Method for establishing carbon emission quota model

Abstract

The invention discloses a method for establishing a carbon emission quota model. The method comprises the following steps of: 1, acquiring an electric quantity marginal emission factor and a capacity marginal emission factor of i units; 2, carrying out normalization processing, and converting an absolute value of the index into a relative value; 3, calculating the proportion of the i unit in the emission factor under the j emission factor; 4, calculating the information entropy of each emission factor; 5, determining the weight corresponding to each emission factor; 6, calculating a carbon emission quota coefficient corresponding to the group i; and 7, calculating the carbon emission quota of the unit according to the obtained carbon emission quota coefficient. According to the carbon emission quota allocation technology based on the entropy method, an excessively subjective processing method for weighting and averaging the electric quantity marginal emission factors and the capacity marginal emission factors is avoided to a certain extent.

Description

Technical Field

[0001] The present invention belongs to the field of power system and automation research, and in particular, relates to a method for determining the carbon emission quota in advance so as to reasonably arrange the power generation plan of the unit. The present invention is a divisional application of an invention patent entitled "A day-ahead optimization scheduling method based on entropy method" (application number 2022109121843). Background Art

[0002] With the continuous growth of power load and the continuous expansion of the scale of power system, the optimal dispatch of power system faces a series of modeling and solving problems in actual engineering. Under the background of the implementation of carbon trading mechanism of power system, constructing carbon trading model of emission subject in the day-ahead optimal dispatch problem has important practical engineering significance for the implementation of emission reduction. How to accurately predict carbon price and determine carbon emission quota in advance based on carbon trading information, so as to reasonably arrange the power generation plan of the unit is an urgent problem to be solved.

[0003] As an important market trading mechanism for reducing CO2 emissions, the tradable quota system allows high-emission participants to purchase carbon emission quotas sold by low-emission participants, provided that the total amount of emissions does not exceed the quota, thereby limiting the carbon emissions of excess emission entities through additional carbon trading costs. As one of the important industries for controlling carbon emissions, the power system considers limiting the carbon emissions of emission entities and establishing a carbon trading model between emission entities in the power system's day-ahead optimal dispatch model, which has important practical engineering significance for implementing emission reductions.

[0004] In the power system carbon trading market, if the initial allocation of carbon emission quotas is too low, it will limit the enthusiasm of power generation companies and be detrimental to social and economic development; if the carbon emission quotas are too low, the corresponding emission reduction effect cannot be achieved. In order to mobilize the enthusiasm of emission reduction on the power supply side, the allocation of power system carbon emission quotas mostly adopts a free quota mechanism. The existing free quota mechanisms mainly include the historical method and the baseline method.

[0005] The historical method uses the historical average carbon emissions of the generating unit as a benchmark to determine the carbon emission quota allocated to the unit in the future. Therefore, the carbon emission quota allocated to the unit is proportional to its historical carbon emissions. However, the historical method has the following main problems: First, it is difficult to obtain historical emission data with reference value for newly commissioned units or units that take emission reduction measures (such as carbon capture power plant units); second, the carbon emission quota obtained by the unit is positively correlated with the historical emissions. The higher the historical emissions, the more carbon emission quota the unit has. In the long run, the emission reduction effect of the historical method is not obvious.

[0006] The baseline method determines the allocation quota of each unit according to the unit capacity and fuel type, and allocates quotas according to the actual power generation. The baseline method generally uses the regional power grid baseline emission factor (electricity marginal emission factor and capacity marginal emission factor) as the basis for the allocation quota, where the electricity marginal emission factor is calculated based on the total net power generation, fuel type and total fuel consumption of all units (excluding low operating cost and must-operate units). Compared with the historical method, the baseline method allocates quotas from the perspective of carbon emissions generated per unit of electricity, avoiding the "whipping the fast bull" problem of the historical method, and the allocation method is more flexible and more reasonable. Wang Yong, Wang Dingyu, Chen Yanru. The impact of different quota allocation methods on the operation of China's carbon trading market - an investigation based on the perspective of liquidity, volatility and effectiveness points out that the baseline method can better reflect fairness and efficiency, but the data requirements are high, and when the industry products are complex, the baseline is not easy to determine, which also increases the complexity of corporate measurement reports.

[0007] The baseline method avoids the problems of the historical method and is more conducive to emission reduction than the historical method. However, the weighted average processing method of the marginal emission factor of electricity and the marginal emission factor of capacity is too subjective. In view of the overly simple and subjective processing of the baseline emission factor by the existing carbon emission quota allocation method, the present invention proposes a carbon emission quota allocation strategy based on the entropy method. The entropy method is essentially an objective weighting method, and its core idea is to determine the weights of different indicators based on data. Summary of the invention

[0008] The purpose of the present invention is to solve the technical problem that the baseline method of weighted averaging the marginal emission factor of electricity and the marginal emission factor of capacity is too simple and subjective, resulting in unreasonable allocation of carbon emission quotas in the power system, and to provide a day-ahead optimization scheduling method based on the entropy method and a method for establishing a carbon emission quota model.

[0009] A day-ahead optimal scheduling method based on entropy method comprises the following steps:

[0010] Step 1: Establish a carbon emission quota model;

[0011] Step 2: Obtain the carbon trading costs of different types of power generation and coordinate the carbon emission differences of different types of units;

[0012] Step 3: Establish a day-ahead optimal scheduling model considering the tradable carbon emission quota system.

[0013] For n units and m emission factors, use x i,j Representing the j-th emission factor of unit i (i = 1, 2, ..., n; j = 1, 2, ..., m), establishing a carbon emission quota model includes the following steps:

[0014] Step 1-1) Obtain the marginal emission factor of electricity and capacity of the i-th unit;

[0015] Step 1-2) Perform normalization to convert the absolute value of the indicator into a relative value;

[0016] Step 1-3) Calculate the proportion of the i-th unit under the j-th emission factor;

[0017] Step 1-4) Calculate the information entropy of each emission factor;

[0018] Step 1-5) Determine the weight corresponding to each emission factor;

[0019] Step 1-6) The carbon emission quota coefficient corresponding to computer group i;

[0020] Step 1-7) The carbon emission quota of the computer group is calculated based on the carbon emission quota coefficient obtained in step 1-6).

