Coordinated optimization method of unit commitment and branch switching considering short-circuit current constraints

Abstract

A collaborative optimization method for unit combination and branch switching taking into account short-circuit current constraints, step 1: construct a carbon emission model based on the CDM mechanism, and calculate the initial carbon emission quota and carbon emission cost allocated to the computer group; step 2: establish the objective function of the unit combination based on multi-scenario expectations, and transform the short-circuit current constraint into a penalty function and add it to the objective function; step 3: adopt the short-circuit current constraint and the constraint conditions of the CDM trading mechanism to obtain the branch switching plan and realize the collaborative optimization of the system; the present invention constructs a unit combination and branch switching collaborative optimization model taking into account short-circuit current constraints. Compared with the traditional unit combination strategy, the model is more comprehensive in terms of taking low carbon into account, and can provide a branch switching plan for the current over-limit node, so that the current of the over-limit node is reduced to below the limit value. It is suitable for solving the unit combination optimization problem involving large-scale new energy grid connection, can significantly improve the current over-limit problem, reduce carbon emissions, and is more conducive to the low-carbon and economical operation of the unit.

Description

Technical Field

[0001] The invention belongs to the field of power system unit optimization operation and calculation technology, and particularly relates to a unit combination and branch switching coordinated optimization method taking short-circuit current constraints into account. Background Art

[0002] As the structure of power systems becomes increasingly complex, it is of great significance to study how to improve the problem of node current exceeding the limit in the system and how to optimize the output distribution of generator sets so that the power grid can balance stability and low carbon during operation.

[0003] The unit combination model generally satisfies constraints such as system power balance, spare capacity and minimum start-stop time, and then determines the start-stop status of the unit and the specific output of the unit in each period, so that the total power generation cost is minimized or the benefit is maximized within the dispatching cycle. With the development of new power systems, the complexity of unit combination and network structure increases, which will lead to a series of problems in the operation process of power system units, such as unit dispatch optimization problems in the system and short-circuit current exceeding the standard due to the tight grid structure. In this context, how to respond to the national sustainable development policy, further explore the low-carbon economic potential of unit combination, and how to limit the short-circuit current in the system through branch switching is of great significance. Therefore, the traditional unit combination model with economy as the goal is no longer applicable. Green and low-carbon performance should be taken into account. By constructing an economic and low-carbon uncertain unit combination model, the problem of breaking current exceeding the limit in the system is expressed in the form of node self-impedance, which will also have an impact on the traditional unit combination research. Summary of the invention

[0004] In order to overcome the defects of the prior art, the purpose of the present invention is to provide a coordinated optimization method for unit combination and branch switching taking into account short-circuit current constraints. The optimization method considers wind power and electric vehicle factors, adopts the Clean Development Mechanism (CDM) to distribute the output of generator sets, and limits the short-circuit current in the system. The optimization result of the output distribution of the generator sets is obtained by calculating the relevant constraints such as CO2 emission costs and carbon emission quotas, carbon emission trading, and the circuit-breaking current represented by the node self-impedance. The method can provide a reasonable branch switching scheme for the current over-limit node and can significantly reduce the carbon emissions in the unit output, providing a reliable solution for the optimization of unit output under the condition of new energy grid connection.

[0005] In order to achieve the above object, the technical solution of the present invention is:

[0006] The method for coordinated optimization of unit commitment and branch switching taking into account short-circuit current constraints comprises the following steps:

[0007] Step 1: Construct a carbon emission model based on the CDM mechanism

[0008] First, the CO2 emission quota of the power system is predicted, and the initial carbon emission quota is allocated to each unit in the system using the benchmark value:

[0009] E D,t =ξP LD,t (1)

[0010] In the formula, E D,t is the initial emission quota allocated to each entity in the system calculated using the benchmark value D during period t; ξ is the carbon emission quota per unit of electricity, which is determined by the electricity marginal factor and the capacity marginal emission factor; P LD,t is the load of the system in the tth period;

[0011] Based on the CDM mechanism, the carbon emission cost of the system in period t is:

[0012]

[0013] In the formula, K CDM , K P They are the unit carbon emission trading price and the penalty for excess emission under the CDM mechanism; is the upper limit of the emission quota traded by the system through the CDM mechanism in the tth period under scenario s, allocated according to the load proportion in the operation cycle; It is the carbon emission calculated using actual value in the tth period under scenario s.

