Park network reconstruction method considering conditional value-at-risk

Abstract

The invention discloses a park network reconstruction method considering conditional value-at-risk. The method comprises the following steps: 1) constructing a park network reconstruction model considering distributed power supply and load uncertainty; 2) constructing a park conditional value-at-risk model based on a risk measurement theory, and adding the conditional value-at-risk model into the park network reconstruction model; 3) constructing a park network reconstruction model repair strategy constraint considering the conditional value-at-risk; and 4) solving the park network fault reconstruction optimization model to obtain an optimal reconstruction scheme of the park network. According to the method, the park network reconstruction optimization model considering the conditional value-at-risk is constructed, the park network optimal reconstruction scheme is obtained, and the safety and reliability of park system operation are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system network reconstruction, and in particular relates to a park network reconstruction method considering conditional risk value, which can be used to improve the safety and reliability of park system operation. Background Art

[0002] With the rapid development of society, the power load in the park is showing a rapid growth trend, and the requirements for power supply reliability and power quality are becoming higher and higher. Traditional deterministic network reconstruction methods are often optimized based on predicted loads, which is difficult to deal with the risks brought by load fluctuations and uncertainty in the output of distributed power sources, and it is difficult to meet the actual needs of the park's power supply.

[0003] Conditional Value-at-Risk (CVaR) is a commonly used risk measurement indicator that can reflect the expected value of potential losses exceeding a certain confidence level. Compared with traditional risk assessment methods, it can more comprehensively characterize risks. Introducing CVaR into the campus network reconstruction model can quantify the risks brought by the uncertainty of distributed power sources, effectively reduce the operating risks of the system under extreme conditions, and improve the reliability of campus power supply. Summary of the invention

[0004] Technical solution, in order to solve the technical problems mentioned in the above background technology, the present invention proposes a campus network reconstruction method considering conditional risk value, the method comprising the following steps:

[0005] Step 1: Construct a campus network reconstruction model that considers the uncertainty of distributed power sources and loads;

[0006] Step 2: Construct a park conditional risk value model based on risk measurement theory, and add the conditional risk value model to the park network reconstruction model;

[0007] Step 3: Construct a repair strategy constraint for the campus network reconstruction model considering the conditional risk value;

[0008] Step 4: Solve the campus network fault reconstruction optimization model to obtain the optimal reconstruction plan for the campus network.

[0009] In step 1, a campus network reconstruction model considering the uncertainty of distributed power sources is constructed. The specific process is as follows:

[0010] Step 1.1 takes the minimization of the sum of the park operation cost, the park network loss cost, and the switch action loss cost in the park as the goal, and constructs the objective function of the park network reconstruction model considering the uncertainty of distributed power supply and load:

[0011]

[0012] In the formula, i and j represent the nodes where the park is located; ij represents the line where the park is located; k represents the scene; t represents the time period; F represents the total operating cost of the park; represents the park operation cost; Indicates the campus network loss cost; represents the switching loss cost in the park; α ij,k represents the connectivity status of line ij in scenario k, α ij,k = 0 means that the switch of line ij is turned on in scenario k, α ij,k =1 means the switch of line ij is closed in scenario k; α ij,k,t Indicates the connectivity status of line ij in scenario k during time period t;

[0013] Step 1.2 constructs the constraints of the campus network reconstruction model considering the uncertainty of distributed power sources and loads, including the campus network radial structure constraints, power flow constraints, line current constraints and node voltage constraints.

