Method and device for active voltage and frequency support of grid-forming energy storage under traction load scenarios, medium

Abstract

The application discloses a network-constructing energy storage voltage and frequency active support method and equipment, medium, and relates to the technical field of electrified railways. The method comprises the following steps: calculating the external power disturbance of a grid-connected point; establishing a system voltage and frequency dynamic prediction model based on a discrete state space and obtaining a state quantity prediction result; establishing a multi-objective optimization function; predicting the power variation of the traction load in a future control time domain and updating the external power disturbance, and meanwhile, based on the power variation, constructing a network-constructing energy storage standby power; establishing a network-constructing energy storage secondary control basic constraint set; forming a quadratic programming problem by combining the multi-objective optimization function with the basic constraint set, obtaining network-constructing energy storage active and reactive power increment control instructions, issuing the control instructions to an energy storage controller for execution and updating the system operation state, and feeding back the execution result to the state space prediction model. The application improves the voltage and frequency support capability of the network-constructing energy storage, and guarantees the safe and stable operation of the power system.

Description

Technical Field

[0001] This invention relates to the field of electrified railway technology, specifically to a method, equipment, and medium for active support of grid-connected energy storage voltage and frequency under traction load scenarios. Background Technology

[0002] In recent years, with the rapid growth of installed capacity of new energy sources, wind power, photovoltaic and other new energy power plants have become an important part of the power system. However, new energy sources are characterized by randomness, volatility and intermittency. Their large-scale integration leads to a decrease in system inertia, forming a high-proportion weak power grid. This weakens the system's frequency regulation and voltage support capabilities, making it prone to problems such as frequency deviation, voltage fluctuation and even large-scale power outages after disturbances, posing challenges to the safe and stable operation of the power system.

[0003] Furthermore, with the continuous expansion of electrified railways, traction loads have gradually become an important dynamic load in the power grid. Compared with conventional loads, traction loads are characterized by frequent power fluctuations, strong impact, and frequent switching between traction and regenerative braking conditions. Both their active and reactive power exhibit significant dynamic changes, further increasing the difficulty of system frequency regulation and voltage control. Grid-based energy storage, due to its rapid power response capability and ability to actively support voltage and frequency, has become an important technical means to enhance the active support capability of new energy power plants and ensure stable system operation. Existing frequency and voltage support methods based on grid-based energy storage are mostly designed for conventional load fluctuations or power grid fault scenarios, without fully considering the impact of the dynamic characteristics of traction loads on system frequency and voltage.

[0004] Therefore, to address the issue of active voltage and frequency support for grid-connected energy storage in scenarios with traction loads, it is urgent to propose a control method that considers the dynamic characteristics of traction loads in order to improve the voltage and frequency support capabilities of grid-connected energy storage and ensure the safe and stable operation of the power system. Summary of the Invention

[0005] This invention addresses the issue that most methods for supporting the frequency and voltage of grid-connected energy storage in scenarios with traction loads are designed for conventional load fluctuations or grid faults, without fully considering the impact of the dynamic characteristics of traction loads on system frequency and voltage. It provides an active voltage and frequency support method, equipment, and medium for grid-connected energy storage in traction load scenarios, which improves the voltage and frequency support capability of grid-connected energy storage and ensures the safe and stable operation of the power system.

[0006] Firstly, a method for actively supporting the voltage and frequency of grid-connected energy storage under traction load scenarios is provided, the method comprising:

[0007] Based on the active and reactive power, active reference power and reactive reference power of the traction load node, calculate the external power disturbance at the grid connection point.

[0008] Considering the external power disturbance and the external voltage disturbance, a dynamic prediction model of system voltage frequency based on discrete state space is established and the prediction results of state variables are obtained.

[0009] Based on the state variable prediction results, a multi-objective optimization function is established with the active power increment and reactive power increment of grid-type energy storage as optimization variables and the goal of minimizing the smoothness of frequency deviation, voltage deviation and control increment changes.

[0010] Based on the current and historical traction load change trends, the power change of the traction load in the future control time domain is predicted and the external power disturbance is updated. At the same time, based on the power change, a grid-type energy storage backup power is constructed.

