Short-circuit current optimization method and system considering coordination between new energy and conventional measures

Abstract

The present invention relates to the technical field of short-circuit current optimization, and specifically discloses a short-circuit current optimization method and system that considers the coordination of new energy and conventional measures, the method comprising: converting the uncertainty of the prediction error of new energy into a certain typical scenario; adding the typical scenario with three-phase short-circuit current exceeding the standard to the short-circuit current exceeding the standard operation scenario concentration; considering conventional measures and new energy measures for suppressing short-circuit current, taking the minimum total control cost as the optimization goal, constructing a short-circuit current comprehensive coordination optimization model; solving the short-circuit current comprehensive coordination optimization model based on a particle swarm optimization algorithm of a hybrid update strategy, obtaining N control measures with the minimum control cost, and obtaining the final short-circuit current optimization control measure through safety verification. The present invention adjusts the reactive current support coefficient and maximum reactive current of new energy under new energy measures, which can reduce the cost of conventional short-circuit current control and reduce the impact on the safety and stability of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of short-circuit current optimization, and in particular to a short-circuit current optimization method and system that considers the coordination of new energy and conventional measures. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] As the scale of power grids continues to expand, the problem of excessive short-circuit current has become increasingly prominent. The expansion of power grids is usually accompanied by the extension of transmission lines, the increase in substations, and the deepening of power grid interconnection. Under the combined effect of these factors, the short-circuit current level of the power grid has also increased. The phenomenon of excessive short-circuit current has become increasingly serious, posing a serious threat to the safe and stable operation of the power grid.

[0004] Excessive short-circuit current may cause the circuit breaker to fail to work properly. The rated short-circuit breaking capacity of the circuit breaker is limited. When the short-circuit current exceeds its design capacity, the circuit breaker may not be able to cut off the fault current in time, causing damage to electrical equipment and even fire and other safety accidents. In addition, excessive short-circuit current will also affect the overall safe and stable operation of the power grid, and may cause local or large-scale power outages in the power grid, seriously affecting social and economic activities and residents' lives. Therefore, optimizing short-circuit current to ensure the safe and stable operation of the power grid has become an important task in the operation of the power system.

[0005] When faced with the challenge of excessive short-circuit current, traditional dispatching and operation strategies often reduce the short-circuit current level by weakening the grid structure, but this approach may lead to reduced reliability of local power grid power supply, unbalanced power flow distribution, and difficulty in implementing the main transformer outage plan. These problems make the demand for short-circuit current control increasingly difficult to meet. With the grid-connected operation of a high proportion of new energy and a high proportion of power electronic equipment, the characteristics of short-circuit current have become more time-varying and complex, making it difficult for short-circuit current control measures calculated offline based on typical methods to adapt to the real-time changing grid status. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a short-circuit current optimization method and system that takes into account the coordination of new energy and conventional measures, giving full play to the flexible adjustment capabilities of power electronic equipment such as new energy and energy storage, and realizing the adjustable and controllable short-circuit current.

[0007] In some embodiments, the following technical solutions are adopted:

[0008] A short-circuit current optimization method considering the coordination of new energy and conventional measures, comprising:

[0009] Model the probability distribution of power forecast errors of renewable energy sources, and transform the forecast error uncertainty of renewable energy sources into certain typical scenarios through sampling and clustering;

[0010] Calculate the three-phase short-circuit current of each node in each typical scenario, and add typical scenarios with excessive three-phase short-circuit current to the short-circuit current excessive operation scenario set;

[0011] For each scenario in the short-circuit current exceeding standard operation scenario set, conventional measures and new energy measures for suppressing short-circuit current are considered, and a comprehensive coordination optimization model for short-circuit current is constructed with the minimum total control cost as the optimization goal;

[0012] The particle swarm optimization algorithm based on the hybrid update strategy solves the comprehensive coordinated optimization model of short-circuit current and obtains N control measures with the minimum control cost. The safety of the N control measures is verified to obtain the final short-circuit current optimization control measures.

[0013] As an optional solution, the prediction error distribution probability of new energy is modeled, including:

[0014] If the power prediction error of a certain station has no correlation with other stations, a single station prediction error distribution model is constructed: the normal distribution is used to characterize the power prediction error of photovoltaic and load, and the Beta distribution is used to characterize the power prediction error of wind power.

