AC / DC hybrid power distribution network operation risk assessment method and system

Abstract

The invention discloses an AC / DC hybrid power distribution network operation risk assessment method, which comprises the steps of collecting renewable energy output data and load data in a power distribution network, inputting the data into a pre-constructed element performance model, and outputting a renewable energy output and load probability density function; according to the probability density function, adopting a Monte Carlo simulation method to simulate and generate an initial scene set; determining a target scene set from the initial scene set by using a kmeans clustering method; based on the target scene set, performing operation risk assessment under each scene to obtain a risk assessment result of the power distribution network; the method can provide a reference basis for risk prevention and control of the power distribution network, improves the capability of the power grid to deal with complex scenes, and improves the reliability and stability of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an AC / DC hybrid distribution network operation risk assessment method and system. Background Art

[0002] With the introduction of the "carbon peak and carbon neutrality" goals, the proportion of renewable energy such as wind power generation and photovoltaic power generation in the power system will continue to increase, resulting in changes in the steady-state operation characteristics of the distribution network. The uncertainty at the source and load ends of the power grid will increase significantly. Therefore, urban distribution networks face multiple challenges such as distributed renewable energy access, reliable and stable supply of electricity, diversified load demands, and changes in grid structure. Various faults and risks will also occur in the operation of the power grid. As an important part of the urban distribution network, the safe and stable operation of the AC / DC hybrid distribution network is crucial to the stability of the entire power system. Therefore, a method that can effectively assess the operating risks of AC / DC hybrid distribution networks is needed. Summary of the invention

[0003] The embodiment of the present invention provides an AC / DC hybrid distribution network operation risk assessment method, which can provide a reference basis for distribution network risk prevention and control, enhance the ability of the power grid to cope with complex scenarios, and thus improve the reliability and stability of the power grid.

[0004] In a first aspect, an embodiment of the present invention provides an AC / DC hybrid distribution network operation risk assessment method, comprising:

[0005] Collect renewable energy output and load data in the distribution network, input them into the pre-built component performance model, and output the probability density function of renewable energy output and load;

[0006] According to the probability density function, a Monte Carlo simulation method is used to simulate and generate an initial scene set;

[0007] Determine a target scene set from the initial scene set using a kmeans clustering method;

[0008] Based on the target scenario set, an operation risk assessment is performed in each scenario to obtain a risk assessment result of the distribution network.

[0009] Furthermore, the renewable energy output data and load data in the distribution network are collected and input into a pre-built component performance model, and the probability density function of the renewable energy output and load is output, including:

[0010] Obtaining component performance parameters according to historical operation data of each component in the distribution network, and constructing a component performance model based on the component performance parameters;

[0011] The renewable energy output data and load data are collected and input into the component performance model, and the probability density function of the renewable energy output and load is output.

[0012] Furthermore, the method of using the kmeans clustering method to determine the target scene set from the initial scene set includes:

[0013] Setting the target number of clusters, and selecting a number of scenes from the initial scene set as initial cluster center points; wherein the number of initial cluster center points is the target number of clusters;

[0014] Calculating the distance between each scene in the initial scene set and the initial center point of the cluster, and assigning each scene to a cluster corresponding to the initial center point of the cluster according to the distance, to obtain an initial clustering result;

[0015] Iterative processing is performed based on the initial clustering result until a preset convergence condition is met to obtain a clustered target scene set.

[0016] Furthermore, based on the target scenario set, the operation risk assessment is performed in each scenario to obtain the risk assessment result of the distribution network, including:

[0017] Construct a risk optimization operation model with the goal of minimizing the operation risk of the distribution network;

[0018] For each operation scenario in the target scenario set, the risk optimization operation model is used to simulate and obtain a simulation operation result;

[0019] Based on the simulation operation results, a risk assessment result of the distribution network is calculated.