[0014] Step 2: Establish the unit commitment objective function based on short-circuit current constraints

[0015] Based on the goal of minimizing the expected cost of multiple scenarios and considering the short-circuit current constraint, the unit operating state is selected, and the total objective function is expressed as:

[0016]

[0017] S i,t is the startup cost of generator set i in the tth period;

[0018] u i,t is the start and stop status of generator set i in the tth period;

[0019] is the coal consumption function of unit i;

[0020] C PM,i is the emission cost of PM2.5;

[0021] is the CO2 emission cost in period t under scenario s;

[0022] is the total charging cost of the electric vehicle at time period t in scenario s;

[0023] W t s Penalty costs for wind curtailment;

[0024] λz is the penalty factor;

[0025] Z imin To meet the minimum node self-impedance under the short-circuit current constraint;

[0026] Z ii is the node self-impedance of node i;

[0027] Step 3: Use the short-circuit current constraint and the CDM trading mechanism constraint to complete the optimization

[0028] The calculation of short-circuit current exceeding the limit is to calculate the current size of each node through the node self-impedance, filter out the nodes exceeding the limit by setting the limit value, and obtain the branch circuit breaking plan through calculation, so that the current of the node exceeding the limit is reduced to below the limit value;

[0029] The constraints of the adopted CDM trading mechanism include: the power balance constraint of the system as shown in formula (4); the spinning reserve constraint as shown in formula (5); the output constraint of each unit at different times as shown in formula (6); the ramp constraint as shown in formula (7); the minimum start and stop time constraint of the unit as shown in formula (8)-(9); the number of electric vehicles that can be charged and discharged as shown in formula (10)-(11); the electric vehicle charging and discharging time constraint as shown in formula (12)-(13); the wind farm abandoned wind volume constraint as shown in formula (14); the carbon emission reduction target constraint for each period as shown in formula (15); the CDM emission reduction trading volume constraint as shown in formula (16);

[0030]

[0031]

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] In the formula, is the total basic load of the system at the tth moment; R t is the system spinning reserve demand at the tth moment; are the upper and lower limits of the active output of generator set i; DR i , UR i They are the minimum downward adjustment and maximum upward adjustment of active power of generator set i at a moment, respectively; are the time that generator set i is in operation and shutdown state before the tth moment respectively; are the minimum allowable running time and minimum allowable downtime of generator set i respectively; N max d,t are the maximum number of electric vehicles that can be charged and discharged in the tth period, respectively; is the total number of all rechargeable electric vehicles in a scheduling cycle; is the total number of all dischargeable electric vehicles in the dispatch cycle; Δt c is the average charging time, Δt d is the average discharge time; is the predicted wind power output power of wind farm w in time period t in scenario s; The amount of wind power abandoned by the wind farm;

[0044] The DC power flow constraints of the network are shown in equations (17)-(18); the node power balance constraints of the system are shown in equation (19); the active power constraints of the lines in the system are shown in equation (20); the network integrity constraints are shown in equation (21); the short-circuit current constraints are shown in equation (22); and the branch switching times constraints are shown in equation (23):

[0045]

[0046]

[0047]

[0048]

[0049]

[0050] z ii,t ≥1 / I i,max (twenty two)

[0051]

[0052] In the formula, represents the voltage phase angle of the tth period under scenario s; X ij is the impedance value of branch ij; P is the active power flowing through branch ij in the tth period under scenario s; M represents a larger number; the set of branches connected to node i is represented by U(i); Z ij,t is the switching state of branch ij in period t. ij,t =1 means branch ij is in operation, Z ij,t =0 means that branch ij is in disconnected state; when Z ij,t = 1, equations (17) and (18) are standard DC power flow models: (θ i,t -θ j,t ) / X ij -P ij,t =0, and when Z ij,t = 0, equations (17) and (18) satisfy the constraints respectively; Z ik,t represents the disconnection state of line ik in time period t, where node k is in the set U(i); z ii,t is the self-impedance of node i in time period t; I i,max is the maximum short-circuit current of the node; N ts The maximum number of times a branch can be opened and closed in a working day. Each branch can be switched at most twice during the scheduling cycle.