[0015] If the power prediction errors between different stations are correlated, a joint distribution model of power prediction errors of multiple stations is constructed; specifically:

[0016] Collect actual power generation data and predicted power generation data of wind farms and calculate prediction errors;

[0017] Select an appropriate marginal distribution function for the prediction error of each new energy station; the prediction error of the i-th new energy station ΔP i The marginal distribution function is expressed as F i (ΔP i );

[0018] Select the appropriate Copula function and estimate the parameters of each marginal distribution function and Copula function;

[0019] Construct a joint distribution, using the selected Copula function and estimated parameters, combined with the marginal distribution function of the prediction error of each new energy station, to construct a joint distribution of multivariate random variables:

[0020] ;

[0021] Where C is the Copula function, F iis the marginal distribution function of the ith variable, is the parameter of the Copula function, and n is the number of renewable energy stations with relevant power prediction errors.

[0022] As an optional solution, the uncertainty of the prediction error of new energy is transformed into a certain typical scenario through sampling and clustering, specifically:

[0023] The Latin hypercube sampling method is used to sample the power prediction errors of all new energy sources based on the constructed prediction error distribution probability model, and then superimposed with the predicted power to obtain a large number of operation scenarios;

[0024] The clustering algorithm clusters the scenes and obtains M typical scenes.

[0025] As an optional solution, the typical scenario in which the three-phase short-circuit current exceeds the standard is specifically: if in the typical scenario, the three-phase short-circuit current at a certain node exceeds the circuit breaker breaking current, then the typical scenario is a typical scenario in which the three-phase short-circuit current exceeds the standard.

[0026] As an optional solution, taking the minimum total control cost as the optimization goal, a short-circuit current comprehensive coordination optimization model is constructed, specifically:

[0027] ;

[0028] in, , and They are the control cost functions of line disconnection control cost, unit start-stop control cost and adjustment of new energy parameters; For the l The control cost of the line disconnection measure is y m is the control cost generated by the start-stop measures of the mth unit, z n N is the control cost generated by adjusting the nth new energy parameter; L is the number of line disconnection measures, N G is the number of unit start-stop control measures, N R To adjust the number of new energy parameter measures.

[0029] As an optional scheme, the constraints of the short-circuit current comprehensive coordinated optimization model include: short-circuit current constraints, line state constraints, unit state constraints, upper and lower limit constraints of new energy parameters, system balance constraints, reactive voltage constraints and grid topology connectivity constraints.

[0030] In other embodiments, the following technical solutions are adopted:

[0031] A short-circuit current optimization system considering the coordination of new energy and conventional measures, comprising:

[0032] The prediction error distribution model building module is used to model the prediction error distribution probability of new energy. Through sampling and clustering, the prediction error uncertainty of new energy is transformed into a certain typical scenario.

[0033] The short-circuit current exceeding standard calculation module is used to calculate the three-phase short-circuit current of each node in each typical scenario, and add the typical scenarios with three-phase short-circuit current exceeding standard to the short-circuit current exceeding standard operation scenario set;

[0034] The short-circuit current comprehensive coordination optimization model construction module is used to consider conventional measures and new energy measures for suppressing short-circuit current for each scenario in the short-circuit current exceeding standard operation scenario set, and to construct a short-circuit current comprehensive coordination optimization model with the minimum total control cost as the optimization goal;

[0035] The short-circuit current optimization control module is used to solve the short-circuit current comprehensive coordination optimization model based on the particle swarm optimization algorithm of the hybrid update strategy, obtain N control measures with the minimum control cost, perform safety verification on the N control measures, and obtain the final short-circuit current optimization control measures.

[0036] The short-circuit current comprehensive coordinated optimization model includes conventional measures and new energy measures for suppressing short-circuit current;

[0037] The conventional measures include grid structure adjustment and unit start-up and shutdown; the grid structure adjustment reduces the short-circuit current of the node exceeding the standard by disconnecting the transmission lines around the node with the short-circuit current exceeding the standard and changing the grid structure; the unit start-up and shutdown reduces the short-circuit current of the node exceeding the standard by shutting down the units around the node with the short-circuit current exceeding the standard;

[0038] The new energy measures include changing the reactive current support coefficient K of the new energy low voltage ride-through control strategy. q and reactive current maximum limit I qmax ; By adjusting the K of new energy q and I qmax value, changes the reactive injection current during the grid fault period, and then reduces the current of the node where the short-circuit current exceeds the standard, achieving the purpose of controlling the short-circuit current.

