Source network load storage state sensing method, system and device considering distributed power supply control characteristics, and storage medium

Abstract

The invention discloses a source network load storage state sensing method, system and device considering distributed power supply control characteristics, and a storage medium, and the method comprises the steps: collecting the multi-dimensional measurement data of a power grid containing a distributed power supply, and constructing a line measurement model based on the non-linear relation between the measurement data of different dimensions of the power grid and a system state variable; identifying key influence factors of the control characteristics of the distributed power converter on the system state, and establishing a converter measurement model based on the key influence factors; constructing a system state variable estimation model by combining the converter measurement model and the line measurement model and taking minimization of a system state variable as a target function; the system state variable estimation model is solved, the optimal state variable of the system is obtained, and a system state sensing result reflecting the actual operation condition is obtained. According to the method, the state of the industrial park source network load storage system can be accurately estimated, the sensing capability of the industrial park to the source network load storage system is improved, and more reliable data support is provided for subsequent control and optimization of the industrial park.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of state estimation, and in particular to a source-grid-load-storage state perception method, system, device and storage medium considering control characteristics of distributed power sources. BACKGROUND

[0002] With the promotion of energy transformation, the proportion of distributed power sources in the source-grid-load-storage system in industrial parks is increasing, and the industrial park power distribution network often faces the problem of poor accuracy of source-grid-load-storage system state estimation caused by the uncertainty and randomness of distributed power source output. The uncertainty of distributed power sources is affected by natural factors such as light intensity. This uncertainty will directly affect the accuracy of the state estimation of the source-grid-load-storage system in the industrial park, causing an error that cannot be ignored. Therefore, there is an urgent need for a source-grid-load-storage state perception method considering the control characteristics of distributed power sources to accurately estimate the state of the source-grid-load-storage system in the industrial park and improve the perception ability of the source-grid-load-storage system in the industrial park. SUMMARY

[0003] In view of the above existing problems, the present application is proposed. Therefore, the present application provides a source-grid-load-storage state perception method, system, device and storage medium considering the control characteristics of distributed power sources to solve the problem of poor accuracy of source-grid-load-storage system state estimation.

[0004] To solve the above technical problems, the present application provides the following technical solutions:

[0005] In a first aspect, the present application provides a source-grid-load-storage state perception method considering the control characteristics of distributed power sources, comprising: collecting multi-dimensional measurement data of a power grid containing distributed power sources, and constructing a line measurement model based on the nonlinear relationship between the measurement data of different dimensions of the power grid and the system state variables;

[0006] Identifying key influencing factors of the control characteristics of the distributed power source converter on the system state, and establishing a converter measurement model based on the key influencing factors;

[0007] Combining the converter measurement model and the line measurement model, a system state variable estimation model is constructed with the minimization of the system state variables as the objective function;

[0008] Solving the system state variable estimation model to obtain the optimal state variables of the system and obtaining the system state perception result reflecting the actual operating conditions.

[0009] As a preferred scheme of the source-grid-load-storage state perception method considering the control characteristics of distributed power sources, the collection of multi-dimensional measurement data of the power grid containing distributed power sources includes: node voltage amplitude, branch current amplitude, node injected power and branch power.

[0010] As a preferred scheme of the source network load storage state perception method considering the control characteristics of the distributed power source, in the method, the line measurement model is constructed based on the nonlinear relationship between the measurement data of different dimensions of the power grid and the system state variables, and the line measurement model includes: the node voltage amplitude measurement is the sum of the actual voltage amplitude of any node three-phase and the corresponding measurement error;

[0011] The branch power measurement is the sum of the actual power value calculated by the alternating current flow and the corresponding measurement error of the active and reactive power measurement values of the branch three-phase, the voltage amplitude of the line two end nodes, the voltage phase angle difference, the admittance parameter of the line and the line-to-ground admittance;

[0012] The node injection power measurement is the sum of the algebraic sum of the power of all branches connected to the node, the reactive power consumed by the node-to-ground admittance and the corresponding measurement error;

[0013] The branch current amplitude measurement is the sum of the calculation value of the square measurement value of the branch current amplitude and the corresponding measurement error.

[0014] As a preferred scheme of the source network load storage state perception method considering the control characteristics of the distributed power source, in the method, identifying the key influencing factors of the control characteristics of the distributed power source converter on the system state includes:

[0015] The total reactive power output of the control converter and the positive sequence voltage amplitude of the grid-connected point satisfy a segmented linear droop control relationship;

[0016] When the positive sequence voltage amplitude of the grid-connected point is lower than the lower limit of the voltage droop control, the converter outputs the maximum reactive power; when the voltage rises from the lower limit of the droop control to the lower limit of the dead zone, the reactive power output of the converter decreases linearly with the rise of the voltage; when the voltage is in the voltage dead zone, the converter does not perform reactive power regulation and maintains a fixed reactive power output; when the voltage continues to rise from the upper limit of the dead zone to the upper limit of the droop control, the reactive power output of the converter decreases linearly with the rise of the voltage;

[0017] When the positive sequence voltage amplitude of the grid-connected point is higher than the upper limit of the droop control, the converter absorbs the maximum reactive power.