[0011] Based on the state quantity prediction results and the reserve power, a set of basic constraints for secondary control of grid-type energy storage is established.

[0012] The multi-objective optimization function and the set of basic constraints are combined to form a quadratic programming problem, which yields incremental control commands for active and reactive power in the grid-type energy storage. These commands are then sent to the energy storage controller for execution and to update the system's operating status. The execution results are fed back to the state-space prediction model.

[0013] In some embodiments, based on the active and reactive power of the traction load node, and the active and reactive reference power, the external power disturbance at the grid connection point is calculated, including:

[0014] Establish a power flow model that includes the access of new energy power plants to the power system, and calculate the active power reference power and reactive power reference power based on the power flow model;

[0015] The differences between the active power and reactive power of the traction load node at the current moment, the active power reference power, and the reactive power reference power are calculated respectively, and the active power disturbance and reactive power disturbance at the grid connection point are calculated.

[0016] In some embodiments, considering the external power disturbance and the external voltage disturbance, a system voltage frequency dynamic prediction model based on discrete state space is established, including:

[0017] The voltage / frequency deviation at the grid connection point of the new energy power station, the active power output and reactive power output of the grid-type energy storage, and the state of charge of the energy storage are used as state variables.

[0018] The reference values ​​for the incremental active power output and reactive power output of grid-type energy storage are used as control quantities.

[0019] Considering the external power disturbance and the external voltage disturbance, a dynamic prediction model for system voltage frequency based on discrete state space is established.

[0020] In some embodiments, the multi-objective optimization function is:

[0021] ;

[0022] in, For frequency deviation weighting; As the voltage deviation weight; and These are the active power cost coefficient and reactive power cost coefficient for grid-type energy storage, respectively. and These are the changes in active power and reactive power calculated by the k-model predictive control during the current control period, respectively. and These are the active control increment and reactive control increment calculated in the previous control cycle, respectively. and These are the active power control command smoothing weight coefficient and the reactive power control command smoothing weight coefficient, respectively.

[0023] In some embodiments, constructing grid-based energy storage backup power based on the power change includes:

[0024] Based on the traction load input filtering time constant and control cycle, the prediction feedforward fusion coefficient is calculated:

[0025] ;

[0026] in, To predict the feedforward fusion coefficient, To control the cycle, Input the filter time constant for the traction load;

[0027] Based on the predicted feedforward fusion coefficients, the active power fusion disturbance and reactive power fusion disturbance are calculated respectively:

[0028] ;

[0029] ;

[0030] in, , These represent the predicted future equivalent changes in active power and reactive power, respectively, under the current control period k. , These are the active power control increment and the reactive power control increment, respectively. , The predicted future equivalent active power changes under the next control period k+1 are respectively:

[0031] Based on the fused disturbance, calculate the maximum active power reserve and the maximum reactive power reserve:

[0032] ;

[0033] ;

[0034] in, , These are the maximum active power reserve and the maximum reactive power reserve, respectively. , These are the current active power compensation allowable bandwidth and the current reactive power compensation allowable bandwidth, respectively.

[0035] In some embodiments, the basic constraint set includes: active power capacity constraints, active and reactive power ramp rate constraints, reactive power dynamic margin constraints, energy storage state of charge operation constraints, active power regulation command direction constraints, and reactive power regulation command direction constraints, wherein...

[0036] The active power capacity constraint is as follows:

[0037] ;

[0038] in, This represents the actual active power output of the grid-type energy storage system at the current moment. This represents the change in the active power output of the grid-type energy storage system at the next moment.

[0039] The active ramp rate constraint is:

[0040] ;

[0041] in, Active power ramp-up rate constraint for grid-type energy storage; The sampling period for model predictive control;

[0042] The reactive ramp rate constraint is:

[0043] ;

[0044] in, The reactive power ramp rate constraint for grid-type energy storage; The sampling period for model predictive control; This represents the change in reactive power output of the grid-type energy storage system at the next moment.

[0045] The reactive power dynamic margin constraint is:

[0046] ;

[0047] ;

[0048] in, This represents the actual reactive power output of the grid-type energy storage system at the next moment. This represents the upper limit of reactive power output for grid-type energy storage at the current moment.