[0039] In other embodiments, the following technical solutions are adopted:

[0040] A terminal device includes a processor and a memory, wherein the processor is used to implement instructions; the memory is used to store multiple instructions, wherein the instructions are suitable for the processor to load and execute the above-mentioned short-circuit current optimization method considering the coordination of new energy and conventional measures.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] (1) The present invention takes into account the uncertainty of power prediction error, constructs a prediction error distribution model, and adopts a sampling method and a clustering algorithm to obtain typical operating scenarios, thereby transforming the prediction error uncertainty of new energy into a certain typical scenario, thereby reducing the impact of power prediction error uncertainty on short-circuit current calculation and subsequent short-circuit current optimization.

[0043] (2) In the process of short-circuit current optimization, the present invention coordinates and optimizes conventional measures and new energy measures, and combines the flexible and considerable control capabilities of new energy power equipment to provide new methods and means for the controllable and adjustable short-circuit current. By adjusting the reactive current support coefficient and maximum reactive current of new energy under new energy measures, the cost of conventional short-circuit current control can be reduced, and the impact on the safety and stability of the power grid can be reduced.

[0044] (3) The present invention uniformly converts the costs of different control measures into control costs to measure the indicators between different schemes, takes the minimum total control cost as the optimization goal, and constructs a comprehensive coordinated optimization model for short-circuit current; uses a particle swarm optimization algorithm based on a hybrid update strategy to solve the model and obtain N control measures with the minimum control cost; then, through safety verification, measures that will cause the safety and stability collapse of the power grid are deleted to achieve optimal control of the short-circuit current.

[0045] Other features and advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flow chart of a short-circuit current optimization method considering coordination between new energy and conventional measures in an embodiment of the present invention;

[0047] Figure 2 Schematic diagram of the process of solving the short-circuit current comprehensive coordinated optimization model using the particle swarm optimization algorithm based on the hybrid update strategy. DETAILED DESCRIPTION

[0048] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0049] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0050] Embodiment 1

[0051] In one or more embodiments, a short-circuit current optimization method considering the coordination of new energy and conventional measures is disclosed, combined with Figure 1 , specifically including the following process:

[0052] S101: Model the probability distribution of power prediction errors of renewable energy sources, and transform the prediction error uncertainty of renewable energy sources into certain typical scenarios through sampling and clustering.

[0053] Specifically, the new energy power prediction data can be obtained through existing technologies (for example, machine learning methods such as generative adversarial networks). There are two types of uncertainty models for power prediction errors:

[0054] (1) The power prediction error of a certain station has no correlation with that of other stations. A single station prediction error distribution model is constructed.

[0055] The PV and load power prediction errors generally follow a normal distribution, as shown in formula (1).

[0056] (1)

[0057] Where ΔP is the predicted power error, μ and σ are the expectation and variance of the normal distribution, corresponding to the predicted power of photovoltaic (or load) and the variance of the predicted error. μ and σ can be given by other existing power prediction methods.

[0058] Wind power forecast error is suitable for Beta distribution, as shown in the formula:

[0059] (2)

[0060] Where ΔP is the predicted power error, α and β are the shape and scale parameters of the Beta distribution, and N d is the normalization factor.

[0061] (2) When the power prediction errors between different stations are correlated, a joint distribution model of multi-station power prediction errors is constructed.

[0062] The steps for constructing the joint distribution model of multi-station power prediction errors are as follows:

[0063] ① Collect actual power generation data and predicted power generation data of wind farms and calculate the prediction error;

[0064] ② Select a suitable marginal distribution function for the prediction error of each new energy station, such as normal distribution, Weil distribution, etc. The prediction error ΔP of the i-th new energy station i The marginal distribution function is expressed as Fi (ΔP i );

[0065] ③Choose a suitable Copula function, such as Gaussian Copula, t-Copula, Clayton Copula, Gumbel Copula, etc.;

[0066] ④ For each marginal distribution and Copula function, its parameters need to be estimated. The parameters of the marginal distribution can be obtained by maximum likelihood estimation, moment estimation and other methods. The parameters of the Copula function are usually estimated by maximum likelihood estimation or methods based on empirical distribution.