[0018] As a preferred scheme of the source network load storage state perception method considering the distributed power supply control characteristics, wherein: the converter measurement model is established based on the key influencing factors, including: a segmented non-smooth function model is established based on the identified key influencing factors, and a fitting function is used to approximate the segmented function for smooth processing, and the smoothed droop control function is used as the converter measurement model, and the fitted droop control function is expressed as:

[0019]

[0020] wherein Q max represents the upper limit of the three-phase total reactive power Q sum ; U h and U l respectively represent the upper and lower limits of the voltage droop control; U max and U min respectively represent the upper and lower limits of the bus voltage amplitude; k dr1 and k dr2 respectively represent the droop control coefficients, and k dr1 < 0, and k dr2 < 0.

[0021] The beneficial effect of the preferred technical scheme is that by modeling the source network load storage state perception of the industrial park considering the distributed power supply control characteristics, the state of the source network load storage system can be accurately and reliably estimated, and more reliable data support is provided for the subsequent control and optimization of the industrial park.

[0022] As a preferred scheme of the source network load storage state perception method considering the distributed power supply control characteristics, wherein: the converter measurement model and the line measurement model are combined to construct a system state variable estimation model with the minimization of the system state variable as the objective function, including:

[0023] The objective function is expressed as:

[0024]

[0025] wherein m is the number of measurements, R is a diagonal matrix composed of the variance of the measurement error, z i is the measurement value, h i (x) is a nonlinear measurement function connecting the measurement value and the state variable, and is the variance.

[0026] The beneficial effect of the preferred technical scheme is that the dead zone and amplitude limiting characteristics in the droop control are identified as the key factors affecting the system state perception, and by establishing the non-smooth segmented function model and subsequent smoothing processing, it is ensured that the constructed converter measurement model can reflect the true physical characteristics and meet the feasibility requirements of numerical calculation.

[0027] Secondly, the present invention provides a source-grid-load-storage state sensing system that considers the control characteristics of distributed power sources, including: a line measurement model establishment module, used to collect multi-dimensional measurement data of the power grid containing distributed power sources, and to construct a line measurement model based on the nonlinear relationship between the measurement data of different dimensions of the power grid and the system state variables;

[0028] The converter measurement model establishment module is used to identify the key influencing factors of the control characteristics of the distributed power converter on the system state, and to establish a converter measurement model based on the key influencing factors.

[0029] The system state variable estimation model building module is used to combine the converter measurement model and the line measurement model to construct a system state variable estimation model with minimizing the system state variables as the objective function.

[0030] The calculation module is used to solve the system state variable estimation model, obtain the optimal system state variables, and obtain the system state perception results that reflect the actual operating conditions.

[0031] As a preferred embodiment of the source-grid-load-storage state sensing system considering the control characteristics of distributed power sources according to the present invention, wherein:

[0032] The converter measurement model establishment module is also used for:

[0033] The total reactive power output of the converter and the positive sequence voltage amplitude at the grid connection point satisfy a piecewise linear droop control relationship.

[0034] When the positive sequence voltage amplitude at the grid connection point is lower than the lower limit of the voltage droop control, the converter outputs reactive power at its maximum capacity; when the voltage rises from the lower limit of the droop control to the lower limit of the dead zone, the reactive power output of the converter decreases linearly with the increase of the voltage; when the voltage is in the voltage dead zone, the converter does not perform reactive power regulation and maintains a fixed reactive power output; when the voltage continues to rise from the upper limit of the dead zone to the upper limit of the droop control, the reactive power output of the converter decreases linearly with the increase of the voltage.

[0035] When the positive sequence voltage amplitude at the grid connection point is higher than the droop control upper limit, the converter absorbs reactive power at its maximum capacity.

[0036] Thirdly, the present invention provides an electronic device, comprising:

[0037] Memory and processor;

[0038] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the source-grid-load-storage state sensing method that takes into account the characteristics of distributed power supply control.

[0039] In a fourth aspect, the present application provides a computer readable storage medium storing computer executable instructions, which, when executed by a processor, implement the steps of the source-grid-load-storage state perception method considering the control characteristics of distributed power sources.