[0049] The operational constraints for the charged energy storage state are:

[0050] ;

[0051] ;

[0052] in, The state of the energy storage SOC at the next moment; This represents the minimum SOC (State of Charge) for energy storage. This represents the maximum value of the energy storage SOC. For energy storage capacity, This refers to the charge / discharge efficiency.

[0053] The active power regulation command direction constraint and the reactive power regulation command direction constraint are respectively:

[0054] ;

[0055] ;

[0056] in, and These are the actual values ​​of the frequency and voltage at the grid connection point at the next moment, respectively. and These are the frequency reference value and voltage reference value for the grid connection point at the next moment, respectively.

[0057] In some embodiments, the method further includes: dynamically adjusting the frequency deviation weight and the voltage deviation weight according to the relative severity of the frequency deviation and the voltage deviation, wherein the frequency deviation weight and the voltage deviation weight are adjusted based on the following formula:

[0058] ;

[0059] ;

[0060] in, For frequency deviation weighting, As the voltage deviation weight, For frequency, For voltage, The predicted value of the variable at time k+i is given at time k. , These are the grid connection point frequency reference value and voltage reference value, respectively. This represents the maximum frequency change. This represents the maximum voltage change.

[0061] In some embodiments, the method further includes: establishing charging and discharging direction constraints and power change smoothing constraints for grid-type energy storage; when the system deviation does not reach a preset severity level, restricting frequent reverse switching of energy storage across the zero power point; wherein the charging and discharging direction constraints and the power change smoothing constraints are:

[0062] ;

[0063] ;

[0064] in, , Let be the active power at time k and time k+1, respectively. , Let be the reactive power at time k and time k+1, respectively. Let k be the frequency change value. Let k be the voltage change at time k. The preset frequency deviation threshold; This is the preset voltage deviation threshold.

[0065] Secondly, a grid-connected energy storage voltage and frequency active support device for traction load scenarios is provided, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0066] Thirdly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and the computer program, when executed by a processor, implements the method described in any of the above embodiments.

[0067] Compared with the prior art, the present invention has the following advantages and beneficial effects: through the coordinated control of the secondary voltage and frequency of grid-type energy storage, the active and reactive power output of grid-type energy storage can be dynamically adjusted, suppressing the frequency deviation and voltage fluctuation caused by the impact of traction load in weak grids, which helps to ensure the safe and stable operation of weak grids with traction loads. Attached Figure Description

[0068] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0069] Figure 1 A schematic diagram of the structure of a new energy power station integrating wind power, photovoltaic power, and grid-connected energy storage is shown.

[0070] Figure 2 A flowchart of an active voltage and frequency support method for grid-connected energy storage under traction load scenarios, according to an embodiment of the present invention, is shown.

[0071] Figure 3 A structural block diagram of a grid-connected energy storage voltage and frequency active support device under a traction load scenario according to an embodiment of the present invention is shown.

[0072] Figure 4 A schematic diagram of a grid-connected energy storage voltage and frequency active support device under a traction load scenario is shown according to an embodiment of the present invention. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are only for explaining this invention and are not intended to limit this invention.

[0074] On the one hand, the present invention provides a method for actively supporting the voltage and frequency of grid-connected energy storage under traction load scenarios. Figure 1 A schematic diagram of the structure of a new energy power station integrating wind power, photovoltaic power, and grid-connected energy storage is shown. Figure 2 A flowchart illustrating an active voltage and frequency support method for grid-connected energy storage under traction load scenarios, according to an embodiment of the present invention, is shown. (Reference) Figure 2 The active support method for grid-connected energy storage voltage frequency under this traction load scenario includes: S10 to S60.

[0075] The following provides a detailed explanation of S10 to S60.

[0076] In S10, the external power disturbance at the grid connection point is calculated based on the active and reactive power, active reference power, and reactive reference power of the traction load nodes. Specifically, calculating the external power disturbance at the grid connection point includes: establishing a power flow model including the access of new energy power plants to the power system, and calculating the active reference power and reactive reference power based on the power flow model; and calculating the power disturbance at the grid connection point based on the active and reactive power of the traction load nodes, the active reference power, and the reactive reference power.

