Method and system for evaluating high-proportion distributed photovoltaic access power grid

Abstract

The invention provides a high-proportion distributed photovoltaic access power grid assessment method and system, and the method comprises the steps: obtaining an access request, constructing a distributed photovoltaic grid-connected point voltage function, and obtaining an impact factor related to the voltage according to the distributed photovoltaic grid-connected point voltage function; acquiring a second key parameter related to the voltage stability; analyzing the relation between the VSG frequency change and the inertia constant after the VSG-based distributed photovoltaic grid-connected point voltage model is subjected to power disturbance, and constructing a multi-target optimization function taking the minimum inertia constant and the frequency change rate as targets; and constructing a power constraint condition about the fluctuating photovoltaic and uncertain load for the multi-objective optimization function, and solving the multi-objective optimization function and the power constraint condition to obtain an optimal inertia constant and an optimal frequency change rate under the current working condition. According to the embodiment of the invention, the method has remarkable advantages when the actual problems of unstable voltage and the like caused by high-proportion distributed photovoltaic access to the power grid are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid voltage assessment, and in particular to a method and system for assessing high-proportion distributed photovoltaic access to a power grid. Background Art

[0002] With the advancement of global energy transformation and sustainable development strategies, distributed photovoltaics have been widely used due to their clean, renewable, flexible and other advantages. More and more distributed photovoltaic power sources are connected to the power grid, providing additional power supply to the grid.

[0003] However, its access also brings challenges to the grid voltage stability. The volatility and intermittency of photovoltaic power generation can easily lead to sharp fluctuations in grid voltage, damage equipment and affect users' normal electricity consumption. At the same time, it may also change the load characteristics of the grid, affect frequency stability, and thus threaten the safety of the grid and even cause a collapse. In order to deal with the grid voltage stability problems caused by distributed photovoltaic access, a series of optimization methods need to be adopted. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for evaluating the access of a high proportion of distributed photovoltaic power to a power grid, aiming to solve the voltage fluctuation of the power grid and alleviate the power quality problem in the power system.

[0005] In a first aspect, the present invention provides a method for evaluating high-proportion distributed photovoltaic access to a power grid, the method comprising:

[0006] Acquire at least one first key parameter of the target distributed photovoltaic grid-connected, and construct a distributed photovoltaic grid-connected point voltage function according to the at least one first key parameter, so as to obtain an influencing factor related to voltage according to the distributed photovoltaic grid-connected point voltage function;

[0007] Constructing a VSG-based distributed photovoltaic grid-connected point voltage model according to the influencing factors, and acquiring a second key parameter related to voltage stability according to the VSG-based distributed photovoltaic grid-connected point voltage model;

[0008] The relationship between the VSG frequency change and the inertia constant of the VSG-based distributed photovoltaic grid-connected point voltage model after power disturbance is analyzed, and a model is constructed based on the analysis results to minimize the inertia constant Y and the frequency change rate f. B is the multi-objective optimization function of the target;

[0009] Power constraints on fluctuating photovoltaics and uncertain loads are constructed for the multi-objective optimization function, and the multi-objective optimization function and the power constraints are solved to obtain the optimal inertia constant and the optimal frequency change rate under the current operating conditions, so as to update the second key parameter according to the optimal inertia constant and the optimal frequency change rate.

[0010] Furthermore, the step of obtaining at least one first key parameter of the target distributed photovoltaic grid-connected system, and constructing a distributed photovoltaic grid-connected point voltage function according to the at least one first key parameter, so as to obtain an influencing factor related to voltage according to the distributed photovoltaic grid-connected point voltage function includes:

[0011] The voltage function of the distributed photovoltaic grid-connected point is constructed according to the following formula:

[0012]

[0013] in, represents the rated value of the grid voltage, k represents the ratio of the actual grid voltage amplitude to the rated value, R g and L g represents the equivalent resistance and inductance of the grid, a represents the difference between the load and the active power of photovoltaic and energy storage, b represents the difference between the reactive power of the load and the reactive power emitted by the VSG, c represents the square of the grid current value, L g represents the equivalent inductance of the power grid, w represents the angular frequency,

[0014] The influencing factors include photovoltaic output power, load power, and grid voltage amplitude.