[0053] The costs of each part in formula (3) in step 2 are specifically as follows:

[0054] (1) Fuel costs

[0055] The fuel cost of unit operation is expressed as a quadratic function of unit output:

[0056]

[0057] Where: is the coal consumption function of unit i; where a i , b i 、c i is the fuel cost coefficient of unit i, in units of t / (MW 2 h), t / (MW h) and t / h; is the active power output of unit i at time period t under scenario s;

[0058] (2) Start - stop cost

[0059] The start - up cost function of the unit is expressed as a step function with respect to the unit's outage time:

[0060]

[0061]

[0062] In the formula, S i,t is the start - up cost of generator set i in the t - th period; is the hot start - up cost of generator set i; The cold start - up cost of generator set i; is the continuous outage time of generator set i before the t - th period; is the transition time between the hot start - up and cold start - up of generator set i; is the minimum allowable outage time of generator set i; is the cold start - up time of generator set i;

[0063] The linearized calculation is expressed as:

[0064]

[0065]

[0066]

[0067] When time t = n, it represents the initial start - stop state of the unit; when t < n, it represents the state of the (n - t) period in the previous cycle;

[0068] During the operation of the unit, the shutdown cost of the generator set is a constant value, and its magnitude can be ignored.

[0069] (3) Charging cost function of electric vehicles

[0070]

[0071] In the formula, is the total charging cost of the electric vehicle at time t in scenario s; e c,t and e d,t are the charging and discharging prices of the electric vehicle at time t respectively; are the total charging and discharging quantities of the electric vehicle at time t in scenario s respectively; P c and P d are the average charging power and discharging power of the electric vehicle respectively; Δt is the duration of period t; (4) Penalty cost for curtailed wind

[0072]

[0073] (5) Pollutant emission costs

[0074] The PM2.5 emissions and CO2 emissions of the unit are shown in equations (32) and (33):

[0075]

[0076]

[0077] In the formula, is the PM2.5 emission of unit i in the tth time period under scenario s; Aar is the average coal received ash content (%), which is taken as 20; ω is the conversion coefficient of coal ash to PM2.5 (%), which is taken as 5.1; η is the PM2.5 removal efficiency of measures taken (%), which is taken as 99; PM2.5 emission and coal consumption of unit i, where, α i , β i , γ i The carbon emission coefficient of unit i is determined by the carbon emission efficiency of the unit;

[0078]

[0079]

[0080] In the formula, C PM,i is the emission cost of PM2.5; C e is the emission penalty factor of PM2.5; is the CO2 emission cost in period t under scenario s; f is the actual CO2 emission of the system and quota E D,t Function of

[0081] (6) Penalty function

[0082] The node impedance matrix is ​​used to represent the node current. The circuit breaker current constraint is converted into a penalty function and then added to the objective function:

[0083] λ z ∑ i∈τ (Z imin -Z ii ) (36)

[0084] Where λz is the penalty factor. If Z ii ≥Z imin , it means that node i satisfies the short-circuit current constraint, then λ z =0, otherwise, if Z ii ≤Z imin , then λ z Take an infinite number and assume that λ z =1010 ; Z imin is the minimum node self-impedance under the short-circuit current constraint; Z ii is the node self-impedance of node i; τ is the set of nodes where the short-circuit current exceeds the limit.