[0014] In step 1.1,

[0015] (a) Park operation costs

[0016]

[0017] In the formula, s represents the load importance level; K represents the total number of scenarios; T represents the set of fault periods; D represents the set of nodes in the park island; L represents the set of load nodes in the park; represents the dispatching cost of the photovoltaic power station at node i in scenario k at time t; δ PV Represents the unit operating cost of photovoltaics; represents the predicted active power output of the photovoltaic power station at node i in scenario k at time t; represents the amount of photovoltaic abandonment at node i at time t; ζ PV Indicates the unit penalty cost of abandoning light; represents the dispatching cost of wind turbines at node i in scenario k at time t; δ WT It represents the unit operating cost of wind power; represents the predicted active power output of the wind turbine at node i in scenario k at time t; represents the amount of wind power abandoned at node i at time t; WT represents the unit penalty cost of wind curtailment; represents the load shedding cost of scenario k at node i at time t; It represents the unit cost of shedding load of level s; represents the load shedding of level s load at node i at time t; ε k represents the probability of scene k appearing;

[0018] In step 1.1,

[0019] (b) Campus network loss costs

[0020]

[0021] Where n represents the total number of nodes in the park; N(i) represents the set of nodes in the park; g ij and b ij Respectively represent the conductance and susceptance of line ij; θ ij,k represents the voltage phase angle difference between node i and node j in scenario k; V i,k and V j,k They represent the voltages at nodes i and j in scenario k respectively;

[0022] In step 1.1,

[0023] (c) Switching cost in the park

[0024]

[0025] In the formula, δ α Indicates the actual cost of each switching action converted into loss, T f represents the set of fault periods, I is the set of campus nodes, α ij,k,t-1 Indicates the connectivity status of line ij in scenario k during time period t-1;

[0026] In step 1.2,

[0027] (a) Constraints of the radial structure of the campus network

[0028] β ij,k +β ji,k =α l,k (8)

[0029]

[0030] β hj =0 j∈N(h) (10)

[0031] β ij,k ∈{0,1}(11)

[0032] 0≤α l,k ≤1 (12)

[0033]

[0034] In the formula, l represents the line where the park is located; h represents the upper-level node connected; N(h) is the set of park nodes;

[0035] β ij,krepresents the hierarchical relationship between node i and node j in scene k, β ij,k =1 means that in scenario k, node j is the parent node of node i, and node i is the child node of node j. Conversely, β ij,k =0 means that in scenario k, node j is not the parent node of node i, and node i is not the child node of node j; β ji,k Represents the hierarchical relationship between node j and node i in scene k; α l,k represents the switch state of line l in scenario k; α l,k =0 and α l,k =1 indicates that the switch of line l is in the open and closed states in scenario k, and all its nodes are connected to node h; n is the total number of lines in the park; m is the total number of switches in the park including tie switches;

[0036] In step 1.2,

[0037] (b) Power flow constraints

[0038]

[0039] Where P i,k and Q i,k Respectively represent the active power and reactive power at node i in scenario k; P DGi,k and Q DGi,k They represent the active and reactive components of the distributed generation at node i in scenario k respectively; P Di,k and Q Di,k They represent the active power and reactive power of the load at node i in scenario k respectively;

[0040] In step 1.2,

[0041] (c) Line current constraints

[0042] I l,k |≤I l,kmax (16)

[0043] In the formula, I l,k represents the current of branch l in scenario k; I lmax represents the maximum allowable current of branch l under scenario k;

[0044] In step 1.2,

[0045] (d) Node voltage constraints

[0046] V i,kmin ≤V i,k ≤V i,kmax (17)

[0047] Where V i,kmin and V i,kmaxThey represent the minimum and maximum allowed voltage of node i in scenario k respectively.

[0048] In step 2, a park conditional risk value model based on risk measurement theory is constructed, and the conditional risk value model is added to the park network reconstruction model. The specific process is as follows:

[0049] Step 2.1 Construct a park conditional risk value model based on risk measurement theory, including the park risk value model and the park conditional risk value model:

[0050] Step 2.2: Add the conditional risk value model to the campus network reconstruction model to construct a campus network reconstruction model that considers the conditional risk value.

[0051]

[0052] z i,k ≥0 (25)

[0053]

[0054] In the formula, δ i represents the conditional risk value at node i; φ i represents the risk value at node i; z i,k represents the value at which the cost exceeds the risk value at node i in scenario k; C all represents the total cost of fault reconstruction based on the conditional risk value, W represents the risk preference coefficient, and its value range is W∈[0,1]. The closer W is to 1, the more it avoids taking too much risk, and the closer W is to 0, the more it is willing to take more risk.

