New energy power grid short-circuit current control method based on inertial particle swarm optimization

Abstract

The invention provides a new energy power grid short-circuit current control method based on an inertial particle swarm algorithm, and relates to the technical field of electric power. The method comprises the following steps: firstly, calculating system maximum short-circuit current supplied by an alternating-current source, and judging whether the current exceeds an allowable threshold; if the standard exceeds the standard and is caused by the energy storage power station, an optimization model with the maximum energy storage total capacity as the target is constructed, and capacity, short-circuit current and voltage constraints are set; iterative solution is carried out by adopting an inertial particle swarm algorithm, particle positions and speeds are dynamically updated, and a global optimal energy storage configuration scheme is obtained; and finally, regulating and controlling the access capacity of the energy storage power station and the current-limiting parameter of the converter to ensure that the short-circuit current contribution is lower than the system residual capacity. By optimizing the energy storage configuration and considering the short-circuit current suppression and the capacity utilization rate, the problem that the short-circuit current of the high-proportion new energy power grid exceeds the standard is solved, and the safety and the economical efficiency of the system are improved.

Description

Technical Field

[0001] This invention relates to the field of power technology, specifically to a short-circuit current control method for new energy power grids based on inertial particle swarm optimization algorithm. Background Technology

[0002] Against the backdrop of accelerated construction of new power systems, the high proportion of renewable energy grid connection and the widespread application of power electronic equipment have fundamentally changed the short-circuit current characteristics of the power grid. With the large-scale integration of renewable energy plants such as wind power and photovoltaic power, as well as energy storage power stations, the short-circuit current level of the power grid in some areas has gradually approached or even exceeded the rated breaking capacity of switchgear, seriously threatening the safe operation of the power grid. Traditional rigid control measures such as line shutdown and loop-breaking operation can temporarily alleviate the problem of excessive short-circuit current, but they are difficult to adapt to the dynamic changes in the power grid operation mode and will significantly weaken the reliability of power supply and the renewable energy absorption capacity of the power grid. Especially in receiving-end power grids and areas rich in renewable energy, the complex control characteristics of power electronic equipment make the short-circuit current highly coupled with equipment parameters and control strategies, further increasing the difficulty of precise short-circuit current control.

[0003] Existing short-circuit current control technologies face three major challenges: First, the voltage-controlled current source characteristics of power electronic devices render traditional synchronous machine-based short-circuit current calculation methods ineffective, necessitating the development of new calculation models adapted to multi-terminal power supply systems. Second, existing control methods often employ fixed limits or local optimization strategies, lacking a collaborative optimization mechanism for system-level short-circuit current and energy storage capacity, making it difficult to maximize renewable energy utilization while ensuring system safety. Third, conventional optimization algorithms are prone to getting trapped in local optima when solving high-dimensional nonlinear constraint problems, failing to effectively balance the relationship between short-circuit current suppression and the economic viability of energy storage configuration. These problems severely restrict the safe and stable operation of high-proportion renewable energy power grids, urgently requiring the development of a flexible short-circuit current control method that can adapt to changes in grid operation modes and balance safety and economy.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a short-circuit current control method for new energy power grids based on inertial particle swarm optimization algorithm, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A short-circuit current control method for new energy power grids based on inertial particle swarm optimization algorithm, comprising the following steps:

[0008] Step 1: In a multi-terminal power system that includes new energy power plants and energy storage power stations, calculate the maximum short-circuit current supplied by the AC source within the multi-terminal power system.

[0009] Step 2: Determine whether the maximum short-circuit current of the system exceeds the allowable threshold. If it exceeds the allowable threshold and is caused by the grid connection of the energy storage power station, then construct a short-circuit current optimization control model with the goal of maximizing the total capacity of the energy storage power station.

[0010] Step 3: Set constraints including energy storage capacity constraints, short-circuit current below the maximum threshold constraints, short-circuit current below the minimum threshold constraints, and node voltage constraints;

[0011] Step 4: Use the inertial particle swarm optimization algorithm to solve the short-circuit current optimization control model. By iteratively updating the particle position and velocity, obtain the globally optimal energy storage configuration scheme that satisfies the constraints.

[0012] Step 5: Adjust the access capacity of the energy storage power station based on the optimization results to achieve short-circuit current control.

[0013] Furthermore, the method for obtaining the maximum short-circuit current of the system is as follows: determine the nodes of the multi-terminal power system, calculate the short-circuit current of each node, wherein the nodes include the AC power grid connection point, the energy storage power station grid connection point and the new energy collection bus, and take the maximum value of the short-circuit current of all AC power grid connection points as the maximum short-circuit current of the system, wherein the short-circuit current is the ratio of voltage to reactance at the corresponding node.

[0014] Furthermore, if the maximum short-circuit current of the multi-terminal power system does not exceed the allowable threshold, the process is terminated; if the maximum short-circuit current of the multi-terminal power system exceeds the allowable threshold and the energy storage power station is not connected to the grid, it is determined that there is a defect and a warning signal is generated.

[0015] Furthermore, detecting whether a short circuit is caused by the grid connection of an energy storage power station specifically includes:

[0016] When the energy storage power station is not connected to the grid, the maximum short-circuit current of all AC power grid-connected nodes does not exceed the allowable threshold. However, after the energy storage power station is connected to the grid, if the maximum short-circuit current of all AC power grid-connected nodes exceeds the allowable threshold, it is determined that the excessive short-circuit current is caused by the grid connection of the energy storage power station. Conversely, if the maximum short-circuit current of all AC power grid-connected nodes still does not exceed the allowable threshold, it is determined that the excessive short-circuit current is not caused by the grid connection of the energy storage power station.