[0021] In step 1-3), the proportion of the i-th unit under the j-th emission factor is:

[0022]

[0023] Where: p i,j represents the proportion of the i-th unit under the j-th emission factor to the emission factor j; r i,j Represents x i,j Normalized value;

[0024] In step 1-4), the information entropy of each emission factor is calculated using the following formula;

[0025]

[0026] Where: s j represents the information entropy of the jth emission factor; p i,j is the proportion of the i-th unit under the j-th emission factor to the emission factor j;

[0027] In step 1-5), the weights corresponding to each emission factor are determined using the following formula:

[0028]

[0029] Where: w j represents the weight corresponding to the jth emission factor; s j represents the information entropy of the j-th emission factor;

[0030] In step 1-6), the carbon emission quota coefficient corresponding to group i is calculated using the following formula:

[0031]

[0032] Where: e i represents the carbon emission quota coefficient corresponding to the i-th unit; w j represents the weight corresponding to the jth emission factor; r i,j Represents x i,j Normalized value;

[0033] In step 1-7), for the thermal power units participating in carbon trading, the allocated carbon emission quota is shown in formula (6):

[0034] Q Gi,t =e i P Gi,t Δt (6)

[0035] Where: Q Gi,t Indicates the carbon emission quota allocated to thermal power units participating in carbon trading; i represents the carbon emission quota coefficient corresponding to the i-th unit; P Gi,t It represents the active power output of unit i in period t; Δt represents the time step.

[0036] In step 1-7), if the wind turbine is involved in carbon trading, the carbon emission quota of wind power is Q W,t for:

[0037] Q W,t =e i (P W,t +ξ t )Δt (7)

[0038] Where: Q W,t represents the carbon emission quota of wind power; e i represents the carbon emission quota coefficient corresponding to the i-th unit; P W,t represents the wind power forecast value during period t; ξ t represents the wind power prediction error in period t; Δt represents the time step.

[0039] Wind power forecast error Standard deviation δ W,t The calculation formula is:

[0040] δ W,t =0.2P W,t +0.02P WT (7)

[0041] Where: W,t represents standard deviation; P W,t represents the wind power forecast value during period t; P WT Represents the total installed capacity of wind power.

[0042] In step 2, the carbon trading costs of thermal power and wind power are included:

[0043] 2-1) Regarding the cost of thermal power carbon trading:

[0044] Thermal power generation participating in carbon trading generates additional costs, specifically:

[0045] FC Gi,t =p t (E Gi,t -Q Gi,t ) (8)

[0046] Where: FC Gi,t represents the carbon trading cost of thermal power unit i in period t; p t represents the carbon trading price in period t; E Gi,t It represents the actual carbon emissions of unit i in period t, specifically E i,t =μ i P i,t 2 +ν i P i,t +ω i , where μ i , ν i ,ω i represents the carbon emission coefficient of unit i; Q Gi,t Indicates the carbon emission quota allocated to thermal power units participating in carbon trading;

[0047] 2-2) Regarding wind power carbon trading costs:

[0048] The increase in carbon emissions from thermal power units caused by the uncertainty of wind power is regarded as the carbon emissions from wind power, specifically:

[0049] E W,t =μ i (ζ W P W,t ) 2 +ν i (ζ W P W,t )+ω i (9)

[0050] Where: E W,t represents the implicit carbon emissions of wind power during period t; W Represents wind power reserve capacity factor; P W,t represents the wind power forecast value during period t; it is assumed that the backup is provided by unit i during period t; μ i , ν i ,ω i represents the carbon emission coefficient of unit i;

[0051] It can be seen that the cost of wind power participating in carbon trading is:

[0052] FC W,t =p t (E W,t -Q W,t ) (9)

[0053] Where: FC W,t represents the carbon trading cost of wind power during period t; p t represents the carbon trading price in period t; E W,t represents the implicit carbon emissions of wind power in period t; Q W,t Represents the carbon emission quota of wind power.

[0054] The established day-ahead optimal scheduling model considering the tradable carbon emission quota system is as follows:

[0055] 3-1) Objective function:

[0056] The day-ahead optimal dispatch model takes the minimum total system cost within the dispatch period as the optimization goal, which includes the operating cost F of the thermal power unit. G , Carbon trading costs of thermal power units FC G , Spinning reserve cost F generated by wind power access R and wind farm carbon trading costs FC W :

[0057] minF=min(F G +FC G +F R +FC W ) (10)

[0058] Carbon trading costs of thermal power units G The calculation is:

[0059]

[0060] Where: FC G represents the carbon trading cost of thermal power units; N represents the number of thermal power units in the system, T represents the time; FC Gi,t represents the carbon trading cost of thermal power unit i in period t; E Gi,t represents the actual carbon emissions of unit i in period t; Q Gi,t Indicates the carbon emission quota allocated to thermal power units participating in carbon trading;

[0061] In addition, the randomness of wind power output leads to additional spinning reserve costs F R , the specific calculation is:

[0062]

[0063] Where: F R The randomness of wind power output leads to additional spinning reserve costs; C R is the price of spinning reserve capacity; ε t is the credibility level of wind power forecast output during period t; W,t represents the wind power forecast value during period t;

[0064] Carbon trading costs of wind power FC W for:

[0065]

[0066] Where: FC W represents the carbon trading cost of wind power; FC W,t represents the carbon trading cost of wind power during period t; p2 represents the carbon price when selling quotas; E W,t represents the implicit carbon emissions of wind power in period t; Q W,t Represents the carbon emission quota of wind power.

[0067] Step 3-2) Constraints

[0068] Step 3-2-1) Unit constraints

[0069] The unit output constraints, minimum continuous start-stop time constraints, and unit climbing / slip constraints of conventional thermal power are shown in formulas (14) to (16) respectively;

[0070]

[0071] Where: P Gi,t It represents the active power output of unit i in period t; Indicates the lower limit of active output of unit i; Represents the upper limit of active output of unit i.

[0072]

[0073] Where: represents the continuous startup time of the i-th unit in period t-1; represents the continuous downtime of the i-th unit in period t-1; Indicates the minimum continuous start time of unit i; represents the minimum continuous downtime of unit i; U Gi,t-1 represents the start and stop status of the i-th unit in the t-1 period; U Gi,t Represents the start and stop status of the i-th unit in period t.

[0074]

[0075] Where: P Gi,trepresents the active power output of unit i in period t; P Gi,t-1 represents the active power output of unit i in period t-1; RU i Represents the maximum ramping capability of active power of unit i in period t; RD i It represents the maximum ramping capacity of active power of unit i in period t.

[0076] Wind farms must meet the following constraints during operation:

[0077]

[0078] Where: P W,t represents the wind power forecast value during period t; ξ t represents the wind power forecast error in period t; is the maximum power of the wind farm, that is, the installed capacity of wind power.