[0085] Advantages of the present invention:

[0086] The present invention proposes a method for coordinated optimization of unit combination and branch switching taking into account short-circuit current constraints. It mainly proposes an uncertain unit combination method taking into account the CDM mechanism. Compared with the traditional unit combination method, it considers low-carbon factors more comprehensively, namely, two low-carbon factors of large-scale wind farms and electric vehicles, strengthens the connection between low-carbon factors and thermal power units, and promotes the rationalization of energy structure; through the calculation of relevant constraints such as carbon emission trading, CO2 emission costs, and carbon emission quotas, combined with short-circuit current constraints and branch switching methods, the problem of excessive short-circuit current in the system is studied. It reduces the operating cost of the unit and can significantly reduce the carbon emissions in the unit output, providing an optimized unit combination method for the low-carbon and economic operation of the transmission system units. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 It is a solution flow chart of the present invention.

[0088] Figure 2 This is the wiring diagram for the IEEE 39 node system. DETAILED DESCRIPTION

[0089] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0090] A coordinated optimization method for unit combination and branch switching considering short-circuit current constraints is shown in the attached figure. Figure 1 As shown, the following steps are included:

[0091] Step 1: Construct a carbon emission model based on the CDM mechanism and calculate the initial carbon emission quota and carbon emission cost.

[0092] Predict the CO2 emission quota of the power system and use the benchmark value to allocate the initial carbon emission quota to each unit in the system:

[0093] E D,t =ξP LD,t (1)

[0094] In the formula, E D,t is the initial emission quota allocated to each entity in the system calculated using the benchmark value D during period t; ξ is the carbon emission quota per unit of electricity, which is determined by the electricity marginal factor and the capacity marginal emission factor; P LD,t is the load of the system in the tth period;

[0095] Based on the CDM mechanism, the carbon emission cost of the system in period t is:

[0096]

[0097] In the formula, K CDM , K P They are the unit carbon emission trading price and the penalty for excess emission under the CDM mechanism; is the upper limit of the emission quota traded by the system through the CDM mechanism in the tth period under scenario s, allocated according to the load proportion in the operation cycle; It is the carbon emission calculated using actual value in the tth period under scenario s.

[0098] Step 2: The objective function of the unit commitment is established based on multi-scenario expectations, and the short-circuit current constraint is converted into a penalty function and added to the objective function.

[0099] In order to comprehensively analyze the optimization results of the unit output, this patent takes the minimum expected cost of multiple scenarios as the goal, considers the short-circuit current constraint, selects the unit operating state, and the total objective function is expressed as:

[0100]

[0101] S i,t is the startup cost of generator set i in the tth period;

[0102] u i,t is the start and stop status of generator set i in the tth period;

[0103] is the coal consumption function of unit i;

[0104] C PM,i is the emission cost of PM2.5;

[0105] is the CO2 emission cost in period t under scenario s;

[0106] is the total charging cost of the electric vehicle at time period t in scenario s;

[0107] W t s Penalty costs for wind curtailment;

[0108] λz is the penalty factor;

[0109] Z imin To meet the minimum node self-impedance under the short-circuit current constraint;

[0110] Z ii is the node self-impedance of node i;

[0111] Step 3: Using the short-circuit current constraint and the constraints of the CDM trading mechanism, the proposed method is used to obtain the branch switching plan to achieve coordinated optimization of the system.

[0112] Based on the constraints of the CDM mechanism, a constraint to limit the short-circuit current exceeding the standard is constructed to ensure the reliability of the unit operation. The calculation of the short-circuit current exceeding the standard is to calculate the current size of each node through the node self-impedance, filter out the nodes exceeding the limit by setting the limit value, and obtain the branch circuit breaking plan by calculation, so that the current of the node exceeding the limit is reduced to below the limit value.

[0113] The constraints of the adopted CDM trading mechanism include: the power balance constraint of the system as shown in formula (4); the spinning reserve constraint as shown in formula (5); the output constraint of each unit at different times as shown in formula (6); the ramp constraint as shown in formula (7); the minimum start and stop time constraint of the unit as shown in formula (8)-(9); the number of electric vehicles that can be charged and discharged as shown in formula (10)-(11); the electric vehicle charging and discharging time constraint as shown in formula (12)-(13); the wind farm abandoned wind volume constraint as shown in formula (14); the carbon emission reduction target constraint for each period as shown in formula (15); the CDM emission reduction trading volume constraint as shown in formula (16);