[0040] Compared with the prior art, the present application has the beneficial effects that: by establishing a weighted least squares state estimation model, analyzing a line measurement model in state estimation, and establishing a converter measurement model in state estimation, the control characteristics of the converter are analyzed. Under the premise of considering the control characteristics of distributed power sources, the source-grid-load-storage state perception of the industrial park is modeled, the state of the source-grid-load-storage system of the industrial park is accurately estimated, the perception ability of the source-grid-load-storage system of the industrial park is improved, and more reliable data support is provided for subsequent control and optimization of the industrial park. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0042] Figure 1 A three-phase line equivalent model of the source-grid-load-storage state perception method considering the control characteristics of distributed power sources according to an embodiment of the present application;

[0043] Figure 2 A three-phase line equivalent model of the source-grid-load-storage state perception method considering the control characteristics of distributed power sources according to an embodiment of the present application;

[0044] Figure 3 A three-phase line equivalent model of the source-grid-load-storage state perception method considering the control characteristics of distributed power sources according to an embodiment of the present application;

[0045] Figure 4 A three-phase line equivalent model of the source-grid-load-storage state perception method considering the control characteristics of distributed power sources according to an embodiment of the present application;

[0046] Figure 5 A three-phase line equivalent model of the source-grid-load-storage state perception method considering the control characteristics of distributed power sources according to an embodiment of the present application;

[0047] Figure 6 A three-phase line equivalent model of the source-grid-load-storage state perception method considering the control characteristics of distributed power sources according to an embodiment of the present application;

[0048] Figure 7 A converter segmented droop control function schematic diagram of a source network load storage state perception method considering the control characteristics of distributed power sources according to an embodiment of the present application;

[0049] Figure 8 A segmented non-smooth function fitting before and after comparison diagram of a source network load storage state perception method considering the control characteristics of distributed power sources according to an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0051] Embodiment 1, reference Figures 1-5 According to an embodiment of the present application, the embodiment provides a source network load storage state perception method considering the control characteristics of distributed power sources, as shown in Figure 1 , comprising:

[0052] S100: Collecting multi-dimensional measurement data of power grid containing distributed power sources, and constructing a line measurement model based on the nonlinear relationship between the measurement data of different dimensions of the power grid and the system state variables;

[0053] S200: Identifying key influencing factors of converter control characteristics of distributed power sources on system state, and establishing a converter measurement model based on the key influencing factors;

[0054] S300: Combining the converter measurement model and the line measurement model, and constructing a system state variable estimation model with the minimization of system state variables as the objective function;

[0055] S400: Solving the system state variable estimation model, obtaining the optimal state variables of the system, and obtaining the system state perception result reflecting the actual operating condition.

[0056] It should be noted that the uncertainty of the distributed power supply is affected by natural factors such as light intensity. This uncertainty will directly affect the accuracy of the source network load storage state estimation of the industrial park, causing an error that cannot be ignored. The present application establishes a weighted least squares state estimation model, analyzes the line measurement model in state estimation, and establishes the converter measurement model in state estimation, and analyzes the control characteristics of the converter. Under the premise of considering the control characteristics of the distributed power supply, the state perception modeling of the source network load storage system of the industrial park is carried out, the state of the source network load storage system of the industrial park is accurately estimated, the perception ability of the source network load storage system of the industrial park is improved, and more reliable data support is provided for the subsequent control and optimization of the industrial park.

[0057] In the embodiment of the present application, the multi-dimensional measurement data of the power grid containing distributed power supply collected in step S100 includes: node voltage amplitude, branch current amplitude, node injected power and branch power.

[0058] In the embodiment of the present application, the line measurement model is constructed based on the nonlinear relationship between the measurement data of different dimensions of the power grid and the system state variables in step S100, including: the node voltage amplitude measurement is the sum of the actual voltage amplitude of any node three-phase and its corresponding measurement error;

[0059] The branch power measurement is the sum of the actual power value calculated by the alternating current flow and its corresponding measurement error, and the actual power value is calculated by the sum of the power of all branches connected to the node, the reactive power consumed by the ground admittance of the node, and the sum of the measurement error;

[0060] The node injected power measurement is the sum of the measurement value of the node injected active and reactive power, and the sum of the measurement error, and the measurement value of the node injected active and reactive power is the algebraic sum of the power of all branches connected to the node and the ground admittance of the node;

[0061] The branch current amplitude measurement is the square measurement value of the branch current amplitude, and the square measurement value of the branch current amplitude is the sum of the calculation value of the square sum of the real part and the imaginary part of the branch current and its corresponding measurement error.