[0079] 3-2-2) Power balance constraints

[0080]

[0081] Where: U Gi,t represents the start and stop status of the i-th unit in period t; P Gi,t represents the active power output of unit i in period t; P L,t represents the predicted load of the system in period t; P W,t Represents the wind power forecast value in period t.

[0082] Step 3-2-3) System backup constraints

[0083]

[0084] Where: Indicates the upper limit of active output of unit i; P Gi,t represents the active power output of unit i in period t; ζ W Indicates wind power reserve factor; P W,t represents the predicted load of wind power in period t; Indicates the lower limit of active output of unit i.

[0085] Step 3-2-4) Network security constraints

[0086] The network security constraints after wind power access are:

[0087]

[0088] Where: P l,min G represents the minimum transmission capacity allowed by line l in the base state; l,i represents the power transfer distribution factor of unit i to line l; PGi,t N = active power output of unit i in period t; W is the number of wind farms; G l,j P represents the power transfer distribution factor of wind farm j to line l; Wj,t represents the power of the jth wind farm; G l,d P represents the power transfer distribution factor of load d to line l; Ld,t represents the load value of node d during period t; P l,max It represents the maximum transmission capacity allowed by line l in the base state.

[0089] A method for establishing a carbon emission quota model, for n units, m emission factors, using x i,j Representing the j-th emission factor of unit i (i = 1, 2, ..., n; j = 1, 2, ..., m), establishing a carbon emission quota model includes the following steps:

[0090] Step 1: Obtain the marginal emission factor of electricity and capacity of the i-th unit;

[0091] Step 2: Perform normalization to convert the absolute value of the indicator into a relative value;

[0092] Step 3: Calculate the proportion of the i-th unit under the j-th emission factor;

[0093] Step 4: Calculate the information entropy of each emission factor;

[0094] Step 5: Determine the weights corresponding to each emission factor;

[0095] Step 6: Carbon emission quota coefficient corresponding to computer group i;

[0096] Step 7: Calculate the carbon emission quota of the computer group based on the obtained carbon emission quota coefficient.

[0097] In step 3:, the proportion of the i-th unit under the j-th emission factor to the emission factor is:

[0098]

[0099] Where: p i,j represents the proportion of the i-th unit under the j-th emission factor to the emission factor j; r i,j Represents x i,j Normalized value;

[0100] In step 4:, the information entropy of each emission factor is calculated using the following formula;

[0101]

[0102] Where: sj represents the information entropy of the jth emission factor; p i,j is the proportion of the i-th unit under the j-th emission factor to the emission factor j;

[0103] In step 5:, the weights corresponding to each emission factor are determined using the following formula:

[0104]

[0105] Where: w j represents the weight corresponding to the jth emission factor; s j represents the information entropy of the j-th emission factor;

[0106] In step 6:, the carbon emission quota coefficient corresponding to group i is calculated using the following formula:

[0107]

[0108] Where: e i represents the carbon emission quota coefficient corresponding to the i-th unit; w j represents the weight corresponding to the jth emission factor; r i,j Represents x i,j Normalized value;

[0109] In step 7:, for the thermal power units participating in carbon trading, the allocated carbon emission quota is shown in formula (6):

[0110] Q Gi,t =e i P Gi,t Δt (6)

[0111] Where: Q Gi,t Indicates the carbon emission quota allocated to thermal power units participating in carbon trading; i represents the carbon emission quota coefficient corresponding to the i-th unit; P Gi,t It represents the active power output of unit i in period t; Δt represents the time step.

[0112] In step 7, if the wind turbine is participating in carbon trading, the carbon emission quota of wind power Q W,t for:

[0113] Q W,t =e i (P W,t +ξ t )Δt (7)

[0114] Where: Q W,t represents the carbon emission quota of wind power; e i represents the carbon emission quota coefficient corresponding to the i-th unit; P W,trepresents the wind power forecast value during period t; ξ t represents the wind power prediction error in period t; Δt represents the time step.

[0115] Wind power forecast error Standard deviation δ W,t The calculation formula is:

[0116] δ W,t =0.2P W,t +0.02P WT (8)

[0117] Where: W,t represents standard deviation; P W,t represents the wind power forecast value during period t; P WT Represents the total installed capacity of wind power.

[0118] Compared with the prior art, the present invention has the following technical effects:

[0119] 1) A carbon emission quota allocation method based on the entropy method is proposed. Taking into account the additional carbon emissions brought by wind power access to conventional thermal power units, wind power is taken as the main body of carbon trading and a day-ahead optimization scheduling model is constructed;

[0120] 2) The carbon emission quota allocation strategy based on the entropy method avoids the overly subjective treatment method of weighted average of marginal emission factors of electricity and capacity to a certain extent, and reduces the carbon emissions of the system by limiting the quota of high-emission units within a certain range;

[0121] 3) Consider the calculation of hidden carbon emissions of wind power after wind power is connected, so that wind power can participate in carbon trading to obtain income to offset part of the carbon trading costs of thermal power units, and give priority to scheduling units with low carbon emission factors in the scheduling model, thereby reducing the total operating cost of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0122] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0123] Figure 1 It is the wiring diagram of the IEEE-30 node system connected to the wind farm;

[0124] Figure 2 It is the load and wind power forecast diagram for each period of the IEEE-30 system;

[0125] Figure 3 It is the output diagram of the unit optimized for scheduling according to the present invention;

[0126] Figure 4 This is a comparison chart of the output plan of G1 unit under scenario 1 and scenario 3;

[0127] Figure 5This is a comparison chart of the output plan of G2 unit under scenario 1 and scenario 3;

[0128] Figure 6 This is a comparison chart of the output plan of G3 unit under scenario 1 and scenario 3;

[0129] Figure 7 This is a comparison chart of the output plan of G4 unit under scenario 1 and scenario 3;

[0130] Figure 8 This is a comparison chart of the output plan of G5 unit under scenario 1 and scenario 3;

[0131] Fig. 9 This is a comparison chart of the output plan of G6 unit under scenario 1 and scenario 3;

[0132] Fig.10 This is a comparison chart of the costs of each time period for Scenario 1 and Scenario 3 of the IEEE-30 system;

[0133] Fig.11 This is a comparison chart of system carbon emissions for IEEE-30 system scenario 1 and scenario 3;

[0134] Fig.12 This is a comparison chart of the carbon emission quota of the G1 unit under scenario 2 and scenario 3;

[0135] Fig.13 This is a comparison chart of the carbon emission quota of the G2 unit under scenario 2 and scenario 3;

[0136] Fig.14 This is a comparison chart of the carbon emission quota of the G3 unit under scenario 2 and scenario 3;