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127] In the formula, is the total basic load of the system at the tth moment; R t is the system spinning reserve demand at the tth moment; are the upper and lower limits of the active output of generator set i; DR i , UR i They are the minimum downward adjustment and maximum upward adjustment of active power of generator set i at a moment, respectively; are the time that generator set i is in operation and shutdown state before the tth moment respectively; are the minimum allowable running time and minimum allowable downtime of generator set i respectively; N max d,t are the maximum number of electric vehicles that can be charged and discharged in the tth period, respectively; is the total number of all rechargeable electric vehicles in a scheduling cycle; is the total number of all dischargeable electric vehicles in the dispatch cycle; Δt c is the average charging time, Δt d is the average discharge time; is the predicted wind power output power of wind farm w in time period t in scenario s; The amount of wind power abandoned by the wind farm;

[0128] Considering the branch switching method to disconnect the line, the DC power flow constraints of the network are shown in equations (17)-(18); the node power balance constraints of the system are shown in equation (19); the active power constraints of the lines in the system are shown in equation (20); the network integrity constraints are shown in equation (21); the short-circuit current constraints are shown in equation (22); and the branch switching times constraints are shown in equation (23):

[0129]

[0130]

[0131]

[0132]

[0133]

[0134] z ii,t ≥1 / I i,max (twenty two)

[0135]

[0136] In the formula, represents the voltage phase angle of the tth period under scenario s; X ij is the impedance value of branch ij; is the branch active power flowing through branch ij in the tth period under scenario s; M represents a larger number; the set of branches connected to node i is represented by U(i); Z ij,t is the switching state of branch ij in period t. ij,t =1 means branch ij is in operation, Z ij,t =0 means that branch ij is in disconnected state; when Z ij,t = 1, equations (17) and (18) are standard DC power flow models: (θ i,t -θ j,t ) / X ij -P ij,t =0, and when Z ij,t = 0, equations (17) and (18) satisfy the constraints respectively; Z ik,t represents the disconnection state of line ik in time period t, where node k is in the set U(i); z ii,t is the self-impedance of node i in time period t; I i,max is the maximum short-circuit current of the node; N ts It is the maximum number of times a branch can be opened and closed in a working day. Each branch can be switched at most twice in a scheduling cycle.

[0137] The calculation of carbon emission costs described in formula (2) is specifically divided into the following four cases:

[0138] (1) When When the allocated carbon emission quota is less than the actual carbon emission, and the emission difference exceeds the upper limit of the emission quota that the system can obtain through CDM transactions at that moment, in addition to purchasing emission quotas through CDM transactions, it is also necessary to purchase the corresponding emission quotas of the excess part by paying penalties;

[0139] (2) When When the allocated carbon emission quota is less than the actual carbon emission, but the emission difference does not exceed the emission quota limit of the system CDM transaction at that moment, it is only necessary to purchase emission quotas through CDM transactions;

[0140] (3) When 0 <E s c,t -E D,t When the actual emission is greater than the allocated carbon emission quota, it means that the power system can make a profit by selling excess emission quotas. There are two situations for excess emission quotas. One is when the difference is less than the upper limit of the emission quota that the system can obtain through CDM mechanism transactions at that moment, that is, When E s c,t -E D,t Profit is calculated by multiplying the transaction price.

[0141] (4) Another situation is when the difference is greater than the upper limit of the emission quota that the system can obtain through CDM trading at that moment, that is, When only through Profit is the product of the CDM transaction price.

[0142] The costs of each part in formula (3) in step 2 are specifically as follows:

[0143] (1) Fuel costs

[0144] The fuel cost of unit operation is expressed as a quadratic function of unit output:

[0145]

[0146] Where: is the coal consumption function of unit i; where a i , b i 、c i is the fuel cost coefficient of unit i, in units of t / (MW 2 h), t / (MW h) and t / h; is the active power output by unit i at time period t under scenario s.