[0067] ⑤ Construct a joint distribution, using the selected Copula function and estimated parameters, combined with the marginal distribution function of each variable, to construct a joint distribution of multivariate random variables. The joint distribution function can be expressed as:

[0068] ;

[0069] Where C is the Copula function, F i is the marginal distribution function of the ith variable, is the parameter of the Copula function, and n is the number of renewable energy stations with relevant power prediction errors.

[0070] In this embodiment, Latin hypercube sampling is adopted to sample the power prediction errors of all renewable energy sources based on the constructed joint error distribution model and single-station prediction error distribution model, and superimpose them with the predicted power to obtain a large number of operation scenarios.

[0071] Clustering algorithms such as k-means are used to cluster the scenes and M typical scenes are obtained.

[0072] The distribution of typical scenarios should conform to the original prediction error distribution model as much as possible. The number of typical scenarios is related to the maximum allowable error. If the variance of the original distribution is α, and the maximum allowable error of the project is 5%, it is necessary to ensure that the variance of the reduced typical scenarios is between 0.95α-1.05α. The smaller the error, the more typical scenarios are required. The specific execution steps are as follows:

[0073] ① Set the initial typical scenario M0 and the maximum allowable error.

[0074] ② Use Kmeans algorithm to cluster to obtain M0 typical scenarios, calculate the maximum value ε0 of the variance of the power prediction error corresponding to M0 scenarios, and compare it with the variance ε of the original distribution.

[0075] ③ If the error between ε0 and ε exceeds the maximum allowable error, the number of typical scenes obtained is increased until the maximum allowable error is met.

[0076] S102: Calculate the three-phase short-circuit current of each node in each typical scenario, and add typical scenarios with excessive three-phase short-circuit current to a set of short-circuit current excessive operation scenarios.

[0077] In this embodiment, a short-circuit current calculation tool is used to calculate the three-phase short-circuit current of each node in the typical scenario one by one. If there is a node in a typical scenario where the three-phase short-circuit current exceeds the circuit breaker breaking current, the typical scenario is added to the short-circuit current exceeding standard operation scenario set for subsequent short-circuit current optimization.

[0078] S103: for each scenario in the short-circuit current exceeding standard operation scenario set, a short-circuit current comprehensive coordination optimization model is constructed with the minimum total control cost as the optimization goal;

[0079] In this embodiment, short-circuit current optimization decisions are made for the scenarios in the short-circuit current exceeding standard operation scenario set one by one; first, a short-circuit current comprehensive coordination optimization decision model is constructed, and then a particle swarm optimization algorithm based on a hybrid update strategy is used to solve the comprehensive coordination optimization decision model to obtain N control measures with the lowest control cost. Starting from the solution with the lowest control cost, N-2 checks are performed to delete measures that will cause the safety and stability of the power grid to collapse.

[0080] Specifically, if new energy can limit the short-circuit current at the nodes that exceed the standard, and there are no other safety and stability issues (such as voltage) after the scheme is verified, new energy measures will be adopted; if new energy measures alone cannot limit the short-circuit current to the target range (or may cause safety and stability problems), conventional measures need to be adopted in combination with new energy to construct a comprehensive coordinated optimization model for short-circuit current.

[0081] Conventional measures include grid structure adjustment and unit start-up and shutdown. Disconnecting the line causes the structure of the grid to change, and the impedance distribution of the system will change, which in turn causes the size and distribution of the short-circuit current to change. If the structural adjustment makes the grid more tightly connected and the system impedance is reduced, the short-circuit current may increase. On the contrary, if the structural adjustment makes the grid more decentralized, the system impedance increases, and the short-circuit current may decrease. Therefore, the transmission lines around the nodes where the short-circuit current exceeds the standard can be disconnected, and the grid structure can be changed to reduce the short-circuit current at the exceeding node. For the unit start-up and shutdown control measures, when the generator set is started and connected to the grid, the total power generation capacity of the system will be increased, which may reduce the equivalent impedance of the system and cause the short-circuit current to increase. On the contrary, when the generator set withdraws from the grid (shutdown), the total power generation capacity of the system decreases, the equivalent impedance increases, and the short-circuit current may decrease. Therefore, the short-circuit current at the exceeding node can be reduced by stopping the units around the node where the short-circuit current exceeds the standard.