[0137] Fig.15 This is a comparison chart of the carbon emission quota of the G4 unit under scenario 2 and scenario 3;

[0138] Fig.16 This is a comparison chart of the carbon emission quota of the G5 unit under scenario 2 and scenario 3;

[0139] Fig.17 This is a comparison chart of the carbon emission quota of the G6 unit under scenario 2 and scenario 3;

[0140] Fig.18 It is the load and wind power forecast diagram for each period of the IEEE-118 system;

[0141] Fig.19 This is a comparison chart of system carbon emissions at different time periods for IEEE-118 system scenarios 1 and 3;

[0142] Fig. 20 This is a comparison chart of the total system cost in each period of IEEE-118 system scenario 1 and scenario 3;

[0143] Fig.21 This is a comparison chart of the carbon emission quotas of each unit in Scenario 2 and Scenario 3 of the IEEE-118 system. DETAILED DESCRIPTION

[0144] A day-ahead optimal scheduling method based on entropy method comprises the following steps:

[0145] Step 1: Establish a carbon emission quota model;

[0146] Step 2: Obtain the carbon trading costs of different types of power generation and coordinate the carbon emission differences of different types of units;

[0147] Step 3: Establish a day-ahead optimal scheduling model considering the tradable carbon emission quota system.

[0148] For n units and m emission factors, use x i,j Representing the j-th emission factor of unit i (i = 1, 2, ..., n; j = 1, 2, ..., m), establishing a carbon emission quota model includes the following steps:

[0149] Step 1-1) Obtain the marginal emission factor of electricity and capacity of the i-th unit;

[0150] Step 1-2) Perform normalization to convert the absolute value of the indicator into a relative value;

[0151] Step 1-3) Calculate the proportion of the i-th unit under the j-th emission factor;

[0152] Step 1-4) Calculate the information entropy of each emission factor;

[0153] Step 1-5) Determine the weight corresponding to each emission factor;

[0154] Step 1-6) The carbon emission quota coefficient corresponding to computer group i;

[0155] Step 1-7) The carbon emission quota of the computer group is calculated based on the carbon emission quota coefficient obtained in step 1-6).

[0156] In step 1-3), the proportion of the i-th unit under the j-th emission factor is:

[0157]

[0158] Where: p i,j represents the proportion of the i-th unit under the j-th emission factor to the emission factor j; r i,j Represents x i,j Normalized value;

[0159] In step 1-4), the information entropy of each emission factor is calculated using the following formula;

[0160]

[0161] Where: s j represents the information entropy of the jth emission factor; p i,j is the proportion of the i-th unit under the j-th emission factor to the emission factor j;

[0162] In step 1-5), the weights corresponding to each emission factor are determined using the following formula:

[0163]

[0164] Where: w j represents the weight corresponding to the jth emission factor; s j represents the information entropy of the j-th emission factor;

[0165] In step 1-6), the carbon emission quota coefficient corresponding to group i is calculated using the following formula:

[0166]

[0167] Where: e i represents the carbon emission quota coefficient corresponding to the i-th unit; w j represents the weight corresponding to the jth emission factor; r i,j Represents x i,j Normalized value;

[0168] In step 1-7), for the thermal power units participating in carbon trading, the allocated carbon emission quota is shown in formula (5):

[0169] Q Gi,t =e i P Gi,t Δt (5)

[0170] Where: Q Gi,t Indicates the carbon emission quota allocated to thermal power units participating in carbon trading; i represents the carbon emission quota coefficient corresponding to the i-th unit; P Gi,t It represents the active power output of unit i in period t; Δt represents the time step.

[0171] In addition, under the trend of clean energy system development, the study of the initial carbon emission quota allocation mechanism oriented to intermittent energy generation has a positive effect on promoting the consumption of clean energy. However, the volatility of intermittent energy output leads to an essential difference between it and thermal power units in the allocation of carbon emission quotas. Taking wind power as an example, step 7 considers combining the unit electricity carbon emission quota coefficient of thermal power and the wind power prediction error to calculate the carbon emission quota Q of wind power. W,t , specifically:

[0172] Q W,t =e i (P W,t +ξ t )Δt (6)

[0173] Where: Q W,t represents the carbon emission quota of wind power; e i represents the carbon emission quota coefficient corresponding to the i-th unit; P W,t represents the wind power forecast value during period t; ξ t represents the wind power prediction error in period t; Δt represents the time step.

[0174] Wind power forecast error Standard deviation δ W,t The calculation formula is:

[0175] δ W,t =0.2P W,t +0.02P WT (7)

[0176] Where: W,t represents standard deviation; P W,t represents the wind power forecast value during period t; P WT Represents the total installed capacity of wind power.

[0177] In step 2, the carbon trading costs of thermal power and wind power are included:

[0178] 2-1) Regarding the cost of thermal power carbon trading:

[0179] Thermal power generation participating in carbon trading generates additional costs, specifically:

[0180] FC Gi,t =p t (E Gi,t -Q Gi,t ) (8)

[0181] Where: FC Gi,t represents the carbon trading cost of thermal power unit i in period t; p t represents the carbon trading price in period t; E Gi,t It represents the actual carbon emissions of unit i in period t, specifically E i,t =μ i P i,t 2 +ν i P i,t +ω i , where μ i , ν i ,ω i represents the carbon emission coefficient of unit i; QGi,t Indicates the carbon emission quota allocated to thermal power units participating in carbon trading;

[0182] 2-2) Regarding wind power carbon trading costs:

[0183] In the actual power generation process, intermittent energy represented by wind power and photovoltaics does not produce carbon emissions, but the large-scale access of intermittent energy brings great uncertainty to the power generation side of the power system. For this reason, conventional thermal power units are required to provide more backup capacity to weaken the impact of intermittent energy power fluctuations, which leads to an additional increase in carbon emissions from thermal power units.

[0184] Therefore, the increase in carbon emissions from thermal power units caused by the uncertainty of wind power is considered to be the carbon emissions of wind power, specifically:

[0185] E W,t =μ i (ζ W P W,t ) 2 +ν i (ζ W P W,t )+ω i (9)

[0186] Where: E W,t represents the implicit carbon emissions of wind power during period t; W Represents wind power reserve capacity factor; P W,t represents the wind power forecast value during period t; it is assumed that the backup is provided by unit i during period t; μ i , ν i ,ω i represents the carbon emission coefficient of unit i;

[0187] It can be seen that the cost of wind power participating in carbon trading is:

[0188] FC W,t =p t (E W,t -Q W,t ) (10)

[0189] Where: FC W,t represents the carbon trading cost of wind power during period t; p t represents the carbon trading price in period t; E W,t represents the implicit carbon emissions of wind power in period t; Q W,t Represents the carbon emission quota of wind power.