[0017] Furthermore, determining the objective function and constraints specifically includes:

[0018] The objective function is: Where F represents the objective function, S PVnThis represents the access capacity of the nth energy storage power station, where n is the index of the energy storage power station, N is the total number of energy storage power stations, and n∈[1,N];

[0019] Set four constraints:

[0020] First, the circuit breakers are classified according to their location: AC source output circuit breakers, energy storage power station output circuit breakers, and two-phase short-circuit fault circuit breakers.

[0021] a. The specific energy storage capacity constraint is: 0 ≤ S PVn ≤S PV,max , among which, S PV,max This represents the maximum capacity of a single energy storage power station connected to the power grid.

[0022] b. Short-circuit current below the maximum threshold constraint includes AC source outlet fault constraint and energy storage power station outlet fault constraint. The AC source outlet fault constraint is specifically as follows: Where S represents the capacity of the energy storage power station closest to the circuit breaker to be analyzed, and the circuit breaker to be analyzed is an AC source output circuit breaker, an energy storage power station output circuit breaker, or a two-phase short-circuit fault circuit breaker. In this case, the circuit breaker to be analyzed is... k Lvrt I represents the low-voltage ride-through reactive power support coefficient of the energy storage converter in the energy storage power station closest to the circuit breaker being analyzed. max Indicates the maximum allowable short-circuit current of the system. This indicates that when a three-phase short-circuit fault occurs, current flows through the AC source output circuit breaker. The short-circuit current value is calculated based on the energy storage power station capacity and the low-voltage ride-through reactive power support coefficient. Indicates the yth ac One AC power output circuit breaker, y ac Index of AC source output circuit breakers;

[0023] The specific fault constraints at the outlet of the energy storage power station are as follows: in, This indicates that when a three-phase short-circuit fault occurs, current flows through the circuit breaker at the outlet of the energy storage power station. The short-circuit current value, Indicates the yth ess One energy storage power station output circuit breaker, y ess Index of circuit breakers at the outlet of energy storage power stations;

[0024] c. The minimum threshold constraint for short-circuit current is specifically as follows: Among them, I min This indicates the minimum allowable short-circuit current of the system. This indicates that when a two-phase short-circuit fault occurs, current flows through the two-phase short-circuit fault circuit breaker. The short-circuit current value, Indicates the ythphase A two-phase short-circuit fault circuit breaker, y phase Index of circuit breakers for two-phase short-circuit faults;

[0025] d. The node voltage constraints are as follows: This indicates that when a three-phase short-circuit fault occurs, current flows through the outlet circuit breaker of the energy storage power station. The bus G voltage sag factor, This indicates that when a three-phase short-circuit fault occurs, current flows through the outlet circuit breaker of the energy storage power station. The voltage drop factor of bus C.

[0026] Furthermore, the use of the inertial particle swarm optimization algorithm specifically includes:

[0027] K particles are randomly generated. Each particle includes a position vector and a velocity vector. The position vector represents a set of energy storage power station capacity configuration schemes, and the velocity vector represents the direction and magnitude of capacity adjustment.

[0028] Analyze the capacity configuration scheme of each particle, obtain the total capacity of the energy storage power station corresponding to each particle, the short-circuit current value flowing through each circuit breaker to be analyzed, and the voltage drop coefficient. Calculate the fitness value of each particle according to the objective function and constraints.

[0029] The particle velocity is updated based on the inertia weight and the learning factor, using the following formula:

[0030] v d (i,t)=ωv d (i,t-1)+c1rand(0,1)[pbest(i,t-1)-p(i,t-1)]+c2rand(0,1)[gbest(i,t-1)-p(i,t-1)]

[0031] Among them, v d (i,t) represents the velocity component of the d-th dimension of the i-th particle in the t-th iteration, where i represents the particle index, t represents the index of the iteration round, d represents the index of the velocity component dimension, d=1 represents the adjustment direction, d=2 represents the adjustment magnitude, ω is the inertia weight, c1 and c2 are learning factors, rand(0,1) represents the random number function, i.e., taking random numbers between [0,1], pbest(i,t-1) represents the historical individual best position of the i-th particle up to the t-1th iteration, gbest(i,t-1) represents the historical global best position up to the t-1th iteration, and p(i,t-1) represents the position of the i-th particle in the t-1th iteration;

[0032] The particle position is corrected based on the updated velocity, using the following formula:

[0033] p(i,t)=p(i,t-1)+v(i,t)

[0034] Where p(i,t) represents the position of the i-th particle in the t-th iteration, and v(i,t) represents the velocity of the i-th particle in the t-th iteration;

[0035] The method for obtaining the historical individual optimal position is as follows: up to the current iteration round, for any particle, sort the fitness values ​​of the particle in each iteration round, and take the position with the largest fitness value as the historical individual optimal position of the particle. The method for obtaining the historical global optimal position is as follows: up to the current iteration round, count the historical individual optimal positions of all particles, and take the position with the largest fitness value as the historical global optimal position.