[0055] In step 2.1,

[0056] (a) Park risk value model

[0057]

[0058] VaR ξ =min{λ∈R:ψ(x,λ)≥ξ} (19)

[0059] Where x and y represent decision variables and random variables respectively; f(x, y) represents the loss function; λ represents the boundary value; ψ(x, λ) represents the distribution function; ε(y) represents the probability density function; ξ represents the confidence level; VaR ξ represents the risk value at the confidence level ξ.

[0060] In step 2.1,

[0061] (b) Park Conditional Value-at-Risk Model

[0062]

[0063]

[0064] In the formula, CVaR ξ represents the conditional risk value under the confidence level ξ; at this time, λ represents the risk value; F ξ (x,λ) and represents the transformation function of the conditional risk value under the confidence level ξ; q represents the number of samples;

[0065] In step 3, a repair strategy constraint of the campus network reconstruction model considering the conditional risk value is constructed, which is expressed as follows:

[0066]

[0067] Where, z represents the number of maintenance teams; L f represents the set of faulty lines; Z represents the set of maintenance teams; and Represents the switch state variables in the set of faulty line switches of line l at time t and time t+1.

[0068] In step 1, the specific process of solving the campus network fault reconstruction optimization model and obtaining the optimal campus network reconstruction solution is as follows:

[0069] Solve the campus network reconstruction optimization model formulas (1)-(30) that consider the conditional risk value and add the repair strategy constraints, and determine whether the constraints formulas (8)-(17) and formulas (28)-(30) are satisfied. If satisfied, stop the network reconstruction and output the optimal reconstruction plan for the campus network. If not satisfied, gradually cut the load from low to high according to the load importance, and continue the network reconstruction process until the optimal reconstruction plan for the campus network is obtained.

[0070] The present invention provides an electronic device, comprising one or more processors for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors execute the aforementioned campus network reconstruction method considering conditional risk value.

[0071] The present invention provides a storage medium in which a computer program is stored, wherein the computer program is configured to execute a campus network reconstruction method taking into account conditional risk value when running.

[0072] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0073] The present invention constructs a park network reconstruction optimization model that takes conditional risk value into consideration, reduces the risks brought by the uncertainty of distributed power sources, obtains the optimal reconstruction plan for the park network, improves the safety and reliability of park system operation, broadens the park network reconstruction method, and improves the clean energy consumption rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 is a flow chart of the method of the present invention;

[0075] Figure 2 Schematic diagram of the IEEE 33-node power grid test system in the embodiment. DETAILED DESCRIPTION

[0076] The following is combined with Figure 1-2 And specific embodiments are provided to further illustrate the present invention. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0077] The present invention designs a campus network reconstruction method considering conditional risk value, such as Figure 1 As shown, the method comprises the following steps:

[0078] Step 1: Construct a campus network reconstruction model that considers the uncertainty of distributed power sources and loads;

[0079] Step 2: Construct a park conditional risk value model based on risk measurement theory, and add the conditional risk value model to the park network reconstruction model;

[0080] Step 3: Construct a repair strategy constraint for the campus network reconstruction model considering the conditional risk value;

[0081] Step 4: Solve the campus network fault reconstruction optimization model to obtain the optimal reconstruction plan for the campus network.

[0082] Furthermore, the specific process of constructing the campus network reconstruction model considering the uncertainty of distributed power sources in step 1 is as follows:

[0083] Step 1.1 takes the minimization of the sum of the park operation cost, the park network loss cost, and the switch action loss cost in the park as the goal, and constructs the objective function of the park network reconstruction model considering the uncertainty of distributed power supply and load:

[0084]

[0085] In the formula, i and j represent the nodes where the park is located; ij represents the line where the park is located; k represents the scene; t represents the time period; F represents the total operating cost of the park; represents the park operation cost; Indicates the campus network loss cost; represents the switching loss cost in the park; α ij,k represents the connectivity status of line ij in scenario k, α ij,k = 0 means that the switch of line ij is turned on in scenario k, α ij,k =1 means the switch of line ij is closed in scenario k; α ij,k,t Indicates the connectivity status of line ij in scenario k during time period t;