[0062] As shown in the example, Figure 2 As shown in the figure, is the phase node voltage amplitude measurement value of node i; is the phase node voltage amplitude measurement corresponding measurement error of node i. represents the phase sequence; and are the phase branch active power measurement value and branch reactive power measurement value between node i and node j; and are the measured values of the active and reactive power of phase a branch between node i and node j, respectively; are the actual values of the active and reactive power of phase a branch between node i and node j, respectively; are the measurement errors of the active and reactive power of phase a branch between node i and node j, respectively; are the measured values of the active and reactive power of phase a branch between node i and node j, respectively; are the actual values of the active and reactive power of phase a branch between node i and node j, respectively; are the phase voltage magnitudes of node i and node j, respectively; wherein is the phase voltage phase angle of node i; is the difference between the phase voltage phase angle of node i and the phase voltage phase angle of node j; is the element in the node admittance matrix Y ij ; y0 represents the line-to-ground admittance; the directions of the active and reactive power flow are from node i to node j.

[0063] Further, define the quantities with superscript m as the measurements of the corresponding electrical quantities. The measurement equations include: the node voltage magnitude, the branch current magnitude, the node injected power, and the branch power measurement equations.

[0064] Specifically, the measurement equation of the node voltage magnitude measurement is represented as:

[0065]

[0066] wherein, is the measured value of the phase a node voltage magnitude of node i; is the measured value of the phase a node voltage magnitude of node i; is the measurement error of the phase a node voltage magnitude measurement of node i. The measurement equation of the branch active and reactive power measurement is represented as:

[0067]

[0068] wherein,

[0069] represents the phase sequence; are the measured values of the active and reactive power of phase a branch between node i and node j, respectively; are the actual values of the active and reactive power of phase a branch between node i and node j, respectively; are the measurement errors of the active and reactive power of phase a branch between node i and node j, respectively; are the measured values of the active and reactive power of phase a branch between node i and node j, respectively; are the actual values of the active and reactive power of phase a branch between node i and node j, respectively; are the measurement errors of the active and reactive power of phase a branch between node i and node j, respectively; are the measured values of the active and reactive power of phase a branch between node i and node j, respectively; are the actual values of the active and reactive power of phase a branch between node i and node j, respectively; are the measurement errors of the active and reactive power of phase a branch between node i and node j, respectively; are the measured values of the active and reactive power of phase a branch between node i and node j, respectively; are the phase voltage magnitudes of node i and node j, respectively; wherein is the phase voltage phase angle of node i; is the difference between the phase voltage phase angle of node i and the phase voltage phase angle of node j; Yij= yij+ jyji ij where y0represents the line-to-ground admittance; the branch power flow directions are from node i to node j.

[0070] The measurement equations for the node injection active and reactive power measurements are given by:

[0071]

[0072] where, and represent the phase injection active power measurement and injection reactive power measurement at node i, respectively; and represent the phase injection active power actual value and injection reactive power actual value at node i, respectively; and represent the phase node injection active and reactive measurement errors at node i, respectively; represents the phase line-to-ground admittance at node i.

[0073] The measurement equations for the branch current magnitude measurements are given by:

[0074]

[0075] where, is the real part of the phase branch current between node i and node j; is the imaginary part of the phase branch current between node i and node j; is the phase branch current magnitude measurement between node i and node j; is the phase branch current magnitude actual value between node i and node j; is the phase branch current magnitude measurement error corresponding to the square of the branch current magnitude measurement between node i and node j.

[0076] For zero injection nodes, the zero injection equality constraints are added and represented by:

[0077]

[0078] It should be noted that a large number of distributed photovoltaic (PV) systems exist in the current distribution network. As a crucial component for distributed PV grid connection, detailed modeling of the converter is of great significance. PV grid connection typically uses voltage source converters (VSCs), which, compared to traditional converters, offer advantages such as adjustable output voltage, controllable output current, and fast response speed. Current research on VSC droop control largely focuses on power flow, with less research on state estimation. Voltage source converters are typically modeled as a combined model of "transformer + filter + phase shifter + inverter." Existing QV droop control, which includes reactive power limiting, maintains a constant active power on the AC bus, ensuring that the reactive power and voltage amplitude on the AC bus satisfy the droop control function, but it does not consider dead zone. Compared to master-slave control and voltage margin control, droop control can instantly adjust the power flow direction of each converter station without requiring real-time communication between them, offering greater flexibility. Furthermore, droop control is independent of the master station, eliminating the problem of abnormal operating conditions in the flexible DC system due to master station shutdown.

[0079] like Figure 3 As shown, the steady-state model of the converter: Distributed photovoltaic power generation achieves DC to AC conversion via an inverter (U c The transformer undergoes a transformation, passing through a filter and then a transformer before being connected to the grid. The transformer is connected in group Dy11, with a delta connection on the high-voltage side. Figure 3 As shown. For grid connection point, and The area between them is a line model, representing the short line between the high-voltage side outlet of the transformer and the grid connection point.