[0020] Furthermore, the constraints of the risk optimization operation model include at least one of AC / DC distribution network flow constraints, load shedding constraints and component operation constraints; wherein the AC / DC distribution network flow constraints include AC network flow constraints and DC network flow constraints, and the component operation constraints include converter operation constraints, converter power balance constraints, energy storage system operation constraints and system safety operation constraints.

[0021] Furthermore, the risk assessment result of the distribution network is calculated based on the simulation operation result, including:

[0022] Acquire the simulation operation result; wherein the simulation operation result includes the load shedding sequence, voltage deviation value sequence and renewable energy abandonment sequence of each node in the distribution network;

[0023] Based on the load shedding sequence, the voltage deviation value sequence, the renewable energy power abandonment sequence and the scenario probability of each of the operating scenarios, a risk assessment result is calculated; wherein the risk assessment result includes load shedding risk, voltage deviation risk and renewable energy power abandonment risk.

[0024] In a second aspect, an embodiment of the present invention provides an AC / DC hybrid distribution network operation risk assessment system, comprising:

[0025] A data acquisition module is used to collect renewable energy output data and load data in the distribution network, and input them into a pre-built component performance model to output the probability density function of renewable energy output and load;

[0026] A scene generation module, used to simulate and generate an initial scene set according to the probability density function using a Monte Carlo simulation method;

[0027] A scene selection module, used for determining a target scene set from the initial scene set using a kmeans clustering method;

[0028] The risk assessment module is used to perform an operation risk assessment in each scenario based on the target scenario set to obtain a risk assessment result of the distribution network.

[0029] Furthermore, the data acquisition module is also used for:

[0030] Obtaining component performance parameters according to historical operation data of each component in the distribution network, and constructing a component performance model based on the component performance parameters;

[0031] The renewable energy output data and load data are collected and input into the component performance model, and the probability density function of the renewable energy output and load is output.

[0032] Furthermore, the scene selection module is also used for:

[0033] Setting the target number of clusters, and selecting a number of scenes from the initial scene set as initial cluster center points; wherein the number of initial cluster center points is the target number of clusters;

[0034] Calculating the distance between each scene in the initial scene set and the initial center point of the cluster, and assigning each scene to a cluster corresponding to the initial center point of the cluster according to the distance, to obtain an initial clustering result;

[0035] Iterative processing is performed based on the initial clustering result until a preset convergence condition is met to obtain a clustered target scene set.

[0036] Furthermore, the risk assessment module is also used to:

[0037] Construct a risk optimization operation model with the goal of minimizing the operation risk of the distribution network;

[0038] For each operation scenario in the target scenario set, the risk optimization operation model is used to simulate and obtain a simulation operation result;

[0039] Based on the simulation operation results, a risk assessment result of the distribution network is calculated.

[0040] Compared with the prior art, an AC / DC hybrid distribution network operation risk assessment method provided by an embodiment of the present invention has the following beneficial effects: by collecting renewable energy output data and load data in the distribution network and inputting them into a pre-built component performance model, a probability density function of renewable energy output and load is output; according to the probability density function, a Monte Carlo simulation method is used to simulate and generate an initial scenario set; a kmeans clustering method is used to determine a target scenario set from the initial scenario set; based on the target scenario set, an operation risk assessment is performed under each scenario to obtain a risk assessment result of the distribution network; a reference basis can be provided for risk prevention and control of the distribution network, and the ability of the power grid to cope with complex scenarios is enhanced, thereby improving the reliability and stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical features of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 It is a flow chart of a method for assessing the operation risk of an AC / DC hybrid distribution network according to an embodiment of the present invention;

[0043] Figure 2 is a schematic diagram of an AC / DC hybrid distribution network system of an AC / DC hybrid distribution network operation risk assessment method according to an embodiment of the present invention;

[0044] Figure 3 It is a schematic diagram of probability distribution of load shedding risk in a method for assessing operation risk of an AC / DC hybrid distribution network according to an embodiment of the present invention;