[0086] (a) Park operation costs

[0087]

[0088] In the formula, s represents the load importance level; K represents the total number of scenarios; T represents the set of fault periods; D represents the set of nodes in the park island; L represents the set of load nodes in the park; represents the dispatching cost of the photovoltaic power station at node i in scenario k at time t; δ PV Represents the unit operating cost of photovoltaics; represents the predicted active power output of the photovoltaic power station at node i in scenario k at time t; represents the amount of photovoltaic abandonment at node i at time t; ζ PV Indicates the unit penalty cost of abandoning light; represents the dispatching cost of wind turbines at node i in scenario k at time t; δ WT It represents the unit operating cost of wind power; represents the predicted active power output of the wind turbine at node i in scenario k at time t; represents the amount of wind power abandoned at node i at time t; WT represents the unit penalty cost of wind curtailment; represents the load shedding cost of scenario k at node i at time t; It represents the unit cost of shedding load of level s; represents the load shedding of level s load at node i at time t; ε k represents the probability of scene k appearing;

[0089] (b) Campus network loss costs

[0090]

[0091] Where n represents the total number of nodes in the park; N(i) represents the set of nodes in the park; g ij and b ij Respectively represent the conductance and susceptance of line ij; θ ij,k represents the voltage phase angle difference between node i and node j in scenario k; Vi,k and V j,k They represent the voltages at nodes i and j in scenario k respectively;

[0092] (c) Switching cost in the park

[0093]

[0094] In the formula, δ α Indicates the actual cost of each switching action converted into loss, T f represents the set of fault periods, I is the set of campus nodes, α ij,k,t-1 Indicates the connectivity status of line ij in scenario k during time period t-1;

[0095] Step 1.2 constructs the constraints of the campus network reconstruction model considering the uncertainty of distributed power sources and loads, including the campus network radial structure constraints, power flow constraints, line current constraints, and node voltage constraints:

[0096] (a) Constraints of the radial structure of the campus network

[0097] β ij,k +β ji,k =α l,k (8)

[0098]

[0099] β hj =0 j∈N(h) (10)

[0100] β ij,k ∈{0,1}(11)

[0101] 0≤α l,k ≤1 (12)

[0102]

[0103] In the formula, l represents the line where the park is located; h represents the upper-level node connected; N(h) is the set of park nodes;

[0104] β ij,k represents the hierarchical relationship between node i and node j in scene k, β ij,k =1 means that in scenario k, node j is the parent node of node i, and node i is the child node of node j. Conversely, β ij,k =0 means that in scenario k, node j is not the parent node of node i, and node i is not the child node of node j; β ji,k Represents the hierarchical relationship between node j and node i in scene k; α l,k represents the switch state of line l in scenario k; α l,k =0 and αl,k =1 indicates that the switch of line l is in the open and closed states in scenario k, and all its nodes are connected to node h; n is the total number of lines in the park; m is the total number of switches in the park including tie switches;

[0105] (b) Power flow constraints

[0106]

[0107] Where P i,k and Q i,k Respectively represent the active power and reactive power at node i in scenario k; P DGi,k and Q DGi,k They represent the active and reactive components of the distributed generation at node i in scenario k respectively; P Di,k and Q Di,k They represent the active power and reactive power of the load at node i in scenario k respectively;

[0108] (c) Line current constraints

[0109] |I l,k |≤I l,kmax (16)

[0110] In the formula, I l,k represents the current of branch l in scenario k; I lmax represents the maximum allowable current of branch l under scenario k;

[0111] (d) Node voltage constraints

[0112] V i,kmin ≤V i,k ≤V i,kmax (17)

[0113] Where V i,kmin and V i,kmax They represent the minimum and maximum allowed voltage of node i in scenario k respectively.