[0080] Converter measurements include active and reactive power measurements of the branches of the grid-connected line. The measurement equations are as follows:

[0081]

[0082] in, Indicates phase sequence; and They are the grid connection points With nodes Between The measured values ​​of active power and reactive power of the branch circuit; and They are the grid connection points With nodes Between Actual values ​​of active power and reactive power of the branch circuit; and They are the grid connection points With nodes Between Measurement errors of the active power and the reactive power of the branch.

[0083] In the steady state, the following constraints are satisfied. The power balance constraint at bus

[0084]

[0085] where '*' denotes the conjugate of a complex number; and P sg and P sf denote the three-phase voltages at the bus and the bus denote the three-phase active power flowing from the bus sg and the bus sf denote the three-phase reactive power flowing from the bus and the bus denote the three-phase reactive power flowing from the bus grid-s denotes the admittance matrix between the bus and the bus .

[0086] The power balance constraint at the bus

[0087]

[0088] where P and P fs and P fc denote the three-phase voltages at the bus and the bus denote the three-phase active power flowing from the bus and the bus denote the three-phase active power flowing from the bus fs and P fc denote the three-phase reactive power flowing from the bus and the bus denote the three-phase reactive power flowing from the bus and the bus denote the three-phase reactive power flowing from the bus f denotes the three-phase reactive power flowing from the bus to the ground, and B f denotes the ground admittance.

[0089] The power balance constraint at the bus

[0090]

[0091] where P PV and Q​​​PV represent the three-phase active and reactive power of the distributed photovoltaic, respectively.

[0092] In steady state, the zero sequence current of the converter is zero, which means that the A, B, C three-phase voltages are completely symmetrical. For the bus Add three-phase symmetry constraints, which are mathematically expressed as follows:

[0093]

[0094] where, U c-A , U c-B , U c-C represent the A, B, C three-phase voltage amplitudes of the bus , respectively; θ c-A , θ c-B , θ c-C represent the A, B, C three-phase voltage phase angles of the bus , respectively.

[0095] In addition, since the distributed photovoltaic can only inject active power into the system, and the active power and the reactive power satisfy the bar arc constraint and the capacity limit, as shown in Figure 4

[0096] In the overcurrent operating point in Figure 4 , the distributed photovoltaic operates with full active power, at this time the distributed photovoltaic also outputs reactive power, and the total power exceeds the capacity limit, which belongs to overcurrent operation; in the active limit operating point, the distributed photovoltaic reaches the capacity limit, but at this time the active power and the reactive power are within the safe operating range. As can be seen from Figure 4 , at night the photovoltaic active power is zero, and during the day it satisfies the following mathematical constraints:

[0097]

[0098] where, S PV represents the capacity of the distributed photovoltaic; k PV =cosφ represents the power factor.

[0099] The five control modes of the converter are as follows:

[0100] (1) P PV / Q PV = constant

[0101] P PV / Q PV = k set

[0102] where, k set is the ratio of the three-phase active power and the reactive power of the distributed photovoltaic, which is a constant.

[0103] (2) P​PV Q PV All are constant values

[0104]

[0105] Among them, P set Q set These are the setpoints for the three-phase active and reactive power outputs of distributed photovoltaic systems.

[0106] (3) Power output control

[0107] Add the following to the objective function:

[0108] min(-P PV )

[0109] When the voltage is within the safe range, distributed photovoltaic power should be used to its full potential to improve photovoltaic absorption. When the voltage exceeds the safe operating range, a portion of the active power can be cut off to ensure the safe and stable operation of the system.

[0110] (4) Constant power control

[0111]

[0112] Among them, P PV-A P PV-B P PV-C These represent the active power of phases A, B, and C, respectively; Q PV-A Q PV-B Q PV-C These represent the reactive power of phases A, B, and C, respectively; P sum-set Q sum-set These are the set values ​​for the total active and reactive power of the three phases, respectively.

[0113] (5) Drooping control

[0114] At the bus A droop control is applied to ensure that the total reactive power output of the photovoltaic system and the positive sequence voltage amplitude satisfy the droop control curve. First, the bus is obtained through the phase sequence transformation matrix. The corresponding positive-sequence, negative-sequence, and zero-sequence voltages:

[0115]

[0116] Where A is the phase sequence transformation matrix.

[0117] Sag control curve as follows Figure 5 As shown, Figure 5 China Q sum This represents the total three-phase reactive power of distributed photovoltaic systems. Indicates busbar The positive sequence voltage amplitude.

[0118] The piecewise function expression is:

[0119]

[0120] wherein Q max , Q min respectively represent upper and lower limits of three-phase total reactive power Q sum ; Q db represents reactive power when the voltage is in a dead zone range; U dbh , U dbl respectively represent upper and lower boundaries of the voltage dead zone; U h , U l respectively represent upper and lower boundaries of the voltage droop control; U max , U min respectively represent upper and lower boundaries of the bus voltage amplitude; k dr1 , k dr2 respectively represent droop control coefficients, and k dr1 < 0, k dr2 < 0.