[0036] The out-of-bounds value is corrected. If the capacity of the nth energy storage power station is greater than the maximum capacity of a single energy storage power station connected to the grid, the out-of-bounds value is forcibly set to the maximum capacity of a single energy storage power station connected to the grid. If the particle velocity exceeds the maximum velocity, it is truncated to the boundary value.

[0037] The termination condition is when the current iteration count reaches the maximum iteration count, at which point the globally optimal energy storage configuration scheme is output.

[0038] Furthermore, calculating the fitness value specifically includes:

[0039] The formula for calculating the fitness value is:

[0040]

[0041] Where f(i,t) is the fitness value of the i-th particle in the t-th iteration, F(i,t) is the normalized total capacity of the energy storage power station of the i-th particle in the t-th iteration, λ is the penalty coefficient, and P m Let m be the proximity of the i-th particle to the m-th constraint in the t-th iteration, where m is the constraint index and m∈[1,4].

[0042] The specific method for calculating the proximity amount is as follows:

[0043] The approximation method for the energy storage capacity constraint is as follows:

[0044]

[0045] Where P1(i,t) represents the proximity of the i-th particle to all energy storage stations approaching the energy storage capacity constraint in the t-th iteration, and S PVn (i,t) represents the access capacity of the nth energy storage power station for the i-th particle in the t-th iteration;

[0046] The method for calculating the approximation of the constraint condition where the short-circuit current is below the maximum threshold constraint is as follows:

[0047]

[0048] Among them, P b1 (i,t) represents the approximation of the i-th particle to the AC source outlet fault constraint in the t-th iteration, Y ac The number of circuit breakers at the AC source output;

[0049]

[0050] Among them, P 22 (i,t) represents the proximity of the i-th particle to the constraint condition of the energy storage power station outlet fault in the t-th iteration, Y ess This refers to the number of circuit breakers at the outlet of the energy storage power station.

[0051] P2(i,t)=P 21 (i,t)+P 22 (i,t)

[0052] Where P2(i,t) represents the amount by which the i-th particle approaches the constraint condition that the short-circuit current is below the maximum threshold in the t-th iteration;

[0053] The method for calculating the approximation of the constraint condition where the short-circuit current is below the minimum threshold is as follows:

[0054]

[0055] Where P3(i,t) represents the amount by which the i-th particle approaches the minimum threshold constraint in the t-th iteration, and Y phase The number of circuit breakers for two-phase short-circuit faults;

[0056] The approximation method for the nodal voltage constraint is as follows:

[0057]

[0058] Where P4(i,t) represents the approximation of the i-th particle to the node voltage constraint in the t-th iteration. This indicates that when the i-th particle experiences a three-phase short-circuit fault in the t-th iteration, the current flows through the outlet circuit breaker of the energy storage power station. The bus voltage drop factor M is the bus type, where M ∈ {G, C}.

[0059] Furthermore, the inertia weight update method specifically includes:

[0060] The update formula for the inertia weight is as follows:

[0061]

[0062] Where, ω t Let ω be the inertia weight at the t-th iteration. max For the maximum inertia weight, ω min The minimum inertia weight is T, where t is the current iteration number. max This represents the maximum number of iterations.

[0063] Furthermore, regulating the grid connection capacity of energy storage power stations specifically includes:

[0064] The globally optimal energy storage configuration scheme is distributed to the control systems of each energy storage power station. Based on the capacity configuration results of the globally optimal energy storage configuration scheme, the current limiting parameters of the energy storage converter are dynamically adjusted so that the contribution of short-circuit current is lower than the remaining capacity allowed by the system.

[0065] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0066] This paper addresses the problem of excessive short-circuit current caused by the grid connection of dense renewable energy power plants, and proposes a short-circuit current control method for renewable energy power grids based on the inertial particle swarm optimization algorithm. This method, through the inertial particle swarm optimization algorithm, can identify power plants with excessive short-circuit current and proposes short-circuit current control and control parameter adjustment schemes for renewable energy power plants. It provides a new technical means for short-circuit current control in renewable energy power grids, ensuring the safe and stable operation of power grids with a high proportion of renewable energy. Attached Figure Description

[0067] Figure 1 This is a schematic diagram of the overall method flow of the present invention. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0069] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0070] Example:

[0071] Please see Figure 1 The present invention provides a technical solution:

[0072] A short-circuit current control method for new energy power grids based on inertial particle swarm optimization algorithm, comprising the following steps:

[0073] Step 1: In a multi-terminal power system that includes new energy power plants and energy storage power stations, calculate the maximum short-circuit current supplied by the AC source within the multi-terminal power system.

[0074] In this embodiment, the method for obtaining the maximum short-circuit current of the system is as follows: determine the nodes of the multi-terminal power system, calculate the short-circuit current of each node, the nodes include AC power grid connection points, energy storage power station grid connection points and new energy collection bus, and take the maximum value of the short-circuit current of all AC power grid connection points as the maximum short-circuit current of the system, the short-circuit current being the ratio of voltage to reactance at the corresponding node.

[0075] AC power grid connection point refers to the connection point of traditional synchronous generators (such as thermal power and hydropower) or external power grids; energy storage power station grid connection point refers to the connection point between energy storage AC device and AC system; new energy aggregation bus refers to the centralized grid connection bus of new energy clusters such as wind farms and photovoltaic power stations.

[0076] For each AC power source connection point, calculate its short-circuit current using the following formula:

[0077]

[0078] Where I represents the short-circuit current at the AC power supply grid connection point, U represents the rated voltage at the AC power supply grid connection point, and X represents the equivalent reactance at the AC power supply grid connection point, including the power supply internal resistance, transformer impedance, and line impedance.