[0190] On this basis, the carbon emission differences or surpluses of different types of units can be coordinated through the carbon trading market mechanism. Thermal power units can purchase insufficient carbon emission quotas from the carbon trading market, and wind power units can sell the remaining carbon emission quotas through the carbon trading market to obtain profits.

[0191] In step 3, the day-ahead optimal scheduling model considering the tradable carbon emission quota system is established as follows:

[0192] 3-1) Objective function:

[0193] The day-ahead optimal dispatch model takes the minimum total system cost within the dispatch period as the optimization goal, which includes the operating cost F of the thermal power unit. G , Carbon trading costs of thermal power units FC G , Spinning reserve cost F generated by wind power access R and wind farm carbon trading costs FC W :

[0194] minF=min(F G +FC G +F R +FC W ) (11)

[0195] Carbon trading costs of thermal power units G The calculation is:

[0196]

[0197] Where: FC G represents the carbon trading cost of thermal power units; N represents the number of thermal power units in the system, T represents the time; FC Gi,t represents the carbon trading cost of thermal power unit i in period t; E Gi,t represents the actual carbon emissions of unit i in period t; Q Gi,t Indicates the carbon emission quota allocated to thermal power units participating in carbon trading;

[0198] In addition, the randomness of wind power output leads to additional spinning reserve costs F R , the specific calculation is:

[0199]

[0200] Where: F R The randomness of wind power output leads to additional spinning reserve costs; C R is the price of spinning reserve capacity; ε t is the credibility level of wind power forecast output during period t; W,t represents the wind power forecast value during period t;

[0201] Carbon trading costs of wind power FC W for:

[0202]

[0203] Where: FC W represents the carbon trading cost of wind power; FC W,t represents the carbon trading cost of wind power during period t; p2 represents the carbon price when selling quotas; E W,t represents the implicit carbon emissions of wind power in period t; Q W,t Represents the carbon emission quota of wind power.

[0204] Step 3-2) Constraints

[0205] Step 3-2-1) Unit constraints

[0206] The unit output constraints, minimum continuous start-stop time constraints, and unit climbing / slip constraints of conventional thermal power are shown in formulas (15) to (17) respectively;

[0207]

[0208] Where: P Gi,t It represents the active power output of unit i in period t; Indicates the lower limit of active output of unit i; Represents the upper limit of active output of unit i.

[0209]

[0210] Where: represents the continuous startup time of the i-th unit in period t-1; represents the continuous downtime of the i-th unit in period t-1; Indicates the minimum continuous start time of unit i; represents the minimum continuous downtime of unit i; U Gi,t-1 represents the start and stop status of the i-th unit in the t-1 period; U Gi,t Represents the start and stop status of the i-th unit in period t.

[0211]

[0212] Where: P Gi,t represents the active power output of unit i in period t; P Gi,t-1 represents the active power output of unit i in period t-1; RU i Represents the maximum ramping capability of active power of unit i in period t; RD i It represents the maximum ramping capacity of active power of unit i in period t.

[0213] Wind farms must meet the following constraints during operation:

[0214]

[0215] Where: P W,t represents the wind power forecast value during period t; ξ t represents the wind power forecast error in period t; is the maximum power of the wind farm, that is, the installed capacity of wind power.

[0216] 3-2-2) Power balance constraints

[0217]

[0218] Where: U Gi,t represents the start and stop status of the i-th unit in period t; P Gi,t represents the active power output of unit i in period t; P L,t represents the predicted load of the system in period t; P W,t Represents the wind power forecast value in period t.

[0219] Step 3-2-3) System backup constraints

[0220]

[0221] Where: Indicates the upper limit of active output of unit i; P Gi,t represents the active power output of unit i in period t; ζ W Indicates wind power reserve factor; P W,t represents the predicted load of wind power in period t; Indicates the lower limit of active output of unit i.

[0222] Step 3-2-4) Network security constraints

[0223] The network security constraints after wind power access are:

[0224]

[0225] Where: P l,min G represents the minimum transmission capacity allowed by line l in the base state; l,i represents the power transfer distribution factor of unit i to line l; P Gi,t N = active power output of unit i in period t; W is the number of wind farms; G l,j P represents the power transfer distribution factor of wind farm j to line l; Wj,t represents the power of the jth wind farm; G l,d P represents the power transfer distribution factor of load d to line l;Ld,t represents the load value of node d during period t; P l,max It represents the maximum transmission capacity allowed by line l in the base state.

[0226] The present invention also includes a method for establishing a carbon emission quota model. For n units and m emission factors, x i,j Representing the j-th emission factor of unit i (i = 1, 2, ..., n; j = 1, 2, ..., m), establishing a carbon emission quota model includes the following steps:

[0227] Step 1: Obtain the marginal emission factor of electricity and capacity of the i-th unit;

[0228] Step 2: Perform normalization to convert the absolute value of the indicator into a relative value;

[0229] Step 3: Calculate the proportion of the i-th unit under the j-th emission factor;

[0230] Step 4: Calculate the information entropy of each emission factor;

[0231] Step 5: Determine the weights corresponding to each emission factor;

[0232] Step 6: Carbon emission quota coefficient corresponding to computer group i;

[0233] Step 7: Calculate the carbon emission quota of the computer group based on the obtained carbon emission quota coefficient.

[0234] The entropy method is essentially an objective weighting method. Its core idea is to determine the weights of different indicators based on data. The method of weighted average of marginal emission factors of electricity volume and marginal emission factors of capacity is more objective and more conducive to emission reduction.

[0235] In step 3:, the proportion of the i-th unit under the j-th emission factor to the emission factor is:

[0236]

[0237] Where: p i,j represents the proportion of the i-th unit under the j-th emission factor to the emission factor j; r i,j Represents x i,j Normalized value;

[0238] In step 4:, the information entropy of each emission factor is calculated using the following formula;

[0239]

[0240] Where: s j represents the information entropy of the jth emission factor; p i,jis the proportion of the i-th unit under the j-th emission factor to the emission factor j;

[0241] In step 5:, the weights corresponding to each emission factor are determined using the following formula:

[0242]

[0243] Where: w j represents the weight corresponding to the jth emission factor; s j represents the information entropy of the j-th emission factor;

[0244] In step 6:, the carbon emission quota coefficient corresponding to group i is calculated using the following formula:

[0245]

[0246] Where: e i represents the carbon emission quota coefficient corresponding to the i-th unit; w j represents the weight corresponding to the jth emission factor; r i,j Represents x i,j Normalized value;

[0247] In step 7:, for the thermal power units participating in carbon trading, the allocated carbon emission quota is shown in formula (6):

[0248] Q Gi,t =e i P Gi,t Δt (6)

[0249] Where: Q Gi,t Indicates the carbon emission quota allocated to thermal power units participating in carbon trading; i represents the carbon emission quota coefficient corresponding to the i-th unit; P Gi,t It represents the active power output of unit i in period t; Δt represents the time step.