[0082] New energy measures include changing the reactive current support factor K of the new energy low voltage ride-through control strategy q and reactive current maximum limit I qmax Compared with active current, reactive current has a greater impact on short-circuit current. Under the low voltage ride-through control strategy, the reactive injection current formula of new energy is:

[0083] (3)

[0084] Among them, U PCC is the voltage of the new energy public connection point, U min -U max It is the low voltage ride through operation range, U min is the voltage lower limit, U max The voltage upper limit is usually 0.2-0.9, K q is the reactive current support factor, I N is the rated current of the fan, I qmax It is the maximum reactive output current of the inverter (i.e. the maximum reactive current limit).

[0085] By adjusting the K of the new energy q and I qmax value, changes the reactive injection current during the grid fault period, and then reduces the current of the node where the short-circuit current exceeds the standard, achieving the purpose of controlling the short-circuit current.

[0086] Therefore, there are many control schemes that can be adopted to limit short-circuit current. Different measures have different costs and benefits. The cost of the same type of equipment can be measured by the number of times, but the cost of different measures cannot be measured by the number of times.

[0087] The indicators of different schemes are measured by converting the costs of different control measures into control costs. The optimization goal of building a comprehensive coordinated optimization model for short-circuit current is to minimize the total control cost:

[0088] (4)

[0089] In the formula, C 1. C 2 and C 3 are the control cost functions of line disconnection control cost, unit start-stop control cost and adjustment of new energy parameters. For the l The control cost of the line disconnection measure is y m is the control cost generated by the start-stop measures of the mth unit, z n is the control cost generated by adjusting the nth new energy parameter. L is the number of line disconnection measures, N G is the number of unit start-stop control measures, NR To adjust the number of new energy parameter measures.

[0090] Among them, the unit start and stop control cost C1: is calculated based on the actual operation data of the generator unit, or a typical value is used.

[0091] The cost of breaking the line control C2: The cost of breaking the line is measured by the life of the circuit breaker. Each opening and closing of a 220kV circuit breaker will affect the life of the circuit breaker arc extinguishing chamber and contacts. The cost of breaking the circuit breaker once is:

[0092] (5)

[0093] Among them, I op is the line current during normal operation, I N is the rated breaking current of the circuit breaker, N cb is the rated breaking times of the short-circuit device, C cb Cost of arc extinguishing chamber and contacts of circuit breaker.

[0094] New energy parameter adjustment control cost C3: The parameters that need to be adjusted for new energy include K q and I qmax . There is basically no cost or very little control cost for adjusting the parameters of new energy, and the control cost can be approximately considered to be 0. However, in order to inject as much reactive current as possible, a control cost is set for adjusting the new energy measures in the optimization objective function. The larger the new energy parameter, the greater the reactive current injected into the power grid. When the new energy parameter is set at the upper limit allowed, the control cost is 0. Based on the upper limit, the control cost is generated by lowering the new energy parameter. The smaller the parameter, the greater the control cost. Set the new energy control cost function as:

[0095] (6)

[0096] Among them, K q and I qmax is the reactive current injection coefficient of the new energy source and the maximum reactive current limit, which are variables in the optimization process. q and I qmax is the reactive current injection coefficient of new energy and the upper limit of the maximum reactive current limit, k1 and k2 are proportional coefficients.

[0097] In this embodiment, the constraints of the short-circuit current comprehensive coordinated optimization model are as follows:

[0098] (1) Short-circuit current constraint:

[0099] (7)

[0100] Among them, I f,k is the short-circuit current of node k, V ocis the set of nodes with excessive short-circuit current, I limit is the short-circuit current limit.

[0101] (2) Line status:

[0102] (8)

[0103] Among them, L i is the state of line i, V L A set of candidate lines for controlling short-circuit current.

[0104] (3) Unit status:

[0105] (9)

[0106] Among them, G i is the state of unit i, V G A set of candidate units for controlling short-circuit current.

[0107] (4) Upper and lower limits of new energy parameters:

[0108] (10)

[0109] Among them, K q,i and I m,i is the reactive current support coefficient and the maximum reactive current limit of renewable energy i, K q-min,i , K q-max,i and I m-min,i ,I m-max,i are the adjustable upper and lower limits of the reactive current support coefficient and the maximum reactive current limit of the new energy source i, V RES A collection of candidate new energy sources for controlling short-circuit current.