[0147] (2) Start-Stop Cost

[0148] The startup cost function of the unit is expressed as a step function of the unit downtime:

[0149]

[0150]

[0151] In the formula, S i,t is the startup cost of generator set i in the tth period; is the hot start cost of generator set i; Cold start cost of generator set i; is the continuous downtime of generator set i before the tth period; is the transition time between hot start and cold start of generator set i; is the minimum permissible downtime of generator set i; is the cold start time of generator set i.

[0152] The linearization calculation is expressed as:

[0153]

[0154]

[0155]

[0156] When the time t = n, it represents the initial start-stop state of the unit; when t < n, it represents the state during the n - t period of the previous cycle.

[0157] During the operation of the unit, the shutdown cost of the generator set is a constant value, and its magnitude can be ignored.

[0158] (3) Charging cost function of electric vehicles

[0159]

[0160] In the formula, is the total charging cost of electric vehicles at time t in scenario s; e c,t and e d,t are the charging and discharging prices of electric vehicles at time t respectively; are the total quantities of charging and discharging of electric vehicles at time t in scenario s respectively; P c and P d are the average charging power and discharging power of electric vehicles respectively; Δt is the duration of time t.

[0161] (4) Penalty cost for curtailed wind

[0162]

[0163] (5) Pollutant emission cost

[0164] The PM2.5 emission and CO2 emission of the unit are shown in formulas (32) and (33):

[0165]

[0166]

[0167] In the formula, is the PM2.5 emission of unit i at the t-th time period in scenario s; Aar is the average ash content of received coal (%), taking 20; ω is the conversion coefficient from coal ash to PM2.5 (%), taking 5.1; η is the removal efficiency of PM2.5 by taking measures (%), taking 99; the PM2.5 emission is related to the coal consumption of unit i, where, α i , β i , γ i are the carbon emission coefficients of unit i determined by the carbon emission efficiency of the unit.

[0168]

[0169]

[0170] In the formula, C PM,i is the emission cost of PM2.5; Ce is the emission penalty factor of PM2.5; is the CO2 emission cost in period t under scenario s; f is the actual CO2 emission of the system and quota E D,t function.

[0171] (6) Penalty function

[0172] The node impedance matrix is ​​used to represent the node current, and the open circuit current constraint is converted into a penalty function and attached to the objective function.

[0173] λ z ∑ i∈τ (Z imin -Z ii ) (36)

[0174] Where λz is the penalty factor. If Z ii ≥Z imin , it means that node i satisfies the short-circuit current constraint, then λ z =0, otherwise, if Z ii ≤Z imin , then λ z Take an infinite number and assume that λ z =10 10 ; Z imin is the minimum node self-impedance under the short-circuit current constraint; Z ii is the node self-impedance of node i; τ is the set of nodes where the short-circuit current exceeds the limit.

[0175] The proposed method is tested using the IEEE 39-node system, and it is assumed that the short-circuit current of each node is limited to less than 65 pu. The wiring diagram of the IEEE 39-node system is shown in Figure 2 As shown. Schemes 1 and 2 are set, where Scheme 1 is the traditional unit combination model without considering the CDM mechanism, and Scheme 2 is the proposed unit combination model. The data such as carbon emissions and emission costs can be obtained as shown in Table 1.

[0176] Table 1

[0177]

[0178]

[0179] It can be seen that the carbon emission costs and emissions of the proposed model are much lower than those of the traditional model.

[0180] Based on this, the unit combination and branch switching coordinated optimization method considering short-circuit current constraints proposed in this patent is used for further analysis, and the unit output after coordinated optimization can be obtained. The analysis shows that the system operation after coordinated optimization is not only more stable, but also the output between each energy source and each unit is more reasonable and coordinated. The unit operating cost obtained by the unit combination and branch switching coordinated optimization method considering short-circuit current constraints is compared with the unit operating cost considering only the CDM mechanism, and the results are shown in Table 2.

[0181] Table 2

[0182]

[0183] It can be seen from this that the collaborative optimization method proposed in this patent can not only effectively solve the problem of circuit-breaking current exceeding the limit in the system, but also has better economy based on considering the coordinated output of the units, so that the units can achieve low-carbon economic operation and provide a more suitable operation plan for the scheduling of the units.