[0079] Based on the system topology, a positive-sequence network diagram is drawn, ignoring negative-sequence and zero-sequence impedances (for symmetrical short-circuit calculations). For each AC power source grid connection point, the system is equivalent to a Thevenin circuit, and the equivalent reactance from that node to the system is calculated. The voltage value selection rules are: if per-unit values ​​are used, the voltage is taken as 1 p.u.; if actual values ​​are used, a reference voltage needs to be specified, such as 10.5 kV or 35 kV. Here, the series combination of generator subtransient reactance, transformer impedance, and line impedance is considered to obtain the actual equivalent reactance. All AC power source grid connection points are traversed, and the short-circuit current of all nodes is calculated. All calculation results are compared, and the maximum value is selected as the system's maximum short-circuit current.

[0080] Step 2: Determine whether the maximum short-circuit current of the system exceeds the allowable threshold. If it exceeds the allowable threshold and is caused by the grid connection of the energy storage power station, then construct a short-circuit current optimization control model with the goal of maximizing the total capacity of the energy storage power station.

[0081] In this embodiment, if the maximum short-circuit current of the multi-terminal power system does not exceed the allowable threshold, the system is deemed safe and the process is terminated; if the maximum short-circuit current of the multi-terminal power system exceeds the allowable threshold and the energy storage power station is not connected to the grid, it is determined that there is a defect and a warning signal is generated.

[0082] In this embodiment, detecting whether a short circuit is caused by the grid connection of an energy storage power station specifically includes:

[0083] When the energy storage power station is not connected to the grid, the maximum short-circuit current of all AC power grid-connected nodes does not exceed the allowable threshold. However, after the energy storage power station is connected to the grid, if the maximum short-circuit current of all AC power grid-connected nodes exceeds the allowable threshold, it is determined that the excessive short-circuit current is caused by the grid connection of the energy storage power station. Conversely, if the maximum short-circuit current of all AC power grid-connected nodes still does not exceed the allowable threshold, it is determined that the excessive short-circuit current is not caused by the grid connection of the energy storage power station.

[0084] The defects include those in multi-terminal power supply systems and other structural defects. Multi-terminal power supply system defects include insufficient short-circuit ratios and abnormal impedance matching in new energy units. Other structural defects include unreasonable busbar segmentation and inappropriate transformer impedance selection.

[0085] The warning signals include warning types and warning implementation methods. Warning types include hardware-level warnings, operational warnings, and planning-level warnings. Warning implementation methods include visual warnings, logic-linked warnings, and data reporting warnings. Hardware-level warnings: Indicate the need to replace or upgrade equipment, such as circuit breakers and current-limiting reactors; Operational warnings: Prompt adjustments to operating modes, such as turbine tripping or load transfer; Planning-level warnings: Recommend grid topology modifications or capacity expansion. Visual warnings: Display red alerts and specific over-limit nodes on the SCADA interface; Logic-linked warnings: Automatically trigger preset logic of protection devices, such as blocking energy storage grid connection; Data reporting warnings: Upload defect information to the cloud-based operation and maintenance platform to generate maintenance work orders. For example, if the short-circuit capacity of a wind farm's collector line is detected to exceed the standard, a planning-level warning is triggered, recommending the installation of series reactors. If the bus short-circuit current exceeds the standard and the protection setting is incorrect, a hardware-level warning is triggered and a trip is initiated.

[0086] By optimizing energy storage capacity allocation as described above, energy storage utilization is maximized while ensuring system safety. A multi-level warning mechanism ensures that defects are traceable and manageable, reducing the risk of human error in missing detections.

[0087] When grid connection of an energy storage power station leads to excessive short-circuit current in the system, an optimization control model is constructed with the goal of maximizing the total energy storage capacity. The input of this model is the particle position vector from the particle swarm optimization algorithm, representing the capacity configuration scheme of each energy storage unit. The output includes the total energy storage capacity, i.e., the objective function value, the short-circuit current value at each grid connection point, and the voltage drop coefficient of each node.

[0088] Step 3: Set constraints including energy storage capacity constraints, short-circuit current below the maximum threshold constraints, short-circuit current below the minimum threshold constraints, and node voltage constraints;

[0089] In this embodiment, determining the objective function and constraints specifically includes:

[0090] The objective function is: Where F represents the objective function, S PVn This represents the access capacity of the nth energy storage power station, where n is the index of the energy storage power station, N is the total number of energy storage power stations, and n∈[1,N];

[0091] Set four constraints:

[0092] First, the circuit breakers are classified according to their location: AC source output circuit breakers, energy storage power station output circuit breakers, and two-phase short-circuit fault circuit breakers.

[0093] a. The specific energy storage capacity constraint is: 0 ≤ S PVn ≤S PV,max , among which, S PV,maxThis represents the maximum capacity of a single energy storage power station connected to the grid; this constraint ensures that the capacity of the energy storage power station is within a reasonable range, avoiding the impact on grid stability due to excessive capacity.

