2 is characterized in that: Step S4, geometrically averaging the index values ​​of the secondary indicators corresponding to each primary indicator to obtain the index value of each primary indicator of the evaluation area, specifically: The first-level indicator layer includes First-level indicators, expressed as: ; For each first-level indicator , ,have Secondary indicators are expressed as: ; First level indicator The index value , calculated using formula (9): （9） in: , respectively represent the secondary indicators The indicator value.

5. The method for evaluating the carrying capacity coefficient of wind power and photovoltaic development under multi-factor constraints according to claim 4 is characterized in that: The regional type evaluation model is: ① Select representative first-level indicators from the first-level indicators of the first-level indicator layer: first-level indicator of energy transition pressure 2. First-level indicators of power supply and demand tension and first-level indicators of social and economic development level ; ② Classify each representative first-level indicator: Level 1 indicator for energy transition pressure , divided into two levels: high energy transition pressure and low energy transition pressure; The first-level indicator of power supply and demand tension , divided into three levels: tight power supply, balanced power supply and demand, and sufficient power supply, which correspond to high, medium, and low tensions in power supply and demand; For the first-level indicators of social and economic development level , divided into two levels: low level of social and economic development and high level of social and economic development; ③ Establish the corresponding relationship between the regional type and the level of each representative primary indicator, and obtain the regional type evaluation model: Category 1: areas with tight power supply and high pressure for energy transformation; The second type of region: electricity supply is tight, but energy transformation pressure is low; Category III: balanced electricity supply and demand, with high pressure for energy transformation; Category IV: balanced electricity supply and demand, with low pressure for energy transformation; The fifth type of region: There is sufficient electricity supply, but the level of social and economic development is low; the sixth type of region: There is sufficient electricity supply, but the level of social and economic development is high.

6. The method for evaluating the carrying capacity coefficient of wind power and photovoltaic development under multi-factor constraints according to claim 5 is characterized in that: The range of index values ​​for each representative first-level index is as follows: Level 1 indicator for energy transition pressure , its index value When the energy transition pressure is high, the indicator value When the pressure for energy transformation is low; The first-level indicator of power supply and demand tension , its index value When the power supply is sufficient, the index value When the power supply and demand is balanced, its index value When the power supply is tight; For the first-level indicators of social and economic development level , its index value When the social and economic development level is low, the index value At that time, the level of social and economic development was high.

7. The method for evaluating the carrying capacity coefficient of wind power and photovoltaic development under multi-factor constraints according to claim 4 is characterized in that: The weight of each first-level indicator in the evaluation area is determined by using the first-level indicator weight determination algorithm, which is specifically: Step S5.1, determining a scoring rule for importance between two indicators in the first-level indicator layer; Step S5.2, assigning values ​​to the importance matrix of the evaluation area according to the scoring rule, and obtaining the assigned importance matrix of the evaluation area, which is expressed as: （10） in: represents the importance matrix; Represents the number of first-level indicators in the first-level indicator layer; Represents the first-level indicator Compared with the first-level index The relative importance of , ; The value of satisfies the following relationship: （11） Step S5.3, using formula (11), obtain the first-level index Weight : （12） in: Represents the first-level indicator the relative importance of .

8. The method for evaluating the wind power and photovoltaic development carrying capacity coefficient under multi-factor constraints according to claim 7 is characterized in that: The scoring rules for the importance of each pair of indicators in the first-level indicator layer are as follows: Table: Scoring rules for importance between two indicators It is a 1~9 scale.

9. The method for evaluating the wind power and photovoltaic development carrying capacity coefficient under multi-factor constraints according to claim 7 is characterized in that: Step S6 is specifically as follows: Using formula (13), the wind power and photovoltaic development carrying capacity coefficient of the evaluation area is obtained: （13） in: First-level indicator The indicator value of Develop carrying capacity coefficient for wind and photovoltaic power.

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