[0077] Based on the topology of the power system connected to new energy power stations including wind power, photovoltaic power, and grid-connected energy storage, a power flow model is selected, and traction load data at the initial moment is used to calculate the power flow as the baseline power flow. The baseline power flow includes: active baseline power and reactive baseline power. Subsequently, the traction load at each moment is used as a disturbance relative to the baseline. The calculation of the corresponding disturbance components is shown below:

[0078] (1)

[0079] (2)

[0080] In the formula, and They represent the current time. Active power and reactive power of traction load nodes; and These represent the reference power of the traction load nodes; and These represent the active and reactive disturbances of the traction load node at the current moment, respectively.

[0081] In S20, considering external power disturbances and external voltage disturbances, a dynamic prediction model for system voltage and frequency based on discrete state space is established, and the prediction results of the state variables are obtained. Specifically, the voltage / frequency deviation at the grid connection point of the new energy power plant, the active power output and reactive power output of the grid-connected energy storage, and the state of charge (SOC) of the energy storage are taken as state variables, and the incremental reference values ​​of the active power output and reactive power output of the grid-connected energy storage are taken as control variables. Considering the aforementioned external power disturbances and external voltage disturbances, a dynamic prediction model for system voltage and frequency based on discrete state space is established:

[0082] (3)

[0083] In the formula, These are state variables, including: frequency deviation at the grid connection point of the power station. and voltage deviation Changes in active power output of grid-type energy storage modules and change in reactive power output Energy storage SOC change ; For controllable quantities, including: reference values ​​for changes in active power output of grid-type energy storage. Reference value for change in reactive power output ; As an external disturbance, the impact of traction load disturbance on the grid connection point frequency is quantified; For external voltage disturbances, quantify the impact of traction load disturbances on the grid connection point voltage; matrix The state matrix, the matrix For control matrix, matrix Let be the perturbation matrix, and let the matrix be... This is the external voltage disturbance matrix. The corresponding specific expression is as follows:

[0084] (4)

[0085] (5)

[0086] (6)

[0087] In the formula, This is the baseline capacity for grid-type energy storage devices. This represents the change in active power at the traction load node.

[0088] (7)

[0089] In the formula, The sensitivity of the grid connection point voltage to changes in the reactive power of the traction load is given, and , This refers to the reactive power of the traction load node.

[0090] (8)

[0091] In the formula, Here is the frequency self-attenuation coefficient, and ,in For the system's equivalent inertia, The system frequency damping coefficient is... To control the cycle; Let be the coefficient of active power's influence on frequency, and ; For the first-order active response coefficient of energy storage, and , The active power response time constant for energy storage; The first-order reactive power response coefficient for energy storage. , The sensitivity of the grid connection point voltage to changes in the active power of the traction load is given, and ; Let SOC be the consumption coefficient, and .

[0092] (9)

[0093] (10)

[0094] This is the output vector (observable). This is the output matrix.

[0095] In S30, based on the state variable prediction results, with the active and reactive power increments of the grid-type energy storage as optimization variables, and minimizing the smoothness of frequency deviation, voltage deviation, and control increment changes as objectives, a multi-objective optimization function J is established:

[0096] (11)

[0097] In the formula, This is the frequency deviation weight; the larger the value, the more the controller prioritizes suppressing frequency deviation. This is the voltage deviation weight; the larger the value, the more the controller prioritizes suppressing voltage deviation. and The active power cost factor and reactive power cost factor for grid-type energy storage; and The active power change and reactive power change are predicted and calculated for the current control cycle model. and The control increment calculated from the previous control cycle; and These are the active power control command smoothing weight coefficient and the reactive power control command smoothing weight coefficient, respectively. The larger these values ​​are, the more the model predictive control will avoid sudden changes in commands.

[0098] In S40, based on the current and historical traction load change trends, the power change of the traction load in the future control time domain is predicted and the external power disturbance is updated. At the same time, based on the power change, a grid-type energy storage backup power is constructed.