Claims

1. A coordinated optimization method for unit combination and branch switching taking into account short-circuit current constraints, characterized in that: It includes the following steps: Step 1: Construct a carbon emission model based on the Clean Development Mechanism (CDM); Step 2: Establish a unit commitment objective function based on short-circuit current constraints; Step 3: Complete the optimization by adopting the constraints of short-circuit current and the CDM trading mechanism; Specifically, Step 1 is as follows: Firstly, it is necessary to predict the CO2 emission quota of the power system, and initial carbon emission quotas are allocated to each unit in the system using a benchmark value: The D,t =ξP LD,t (1) In the formula, E D,t The initial emission quota allocated to each entity in the system is calculated using the benchmark value D during period t; ξ is the carbon emission quota per unit of electricity, which is determined by the electricity marginal factor and the capacity marginal emission factor; P LD,t is the load of the system in the tth period; Based on the CDM mechanism, the carbon emission cost of the system at time t is: In the formula, K CDM , K P They are the unit carbon emission trading price and the penalty for excess emission under the CDM mechanism; is the upper limit of the emission quota traded by the system through the CDM mechanism in the tth period under scenario s, allocated according to the load proportion in the operation cycle; is the carbon emissions calculated using actual values ​​in the tth period under scenario s; Specifically, Step 2 is as follows: Based on the goal of minimizing the expected cost in multiple scenarios and considering short-circuit current constraints, the operating states of the units are selected, and the total objective function is expressed as: S i,t is the startup cost of generator set i in the tth period; u i,t is the start and stop status of generator set i in the tth period; is the active power output of unit i at time period t under scenario s; is the coal consumption function of unit i; C PM,i is the emission cost of PM2.5; is the CO2 emission cost in period t under scenario s; is the total charging cost of the electric vehicle at time period t in scenario s; Penalty costs for wind curtailment; λz is the penalty factor; Z imin To meet the minimum node self-impedance under the short-circuit current constraint; Z ii is the node self-impedance of node i.

2. The method for coordinated optimization of unit combination and branch switching taking into account short-circuit current constraints according to claim 1 is characterized in that: The specific costs of each part in Equation (3) in Step 2 are as follows: (1) Fuel cost The fuel cost of unit operation is expressed as a quadratic function of unit output: Where: is the coal consumption function of unit i; where a i 、b i 、c i is the fuel cost coefficient of unit i, in units of t / (MW 2 h), t / (MW h) and t / h; is the active power output of unit i at time period t under scenario s; (2) Start-up and shutdown cost The start-up cost function of the unit is expressed as a step function of the unit's outage time: In the formula, S i,t is the startup cost of generator set i in the tth period; is the hot start cost of generator set i; Cold start cost of generator set i; is the continuous downtime of generator set i before the tth period; is the transition time between hot start and cold start of generator set i; is the minimum permissible downtime of generator set i; is the cold start time of generator set i; The linearized calculation is expressed as: When time t = n, it represents the initial start-up and shutdown state of the unit; when t < n, it represents the state of the n - t period in the previous cycle; During unit operation, the shutdown cost of the generator set is a constant value, and its magnitude can be ignored; (3) Charging cost function of electric vehicles In the formula, is the total charging cost of the electric vehicle at time t in scenario s; e c,t and e d,t are the charging and discharging prices of electric vehicles in time period t respectively; are the total number of electric vehicles charged and discharged in time period t under scenario s; P c and P d are the average charging power and discharging power of electric vehicles respectively; Δt is the duration of time period t; (4) Penalty cost for wind curtailment The amount of wind power abandoned by the wind farm; (5) Pollutant emission cost The PM2.5 emissions and CO2 emissions of the unit are shown in Equations (32) and (33): In the formula, is the PM2.5 emission of unit i in the tth time period under scenario s; Aar is the average coal received ash content (%), which is taken as 20; ω is the conversion coefficient of coal ash to PM2.5 (%), which is taken as 5.1; η is the PM2.5 removal efficiency of measures taken (%), which is taken as 99; PM2.5 emissions and coal consumption of unit i, where, α i , β i , γ i The carbon emission coefficient of unit i is determined by the carbon emission efficiency of the unit; In the formula, C PM,i is the emission cost of PM2.5; C e is the emission penalty factor of PM2.5; is the CO2 emission cost in period t under scenario s; f is the actual CO2 emission of the system and quota E D,t Function of (6) Penalty function The magnitude of the node current is represented in the form of a node impedance matrix, and by transforming the short-circuit current constraint into the form of a penalty function and attaching it to the objective function; λ z ∑ i∈τ (WITH imin -WITH ii ) (36) Where λz is the penalty factor. If Z ii ≥Z imin , it means that node i satisfies the short-circuit current constraint, then λ z =0, otherwise, if Z ii ≤Z imin , then λ z Take an infinite number and assume that λ z =10 10 ; Z imin is the minimum node self-impedance under the short-circuit current constraint; Z ii is the node self-impedance of node i; τ is the set of nodes where the short-circuit current exceeds the limit.