[0250] In step 7, if the wind turbine is participating in carbon trading, the carbon emission quota of wind power Q W,t for:

[0251] Q W,t =e i (P W,t +ξ t )Δt (7)

[0252] Where: Q W,t represents the carbon emission quota of wind power; e i represents the carbon emission quota coefficient corresponding to the i-th unit; P W,t represents the wind power forecast value during period t; ξ trepresents the wind power prediction error in period t; Δt represents the time step;

[0253] Wind power forecast error Standard deviation δ W,t The calculation formula is:

[0254] δ W,t =0.2P W,t +0.02P WT (8)

[0255] Where: W,t represents standard deviation; P W,t represents the wind power forecast value during period t; P WT Represents the total installed capacity of wind power.

[0256] The technical effects of a method for establishing a carbon emission quota model are:

[0257] 1: The carbon emission quota allocation strategy based on the entropy method avoids the overly subjective treatment method of weighted average of marginal emission factors of electricity and capacity to a certain extent;

[0258] 2: Reduce the system’s carbon emissions by limiting the quota of high-emission units to a certain range;

[0259] 3: Consider the calculation of hidden carbon emissions of wind power after wind power is connected, so that wind power can participate in carbon trading to obtain income to offset part of the carbon trading costs of thermal power units, and give priority to scheduling units with low carbon emission factors in the scheduling model, thereby reducing the total operating cost of the system.

[0260] Example:

[0261] Step 1: Simulation and analysis based on IEEE-30 bus system

[0262] Take the IEEE-30 node system connected to the wind farm as an example for simulation analysis. The system wiring diagram is as follows: Figure 1 It includes 6 conventional thermal power units, which are connected to a wind farm with an installed capacity of 150MW at 22 nodes. The load forecast value and wind power forecast value for each period are shown in Figure 2 As shown in Table 1, the marginal emission factors of electricity and capacity of each unit are shown in Table 1. Considering the allocation of carbon emission quotas and the calculation of carbon trading costs, the time step is 1h and the scheduling cycle T is 24h. In addition, the load reserve coefficient ζ L Take it as 0.1, wind power reserve coefficient ζ W The present invention uses the carbon trading price prediction results based on the combined prediction of VMD-LSSVM to simulate the carbon price changes during the scheduling period, that is, the predicted carbon prices from the 401st to the 424th day are used to simulate the carbon trading prices for 24 hours during the scheduling period.

[0263] Table 1 Marginal emission factors of electricity and capacity for each unit in the IEEE-30 system

[0264]

[0265] The relevant solution calculations are completed based on Matlab 2016a software and CPLEX 12.4, and the hardware configuration is CPU i5-7500, 8G running memory and Windows 10 64-bit operating system.

[0266] The weights of the marginal emission factor of electricity and the marginal emission factor of capacity are 0.65 and 0.35 respectively. The carbon quota coefficients corresponding to each unit are shown in Table 2:

[0267] Table 2 Carbon quota coefficients corresponding to each unit in the IEEE-30 system

[0268]

[0269] As can be seen from Table 1, the quota coefficient of thermal power unit G3 is the lowest, while the quota coefficient of unit G5 is the highest. If other factors are not considered, when the system requires the same output level, prioritizing the dispatch of unit G3 can reduce the carbon emissions of the system.

[0270] In addition, the optimized scheduling results of the present invention are as follows Figure 3 shown.

[0271] from Figure 3 It can be seen that thermal power units G1, G2 and G3 bear the basic load of the system, among which G3 has been in full power state during the dispatch period. During the period from t = 1 to t = 6, due to the reduction of load demand, the system adopts the method of reducing the output of G2 to balance the load constraint; and during the period from t = 6 to t = 12, the system constraint is met by increasing the output of G2.

[0272] (1) Comparative analysis

[0273] In order to verify the correctness and effectiveness of the method proposed in this invention, the following three scenarios are set up:

[0274] Scenario 1: Day-ahead optimal scheduling without considering carbon emission trading;

[0275] Scenario 2: Considering carbon emission trading in the power system, a carbon emission quota allocation method using a weighted average of the marginal emission factor of electricity and the marginal emission factor of capacity is used;

[0276] Scenario 3: Considering the carbon emission trading of the power system, a carbon emission quota allocation method based on the entropy method is used, i.e., the model of the present invention.

[0277] 1) Consider the necessity of carbon emissions trading

[0278] In order to verify the necessity of incorporating carbon trading into the day-ahead optimization scheduling model, the total system carbon emissions of scenario 1 and scenario 3 are calculated and analyzed. First, the output plans of each unit G1-G6 under scenario 1 and scenario 3 during the scheduling period are as follows: Figure 4-9 shown.

[0279] from Figure 4-9 It can be seen that compared with scenario 1, the output levels of units G1 and G2 that bear the basic load of the system under scenario 3 are obviously reduced during the entire scheduling cycle, among which the output of G1 is the most obvious, while the output of units G3, G4, G5 and G6 is significantly increased. This is because the carbon emissions of thermal power units are positively correlated with the output of the units. For G1 and G2 with large active output and high carbon emission levels, in order to reduce the system carbon emissions in each scheduling period, the scenario 3 model lowers the output of G1 and G2; while for units G3, G4, G5 and G6 with small output and low carbon emission levels, their corresponding active output is increased. Therefore, incorporating carbon trading into the day-ahead optimization scheduling model is mainly to reduce carbon emissions in each scheduling period by lowering the output of units with higher carbon emission levels, while increasing the output of units with lower carbon emission levels to ensure power supply balance.

[0280] Furthermore, in order to analyze the changes in the total system cost during the dispatch period after carbon trading, the total cost of each period under the two scenarios is calculated, and the comparison chart is as follows: Fig.10 shown.