[0099] Traction load forecasting utilizes traditional mechanistic models, statistical methods, or artificial intelligence and machine learning to predict traction load. Controllers can adjust energy storage constraints or reserve space in advance; therefore, the significance of traction load forecasting is not to replace state-space sensing, but rather to compensate for its lag. Since state-space forecasting typically employs a zero-holding assumption, this information only reflects the impact of current disturbances on the next state, not the future abrupt changes in traction load. Therefore, traction load forecasting results are used to adjust energy storage reserve margins in advance to cope with future traction load changes. Specifically, grid-based energy storage active power reserve can be constructed in the following way.

[0100] First, construct the predictive feedforward fusion coefficients. This coefficient is used to limit the impact of predicted disturbances on the energy storage constraint boundary, avoiding sudden changes in control commands due to prediction errors.

[0101] (12)

[0102] In the formula, Forward fusion coefficient, The sampling period for model predictive control. The input filtering time constant is the traction load.

[0103] Then, based on the predicted feedforward fusion coefficients, the predicted fusion perturbation is calculated:

[0104] (13)

[0105] ;(14)

[0106] In the formula, , These represent the predicted future equivalent changes in active power and reactive power, respectively, under the current control period k. , These are the active power control increment and the reactive power control increment, respectively. , These are the predicted future equivalent active power changes under the next control period k+1.

[0107] Finally, based on the predicted fusion disturbance, the maximum active power reserve and maximum reactive power reserve of the traction load prediction results are calculated:

[0108] (15)

[0109] ; (16)

[0110] In the formula, , These are the maximum active power reserve and the maximum reactive power reserve, respectively. , These are the current active power compensation allowable bandwidth and the current reactive power compensation allowable bandwidth, respectively.

[0111] In S50, a set of basic constraints for secondary control of grid-type energy storage is established based on the energy storage state prediction results and reserve power. This set of basic constraints includes: active power capacity constraints, active power ramp rate constraints, reactive power ramp rate constraints, reactive power dynamic margin constraints, SOC operation constraints, and active power regulation command direction constraints and reactive power regulation command direction constraints, which are used to define the feasible control domain of grid-type energy storage.

[0112] Active capacity constraints:

[0113] (15)

[0114] In the formula, This represents the actual active power output of the grid-type energy storage system at the current moment. This represents the change in active power output of the grid-type energy storage system at the next moment.

[0115] Active ramp rate constraint:

[0116] (16)

[0117] In the formula, Active power ramp-up rate constraint for grid-type energy storage; This is the sampling period for model predictive control.

[0118] Reactive ramp rate constraint:

[0119] (17)

[0120] In the formula, The reactive power ramp rate constraint for grid-type energy storage; The sampling period for model predictive control; This represents the change in reactive power output of the grid-type energy storage system at the next moment.

[0121] Reactive power dynamic margin constraint:

[0122] (18)

[0123] (19)

[0124] In the formula, This represents the actual reactive power output of the grid-type energy storage system at the next moment. This represents the upper limit of reactive power output for grid-type energy storage at the current moment.

[0125] SOC operational constraints:

[0126] Overcharging / discharging of energy storage modules will accelerate their lifespan degradation, so it is necessary to limit the range of SOC variation.

[0127] (20)

[0128] ; (twenty one)

[0129] In the formula, The state of the energy storage SOC at the next moment; This represents the minimum SOC (State of Charge) for energy storage. This represents the maximum value of the energy storage SOC.

[0130] Active power regulation command direction constraints, reactive power regulation command direction constraints:

[0131] To ensure the correct direction of power output during the regulation process of grid-type energy storage, it is necessary to set constraints on the direction of regulation commands:

[0132] ; (twenty two)

[0133] ; (twenty three)

[0134] In the formula, and These are the actual values ​​of the frequency and voltage at the grid connection point at the next moment, respectively. and These are the frequency reference value and voltage reference value for the grid connection point at the next moment, respectively.

[0135] In S60, the multi-objective optimization function and the set of basic constraints are combined to form a quadratic programming problem, which yields the incremental control commands for active and reactive power of the grid-type energy storage. These commands are then sent to the energy storage controller for execution and to update the system's operating status. The execution results are then fed back to the state-space prediction model.