[0045] Figure 4 It is a schematic diagram of probability distribution of renewable energy power abandonment risk in a method for risk assessment of AC / DC hybrid distribution network operation according to an embodiment of the present invention;

[0046] Figure 5 It is a schematic diagram of probability distribution of node voltage deviation risk of an AC / DC hybrid distribution network operation risk assessment method according to an embodiment of the present invention;

[0047] Figure 6 It is a schematic diagram of the spatiotemporal characteristics of load shedding risk in a method for assessing the operation risk of an AC / DC hybrid distribution network according to an embodiment of the present invention;

[0048] Figure 7 It is a schematic diagram of the spatiotemporal characteristics of the risk of power abandonment of renewable energy in a method for risk assessment of operation of an AC / DC hybrid distribution network according to an embodiment of the present invention;

[0049] Figure 8 It is a schematic diagram of the spatiotemporal characteristics of node voltage deviation risk in an AC / DC hybrid distribution network operation risk assessment method according to an embodiment of the present invention;

[0050] Fig. 9 It is a structural schematic diagram of an AC / DC hybrid distribution network operation risk assessment system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] In a first aspect, an embodiment of the present invention provides a method for assessing the operation risk of an AC / DC hybrid distribution network. Figure 1 , which is a flow chart of an embodiment of an AC / DC hybrid distribution network operation risk assessment method provided by the present invention.

[0053] like Figure 1 As shown, the method comprises the following steps:

[0054] S1: Collect renewable energy output and load data in the distribution network, input them into the pre-built component performance model, and output the probability density function of renewable energy output and load;

[0055] S2: According to the probability density function, a Monte Carlo simulation method is used to simulate and generate an initial scene set;

[0056] S3: using kmeans clustering method to determine the target scene set from the initial scene set;

[0057] S4: Based on the target scenario set, perform an operation risk assessment in each scenario to obtain a risk assessment result of the distribution network.

[0058] In the specific implementation, the output data and load data of renewable energy are first collected from the distribution network, and the collected data are input into a pre-built component performance model. The probability density function of renewable energy output and load is output through the component performance model. According to the probability density function, the Monte Carlo simulation method is used to simulate and generate uncertainty scenarios that consider component failure, renewable energy output and power load. The generated scenarios include distribution network load sequences and photovoltaic output sequences for 24 hours a day, which are recorded as the initial scenario set. The kmeans clustering method is used to select a target scenario set from the initial scenario set. Based on the target scenario set, an operation risk assessment is performed under each scenario to obtain a risk assessment result of the distribution network.

[0059] In summary, the present invention collects renewable energy output data and load data in the distribution network, and inputs them into a pre-built component performance model to output the probability density function of renewable energy output and load; according to the probability density function, a Monte Carlo simulation method is used to simulate and generate an initial scenario set; a kmeans clustering method is used to determine a target scenario set from the initial scenario set; based on the target scenario set, an operation risk assessment is performed under each scenario to obtain a risk assessment result of the distribution network; a reference basis can be provided for risk prevention and control of the distribution network, the ability of the power grid to cope with complex scenarios can be enhanced, the safe and stable operation of the power grid can be ensured, the reliability and stability of the power grid can be improved, and a more solid foundation and support can be provided for the future development of the distribution network.

[0060] In an optional implementation, the collecting of renewable energy output data and load data in the distribution network and inputting them into a pre-built component performance model to output a probability density function of renewable energy output and load includes:

[0061] Obtaining component performance parameters according to historical operation data of each component in the distribution network, and constructing a component performance model based on the component performance parameters;

[0062] The renewable energy output data and load data are collected and input into the component performance model, and the probability density function of the renewable energy output and load is output.