[0110] (5) System balance constraints:

[0111] (11)

[0112] Among them, P g,i is the maximum active power output of conventional unit i, P res,j is the maximum active power output of new energy j, P rl,j is the active power of load k, V gen For a normal set in the power-on state, V RES is a collection of new energy sources in the on state, V Load For load collection.

[0113] (6) Reactive voltage constraint: The number of units started is different under different load levels, and the scheme needs to be verified later:

[0114] (12)

[0115] Among them, N1 is the required number of startups when the load level is less than P1, and N2 is the required number of startups when the load level is greater than P1 and less than P2.

[0116] (7) Grid topology connectivity constraints: Not all lines in the system can be cut off. When some lines are cut off, some nodes or areas in the system may become isolated islands, destroying the system connectivity.

[0117] S104: Solving the short-circuit current comprehensive coordination optimization model based on the particle swarm optimization algorithm of the hybrid update strategy to obtain N control measures with the minimum control cost, performing safety verification on the obtained N control measures, and obtaining the final short-circuit current optimization control measures.

[0118] Combination Figure 2 ,The process of solving the short-circuit current comprehensive coordination optimization model by the particle swarm optimization algorithm based on the hybrid update strategy is as follows:

[0119] (1) Screening of short-circuit current measure sets

[0120] According to the location of the node where the short-circuit current exceeds the standard and the adjustable equipment during the actual operation, the control measures that have a great impact on the node where the short-circuit current exceeds the standard are screened to form a set of controllable measures.

[0121] (2) Particle settings

[0122] The particle dimension is related to controllable measures and includes two parts: the first part is the new energy parameters, and the second part is the status of disconnectable lines and shuttable units. Among them, each new energy parameter is a continuous variable, and the line and unit status are 0-1 variables.

[0123] (3) Determine the constraint conditions and calculate the penalty function and particle fitness value

[0124] The calculation of the fitness value is the sum of the control cost of each control measure and the penalty function value in formula (4):

[0125] (13)

[0126] Among them, M represents the penalty function value. When the control measures cannot meet the constraints after implementation, a large value is set. If all constraints can be met after the control measures are implemented, the M value is 0.

[0127] (4) Update particle position and velocity

[0128] The speed update formula of continuous variables is:

[0129] (14)

[0130] Among them, ω is the inertia weight, c1 and c2 are the individual learning factor and social learning factor, r1 is a random value, gb is the optimal position of the example individual, and p i,b represents the historical optimal position of the particle, x represents the current position of the particle, and t represents the number of iterations.

[0131] The position update formula of continuous variables is:

[0132] (15)

[0133] When the 0-1 variable is updated, the speed is first updated according to formula (14), then the Sigmoid function is used to map the speed to the [0,1] interval, and then a new position is generated by a random number:

[0134] (16)

[0135] Then generate a random number (r) between 0 and 1 and update the particle's position:

[0136] (17)

[0137] (5) Exit iteratively and output the control solution

[0138] When the number of iterations exceeds the maximum number of iterations, the iteration is exited, and then the N solutions with the smallest fitness value are output.

[0139] After obtaining the N control measures with the lowest control cost, we start from the plan with the lowest control cost, perform N-2 checks, and delete the measures that will cause the collapse of power grid security and stability.

[0140] Since there is a contradiction between short-circuit current control and voltage stability, short-circuit current control measures will reduce voltage stability. Therefore, the N-2 check after short-circuit current optimization is mainly to check the voltage stability. The voltage stability can be checked according to the following steps:

[0141] ① Apply the measures taken in the solution to the power grid calculation example, modify relevant parameters or shut down the equipment;

[0142] ② Use software such as BPA or PSASP to perform dynamic simulation functions and disconnect two important lines in the system;

[0143] ③ Check the voltage changes of each node in the system over time. If the voltage of the node cannot be restored to the normal operating voltage after the voltage drops, the N-2 check of the scheme fails.

[0144] Embodiment 2

[0145] In one or more embodiments, a short-circuit current optimization system considering coordination of new energy and conventional measures is disclosed, comprising:

[0146] The prediction error distribution model building module is used to model the prediction error distribution probability of new energy. Through sampling and clustering, the prediction error uncertainty of new energy is transformed into a certain typical scenario.