[0114] Furthermore, in step 2, a park conditional risk value model based on risk measurement theory is constructed, and the conditional risk value model is added to the park network reconstruction model. The specific process is as follows:

[0115] Step 2.1 Construct a park conditional risk value model based on risk measurement theory, including the park risk value model and the park conditional risk value model:

[0116] (a) Park risk value model

[0117]

[0118] VaR ξ=min{λ∈R:ψ(x,λ)≥ξ} (19)

[0119] Where x and y represent decision variables and random variables respectively; f(x, y) represents the loss function; λ represents the boundary value; ψ(x, λ) represents the distribution function; ε(y) represents the probability density function; ξ represents the confidence level; VaR ξ represents the risk value at the confidence level ξ.

[0120] (b) Park Conditional Value-at-Risk Model

[0121]

[0122] In the formula, CVaR ξ represents the conditional risk value under the confidence level ξ; at this time, λ represents the risk value; F ξ (x,λ)

[0123] and represents the transformation function of the conditional risk value under the confidence level ξ; q represents the number of samples;

[0124] Step 2.2: Add the conditional risk value model to the campus network reconstruction model to construct a campus network reconstruction model that considers the conditional risk value.

[0125]

[0126] z i,k ≥0 (25)

[0127]

[0128] In the formula, δ i represents the conditional risk value at node i; φ i represents the risk value at node i; z i,k represents the value at which the cost exceeds the risk value at node i in scenario k; C all represents the total cost of fault reconstruction based on the conditional risk value, W represents the risk preference coefficient, and its value range is W∈[0,1]. The closer W is to 1, the more it avoids taking too much risk, and the closer W is to 0, the more it is willing to take more risk.

[0129] Furthermore, in step 3, a repair strategy constraint of the campus network reconstruction model considering the conditional risk value is constructed, which is expressed as follows:

[0130]

[0131] Where, z represents the number of maintenance teams; L f represents the set of faulty lines; Z represents the set of maintenance teams; and Represents the switch state variables in the set of faulty line switches of line l at time t and time t+1.

[0132] Furthermore, the specific process of solving the campus network fault reconstruction optimization model in step 4 and obtaining the optimal reconstruction solution of the campus network is as follows:

[0133] Solve the campus network reconstruction optimization model formulas (1)-(30) that consider the conditional risk value and add the repair strategy constraints, and determine whether the constraints formulas (8)-(17) and formulas (28)-(30) are satisfied. If satisfied, stop the network reconstruction and output the optimal reconstruction plan for the campus network. If not satisfied, gradually cut the load from low to high according to the load importance, and continue the network reconstruction process until the optimal reconstruction plan for the campus network is obtained.

[0134] The present invention provides an electronic device, comprising one or more processors for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors execute the aforementioned campus network reconstruction method considering conditional risk value.

[0135] The present invention provides a storage medium in which a computer program is stored, wherein the computer program is configured to execute a campus network reconstruction method taking into account conditional risk value when running.

[0136] The IEEE33 improved node power grid system is used as an example, and its schematic diagram is as follows Figure 2 As shown. The system base voltage is 12.66kV, and the model includes 32 normally closed switches, 5 tie switches, and a total of 37 branches. This chapter considers adding distributed power sources such as photovoltaics, wind power, and gas turbines to the improved IEEE33-node system, installing photovoltaic systems at nodes 7, 13, and 27, installing wind power systems at nodes 10, 16, 17, 30, and 33, and installing gas turbines at nodes 11, 21, and 33. GAMS software is used to solve the model using the CPLEX solver.

[0137] In order to illustrate the advantages of the campus network reconstruction model considering conditional risk value proposed by the present invention in obtaining the optimal reconstruction plan of the campus network, the traditional campus network reconstruction model without considering conditional risk value is compared. The results are shown in Table 1. Since the traditional model does not consider the conditional risk value, the output of the distributed power source is adjusted to a lower level when reconstructing the fault network, thus generating a higher penalty cost for wind and solar power abandonment, resulting in a higher total cost, while the cost of the campus network reconstruction model considering conditional risk value will be reduced. In general, compared with the campus network reconstruction without considering conditional risk value, the total operating cost of the campus network reconstruction considering conditional risk value is reduced by 16.55%, that is, considering conditional risk value can reduce the cost of campus network reconstruction.