[0094] Short-circuit current exceeding the maximum allowable value may cause malfunction of protection devices or damage to equipment; therefore, short-circuit current must be limited. This embodiment sets constraints separately for circuit breakers in different locations:

[0095] b. Short-circuit current below the maximum threshold constraint includes AC source outlet fault constraint and energy storage power station outlet fault constraint. The AC source outlet fault constraint is specifically as follows: Where S represents the capacity of the energy storage power station closest to the circuit breaker to be analyzed, and the circuit breaker to be analyzed is an AC source output circuit breaker, an energy storage power station output circuit breaker, or a two-phase short-circuit fault circuit breaker. In this case, the circuit breaker to be analyzed is... k Lvrt I represents the low-voltage ride-through reactive power support coefficient of the energy storage converter in the energy storage power station closest to the circuit breaker being analyzed. max Indicates the maximum allowable short-circuit current of the system. This indicates that when a three-phase short-circuit fault occurs, current flows through the AC source output circuit breaker. The short-circuit current value is calculated based on the energy storage power station capacity and the low-voltage ride-through reactive power support coefficient. Indicates the yth ac One AC power output circuit breaker, y ac This is an index for the AC source output circuit breaker; when determining an AC source output fault, the circuit breaker to be analyzed is represented as...

[0096] The specific fault constraints at the outlet of the energy storage power station are as follows: in, This indicates that when a three-phase short-circuit fault occurs, current flows through the circuit breaker at the outlet of the energy storage power station. The short-circuit current value, Indicates the yth ess One energy storage power station output circuit breaker, y ess This is an index of the circuit breakers at the outlet of the energy storage power station; when determining an outlet fault at the energy storage power station, the circuit breaker to be analyzed is represented as...

[0097] c. The minimum threshold constraint for short-circuit current is specifically as follows: Among them, I min This indicates the minimum allowable short-circuit current of the system. This indicates that when a two-phase short-circuit fault occurs, current flows through the two-phase short-circuit fault circuit breaker. The short-circuit current value, Indicates the yth phase A two-phase short-circuit fault circuit breaker, y phaseThis is an index of circuit breakers for two-phase short-circuit faults; when it is determined that a two-phase short-circuit fault has occurred, the circuit breaker to be analyzed is represented as follows:

[0098] d. The node voltage constraints are as follows: This indicates that when a three-phase short-circuit fault occurs, current flows through the outlet circuit breaker of the energy storage power station. The bus G voltage sag factor, This indicates that when a three-phase short-circuit fault occurs, current flows through the outlet circuit breaker of the energy storage power station. The voltage drop factor of bus C.

[0099] In this embodiment, the short-circuit current value is calculated based on the energy storage power station capacity and the low-voltage ride-through reactive power support coefficient. The specific calculation method can adopt the power grid short-circuit current calculation standard, such as IEC 60909 or GB / T 15544.

[0100] Constraining energy storage capacity limits the capacity of individual energy storage power stations, preventing excessive capacity from impacting grid stability. Constraining the upper limit of short-circuit current prevents excessive short-circuit current from damaging equipment or causing protection malfunctions. Constraining the lower limit of short-circuit current ensures that the short-circuit current is sufficiently large to allow protection devices to reliably detect faults. Constraining node voltage maintains grid voltage stability and prevents voltage dips from affecting equipment operation.

[0101] Step 4: Use the inertial particle swarm optimization algorithm to solve the short-circuit current optimization control model. By iteratively updating the particle position and velocity, obtain the globally optimal energy storage configuration scheme that satisfies the constraints.

[0102] In this embodiment, the inertial particle swarm optimization algorithm specifically includes:

[0103] K particles are randomly generated. Each particle includes a position vector and a velocity vector. The position vector represents a set of energy storage power station capacity configuration schemes, and the velocity vector represents the direction and magnitude of capacity adjustment.

[0104] The parameter settings include the maximum number of iterations, the inertia weight range, the learning factor, and the penalty coefficient. Here is a sample example: the maximum number of iterations is set to 100, the inertia weight range is [0.4, 0.9], the learning factor c1,c2 = 2, and the penalty coefficient is 10. The penalty coefficient can be adjusted according to the degree of constraint violation.

[0105] Analyze the capacity configuration scheme of each particle, obtain the total capacity of the energy storage power station corresponding to each particle, the short-circuit current value flowing through each circuit breaker to be analyzed, and the voltage drop coefficient. Calculate the fitness value of each particle according to the objective function and constraints.

[0106] In this embodiment, calculating the fitness value specifically includes:

[0107] The formula for calculating the fitness value is:

[0108]

[0109] Where f(i,t) is the fitness value of the i-th particle in the t-th iteration, F(i,t) is the normalized total capacity of the energy storage power station of the i-th particle in the t-th iteration, λ is the penalty coefficient, and P m Let m be the proximity of the i-th particle to the m-th constraint in the t-th iteration, where m is the constraint index and m∈[1,4].

[0110] The specific method for calculating the proximity amount is as follows:

[0111] The approximation method for the energy storage capacity constraint is as follows:

[0112]

[0113] Where P1(i,t) represents the proximity of the i-th particle to all energy storage stations approaching the energy storage capacity constraint in the t-th iteration, and S PVn (i,t) represents the access capacity of the nth energy storage power station for the i-th particle in the t-th iteration;

[0114] The method for calculating the approximation of the constraint condition where the short-circuit current is below the maximum threshold constraint is as follows:

[0115]

[0116] Among them, P b1 (i,t) represents the approximation of the i-th particle to the AC source outlet fault constraint in the t-th iteration, Y ac The number of circuit breakers at the AC source output;

[0117]

[0118] Among them, P 22 (i,t) represents the proximity of the i-th particle to the constraint condition of the energy storage power station outlet fault in the t-th iteration, Y ess This refers to the number of circuit breakers at the outlet of the energy storage power station.