[0136] Specifically, the objective function and the modified constraints are combined to form a quadratic programming problem, resulting in incremental active and reactive power control commands for the grid-connected energy storage system, which are then sent to the energy storage controller for execution. Simultaneously, the system operating status is updated, including grid connection point voltage, system frequency, primary response output of wind and solar power, and the state of charge (SOC) of the energy storage system. The execution results are then fed back to the state-space prediction model for prediction and optimization of the next control cycle T.

[0137] In some embodiments, the method further includes: dynamically adjusting the frequency deviation weight and the voltage deviation weight according to the relative severity of the frequency deviation and the voltage deviation, wherein the frequency deviation weight and the voltage deviation weight are adjusted based on the following formula:

[0138] ;

[0139] ;

[0140] in, For frequency deviation weighting, As the voltage deviation weight, For frequency, For voltage, The predicted value of the variable at time k+i is given at time k. , These are the grid connection point frequency reference value and voltage reference value, respectively. This represents the maximum frequency change. This represents the maximum voltage change.

[0141] In some embodiments, the method further includes: establishing charging and discharging direction constraints and power change smoothing constraints for grid-type energy storage; when the system deviation does not reach a preset severity level, restricting frequent reverse switching of energy storage across the zero power point; wherein the charging and discharging direction constraints and the power change smoothing constraints are:

[0142] ;

[0143] ;

[0144] in, , Let be the active power at time k and time k+1, respectively. , Let be the reactive power at time k and time k+1, respectively. Let k be the frequency change value. Let k be the voltage change at time k. The preset frequency deviation threshold; This is the preset voltage deviation threshold.

[0145] In this invention, by coordinating the secondary voltage and frequency control of grid-type energy storage, the active and reactive power output of grid-type energy storage can be dynamically adjusted, suppressing frequency deviation and voltage fluctuation caused by traction load impact in weak power grids, which helps to ensure the safe and stable operation of weak power grids containing traction loads.

[0146] On the other hand, the present invention provides an active support device for grid-connected energy storage voltage and frequency in traction load scenarios. Figure 3 A structural block diagram of a grid-connected energy storage voltage-frequency active support device under a traction load scenario, according to an embodiment of the present invention, is shown. (Reference) Figure 3 The device includes: an external power disturbance calculation module, a voltage and frequency dynamic prediction model construction module, a multi-objective optimization function construction module, a grid-type energy storage backup power calculation module, a basic constraint set acquisition module, and a control command planning module.

[0147] The external power disturbance calculation module is used to calculate the external power disturbance at the grid connection point based on the active and reactive power, active reference power, and reactive reference power of the traction load node.

[0148] The voltage frequency dynamic prediction model construction module is used to: consider the external power disturbance and the external voltage disturbance, establish a system voltage frequency dynamic prediction model based on discrete state space, and obtain the state quantity prediction results.

[0149] The multi-objective optimization function construction module is used to: based on the state variable prediction results, take the active power increment and reactive power increment of the grid-type energy storage as optimization variables, and take the minimization of the smoothness of frequency deviation, voltage deviation and control increment changes as objectives to establish a multi-objective optimization function.

[0150] The grid-type energy storage backup power calculation module is used to: predict the power change of the traction load in the future control time domain based on the current and historical traction load change trends and update the external power disturbance, and construct the grid-type energy storage backup power based on the power change.

[0151] The basic constraint set acquisition module is used to: establish a basic constraint set for secondary control of grid-type energy storage based on the state variable prediction results and the reserve power.

[0152] The control command planning module is used to: construct a quadratic programming problem by combining the multi-objective optimization function with the basic constraint set to obtain the active and reactive incremental control commands for the grid-type energy storage, send them to the energy storage controller for execution and update the system operating status, and feed the execution results back to the state space prediction model.

[0153] For other details regarding the active voltage and frequency support equipment for grid-connected energy storage under traction load scenarios, please refer to the previous description of the active voltage and frequency support method for grid-connected energy storage under traction load scenarios, which will not be repeated here.