[0063] Specifically, historical operating data of each component in the distribution network is collected, and component performance parameters are obtained from the historical operating data, including parameters such as component reliability and component state conversion rate. Component performance models of converters and renewable energy units are constructed based on the component performance parameters. The collected output data and load data of renewable energy such as wind power and photovoltaics are input into the component performance model, and the model analyzes and outputs probability density functions of renewable energy output and load.

[0064] Exemplarily, for component e, the performance of the component can be represented by a performance set g = {g1,..., g k}, where the elements in the set are arranged in ascending order of component performance. When the component fails, it will cause the performance of the component to change from g j to g i , where j > i, and the corresponding failure rate is λ ji . When the component failure is repaired, it will cause the performance of the component to recover from g e to g l , where e < l, and the corresponding repair rate is μ el . Based on the Markov method, the following differential equations can be written for the state probabilities of the multi-state component:

[0065]

[0066] where the initial state probability of the component is p k (0) = 1, p k-1 (0) =... = p1(0) = 0. Solving the equations can obtain the state probabilities of the component at t > 0.

[0067] In an alternative embodiment, the use of the kmeans clustering method to determine the target scenario set from the initial scenario set includes:

[0068] Set the number of target clustering clusters, and select several scenarios from the initial scenario set as the initial clustering center points; where the number of the initial clustering center points is the number of the target clustering clusters;

[0069] Calculate the distance from each scenario in the initial scenario set to the initial clustering center point, and assign each scenario to the cluster corresponding to the initial clustering center point according to the distance to obtain the initial clustering result;

[0070] Perform iterative processing based on the initial clustering result until a preset convergence condition is met to obtain the clustered target scenario set.

[0071] Specifically, first set the number of target clustering clusters k, that is, the number of running scenarios in the final target scenario set to be obtained, and select k scenarios {C1, C2, C3,..., C k} from the initial scenario set as the initial clustering center points, and calculate the distance from each scenario in the initial scenario set to each initial clustering center point. The specific formula is as follows:

[0072]

[0073] where dis(X i , C j ) is the distance from scenario Xi to the central scenario Cj distance, m is the data dimension of scene Xi, scene X i The data dimension is equal to scene C i The data dimension.

[0074] After calculating the distance from each scene to the central scene, each scene is assigned to the nearest central scene according to the principle of the shortest distance to obtain the initial clustering result. According to the initial clustering result, each cluster center is calculated as the new cluster center scene, and the iterative operation is repeated until the change of the cluster center scene meets the convergence condition. The final target scene set is obtained as the typical scene of renewable energy output and load. The sum of the scene probabilities of all scenes in the target scene set is 1, as shown below:

[0075]

[0076] Where k represents the number of target scenes, ρ ω represents the probability of scenario ω.

[0077] In an optional implementation, performing an operation risk assessment in each scenario based on the target scenario set to obtain a risk assessment result of the distribution network includes:

[0078] Construct a risk optimization operation model with the goal of minimizing the operation risk of the distribution network;

[0079] For each operation scenario in the target scenario set, the risk optimization operation model is used to simulate and obtain a simulation operation result;

[0080] Based on the simulation operation results, a risk assessment result of the distribution network is calculated.

[0081] Specifically, a risk optimization operation model of the AC / DC distribution network is constructed. The objective function of the risk optimization operation model is to minimize the operation risk within the operation scenario, that is, the sum of the load shedding loss, renewable energy abandonment loss and voltage deviation loss costs is minimized. The objective function is as follows:

[0082]

[0083] in:

[0084]

[0085] Among them, F ω represents the objective function under scenario ω, T is the set of distribution network operation time periods under each simulation scenario, and They are load shedding cost, renewable energy curtailment cost and voltage deviation cost under scenario ω, c RC and are the unit load removal cost of node i, the unit renewable energy curtailment cost of the system and the unit voltage deviation cost of node i, δ ω,i,t is the voltage deviation of node i at time t under scenario ω, and U i are the upper and lower limits of the voltage fluctuation allowed at node i, respectively. is the maximum output value of renewable energy unit g at time t under scenario ω, Nb and Nr are the number of nodes in the distribution network and the number of renewable energy units, respectively.