[0138] Table 1 Comparison of network reconstruction schemes with and without considering conditional risk value

[0139]

[0140] The embodiments are only for illustrating the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A campus network reconstruction method considering conditional risk value, characterized in that: The method comprises the following steps: Step 1: Construct a campus network reconstruction model that considers distributed power sources and load uncertainties; Step 2: Construct a park conditional risk value model based on risk measurement theory, and add the conditional risk value model to the park network reconstruction model; Step 3: Construct a repair strategy constraint for the campus network reconstruction model considering the conditional risk value; Step 4: Solve the campus network fault reconstruction optimization model to obtain the optimal reconstruction plan for the campus network.

2. A campus network reconstruction method considering conditional risk value according to claim 1, characterized in that: In step 1, a campus network reconstruction model considering the uncertainty of distributed power sources is constructed. The specific process is as follows: Step 1.1: Taking the sum of the park operation cost, the park network loss cost, and the switch action loss cost in the park as the minimum, the objective function of the park network reconstruction model considering the uncertainty of distributed power supply and load is constructed: In the formula, i and j represent the nodes where the park is located; ij represents the line where the park is located; k represents the scene; t represents the time period; F represents the total operating cost of the park; represents the park operation cost; Indicates the campus network loss cost; represents the switching loss cost in the park; α ij,k represents the connectivity status of line ij in scenario k, α ij,k = 0 means that the switch of line ij is turned on in scenario k, α ij,k =1 means the switch of line ij is closed in scenario k; α ij,k,t Indicates the connectivity status of line ij in scenario k during time period t; Step 1.2 constructs the constraints of the campus network reconstruction model considering the uncertainty of distributed power sources and loads, including the campus network radial structure constraints, power flow constraints, line current constraints and node voltage constraints.

3. A campus network reconstruction method considering conditional risk value according to claim 2, characterized in that: In step 1.1, (a) Park operation costs In the formula, s represents the load importance level; K represents the total number of scenarios; T represents the set of fault periods; D represents the set of nodes in the park island; L represents the set of load nodes in the park; represents the dispatching cost of the photovoltaic power station at node i in scenario k at time t; δ PV Represents the unit operating cost of photovoltaics; represents the predicted active power output of the photovoltaic power station at node i in scenario k at time t; represents the amount of photovoltaic abandonment at node i at time t; ζ PV Indicates the unit penalty cost of abandoning light; represents the dispatching cost of wind turbines at node i in scenario k at time t; δ WT It represents the unit operating cost of wind power; represents the predicted active power output of the wind turbine at node i in scenario k at time t; represents the amount of wind power abandoned at node i at time t; WT represents the unit penalty cost of wind curtailment; represents the load shedding cost of scenario k at node i at time t; It represents the unit cost of shedding load of level s; represents the load shedding of level s load at node i at time t; ε k represents the probability of scene k appearing.

4. A campus network reconstruction method considering conditional risk value according to claim 2, characterized in that: In step 1.1, (b) Campus network loss costs Where n represents the total number of nodes in the park; N(i) represents the set of nodes in the park; g ij and b ij denote the conductance and susceptance of line ij respectively; θ ij,k represents the voltage phase difference between node i and node j in scenario k; V i,k and V j,k They represent the voltage at node i and node j in scenario k respectively.

5. A campus network reconstruction method considering conditional risk value according to claim 2, characterized in that: In step 1.1, (c) Switching cost in the park In the formula, δ α Indicates the actual cost of each switching action converted into loss, T f represents the set of fault periods, I is the set of campus nodes, α ij,k,t-1 Indicates the connectivity status of line ij in scenario k during time period t-1.