[0015] Further, the VSG-based distributed photovoltaic grid-connected point voltage model includes a BESS, a DC-DC bidirectional converter and a VSC, and the steps of constructing the VSG-based distributed photovoltaic grid-connected point voltage model according to the influencing factors and acquiring a second key parameter related to voltage stability according to the VSG-based distributed photovoltaic grid-connected point voltage model include:

[0016] The VSC is connected to the common point coupling through the step-up transformer and the line impedance. The BESS adjusts the DC bus voltage through the voltage and current control loop through the BDC. The control signal of the BDC DC bus voltage control is as follows:

[0017]

[0018]

[0019]

[0020] D B =K pb ΔI B +k ib ∫ΔI B dt

[0021] Among them, V D is the actual DC bus voltage passing through the low-pass filter, with a cut-off frequency of f cf , VDref is the reference DC bus voltage, ΔV D is the change in DC bus voltage, k pd and k id are the proportional gain constant and integral gain constant of the outer voltage control loop, I Bref is the reference battery current calculated by the outer voltage control loop, I B is the actual battery current, ΔI B The difference between the reference current and the actual battery current, k pb and k ib are the proportional gain constant and integral gain constant of the inner current control loop, D B is the duty cycle of the bidirectional DC-DC converter, s represents the complex frequency domain signal, I Bref (t) represents the variation of the reference battery current over time t;

[0022] The terminal voltage and reactive power control of VSG are generated by the following equation:

[0023] E ref =V ref -Q D Q VSG +K pv (Q ref -Q VSG )+K iv ∫(Q ref -Q VSG )dt

[0024] Among them, K pv ,K iv are the proportional gain and integral gain of VSG voltage control, Q ref and Q VSG are the reactive reference value and reactive power of VSG, E ref is the d-axis voltage reference value in the dq synchronous rotating coordinate system, V ref is the VSG output voltage reference value, Q D is the reactive power droop coefficient;

[0025] The second key parameter includes the reactive power droop coefficient Q D , the reactive power Q generated by VSG VSG , proportional gain K of the voltage control loop pv And the integral gain K iv .

[0026] Furthermore, the relationship between the VSG frequency change and the inertia constant of the distributed photovoltaic grid-connected point voltage model based on the VSG after the power disturbance is analyzed, so as to construct a voltage model to minimize the inertia constant Y and the frequency change rate f according to the analysis results. BThe steps of multi-objective optimization function for target include:

[0027] The active and reactive droop coefficients can be calculated as;

[0028]

[0029] Among them, (f max -f min ) is the acceptable frequency variation range of VSC, P VSGmax is the maximum required active power, (V max -V min ) is the acceptable voltage variation range of VSC;

[0030] The instantaneous f after any power disturbance occurs in the system B The relationship between the value and the inertia constant Y is expressed as;

[0031]

[0032] Furthermore, the relationship between the VSG frequency change and the inertia constant of the distributed photovoltaic grid-connected point voltage model based on the VSG after the power disturbance is analyzed, so as to construct a voltage model to minimize the inertia constant Y and the frequency change rate f according to the analysis results. B The steps of the multi-objective optimization function for the target also include:

[0033] The multi-objective optimization function is constructed according to the following formula:

[0034] Ming 1 (x) = minY

[0035] Ming 2 (x) = minf B

[0036] f B min ≤f B ≤f B max Y min ≤Y≤Y max

[0037]

[0038] Among them, (f B / Y) min and (f B / Y) max Yes B and the reference maximum and minimum values ​​of Y, P 0 and f 0 is the power and frequency of the system, ΔP depends on the real-time P pv and P L.

[0039] Furthermore, the step of constructing power constraints on the fluctuating photovoltaic power and the uncertain load for the multi-objective optimization function includes:

[0040] The output active power of VSG is;

[0041] P i =P s -P pv -P ba -P L -P loss

[0042] P pv The short lead time uncertainty is expressed using a probability distribution function:

[0043]

[0044] The random uncertainty of load occurrence is as follows:

[0045]

[0046] Among them, P pv is the photovoltaic output active power, P ba The energy storage battery outputs active power, P L is the load power, P loss is the system loss; P j is the VSG output active power, υ′ and ω′ are the shape parameters of the probability distribution function, N ω is the normalization factor, y is P pv The occurrence of uncertainty in μ i and l are the mean and standard deviation of load forecast, respectively.