[0119] P2(i,t)=P 21 (i,t)+P 22 (i,t)

[0120] Where P2(i,t) represents the amount by which the i-th particle approaches the constraint condition that the short-circuit current is below the maximum threshold in the t-th iteration;

[0121] The method for calculating the approximation of the constraint condition where the short-circuit current is below the minimum threshold is as follows:

[0122]

[0123] Where P3(i,t) represents the amount by which the i-th particle approaches the minimum threshold constraint in the t-th iteration, and Y phase The number of circuit breakers for two-phase short-circuit faults;

[0124] The approximation method for the nodal voltage constraint is as follows:

[0125]

[0126] Where P4(i,t) represents the approximation of the i-th particle to the node voltage constraint in the t-th iteration. This indicates that when the i-th particle experiences a three-phase short-circuit fault in the t-th iteration, the current flows through the outlet circuit breaker of the energy storage power station. The bus voltage drop factor M is the bus type, where M ∈ {G, C}.

[0127] A larger proximity value indicates that the object is further away from the constraint; therefore, the larger the values ​​of each proximity value, the better.

[0128] The formula for updating particle velocity is:

[0129] v d (i,t)=ωv d (i,t-1)+c1rand(0,1)[pbest(i,t-1)-p(i,t-1)]+c2rand(0,1)[gbest(i,t-1)-p(i,t-1)]

[0130] Among them, v d (i,t) represents the velocity component of the d-th dimension of the i-th particle in the t-th iteration, where i represents the particle index, t represents the index of the iteration round, d represents the index of the velocity component dimension, d=1 represents the adjustment direction, d=2 represents the adjustment magnitude, ω is the inertia weight, c1 and c2 are learning factors, rand(0,1) represents the random number function, i.e., taking random numbers between [0,1], pbest(i,t-1) represents the historical individual best position of the i-th particle up to the t-1th iteration, gbest(i,t-1) represents the historical global best position up to the t-1th iteration, and p(i,t-1) represents the position of the i-th particle in the t-1th iteration;

[0131] The particle position is corrected based on the updated velocity, using the following formula:

[0132] p(i,t)=p(i,t-1)+v(i,t)

[0133] Where p(i,t) represents the position of the i-th particle at the t-th iteration, and v(i,t) represents the velocity of the i-th particle at the t-th iteration. Here, the velocity is the velocity components of the two dimensions obtained earlier, which are combined into a single velocity.

[0134] The method for obtaining the historical individual optimal position is as follows: up to the current iteration round, for any particle, sort the fitness values ​​of the particle in each iteration round, and take the position with the largest fitness value as the historical individual optimal position of the particle. The method for obtaining the historical global optimal position is as follows: up to the current iteration round, count the historical individual optimal positions of all particles, and take the position with the largest fitness value as the historical global optimal position.

[0135] The out-of-bounds value is corrected. If the capacity of the nth energy storage power station is greater than the maximum capacity of a single energy storage power station connected to the grid, the out-of-bounds value is forcibly set to the maximum capacity of a single energy storage power station connected to the grid. If the particle velocity exceeds the maximum velocity, it is truncated to the boundary value.

[0136] The termination condition is when the current iteration count reaches the maximum iteration count, at which point the globally optimal energy storage configuration scheme is output. Alternatively, the planting condition can be set to output the globally optimal solution when the fitness value converges; for example, if the change in the globally optimal solution is less than 1% over 10 consecutive iterations, then the fitness value is considered converged.

[0137] In this embodiment, the inertia weight update method specifically includes:

[0138] The update formula for the inertia weight is as follows:

[0139]

[0140] Where, ω t Let ω be the inertia weight at the t-th iteration. max For the maximum inertia weight, ω min The minimum inertia weight is T, where t is the current iteration number. max This represents the maximum number of iterations.

[0141] Step 5: Adjust the access capacity of the energy storage power station based on the optimization results to achieve short-circuit current control.

[0142] In this embodiment, regulating the access capacity of the energy storage power station specifically includes:

[0143] The globally optimal energy storage configuration scheme is distributed to the control systems of each energy storage power station. Based on the capacity configuration results of the globally optimal energy storage configuration scheme, the current limiting parameters of the energy storage converter are dynamically adjusted to ensure that the contribution of short-circuit current is lower than the system's allowable remaining capacity. This is achieved under the following conditions:

[0144] I PV =f(S) PV * ,k lim )≤I max -I

[0145] Among them, I PV S represents the actual current contributed by the energy storage power station during a fault. PV * For the globally optimal energy storage capacity configuration scheme obtained through inertial particle swarm optimization, k lim I represents the current limiting coefficient of the energy storage converter. max This represents the maximum short-circuit current of the system, and I represents the short-circuit current of the AC source.