6. A campus network reconstruction method considering conditional risk value according to claim 2, characterized in that: In step 1.2, (a) Constraints of the radial structure of the campus network β ij,k +β ji,k ＝α l,k (8) b hj =0j∈N(h)(10) β ij,k ∈{0,1}(11)0≤α l,k ≤1(12) In the formula, l represents the line where the park is located; h represents the upper-level node connected; N(h) is the set of park nodes; β ij,k represents the hierarchical relationship between node i and node j in scene k, β ij,k =1 means that in scenario k, node j is the parent node of node i, and node i is the child node of node j. Conversely, β ij,k =0 means that in scenario k, node j is not the parent node of node i, and node i is not the child node of node j; β ji,k Represents the hierarchical relationship between node j and node i in scene k; α l,k represents the switch state of line l in scenario k; α l,k =0 and α l,k =1 indicates that the switch of line l in scenario k is in the open and closed states, respectively, and all its nodes are connected to node h; n is the total number of lines in the park; m is the total number of switches in the park including tie switches.

7. A campus network reconstruction method considering conditional risk value according to claim 2, characterized in that: In step 1.2, (b) Power flow constraints Where P i,k and Q i,k They represent the active power and reactive power at node i in scenario k respectively; P DGi,k and Q DGi,k They represent the active and reactive components of the distributed generation at node i in scenario k respectively; P Di,k and Q Di,k They represent the active power and reactive power of the load at node i in scenario k respectively.

8. A campus network reconstruction method considering conditional risk value according to claim 2, characterized in that: In step 1.2, (c) Line current constraints I l,k ≤I l,kmax (16) In the formula, I l,k represents the current of branch l in scenario k; I lmax Indicates the maximum allowable current of branch l under scenario k.

9. A campus network reconstruction method considering conditional risk value according to claim 2, characterized in that: In step 1.2, (d) Node voltage constraints In i,kmin ≤V i,k ≤V i,kmax (17) Where V i,kmin and V i,kmax They represent the minimum and maximum allowed voltage of node i under scenario k respectively.

10. A campus network reconstruction method considering conditional risk value according to claim 1, characterized in that: In step 2, a park conditional risk value model based on risk measurement theory is constructed, and the conditional risk value model is added to the park network reconstruction model. The specific process is as follows: Step 2.1 Construct a park conditional risk value model based on risk measurement theory, including the park risk value model and the park conditional risk value model: Step 2.2 adds the conditional risk value model to the campus network reconstruction model to construct a campus network reconstruction model that considers the conditional risk value.

11. A campus network reconstruction method considering conditional risk value according to claim 10, characterized in that: In step 2.1, (a) Park risk value model VaR ξ =min{λ∈R:ψ(x,λ)≥ξ}(19) Where x and y represent decision variables and random variables respectively; f(x, y) represents the loss function; λ represents the boundary value; ψ(x, λ) represents the distribution function; ε(y) represents the probability density function; ξ represents the confidence level; VaR ξ represents the risk value at the confidence level ξ.

12. A campus network reconstruction method considering conditional risk value according to claim 10, characterized in that: In step 2.1, (b) Park Conditional Value-at-Risk Model In the formula, CVaR ξ represents the conditional risk value under the confidence level ξ; at this time, λ represents the risk value; F ξ (x,λ) and represents the transformation function of the conditional risk value under the confidence level ξ; q represents the number of samples.

13. A campus network reconstruction method considering conditional risk value according to claim 10, characterized in that: In step 2.2, z i,k ≥0(25) In the formula, δ i represents the conditional risk value at node i; φ i represents the risk value at node i; z i,k represents the value at which the cost exceeds the risk value at node i in scenario k; C all represents the total cost of fault reconstruction based on the conditional risk value, W represents the risk preference coefficient, and its value range is W∈[0,1]. The closer W is to 1, the more it avoids taking too much risk, and the closer W is to 0, the more it is willing to take more risk.

14. A campus network reconstruction method considering conditional risk value according to claim 1, characterized in that: In step 3, a repair strategy constraint of the campus network reconstruction model considering the conditional risk value is constructed, which is expressed as follows: Where, z represents the number of maintenance teams; L f represents the set of faulty lines; Z represents the set of maintenance teams; and Represents the switch state variables in the set of faulty line switches of line l at time t and time t+1.

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