[0281] from Fig.10 It can be seen that in the period from t = 1 to t = 5, the system cost of scenario 3 is lower than that of scenario 1. The main reason is that the carbon trading income of wind power is greater than the carbon trading cost of thermal power units, which offsets part of the cost; while in the period from t = 10 to t = 24, the dispatching cost of scenario 3 has an additional increase compared with the traditional dispatching cost of scenario 1. This is mainly due to the increase in the output of G3, G4, G5 and G6 in the corresponding period, which leads to the carbon trading cost of thermal power exceeding the carbon trading income of wind power. In the entire dispatching cycle, the total cost of scenario 3 is 18,700 yuan higher than that of scenario 1. Figure 4-9 Although scenario 3 reduces the output of G1 and G2 to limit carbon emissions, the total cost will eventually increase. Therefore, incorporating carbon emissions trading into the day-ahead dispatch model is essentially to reduce the total carbon emissions of the system by reducing the output of high-emission units and increasing the output of low-emission units at the cost of a small increase in total costs.

[0282] Then calculate the total carbon emissions of the system in each scheduling period under scenario 1 and scenario 3 as follows: Fig.11 shown.

[0283] from Fig.11It can be seen that the carbon emissions of scenario 3 are significantly lower than those of scenario 1. During the entire scheduling period, the total carbon emissions of the scenario 1 system are 5616.36 tons, while the total carbon emissions of scenario 3 are 5262.33 tons, which is nearly 354 tons less than that of scenario 1. Therefore, incorporating carbon emission trading into the day-ahead scheduling model can effectively reduce the total carbon emissions of the system.

[0284] 2) Validation of the effectiveness of the carbon emission quota allocation method based on the entropy method

[0285] The output results of each unit G1-G6 under scenario 2 and scenario 3 are shown in Table 3 and Table 4 respectively. In order to verify the effectiveness of the carbon emission quota allocation method based on the entropy method, the total carbon emission quota of each unit G1-G6 under scenario 2 and scenario 3 is calculated, and the specific results are shown in Table 5:

[0286] Table 3 Output results of each unit in IEEE-30 system under scenario 2

[0287]

[0288]

[0289] Table 4 Output results of each unit in IEEE-30 system under scenario 3

[0290]

[0291] Table 5 Carbon emission quotas corresponding to each unit in scenario 2 and scenario 3

[0292]

[0293] As can be seen from Table 5, the carbon emission quota of G1 in Scenario 3 is higher than that in Scenario 2. Comparing the output results of G1 in the two scenarios, it is actually to reduce the carbon emission quota by lowering its output; compared with Scenario 2, the output of units G4 and G6 with lower carbon emission intensity in Scenario 3 has increased significantly, and their corresponding carbon emission quotas have also increased accordingly. In addition, the carbon emission quotas of thermal power units G2, G3, and G5 are higher than those in Scenario 2. This is because the method of calculating the carbon quota coefficient based on the entropy method takes into account the carbon emission characteristics of different units. Compared with the method of directly averaging the carbon emission factors, the increase in its carbon emission quota coefficient leads to a corresponding increase in its carbon emission quota.

[0294] In addition, in order to analyze the impact of the carbon emission quota allocation method in this paper on different units, a detailed comparison of the carbon emission quotas of each unit in scenario 2 and scenario 3 is carried out. The carbon emission quotas of the six thermal power units in the scheduling period are as follows: Figure 12-17 shown.

[0295] from Figure 12-17It can be seen that for unit G1 that bears the basic load, the carbon emission quota of scenario 3 is significantly reduced compared with scenario 2 during the period from t=14 to t=23. The main reason is that the output level of unit G1 is relatively stable under scenario 3 and the total active output is significantly reduced; for unit G2, the carbon emission quota under scenario 3 is significantly increased compared with the quota of scenario 2 during the period from t=8 to t=24. The output levels of the units in the two scenarios are basically the same during this period. The main reason for the increase in G2's quota is that the quota coefficient of scenario 3 is higher than that of scenario 2; for G3, whose unit output has not changed in the two scenarios, the quota of scenario 3 is higher than that of scenario 2, which is also caused by the increase in the quota coefficient; for units G4, G5 and G6, their quotas under scenario 3 are all higher than those in scenario 2. The common reason for the increase in their carbon emission quotas is that they are affected by the increase in the quota coefficient. In addition, G4 and G6 have a significant increase in their output and remain within a certain range due to their backup for wind power access. These two reasons lead to a greater increase in the quotas of G4 and G6 under scenario 3. Therefore, the method proposed in this paper not only optimizes the carbon emission quota of each unit, but also makes the unit output level more stable.

[0296] Step 2: Simulation and analysis based on IEEE-118 node system

[0297] To further verify the effectiveness of the proposed method in a large-scale power system, a simulation analysis is performed on the IEEE-118 node system connected to a wind farm. Nodes 36, 69, and 77 are connected to wind farms with installed capacities of 100MW, 200MW, and 250MW, respectively. The load forecast values ​​and wind power forecast values ​​for each period are shown in Figure 2. Fig.18 As shown. In addition, the load reserve factor ζ L Take it as 0.1, the wind farm reserve coefficient is 0.14, and the carbon price prediction results in step 1 are used to simulate the changes in carbon prices during the scheduling period.

[0298] According to the carbon emission quota allocation method based on the entropy method in this chapter, the weights corresponding to the two emission factors are calculated to be 0.55 and 0.45 respectively, and then the quota coefficients corresponding to each unit are obtained as shown in Table 6.

[0299] Table 6 Carbon quota coefficients corresponding to each unit in the IEEE-118 system

[0300]

[0301]

[0302] As can be seen from Table 6, the quota coefficients of units 27 and 28 are the lowest, while the quota coefficients of units 32, 33, 41, 46 and 49 are the highest, among which the units with quota coefficients of 0.6699 tons / MW·h and 0.6915 tons / MW·h account for 61.4% of the total installed capacity of thermal power units. In addition, for units with smaller characteristic differences in the system, their quota coefficients are very small or even the same.

[0303] Combined with the model of the present invention, the start-stop scheme of each unit of the IEEE-118 system is calculated as shown in Table 7.

[0304] Table 7 Start-up and shutdown schemes for each unit in the IEEE-118 system

[0305]

[0306] As can be seen from Table 7, taking the two groups of units 26 and 51, and 16 and 34 as examples, since their respective parameters are basically the same, the start-stop schemes obtained are also the same. Combined with Table 3, it can be seen that the quota coefficient of the normally open unit is lower than the quota coefficient of the normally shut down unit. It can be seen that when the system requires the same output level, if other factors are not considered, the unit with a low carbon emission quota coefficient is preferentially dispatched in the model of this paper.