[0146] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0147] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0148] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0149] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A short-circuit current control method for new energy power grids based on inertial particle swarm optimization algorithm, characterized in that, The specific steps include: Step 1: In a multi-terminal power system that includes new energy power plants and energy storage power stations, calculate the maximum short-circuit current supplied by the AC source within the multi-terminal power system; Step 2: Determine whether the maximum short-circuit current of the system exceeds the allowable threshold. If it exceeds the allowable threshold and is caused by the grid connection of the energy storage power station, then construct a short-circuit current optimization control model with the goal of maximizing the total capacity of the energy storage power station. Step 3: Set constraints including energy storage capacity constraints, short-circuit current below the maximum threshold constraints, short-circuit current below the minimum threshold constraints, and node voltage constraints; Step 4: Use the inertial particle swarm optimization algorithm to solve the short-circuit current optimization control model. By iteratively updating the particle position and velocity, obtain the globally optimal energy storage configuration scheme that satisfies the constraints. Step 5: Adjust the access capacity of the energy storage power station based on the optimization results to achieve short-circuit current control.

2. The method for short-circuit current control in a new energy power grid based on inertial particle swarm optimization algorithm according to claim 1, characterized in that, The method for obtaining the maximum short-circuit current of the system is as follows: determine the nodes of the multi-terminal power system, calculate the short-circuit current of each node, the nodes include the AC power grid connection point, the energy storage power station grid connection point and the new energy collection bus, and take the maximum value of the short-circuit current of all AC power grid connection points as the maximum short-circuit current of the system, the short-circuit current is the ratio of voltage to reactance at the corresponding node.

3. The method for short-circuit current control in a new energy power grid based on inertial particle swarm optimization algorithm according to claim 1, characterized in that, If the maximum short-circuit current of the multi-terminal power supply system does not exceed the allowable threshold, the process is terminated. If the maximum short-circuit current of the multi-terminal power system exceeds the allowable threshold, and the energy storage power station is not connected to the grid, it is determined to be defective and a warning signal is generated.

4. The method for short-circuit current control in a new energy power grid based on inertial particle swarm optimization algorithm according to claim 3, characterized in that, Detecting whether a short circuit is caused by the grid connection of an energy storage power station specifically includes: When the energy storage power station is not connected to the grid, the maximum short-circuit current of all AC power grid-connected nodes does not exceed the allowable threshold. However, after the energy storage power station is connected to the grid, if the maximum short-circuit current of all AC power grid-connected nodes exceeds the allowable threshold, it is determined that the excessive short-circuit current is caused by the grid connection of the energy storage power station. Conversely, if the maximum short-circuit current of all AC power grid-connected nodes still does not exceed the allowable threshold, it is determined that the excessive short-circuit current is not caused by the grid connection of the energy storage power station.

5. The method for short-circuit current control in a new energy power grid based on inertial particle swarm optimization algorithm according to claim 3, characterized in that, Determining the objective function and constraints specifically includes: The objective function is: Where F represents the objective function, S PVn This represents the access capacity of the nth energy storage power station, where n is the index of the energy storage power station, N is the total number of energy storage power stations, and n∈[1,N]; Set four constraints: First, the circuit breakers are classified according to their location: AC source output circuit breakers, energy storage power station output circuit breakers, and two-phase short-circuit fault circuit breakers. a. The specific energy storage capacity constraint is: 0 ≤ S PVn ≤S PV,max , among which, S PV,max This represents the maximum capacity of a single energy storage power station connected to the power grid. b. Short-circuit current below the maximum threshold constraint includes AC source outlet fault constraint and energy storage power station outlet fault constraint. The AC source outlet fault constraint is specifically as follows: Where S represents the capacity of the energy storage power station closest to the circuit breaker to be analyzed, and the circuit breaker to be analyzed is an AC source output circuit breaker, an energy storage power station output circuit breaker, or a two-phase short-circuit fault circuit breaker. In this case, the circuit breaker to be analyzed is... k Lvrt I represents the low-voltage ride-through reactive power support factor of the energy storage converter in the energy storage power station closest to the circuit breaker being analyzed. max Indicates the maximum allowable short-circuit current of the system. This indicates that when a three-phase short-circuit fault occurs, current flows through the AC source output circuit breaker. The short-circuit current value is calculated based on the energy storage power station capacity and the low-voltage ride-through reactive power support coefficient. Indicates the yth ac One AC power output circuit breaker, y ac Index of AC source output circuit breakers; The specific fault constraints at the outlet of the energy storage power station are as follows: in, This indicates that when a three-phase short-circuit fault occurs, current flows through the circuit breaker at the outlet of the energy storage power station. The short-circuit current value, Indicates the yth ess One energy storage power station output circuit breaker, y ess Index of circuit breakers at the outlet of energy storage power stations; c. The minimum threshold constraint for short-circuit current is specifically as follows: Among them, I min This indicates the minimum allowable short-circuit current of the system. This indicates that when a two-phase short-circuit fault occurs, current flows through the two-phase short-circuit fault circuit breaker. The short-circuit current value, Indicates the yth phase A two-phase short-circuit fault circuit breaker, y phase Index of circuit breakers for two-phase short-circuit faults; d. The node voltage constraints are as follows: This indicates that when a three-phase short-circuit fault occurs, current flows through the outlet circuit breaker of the energy storage power station. The bus G voltage sag factor, This indicates that when a three-phase short-circuit fault occurs, current flows through the outlet circuit breaker of the energy storage power station. The voltage drop factor of bus C.