[0147] The short-circuit current exceeding standard calculation module is used to calculate the three-phase short-circuit current of each node in each typical scenario, and add the typical scenarios with three-phase short-circuit current exceeding standard to the short-circuit current exceeding standard operation scenario set;

[0148] The short-circuit current comprehensive coordination optimization model construction module is used to consider conventional measures and new energy measures for suppressing short-circuit current for each scenario in the short-circuit current exceeding standard operation scenario set, and to construct a short-circuit current comprehensive coordination optimization model with the minimum total control cost as the optimization goal;

[0149] The short-circuit current optimization control module is used to solve the short-circuit current comprehensive coordination optimization model based on the particle swarm optimization algorithm of the hybrid update strategy, obtain N control measures with the minimum control cost, perform safety verification on the N control measures, and obtain the final short-circuit current optimization control measures.

[0150] Among them, the comprehensive coordinated optimization model of short-circuit current includes conventional measures and new energy measures for suppressing short-circuit current;

[0151] Conventional measures include grid structure adjustment and unit start-up and shutdown; the grid structure adjustment reduces the short-circuit current of the node with excessive short-circuit current by disconnecting the transmission lines around the node with excessive short-circuit current and changing the grid structure; the unit start-up and shutdown reduces the short-circuit current of the node with excessive short-circuit current by shutting down the units around the node with excessive short-circuit current;

[0152] New energy measures include changing the reactive current support factor K of the new energy low voltage ride-through control strategy q and reactive current maximum limit I qmax ; By adjusting the K of new energy q and I qmax value, changes the reactive injection current during the grid fault period, and then reduces the current of the node where the short-circuit current exceeds the standard, achieving the purpose of controlling the short-circuit current.

[0153] Embodiment 3

[0154] In one or more embodiments, a terminal device is disclosed, including a server, the server including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the short-circuit current optimization method considering the coordination of new energy and conventional measures in embodiment 1 when executing the program. For the sake of brevity, it will not be described here.

[0155] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0156] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0157] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software.

[0158] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A short-circuit current optimization method considering the coordination of new energy and conventional measures, characterized in that: include: Model the probability distribution of power forecast errors of renewable energy sources, and transform the forecast error uncertainty of renewable energy sources into certain typical scenarios through sampling and clustering; Calculate the three-phase short-circuit current of each node in each typical scenario, and add typical scenarios with excessive three-phase short-circuit current to the short-circuit current excessive operation scenario set; For each scenario in the short-circuit current exceeding standard operation scenario set, conventional measures and new energy measures for suppressing short-circuit current are considered, and a short-circuit current comprehensive coordination optimization model is constructed with the minimum total control cost as the optimization goal; the short-circuit current comprehensive coordination optimization model is constructed with the minimum total control cost as the optimization goal, specifically: ； in, , and They are the control cost functions of line disconnection control cost, unit start-stop control cost and adjustment of new energy parameters; For the l The control cost of the line disconnection measure is y m is the control cost generated by the start-stop measures of the mth unit, z n N is the control cost generated by adjusting the nth new energy parameter; L is the number of line disconnection measures, N G is the number of unit start-stop control measures, N R To adjust the number of new energy parameter measures; The particle swarm optimization algorithm based on the hybrid update strategy solves the comprehensive coordinated optimization model of short-circuit current and obtains N control measures with the minimum control cost. The safety of the N control measures is verified to obtain the final short-circuit current optimization control measures.

2. A short-circuit current optimization method considering the coordination of new energy and conventional measures as claimed in claim 1, characterized in that: Modeling of the forecast error distribution probability of new energy sources, including: If the power prediction error of a certain station has no correlation with other stations, a single station prediction error distribution model is constructed: the normal distribution is used to characterize the power prediction error of photovoltaic and load, and the Beta distribution is used to characterize the power prediction error of wind power.

3. A short-circuit current optimization method considering the coordination of new energy and conventional measures as claimed in claim 1, characterized in that: If the power prediction errors between different stations are correlated, a joint distribution model of power prediction errors of multiple stations is constructed; specifically: Collect actual power generation data and predicted power generation data of wind farms and calculate prediction errors; Select an appropriate marginal distribution function for the prediction error of each new energy station; the prediction error of the i-th new energy station ΔP i The marginal distribution function is expressed as F i (ΔP i ); Select the appropriate Copula function and estimate the parameters of each marginal distribution function and Copula function; Construct a joint distribution, using the selected Copula function and estimated parameters, combined with the marginal distribution function of the prediction error of each new energy station, to construct a joint distribution of multivariate random variables: ； Where C is the Copula function, F i is the marginal distribution function of the ith variable, is the parameter of the Copula function, and n is the number of renewable energy stations with relevant power prediction errors.