[0086] Furthermore, each operation scenario in the target scenario set is simulated using the risk optimization operation model to obtain a simulation operation result, and based on the simulation operation result, a risk assessment result of the distribution network is calculated.

[0087] In an optional embodiment, the constraints of the risk optimization operation model include at least one of AC / DC distribution network flow constraints, load shedding constraints and component operation constraints; wherein the AC / DC distribution network flow constraints include AC network flow constraints and DC network flow constraints, and the component operation constraints include converter operation constraints, converter power balance constraints, energy storage system operation constraints and system safety operation constraints.

[0088] Specifically, the constraints considered in the risk optimization operation model mainly include AC and DC distribution network power flow constraints, load shedding constraints, and component operation constraints. Among them, the AC and DC distribution network power flow constraints include AC network power flow constraints and DC network power flow constraints. The AC network power flow constraints are as follows:

[0089]

[0090]

[0091] Among them, Ω AC represents the set of AC lines, U ω,i,t represents the voltage amplitude of node i at time t in scenario ω, I ω,ij,t represents the current value flowing through line ij at time t in scenario ω, r ji and x ij Respectively represent the resistance and reactance of line ij, P ω,ji,t and Q ω,ij,t They represent the active and reactive power flowing through line ij at time t in scenario ω, and They represent the active and reactive power injected into node i at time t in scenario ω, and They represent the active power of renewable energy unit output and load consumption at node i at time t in scenario ω, and They represent the output of renewable energy units and reactive power consumed by loads at node i at time t under scenario ω, and They represent the active power and reactive power removed from node i at time t in scenario ω, and They respectively represent the active power and reactive power values ​​emitted by the energy storage system at node i at time t in scenario ω.

[0092] The DC network power flow constraints are as follows:

[0093]

[0094] in, represents the voltage value of DC node i at time t in scenario ω, P ω,i,t represents the active power injected into DC node i at time t in scenario ω, represents the conductivity parameter of the DC distribution network line ij, N dc is the number of nodes in the DC distribution network.

[0095] Further, the load shedding constraints are as follows:

[0096] 0≤χ ω,i,t ≤1;

[0097]

[0098] Among them, χ ω,i,t represents the load shedding rate of node i at time t in scenario ω.

[0099] Furthermore, the component operation constraints include converter operation constraints, converter power balance constraints, energy storage system operation constraints and system safety operation constraints, wherein the converter operation constraints are as follows:

[0100]

[0101] in, and They represent the active power and reactive power flowing through converter i at time t in scenario ω, represents the maximum capacity of converter i.

[0102] The reference of converter power balance constraint is as follows:

[0103]

[0104] in, and They represent the active power on the AC and DC sides of the converter at time t in scenario ω, and α represents the loss coefficient of the converter.

[0105] The operating constraints of the energy storage system are as follows:

[0106]

[0107] in, Represents the rated apparent power of the energy storage system s, SOC ω,s,t It represents the state of charge of the energy storage system s at time t in scenario ω, SOC s and They represent the maximum and minimum state of charge of the energy storage system s, respectively. represents the rated energy of the energy storage system s, and They represent the charging and discharging power of the energy storage system s at time t under scenario ω, and They represent the charging and discharging efficiency of the energy storage system s respectively.

[0108] The system safe operation constraints are as follows:

[0109]

[0110] Among them, U min and U max They represent the upper and lower limits of node voltage amplitude for safe operation, I max Indicates the maximum current value allowed to flow through the line.