[0121] In the embodiment of the present application, the key influencing factors of the distributed power converter control characteristics on the system state identified in step S200 include:

[0122] The total reactive power controlled by the converter output and the positive sequence voltage amplitude of the grid-connected point satisfy a piecewise linear droop control relationship;

[0123] When the positive sequence voltage amplitude of the grid-connected point is lower than the lower limit of the voltage droop control, the converter outputs the maximum reactive power; when the voltage rises from the lower limit of the droop control to the lower limit of the dead zone, the reactive power output of the converter linearly decreases with the rise of the voltage; when the voltage is in the voltage dead zone, the converter does not perform reactive power regulation and maintains a fixed reactive power output; when the voltage continues to rise from the upper limit of the dead zone to the upper limit of the droop control, the reactive power output of the converter linearly decreases with the rise of the voltage;

[0124] When the positive sequence voltage amplitude of the grid-connected point is higher than the upper limit of the droop control, the converter absorbs the maximum reactive power.

[0125] In the embodiment of the present application, the establishment of the converter measurement model based on the key influencing factors in step S200 includes: establishing a piecewise non-smooth function model based on the identified key influencing factors, and performing smooth processing on the piecewise function by using a fitting function for approximate fitting, taking the droop control function after the smooth processing as the converter measurement model, and the fitting droop control function is expressed as:

[0126]

[0127] wherein Q maxQ represents the upper limit of the three-phase total reactive power Q sum h U l , U max represent the upper and lower limits of the voltage droop control, respectively min , U dr1 represent the upper and lower limits of the bus voltage amplitude, respectively dr2 , k dr1 represent the droop control coefficients, and k dr2 < 0, k T < 0.

[0128] In an optional embodiment, for an n-node system, the state variable x is expressed as:

[0129] x 2-A = [θ n-B … θ n-C θ 1-A U n-B … U n-C ]

[0130]

[0131] wherein, represents the phase angle of the n-th node; represents the phase voltage amplitude of the n-th node; represents the phase angle of the reference node.

[0132] The measured value z i has the following relationship with the system measurement equation:

[0133] z i = h i (x) + e i

[0134] wherein, h i (x) is a nonlinear measurement function that links the measured value with the state variable; e i represents the measurement error corresponding to the i-th measurement function, e i is an element in a normally distributed random vector with mean 0 and variance .

[0135] Given a set of state variables x, a set of calculated values h i (x) of the measured values can be obtained, and the difference between the given measured value z i and the calculated value is called the residual r i :

[0136] r i = z i - h​​​i (x)

[0137] In the embodiment of the present application, the converter measurement model and the line measurement model are combined in step S300 to construct a system state variable estimation model with the system state variable as the objective function, which comprises:

[0138] The objective function is expressed as:

[0139]

[0140] Wherein, m is the number of measurements, R is a diagonal matrix composed of the variance of measurement errors, z i is the measurement value, h i (x) is a nonlinear measurement function connecting the measurement value with the state variable, is the variance.

[0141] It should be noted that the present application considers the control characteristics of distributed power sources to model the state perception of source-grid-load-storage in industrial parks, more accurately and reliably estimates the state of the source-grid-load-storage system, and provides more reliable data support for subsequent control and optimization of industrial parks.

[0142] Embodiment 2, the above embodiment is a schematic scheme of a source-grid-load-storage state perception method considering the control characteristics of distributed power sources. It should be noted that the technical scheme of the source-grid-load-storage state perception system considering the control characteristics of distributed power sources belongs to the same concept as the technical scheme of the source-grid-load-storage state perception method considering the control characteristics of distributed power sources described above. The technical scheme of the source-grid-load-storage state perception system considering the control characteristics of distributed power sources in the present embodiment is not described in detail, and can be referred to the description of the technical scheme of the source-grid-load-storage state perception method considering the control characteristics of distributed power sources.

[0143] The source-grid-load-storage state perception system considering the control characteristics of distributed power sources in the present embodiment comprises:

[0144] The line measurement model establishment module is used to collect multi-dimensional measurement data of a power grid containing distributed power sources, and construct a line measurement model based on the nonlinear relationship between the measurement data of different dimensions of the power grid and the system state variable;

[0145] The converter measurement model establishment module is used to identify key influencing factors of the control characteristics of the distributed power source converter on the system state, and establish a converter measurement model based on the key influencing factors;

[0146] The system state variable estimation model establishment module is used to combine the converter measurement model and the line measurement model to construct a system state variable estimation model with the system state variable as the objective function;

[0147] The computing module is configured to solve a system state variable estimation model, obtain optimal system state variables, and obtain system state perception results reflecting actual operation conditions.