4. A short-circuit current optimization method considering the coordination of new energy and conventional measures as claimed in claim 1, characterized in that: Through sampling and clustering, the uncertainty of new energy forecast errors is transformed into certain typical scenarios, specifically: The Latin hypercube sampling method is used to sample the power prediction errors of all new energy sources based on the constructed prediction error distribution probability model, and then superimposed with the predicted power to obtain a large number of operation scenarios; The clustering algorithm clusters the scenes and obtains M typical scenes.

5. A short-circuit current optimization method considering coordination between new energy and conventional measures as claimed in claim 1, characterized in that: The typical scenario in which the three-phase short-circuit current exceeds the standard is specifically: if in the typical scenario, the three-phase short-circuit current at a certain node exceeds the circuit breaker breaking current, then the typical scenario is a typical scenario in which the three-phase short-circuit current exceeds the standard.

6. A short-circuit current optimization method considering coordination between new energy and conventional measures as claimed in claim 1, characterized in that: The constraints of the short-circuit current comprehensive coordinated optimization model include: short-circuit current constraints, line state constraints, unit state constraints, upper and lower limit constraints of new energy parameters, system balance constraints, reactive voltage constraints and grid topology connectivity constraints.

7. A short-circuit current optimization system considering the coordination of new energy and conventional measures, characterized in that: include: The prediction error distribution model building module is used to model the prediction error distribution probability of new energy. Through sampling and clustering, the prediction error uncertainty of new energy is transformed into a certain typical scenario. The short-circuit current exceeding standard calculation module is used to calculate the three-phase short-circuit current of each node in each typical scenario, and add the typical scenarios with three-phase short-circuit current exceeding standard to the short-circuit current exceeding standard operation scenario set; The module for constructing a comprehensive coordinated optimization model of short-circuit current is used to consider conventional measures and new energy measures for suppressing short-circuit current for each scenario in the set of short-circuit current exceeding standard operation scenarios, and to construct a comprehensive coordinated optimization model of short-circuit current with the minimum total control cost as the optimization goal; the module for constructing a comprehensive coordinated optimization model of short-circuit current with the minimum total control cost as the optimization goal is specifically as follows: ； in, , and They are the control cost functions of line disconnection control cost, unit start-stop control cost and adjustment of new energy parameters; For the l The control cost of the line disconnection measure is y m is the control cost generated by the start-stop measures of the mth unit, z n N is the control cost generated by adjusting the nth new energy parameter; L is the number of line disconnection measures, N G is the number of unit start-stop control measures, N R To adjust the number of new energy parameter measures; The short-circuit current optimization control module is used to solve the short-circuit current comprehensive coordination optimization model based on the particle swarm optimization algorithm of the hybrid update strategy, obtain N control measures with the minimum control cost, perform safety verification on the N control measures, and obtain the final short-circuit current optimization control measures.

8. A short-circuit current optimization system considering coordination between new energy and conventional measures as claimed in claim 7, characterized in that: The short-circuit current comprehensive coordinated optimization model includes conventional measures and new energy measures for suppressing short-circuit current; The conventional measures include grid structure adjustment and unit start-up and shutdown; the grid structure adjustment reduces the short-circuit current of the node exceeding the standard by disconnecting the transmission lines around the node with the short-circuit current exceeding the standard and changing the grid structure; the unit start-up and shutdown reduces the short-circuit current of the node exceeding the standard by shutting down the units around the node with the short-circuit current exceeding the standard; The new energy measures include changing the reactive current support coefficient K of the new energy low voltage ride-through control strategy. q and reactive current maximum limit I qmax ; By adjusting the K of new energy q and I qmax value, changes the reactive injection current during the grid fault period, and then reduces the current of the node where the short-circuit current exceeds the standard, achieving the purpose of controlling the short-circuit current.

9. A terminal device, comprising a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions, characterized in that: The instructions are suitable for being loaded by a processor and executing the short-circuit current optimization method considering the coordination of new energy and conventional measures as described in any one of claims 1 to 6.

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