[0154] In implementing the functions of the integrated modules described above in hardware, this embodiment of the invention provides another structure for the active support device for grid-connected energy storage voltage and frequency under traction load scenarios as described in the above embodiments. Figure 4 A schematic diagram of a grid-connected energy storage voltage-frequency active support device under a traction load scenario, according to an embodiment of the present invention, is shown. (Reference) Figure 4 The device includes: at least one processor; and at least one memory. The at least one memory is coupled to the at least one processor and stores instructions for execution by the at least one processor, which, when executed by the at least one processor, implement the methods described above.

[0155] A processor can be a set of logic blocks, modules, and circuits that implement or execute the various exemplary logic blocks, modules, and circuits described in connection with embodiments of the present invention. The processor can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in connection with embodiments of the present invention. A processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, etc.

[0156] The memory may be read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0157] In one implementation, the memory can exist independently of the processor. The memory can be connected to the processor via a bus and used to store instructions or program code. When the processor calls and executes the instructions or program code stored in the memory, it can implement the method provided in the embodiments of the present invention. In another implementation, the memory can also be integrated with the processor.

[0158] On the other hand, the present invention also provides a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium) storing computer program instructions that, when executed on a computer, cause the computer to perform the method as described in any of the above embodiments.

[0159] Exemplary examples show that the aforementioned computer-readable storage media may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tapes), optical discs (e.g., compact disks (CDs), digital versatile disks (DVDs), etc.), smart cards, and flash memory devices (e.g., erasable programmable read-only memory (EPROMs), cards, sticks, or key drives, etc.). The various computer-readable storage media described in this invention may represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing, and / or carrying instructions and / or data.

[0160] This invention provides a computer program that, when run on a computer, causes the computer to perform the method of any of the above embodiments.

[0161] This invention provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the method of any of the above embodiments.

[0162] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for actively supporting the voltage and frequency of grid-connected energy storage under traction load scenarios, characterized in that, The method includes: Based on the active and reactive power, active reference power and reactive reference power of the traction load node, calculate the external power disturbance at the grid connection point. Considering the external power disturbance and the external voltage disturbance, a dynamic prediction model of system voltage frequency based on discrete state space is established and the prediction results of state variables are obtained. Based on the state variable prediction results, a multi-objective optimization function is established with the active power increment and reactive power increment of grid-type energy storage as optimization variables and the goal of minimizing the smoothness of frequency deviation, voltage deviation and control increment changes. Based on the current and historical traction load change trends, the power change of the traction load in the future control time domain is predicted and the external power disturbance is updated. At the same time, based on the power change, a grid-type energy storage backup power is constructed. Based on the state quantity prediction results and the reserve power, a set of basic constraints for secondary control of grid-type energy storage is established. The multi-objective optimization function and the set of basic constraints are combined to form a quadratic programming problem, which yields incremental control commands for active and reactive power in the grid-type energy storage. These commands are then sent to the energy storage controller for execution and to update the system's operating status. The execution results are fed back to the state-space prediction model.

2. The method according to claim 1, characterized in that, Based on the active and reactive power, active baseline power, and reactive baseline power of the traction load nodes, calculate the external power disturbance at the grid connection point, including: Establish a power flow model that includes the access of new energy power plants to the power system, and calculate the active power reference power and reactive power reference power based on the power flow model; The differences between the active power and reactive power of the traction load node at the current moment, the active power reference power, and the reactive power reference power are calculated respectively, and the active power disturbance and reactive power disturbance at the grid connection point are calculated.

3. The method according to claim 1, characterized in that, Considering the external power disturbance and the external voltage disturbance, a dynamic prediction model for system voltage frequency based on discrete state space is established, including: The voltage / frequency deviation at the grid connection point of the new energy power station, the active power output and reactive power output of the grid-type energy storage, and the state of charge of the energy storage are used as state variables. The reference values ​​for the incremental active power output and reactive power output of grid-type energy storage are used as control quantities. Considering the external power disturbance and the external voltage disturbance, a dynamic prediction model for system voltage frequency based on discrete state space is established.