6. The method for short-circuit current control in a new energy power grid based on inertial particle swarm optimization algorithm according to claim 5, characterized in that, The inertial particle swarm optimization algorithm specifically includes: K particles are randomly generated. Each particle includes a position vector and a velocity vector. The position vector represents a set of energy storage power station capacity configuration schemes, and the velocity vector represents the direction and magnitude of capacity adjustment. Analyze the capacity configuration scheme of each particle, obtain the total capacity of the energy storage power station corresponding to each particle, the short-circuit current value flowing through each circuit breaker to be analyzed, and the voltage drop coefficient. Calculate the fitness value of each particle according to the objective function and constraints. The particle velocity is updated based on the inertia weight and the learning factor, using the following formula: v d (i,t)=ωv d (i,t-1)+c1rand(0,1)[pbest(i,t-1)-p(i,t-1)]+c2rand(0,1)[gbest(i,t-1)-p(i,t-1)] Among them, v d (i,t) represents the velocity component of the d-th dimension of the i-th particle in the t-th iteration, where i represents the particle index, t represents the index of the iteration round, d represents the index of the velocity component dimension, d=1 represents the adjustment direction, d=2 represents the adjustment magnitude, ω is the inertia weight, c1 and c2 are learning factors, rand(0,1) represents the random number function, i.e., taking random numbers between [0,1], pbest(i,t-1) represents the historical individual best position of the i-th particle up to the t-1th iteration, gbest(i,t-1) represents the historical global best position up to the t-1th iteration, and p(i,t-1) represents the position of the i-th particle in the t-1th iteration; The particle position is corrected based on the updated velocity, using the following formula: p(i,t)=p(i,t-1)+v(i,t) Where p(i,t) represents the position of the i-th particle in the t-th iteration, and v(i,t) represents the velocity of the i-th particle in the t-th iteration; The method for obtaining the historical individual optimal position is as follows: up to the current iteration round, for any particle, sort the fitness values ​​of the particle in each iteration round, and take the position with the largest fitness value as the historical individual optimal position of the particle. The method for obtaining the historical global optimal position is as follows: up to the current iteration round, count the historical individual optimal positions of all particles, and take the position with the largest fitness value as the historical global optimal position. The out-of-bounds value is corrected. If the capacity of the nth energy storage power station is greater than the maximum capacity of a single energy storage power station connected to the grid, the out-of-bounds value is forcibly set to the maximum capacity of a single energy storage power station connected to the grid. If the particle velocity exceeds the maximum velocity, it is truncated to the boundary value. The termination condition is when the current iteration count reaches the maximum iteration count, at which point the globally optimal energy storage configuration scheme is output.

7. The method for short-circuit current control in a new energy power grid based on inertial particle swarm optimization algorithm according to claim 6, characterized in that, The calculation of fitness values ​​specifically includes: The formula for calculating the fitness value is: Where f(i,t) is the fitness value of the i-th particle in the t-th iteration, F(i,t) is the normalized total capacity of the energy storage power station of the i-th particle in the t-th iteration, λ is the penalty coefficient, and P m Let m be the proximity of the i-th particle to the m-th constraint in the t-th iteration, where m is the constraint index and m∈[1,4]. The specific method for calculating the proximity amount is as follows: The approximation method for the energy storage capacity constraint is as follows: Where P1(i,t) represents the proximity of the i-th particle to all energy storage stations approaching the energy storage capacity constraint in the t-th iteration, and S PVn (i,t) represents the access capacity of the nth energy storage power station for the i-th particle in the t-th iteration; The method for calculating the approximation of the constraint condition where the short-circuit current is below the maximum threshold constraint is as follows: Among them, P b1 (i,t) represents the approximation of the i-th particle to the AC source outlet fault constraint in the t-th iteration, Y ac The number of circuit breakers at the AC source output; Among them, P 22 (i,t) represents the approximation of the i-th particle to the constraint condition of the energy storage power station outlet fault in the t-th iteration, Y ess This refers to the number of circuit breakers at the outlet of the energy storage power station. P2(i,t)=P 21 (i,t)+P 22 (i,t) Where P2(i,t) represents the amount by which the i-th particle approaches the constraint condition that the short-circuit current is below the maximum threshold in the t-th iteration; The method for calculating the approximation of the constraint condition where the short-circuit current is below the minimum threshold is as follows: Where P3(i,t) represents the amount by which the i-th particle approaches the minimum threshold constraint in the t-th iteration, and Y phase The number of circuit breakers for two-phase short-circuit faults; The approximation method for the nodal voltage constraint is as follows: Where P4(i,t) represents the approximation of the node voltage constraint by the i-th particle in the t-th iteration. This indicates that when the i-th particle experiences a three-phase short-circuit fault in the t-th iteration, the current flows through the outlet circuit breaker of the energy storage power station. The bus voltage drop factor M is the bus type, where M ∈ {G, C}.

8. A method for controlling short-circuit current in a new energy power grid based on an inertial particle swarm optimization algorithm according to claim 6, characterized in that, The inertia weight update method specifically includes: The update formula for the inertia weight is as follows: Where, ω t Let ω be the inertia weight at the t-th iteration. max For the maximum inertia weight, ω min The minimum inertia weight is T, where t is the current iteration number. max This represents the maximum number of iterations.

9. A method for controlling short-circuit current in a new energy power grid based on an inertial particle swarm optimization algorithm according to claim 1, characterized in that, Regulating the access capacity of energy storage power stations specifically includes: The globally optimal energy storage configuration scheme is distributed to the control systems of each energy storage power station. Based on the capacity configuration results of the globally optimal energy storage configuration scheme, the current limiting parameters of the energy storage converter are dynamically adjusted so that the contribution of short-circuit current is lower than the remaining capacity allowed by the system.

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