[0148] In the embodiment of the present application, the converter measurement model establishing module is further configured to:

[0149] The total reactive power output of the converter and the positive sequence voltage amplitude of the grid-connected point satisfy a piecewise linear droop control relationship.

[0150] When the positive sequence voltage amplitude of the grid-connected point is lower than the lower limit of the voltage droop control, the converter outputs the maximum reactive power; when the voltage rises from the lower limit of the droop control to the lower limit of the voltage dead zone, the reactive power output of the converter decreases linearly with the rise of the voltage; when the voltage is in the voltage dead zone, the converter does not perform reactive power regulation and maintains a fixed reactive power output; when the voltage continues to rise from the upper limit of the dead zone to the upper limit of the droop control, the reactive power output of the converter decreases linearly with the rise of the voltage.

[0151] When the positive sequence voltage amplitude of the grid-connected point is higher than the upper limit of the droop control, the converter absorbs the maximum reactive power.

[0152] The embodiment further provides an electronic device suitable for the source-grid-load-storage state perception method considering the control characteristics of the distributed power supply, and the electronic device comprises:

[0153] The electronic device comprises a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to implement the source-grid-load-storage state perception method considering the control characteristics of the distributed power supply.

[0154] The embodiment further provides a storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the source-grid-load-storage state perception method considering the control characteristics of the distributed power supply.

[0155] The storage medium provided by the embodiment and the source-grid-load-storage state perception method considering the control characteristics of the distributed power supply provided by the above embodiment belong to the same inventive concept, and the technical details not described in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0156] Through the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary universal hardware, and of course can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk or an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0157] Embodiment 3, refer to Figures 6-8 As an embodiment of the present application, this embodiment verifies the beneficial effects of the present application according to specific examples.

[0158] This embodiment takes a 5-node system as an example, and the actual 5-node system topology relationship and measurement configuration are as shown in Figure 6 . Among them, node 1 is the system balance node, nodes 1 and 2 are load regulating transformers, nodes 2, 3, 4 and 5 are load nodes, and there is a distributed photovoltaic grid-connected at node 4. The photovoltaic outlet is connected to the grid point through a converter. The distributed power converter adopts a droop control considering amplitude limiting and dead zone, and uses the smoothing method proposed in the present application to process the non-smooth control function.

[0159] The measurement configuration of the 5-node system is shown in Table 1:

[0160] Table 1 Measurement configuration table of 5-node system

[0161]

[0162] The system includes a total of 66 measurements, including 6 voltage amplitude measurements, 30 branch power measurements, and 30 node injection power measurements.

[0163] The piecewise droop control function of the distributed power converter is as shown in Figure 7 , and the corresponding piecewise droop control function is:

[0164]

[0165] The piecewise non-smooth function represented by the above formula is processed by the smoothing method proposed in the present application, and the function curves before and after fitting are compared, as shown in Figure 8 . Figure 8It can be known that the derivative is continuous and smooth after the piecewise function is smoothed by using the ln fitting function, and convergence failure of the algorithm at the inflection point can be avoided.

[0166] It should be noted that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A source-grid-load-storage state sensing method considering the control characteristics of distributed power sources, characterized in that, include: Collect multidimensional measurement data of power grids containing distributed power sources, and construct line measurement models based on the nonlinear relationship between measurement data of different dimensions of the power grid and system state variables; Identify the key factors that affect the system state by the control characteristics of the distributed power converter, and establish a converter measurement model based on the key factors. Combining the converter measurement model and the line measurement model, a system state variable estimation model is constructed with minimizing the system state variables as the objective function; Solve the system state variable estimation model to obtain the optimal system state variables and obtain the system state perception results that reflect the actual operating conditions.

2. The source-grid-load-storage state sensing method considering the control characteristics of distributed power sources as described in claim 1, characterized in that, The collection of multidimensional measurement data for power grids containing distributed power sources includes: node voltage amplitude, branch current amplitude, node injected power, and branch power.

3. The source-grid-load-storage state sensing method considering the control characteristics of distributed power sources as described in claim 2, characterized in that, The line measurement model is constructed based on the nonlinear relationship between measurement data from different dimensions of the power grid and system state variables. The model includes: the node voltage amplitude measurement is the sum of the actual voltage amplitude of the three phases of any node and its corresponding measurement error. The branch power measurement is the active and reactive power measurement value of the branch in three phases. The active and reactive power measurement values ​​are the voltage amplitude, voltage phase angle difference, line admittance parameters and line susceptance to ground at the nodes at both ends of the line, and the sum of the actual power value obtained by AC power flow calculation and its corresponding measurement error. The node injected power measurement is the measured value of the active and reactive power injected into the node. The measured value of the active and reactive power injected into the node is the sum of the algebraic sum of the power of all branches connected to the node, the reactive power consumed by the node to ground susceptance, and the corresponding measurement error. The measured value of the branch current amplitude is the square of the measured value of the branch current amplitude. The square of the measured value of the branch current amplitude is the sum of the calculated value of the sum of the squares of the real and imaginary parts of the branch current and its corresponding measurement error.