[0111] In an optional implementation, the calculating and obtaining the risk assessment result of the distribution network based on the simulation operation result includes:

[0112] Acquire the simulation operation result; wherein the simulation operation result includes the load shedding sequence, voltage deviation value sequence and renewable energy abandonment sequence of each node in the distribution network;

[0113] Based on the load shedding sequence, the voltage deviation value sequence, the renewable energy power abandonment sequence and the scenario probability of each of the operating scenarios, a risk assessment result is calculated; wherein the risk assessment result includes load shedding risk, voltage deviation risk and renewable energy power abandonment risk.

[0114] Specifically, the simulation operation results output by the risk optimization operation model are obtained, that is, the load shedding sequence, voltage deviation value sequence and renewable energy power abandonment sequence of each node in the distribution network. According to the simulation operation results, the risk assessment results are calculated, and the risk assessment results include load shedding risk, voltage deviation risk and renewable energy power abandonment risk. The calculation formula of the load shedding risk is as follows:

[0115]

[0116]

[0117] The calculation formula of the voltage deviation risk is as follows:

[0118]

[0119]

[0120] The calculation formula for the risk of renewable energy curtailment is as follows:

[0121]

[0122] Among them, ρ ω represents the probability of the ωth scene, N ω is the number of operation scenarios after clustering, T is the total number of time series, LS, RC and VD represent the overall daily load shedding rate, renewable energy curtailment rate and node voltage deviation rate of the whole system under all typical scenarios, respectively. t ,RC t and VD t are the load shedding rate, renewable energy curtailment rate and node voltage deviation rate of the whole system at time t in all typical scenarios, reflecting the time series characteristics of the load shedding risk, renewable energy curtailment risk and node voltage deviation risk of the whole system. i,t ,RC i,t and VD i,t They are respectively the load shedding rate, renewable energy curtailment rate and node voltage deviation rate of node i at time t in all typical scenarios, reflecting the spatiotemporal characteristics of the load shedding risk, renewable energy curtailment risk and node voltage deviation risk of the entire system.

[0123] The AC / DC hybrid distribution network operation risk assessment method of the present application is used to assess the AC / DC hybrid distribution network system consisting of a 33-node AC system and a 30-node DC system. Figure 2, which is a schematic diagram of an AC / DC hybrid distribution network system according to an embodiment of the present invention, wherein two subsystems are connected via a converter, and the connection nodes are node 1 of the AC subsystem and node 13 of the DC subsystem. The system is simulated on a MATLAB platform and solved using the CPLEX toolbox to obtain the node voltage deviation values, node load shedding risks, and renewable energy power abandonment risk quantified values ​​at different time sequences under various typical scenarios.

[0124] For example, see Figure 3 , Figure 4 and Figure 5 , is the probability distribution of the load shedding risk, renewable energy abandonment risk and node voltage deviation risk of the whole system in the embodiment of the present invention over time, see Figure 6 , Figure 7 and Figure 8 , which are schematic diagrams of the spatiotemporal characteristics of the whole system load shedding risk, renewable energy power abandonment risk and node voltage deviation risk according to an embodiment of the present invention. From the spatial scale, it can be seen that the load shedding risk of AC system nodes 1, 7, 10, 12, 13, 14, 15, 16, 18, 27, 31, 32 and 33 is relatively large, the power abandonment risk of renewable energy unit 5 is relatively large, and the node voltage deviation risk of nodes 7-18 and 28-33 is relatively large. From the time scale, it can be seen that the load shedding risk of time periods 12, 13, 19, 20 and 21 is relatively large, the renewable energy power abandonment risk of time periods 2 and 3 is relatively large, and the node voltage deviation risk of time periods 11-18 is relatively large.

[0125] In a second aspect, an embodiment of the present invention provides an AC / DC hybrid distribution network operation risk assessment system, see Fig. 9 , which is a structural diagram of an AC / DC hybrid distribution network operation risk assessment system provided by an embodiment of the present invention.