[0307] (1) Comparative analysis

[0308] In order to verify the correctness and effectiveness of the method proposed in this chapter in a larger system, three scenarios similar to step 1 are set up for analysis.

[0309] 1) Consider the necessity of carbon emissions trading

[0310] In order to verify the necessity of incorporating carbon trading into the day-ahead optimization scheduling model, the system carbon emissions in each period of scenario 1 and scenario 3 are first calculated. The results are as follows: Fig.19 shown.

[0311] from Fig.19 It can be seen that the carbon emissions in each period of scenario 3 are significantly lower than those in scenario 1. During the entire scheduling cycle, the total carbon emissions of the scenario 1 system are 57,286.88 tons, and the total carbon emissions of scenario 3 are 53,675.78 tons, which is 3,611.10 tons less than that of scenario 1. The main reason is that carbon emissions trading is considered in the scenario 3 model, which is essentially to shut down high-emission units or reduce the output of high-emission units, while increasing the output of low-emission units, thereby effectively reducing the total carbon emissions of the system.

[0312] Furthermore, the total system cost for each period under scenario 1 and scenario 3 is calculated, and the comparison chart is as follows: Fig. 20 shown.

[0313] from Fig. 20It can be seen that in the period from t = 1 to t = 4 and from t = 20 to t = 24, the system operation cost of scenario 3 is significantly lower than that of scenario 1. The main reason is that the reduction in load demand during this period reduces the carbon trading cost of thermal power. At the same time, the increase rate of wind power carbon emission quota exceeds the increase rate of its emissions, which makes wind power participate in carbon emission trading to obtain corresponding benefits, thus reducing the total cost. In the period from t = 5 to t = 19, the cost is slightly higher than that of scenario 1. The main reason is that the reduction in wind power output has greatly reduced the benefits of wind power participating in carbon trading. The carbon trading cost of thermal power during this period exceeds the carbon trading benefits of wind power, which ultimately leads to an increase in total costs. During the entire dispatch cycle, the total cost of scenario 3 is 290,900 yuan higher than that of scenario 1. If 40 yuan / ton is used as the penalty fee for not conducting carbon trading, 3611.10 tons of carbon emissions need to bear a fine of 1.444 million yuan. It can be seen that incorporating carbon emission trading into the SCUC model is essentially to reduce the total carbon emissions of the system with a small increase in total costs, which is more economical than paying fines directly without considering carbon trading.

[0314] 2) Validation of the effectiveness of the carbon emission quota allocation method based on the entropy method

[0315] In order to verify the effectiveness of the entropy-based carbon emission quota allocation method in a large-scale power system, the total carbon emission quota of each unit in scenario 2 and scenario 3 during the entire dispatch cycle is calculated. The specific results are as follows: Fig.21 As shown:

[0316] from Fig.21 It can be seen that the carbon emission quotas of units G11, G20, G21, G24, G25, G27, G28, G39, and G40 in scenario 3 are significantly higher than those in scenario 2, among which the changes of G11, G20, G39, and G40 are more prominent. The main reason is that these units are always open during the scheduling cycle, and their total output during the scheduling cycle increases accordingly, thereby increasing their carbon emission quotas. For units that only change in output and whose start-stop status does not change, the changes in their carbon emission quotas in scenarios 2 and 3 are not obvious, which shows that the carbon emission quota allocation method based on the entropy method in this paper is mainly to optimize the carbon emission quotas of each unit by adjusting the start-stop status of the unit. Therefore, the method of the present invention takes into account the proportion of units with different emission characteristics in the system, and uses the start-stop adjustment of the unit to reasonably allocate the carbon emission quotas of each unit, thereby promoting the participation of various entities in carbon emission trading.

Claims

1. A method for establishing a carbon emission quota model, characterized in that: The following steps are involved: Step 1: Obtain the marginal emission factor of electricity and capacity of the i-th unit; Step 2: Perform normalization to convert the absolute value of the indicator into a relative value; Step 3: Calculate the proportion of the i-th unit under the j-th emission factor; Step 4: Calculate the information entropy of each emission factor; Step 5: Determine the weights corresponding to each emission factor; Step 6: Carbon emission quota coefficient corresponding to computer group i; Step 7: Calculate the carbon emission quota of the computer group based on the obtained carbon emission quota coefficient.

2. The method according to claim 1, characterized in that The carbon emission quota model is for n units, m emission factors, and x i,j Represents the jth emission factor of unit i; where i = 1, 2, …, n; j = 1, 2, …, m.

3. The method according to claim 1, characterized in that In step 3, the proportion of the i-th unit under the j-th emission factor is: Where: p i,j represents the proportion of the i-th unit under the j-th emission factor to the emission factor j; r i,j Represents x i,j Normalized value.

4. The method according to claim 1, characterized in that: In step 4, the information entropy of each emission factor is calculated using the following formula: Where: s j represents the information entropy of the jth emission factor; p i,j is the proportion of the i-th unit under the j-th emission factor to the emission factor j.

5. The method according to claim 1, characterized in that In step 5, the weights corresponding to each emission factor are determined using the following formula: Where: w j represents the weight corresponding to the jth emission factor; s j represents the information entropy of the j-th emission factor.

6. The method according to claim 1, characterized in that In step 6, the carbon emission quota coefficient corresponding to group i is calculated using the following formula: Where: e i represents the carbon emission quota coefficient corresponding to the i-th unit; w j represents the weight corresponding to the jth emission factor; r i,j Represents x i,j Normalized value.

7. The method according to claim 1, characterized in that In step 7, for the thermal power units participating in carbon trading, the allocated carbon emission quota is shown in formula (6): Q Gi,t =e i P Gi,t Δt (6) Where: Q Gi,t Indicates the carbon emission quota allocated to thermal power units participating in carbon trading; i represents the carbon emission quota coefficient corresponding to the i-th unit; P Gi,t It represents the active power output of unit i in period t; Δt represents the time step.

8. The method according to claim 1, characterized in that: In step 7, if the wind turbine is participating in carbon trading, the carbon emission quota of wind power Q W,t for: Q W,t =e i (P W,t +ξ t )Δt (7) Where: Q W,t represents the carbon emission quota of wind power; e i represents the carbon emission quota coefficient corresponding to the i-th unit; P W,t represents the wind power forecast value during period t; ξ t represents the wind power prediction error in period t; Δt represents the time step; Wind power forecast error Standard deviation δ W,t The calculation formula is: d W,t =0.2P W,t +0.02P WT (8) Where: W,t represents standard deviation; P W,t represents the wind power forecast value during period t; P WT Represents the total installed capacity of wind power.

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