4. The source-grid-load-storage state sensing method considering the control characteristics of distributed power sources as described in claim 3, characterized in that, Key factors influencing the system state of distributed power converter control characteristics include: The total reactive power output of the converter and the positive sequence voltage amplitude at the grid connection point satisfy a piecewise linear droop control relationship. When the positive sequence voltage amplitude at the grid connection point is lower than the lower limit of the voltage droop control, the converter outputs reactive power at its maximum capacity; when the voltage rises from the lower limit of the droop control to the lower limit of the dead zone, the reactive power output of the converter decreases linearly with the increase of the voltage; when the voltage is in the voltage dead zone, the converter does not perform reactive power regulation and maintains a fixed reactive power output; when the voltage continues to rise from the upper limit of the dead zone to the upper limit of the droop control, the reactive power output of the converter decreases linearly with the increase of the voltage. When the positive sequence voltage amplitude at the grid connection point is higher than the droop control upper limit, the converter absorbs reactive power at its maximum capacity.

5. The source-grid-load-storage state sensing method considering the control characteristics of distributed power sources as described in claim 4, characterized in that, The converter measurement model based on the key influencing factors includes: establishing a piecewise non-smooth function model based on the identified key influencing factors, and using a fitting function to approximate and smooth the piecewise function. The smoothed droop control function is then used as the converter measurement model. The fitted droop control function is expressed as follows: Among them, Q max Q represents the total three-phase reactive power. sum The upper limit of U; h U l These represent the upper and lower bounds of the voltage droop control, respectively; U max U min These represent the upper and lower bounds of the bus voltage amplitude, respectively; k dr1 k dr2 Let k and k represent the droop control coefficients, respectively. dr1 <0,k dr2 <0.

6. The source-grid-load-storage state sensing method considering the control characteristics of distributed power sources as described in claim 5, characterized in that, Combining the converter measurement model and the line measurement model, and with minimizing the system state variables as the objective function, a system state variable estimation model is constructed, including: The objective function is expressed as: Where m is the number of measurements, R is a diagonal matrix of the variances of the measurement errors, and z i h is a measured value. i (x) is a nonlinear measurement function that links the measured value to the state variable. Let Variance be the variance.

7. A source-grid-load-storage state sensing system considering the control characteristics of distributed power sources, applied to the method described in any one of claims 1-6, characterized in that, include: The line measurement model building module is used to collect multi-dimensional measurement data of power grids containing distributed power sources and to build line measurement models based on the nonlinear relationship between measurement data of different dimensions of the power grid and system state variables. The converter measurement model establishment module is used to identify the key influencing factors of the control characteristics of the distributed power converter on the system state, and to establish a converter measurement model based on the key influencing factors. The system state variable estimation model building module is used to combine the converter measurement model and the line measurement model to construct a system state variable estimation model with minimizing the system state variables as the objective function. The calculation module is used to solve the system state variable estimation model, obtain the optimal system state variables, and obtain the system state perception results that reflect the actual operating conditions.

8. The source-grid-load-storage state sensing system considering the control characteristics of distributed power sources as described in claim , characterized in that, The converter measurement model establishment module is also used for: The total reactive power output of the converter and the positive sequence voltage amplitude at the grid connection point satisfy a piecewise linear droop control relationship. When the positive sequence voltage amplitude at the grid connection point is lower than the lower limit of the voltage droop control, the converter outputs reactive power at its maximum capacity; when the voltage rises from the lower limit of the droop control to the lower limit of the dead zone, the reactive power output of the converter decreases linearly with the increase of the voltage; when the voltage is in the voltage dead zone, the converter does not perform reactive power regulation and maintains a fixed reactive power output; when the voltage continues to rise from the upper limit of the dead zone to the upper limit of the droop control, the reactive power output of the converter decreases linearly with the increase of the voltage. When the positive sequence voltage amplitude at the grid connection point is higher than the droop control upper limit, the converter absorbs reactive power at its maximum capacity.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the source-grid-load-storage state sensing method considering the characteristics of distributed power source control as described in any one of claims 1 to 6.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the source-grid-load-storage state sensing method considering the characteristics of distributed power source control as described in any one of claims 1 to 6.

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