[0126] like Fig. 9 As shown, the system comprises:

[0127] The data acquisition module 21 is used to collect the renewable energy output data and load data in the distribution network, and input them into the pre-built component performance model, and output the probability density function of the renewable energy output and load;

[0128] A scene generation module 22, configured to generate an initial scene set by simulation using a Monte Carlo simulation method according to the probability density function;

[0129] A scene selection module 23, used for determining a target scene set from the initial scene set using a kmeans clustering method;

[0130] The risk assessment module 24 is used to perform an operation risk assessment in each scenario based on the target scenario set to obtain a risk assessment result of the distribution network.

[0131] In an optional implementation, the data acquisition module 21 is further used for:

[0132] Obtaining component performance parameters according to historical operation data of each component in the distribution network, and constructing a component performance model based on the component performance parameters;

[0133] The renewable energy output data and load data are collected and input into the component performance model, and the probability density function of the renewable energy output and load is output.

[0134] In an optional implementation, the scene selection module 23 is further used to:

[0135] Setting the target number of clusters, and selecting a number of scenes from the initial scene set as initial cluster center points; wherein the number of initial cluster center points is the target number of clusters;

[0136] Calculating the distance between each scene in the initial scene set and the initial center point of the cluster, and assigning each scene to a cluster corresponding to the initial center point of the cluster according to the distance, to obtain an initial clustering result;

[0137] Iterative processing is performed based on the initial clustering result until a preset convergence condition is met to obtain a clustered target scene set.

[0138] In an optional implementation, the risk assessment module 24 is further configured to:

[0139] Construct a risk optimization operation model with the goal of minimizing the operation risk of the distribution network;

[0140] For each operation scenario in the target scenario set, the risk optimization operation model is used to simulate and obtain a simulation operation result;

[0141] Based on the simulation operation results, a risk assessment result of the distribution network is calculated.

[0142] In an optional embodiment, the constraints of the risk optimization operation model include at least one of AC / DC distribution network flow constraints, load shedding constraints and component operation constraints; wherein the AC / DC distribution network flow constraints include AC network flow constraints and DC network flow constraints, and the component operation constraints include converter operation constraints, converter power balance constraints, energy storage system operation constraints and system safety operation constraints.

[0143] In an optional implementation, the risk assessment module 24 is further configured to:

[0144] Acquire the simulation operation result; wherein the simulation operation result includes the load shedding sequence, voltage deviation value sequence and renewable energy abandonment sequence of each node in the distribution network;

[0145] Based on the load shedding sequence, the voltage deviation value sequence, the renewable energy power abandonment sequence and the scenario probability of each of the operating scenarios, a risk assessment result is calculated; wherein the risk assessment result includes load shedding risk, voltage deviation risk and renewable energy power abandonment risk.

[0146] The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. It should be pointed out that for those skilled in the art, several equivalent obvious variations and / or equivalent substitutions can be made without departing from the technical principles of the present invention. These obvious variations and / or equivalent substitutions should also be regarded as the protection scope of the present invention.

Claims

1. A method for assessing the operation risk of an AC / DC hybrid distribution network, characterized in that: include: Collect renewable energy output and load data in the distribution network, input them into the pre-built component performance model, and output the probability density function of renewable energy output and load; According to the probability density function, a Monte Carlo simulation method is used to simulate and generate an initial scene set; Determine a target scene set from the initial scene set using a kmeans clustering method; Based on the target scenario set, an operation risk assessment is performed in each scenario to obtain a risk assessment result of the distribution network.

2. The AC / DC hybrid distribution network operation risk assessment method according to claim 1, characterized in that: The method collects the renewable energy output data and load data in the distribution network, inputs them into a pre-built component performance model, and outputs the probability density function of the renewable energy output and load, including: Obtaining component performance parameters according to historical operation data of each component in the distribution network, and constructing a component performance model based on the component performance parameters; The renewable energy output data and load data are collected and input into the component performance model, and the probability density function of the renewable energy output and load is output.