3. The method for coordinated optimization of unit combination and branch switching taking into account short-circuit current constraints according to claim 2 is characterized in that: Specifically, Step 3 is as follows: For the calculation of short-circuit current exceeding the limit, the magnitude of the current at each node is calculated through the self-impedance of the node, the over-limited nodes are screened out by setting limits, and the branch outage plan is calculated to reduce the current of the over-limited nodes below the limit value; The constraints adopted by the CDM trading mechanism include: the power balance constraint of the system as shown in Equation (4); the spinning reserve constraint as shown in Equation (5); the output constraints of each unit at different times as shown in Equation (6); the ramping constraint as shown in Equation (7); the minimum start-up and shutdown time constraints of the unit as shown in Equations (8) - (9); the charge and discharge quantity constraints of electric vehicles as shown in Equations (10) - (11); the charge and discharge time constraints of electric vehicles as shown in Equations (12) - (13); the wind curtailment volume constraint of the wind farm as shown in Equation (14); the carbon emission reduction target constraints for each period as shown in Equation (15); the CDM emission reduction trading volume constraint as shown in Equation (16); Where P D,t is the total basic load of the system at the tth moment; R t is the system spinning reserve demand at the tth moment; are the upper and lower limits of the active output of generator set i; DR i , UR i They are the minimum downward adjustment and maximum upward adjustment of active power of generator set i at a moment, respectively; are the time that generator set i is in operation and shutdown state before the tth moment respectively; are the minimum allowable running time and minimum allowable downtime of generator set i respectively; are the maximum number of electric vehicles that can be charged and discharged in the tth period, respectively; is the total number of all rechargeable electric vehicles in a scheduling cycle; is the total number of all dischargeable electric vehicles in the dispatch cycle; Δt c is the average charging time, Δt d is the average discharge time; is the predicted wind power output power of wind farm w in time period t in scenario s; The amount of wind power abandoned by the wind farm; The DC power flow constraints of the network are as shown in Equations (17) - (18); the node power balance constraint of the system is as shown in Equation (19); the active power constraints of the lines in the system are as shown in Equation (20); the network integrity constraint is as shown in Equation (21); the short-circuit current constraint is as shown in Equation (22); the branch switching times constraint is as shown in Equation (23): In the formula, represents the voltage phase angle of the tth period under scenario s; X ij is the impedance value of branch ij; is the branch active power flowing through branch ij in the tth period under scenario s; M represents a larger number; the set of branches connected to node i is represented by U(i); Z ij,t is the switching state of branch ij in period t. ij,t =1 means branch ij is in operation, Z ij,t =0 means that branch ij is in disconnected state; when Z ij,t = 1, equations (17) and (18) are standard DC power flow models: (θ i,t -θ j,t ) / X ij -P ij,t =0, and when Z ij,t = 0, equations (17) and (18) satisfy the constraints respectively; Z ik,t represents the disconnection state of line ik in time period t, where node k is in the set U(i); z ii,t is the self-impedance of node i in time period t; I i,max is the maximum short-circuit current of the node; N ts The maximum number of times a branch can be opened and closed in a working day. Each branch can be switched at most twice during the scheduling cycle.

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