4. The method according to claim 1, characterized in that, The multi-objective optimization function is: ； in, For frequency deviation weighting; As the voltage deviation weight; and These are the active power cost coefficient and reactive power cost coefficient for grid-type energy storage, respectively. and These are the changes in active power and reactive power calculated by the k-model predictive control during the current control period, respectively. and These are the active control increment and reactive control increment calculated in the previous control cycle, respectively. and These are the active power control command smoothing weight coefficient and the reactive power control command smoothing weight coefficient, respectively.

5. The method according to any one of claims 1 to 4, characterized in that, Based on the aforementioned power change, a grid-based energy storage backup power is constructed, including: Based on the traction load input filtering time constant and control cycle, the prediction feedforward fusion coefficient is calculated: ； in, To predict the feedforward fusion coefficient, To control the cycle, Input the filter time constant for the traction load; Based on the predicted feedforward fusion coefficients, the active power fusion disturbance and reactive power fusion disturbance are calculated respectively: ； ； in, , These represent the predicted future equivalent changes in active power and reactive power, respectively, under the current control period k. , These are the active power control increment and the reactive power control increment, respectively. , These are the predicted future equivalent active power changes under the next control period k+1; Based on the fused disturbance, calculate the maximum active power reserve and the maximum reactive power reserve: ； ； in, , These are the maximum active power reserve and the maximum reactive power reserve, respectively. , These are the current active power compensation allowable bandwidth and the current reactive power compensation allowable bandwidth, respectively.

6. The method according to claim 1, characterized in that, The set of basic constraints includes: active power capacity constraints, active and reactive power ramp rate constraints, reactive power dynamic margin constraints, energy storage state of charge operation constraints, active power regulation command direction constraints, and reactive power regulation command direction constraints. The active power capacity constraint is as follows: ； in, This represents the actual active power output of the grid-type energy storage system at the current moment. This represents the change in the active power output of the grid-type energy storage system at the next moment. The active ramp rate constraint is: ； in, Active power ramp-up rate constraint for grid-type energy storage; The sampling period for model predictive control; The reactive ramp rate constraint is: ； in, The reactive power ramp rate constraint for grid-type energy storage; The sampling period for model predictive control; This represents the change in reactive power output of the grid-type energy storage system at the next moment. The reactive power dynamic margin constraint is: ； ； in, This represents the actual reactive power output of the grid-type energy storage system at the next moment. This represents the upper limit of reactive power output for grid-type energy storage at the current moment. The operational constraints for the charged energy storage state are: ； ； in, The state of the energy storage SOC at the next moment; This represents the minimum SOC (State of Charge) for energy storage. This represents the maximum value of the energy storage SOC. For energy storage capacity, For charge and discharge efficiency; The active power regulation command direction constraint and the reactive power regulation command direction constraint are respectively: ； ； in, and These are the actual values ​​of the frequency and voltage at the grid connection point at the next moment, respectively. and These are the frequency reference value and voltage reference value for the grid connection point at the next moment, respectively.

7. The method according to claim 4, characterized in that, The method further includes: dynamically adjusting the frequency deviation weight and the voltage deviation weight according to the relative severity of the frequency deviation and the voltage deviation, wherein the frequency deviation weight and the voltage deviation weight are adjusted based on the following formula: ； ； in, For frequency deviation weighting, As the voltage deviation weight, For frequency, For voltage, The predicted value of the variable at time k+i is given at time k. , These are the grid connection point frequency reference value and voltage reference value, respectively. This represents the maximum frequency change. This represents the maximum voltage change.

8. The method according to claim 1, characterized in that, The method further includes: establishing charging and discharging direction constraints and power change smoothing constraints for grid-type energy storage; when the system deviation does not reach a preset severity level, restricting frequent reverse switching of energy storage across the zero power point; wherein the charging and discharging direction constraints and the power change smoothing constraints are as follows: ； ； in, , Let be the active power at time k and time k+1, respectively. , Let be the reactive power at time k and time k+1, respectively. Let k be the frequency change at time k. Let be the voltage change at time k. The preset frequency deviation threshold; This is the preset voltage deviation threshold.

9. A voltage and frequency active support device for grid-connected energy storage under traction load scenarios, characterized in that, It includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 8.

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