3. The AC / DC hybrid distribution network operation risk assessment method according to claim 1, characterized in that: The method of using the kmeans clustering method to determine the target scene set from the initial scene set includes: Setting the target number of clusters, and selecting a number of scenes from the initial scene set as initial cluster center points; wherein the number of initial cluster center points is the target number of clusters; Calculating the distance between each scene in the initial scene set and the initial center point of the cluster, and assigning each scene to a cluster corresponding to the initial center point of the cluster according to the distance, to obtain an initial clustering result; Iterative processing is performed based on the initial clustering result until a preset convergence condition is met to obtain a clustered target scene set.

4. The AC / DC hybrid distribution network operation risk assessment method according to claim 1, characterized in that: The step of performing an operation risk assessment in each scenario based on the target scenario set to obtain a risk assessment result of the distribution network includes: Construct a risk optimization operation model with the goal of minimizing the operation risk of the distribution network; For each operation scenario in the target scenario set, the risk optimization operation model is used to simulate and obtain a simulation operation result; Based on the simulation operation results, a risk assessment result of the distribution network is calculated.

5. The AC / DC hybrid distribution network operation risk assessment method according to claim 1, characterized in that: The constraints of the risk optimization operation model include at least one of AC / DC distribution network flow constraints, load shedding constraints and component operation constraints; wherein the AC / DC distribution network flow constraints include AC network flow constraints and DC network flow constraints, and the component operation constraints include converter operation constraints, converter power balance constraints, energy storage system operation constraints and system safety operation constraints.

6. The AC / DC hybrid distribution network operation risk assessment method according to claim 4, characterized in that: The step of calculating the risk assessment result of the distribution network based on the simulation operation result includes: Acquire the simulation operation result; wherein the simulation operation result includes the load shedding sequence, voltage deviation value sequence and renewable energy abandonment sequence of each node in the distribution network; Based on the load shedding sequence, the voltage deviation value sequence, the renewable energy power abandonment sequence and the scenario probability of each of the operating scenarios, a risk assessment result is calculated; wherein the risk assessment result includes load shedding risk, voltage deviation risk and renewable energy power abandonment risk.

7. An AC / DC hybrid distribution network operation risk assessment system, characterized in that: include: A data acquisition module is used to collect renewable energy output data and load data in the distribution network, and input them into a pre-built component performance model to output the probability density function of renewable energy output and load; A scene generation module, used to simulate and generate an initial scene set according to the probability density function using a Monte Carlo simulation method; A scene selection module, used for determining a target scene set from the initial scene set using a kmeans clustering method; The risk assessment module is used to perform an operation risk assessment in each scenario based on the target scenario set to obtain a risk assessment result of the distribution network.

8. The AC / DC hybrid distribution network operation risk assessment system according to claim 7, characterized in that: The data acquisition module is also used for: Obtaining component performance parameters according to historical operation data of each component in the distribution network, and constructing a component performance model based on the component performance parameters; The renewable energy output data and load data are collected and input into the component performance model, and the probability density function of the renewable energy output and load is output.

9. The AC / DC hybrid distribution network operation risk assessment system according to claim 7, characterized in that: The scene selection module is also used for: Setting the target number of clusters, and selecting a number of scenes from the initial scene set as initial cluster center points; wherein the number of initial cluster center points is the target number of clusters; Calculating the distance between each scene in the initial scene set and the initial center point of the cluster, and assigning each scene to a cluster corresponding to the initial center point of the cluster according to the distance, to obtain an initial clustering result; Iterative processing is performed based on the initial clustering result until a preset convergence condition is met to obtain a clustered target scene set.

10. The AC / DC hybrid distribution network operation risk assessment system according to claim 7, characterized in that: The risk assessment module is also used to: Construct a risk optimization operation model with the goal of minimizing the operation risk of the distribution network; For each operation scenario in the target scenario set, the risk optimization operation model is used to simulate and obtain a simulation operation result; Based on the simulation operation results, a risk assessment result of the distribution network is calculated.

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