[0047] Furthermore, the step of solving the multi-objective optimization function and the power constraint condition to obtain the optimal inertia constant and the optimal frequency change rate under the current working condition, and updating the second key parameter according to the optimal inertia constant and the optimal frequency change rate includes:

[0048] A stochastic programming model is used to minimize the expected value of the objective function under uncertainty, where the objective function is expressed as:

[0049] g i (y i ):y i ∈G i (x, u), x∈X, u∈U

[0050] The objective function is mathematically reformulated as:

[0051]

[0052] Among them, x / X and μ / U are the input variable set and uncertain decision respectively, Y i (x, u) and E[Y i (x, u)] represent the optimal value and expected value of the uncertain target, respectively.

[0053] In a second aspect, the present invention provides an evaluation system for high-proportion distributed photovoltaic access to a power grid, the system comprising:

[0054] An influencing factor analysis module, used to obtain at least one first key parameter of the target distributed photovoltaic grid-connected, and to construct a distributed photovoltaic grid-connected point voltage function according to the at least one first key parameter, so as to obtain an influencing factor related to voltage according to the distributed photovoltaic grid-connected point voltage function;

[0055] A model building module, used to build a VSG-based distributed photovoltaic grid-connected point voltage model according to the influencing factors, and obtain a second key parameter related to voltage stability according to the VSG-based distributed photovoltaic grid-connected point voltage model;

[0056] The objective function building module is used to analyze the relationship between the VSG frequency change and the inertia constant of the VSG-based distributed photovoltaic grid-connected point voltage model after power disturbance, so as to minimize the inertia constant Y and the frequency change rate f according to the analysis results. B is the multi-objective optimization function of the target;

[0057] A solution module is used to construct power constraints on fluctuating photovoltaics and uncertain loads for the multi-objective optimization function, and solve the multi-objective optimization function and the power constraints to obtain the optimal inertia constant and the optimal frequency change rate under the current operating conditions, so as to update the second key parameter according to the optimal inertia constant and the optimal frequency change rate.

[0058] In a third aspect, the present invention provides a storage medium storing one or more programs, which, when executed by a processor, implement the above-mentioned evaluation method for high-proportion distributed photovoltaic access to the power grid.

[0059] In a fourth aspect, the present invention provides an electronic device, the electronic device comprising a memory and a processor, wherein:

[0060] The memory is used to store computer programs;

[0061] When the processor is used to execute the computer program stored in the memory, the above-mentioned evaluation method for high-proportion distributed photovoltaic access to the power grid is implemented.

[0062] Compared with the prior art, the present invention first analyzes the specific impact of environmental factors such as photovoltaic sunshine intensity, temperature, and key parameters such as load parameters on the voltage of distributed photovoltaic grid-connected nodes based on virtual synchronous generators (VSGs), and then extracts voltage sensitivity factors (influencing factors). On this basis, a VSG grid-connected point voltage model incorporating voltage sensitivity factors is constructed to further explore the impact of changes in key VSG parameters on distributed photovoltaic grid-connected voltage. Next, using the constructed VSG output voltage model, the correlation between the VSG frequency change and the inertia constant after power disturbance is analyzed, and based on this, a method is established to minimize the inertia constant Y and the frequency change rate f. B In addition, the power constraints brought by the fluctuating photovoltaic and uncertain loads are fully considered. Finally, based on the above optimization objectives and constraints, the random programming model and the normal boundary intersection (NBI) solution algorithm are used to obtain the optimal inertia constant and frequency change rate f under random working conditions. B This result can be used to adaptively update VSG parameters to ensure voltage stability at the VSG grid connection point. It shows significant advantages in dealing with practical problems such as voltage instability caused by high-proportion distributed photovoltaic access to the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A flow chart of a method for evaluating a high proportion of distributed photovoltaic power grid access proposed in one embodiment of the present invention;

[0064] Figure 2 It is an overall framework diagram of a method for evaluating a high proportion of distributed photovoltaic access to a power grid in one embodiment of the present invention;

[0065] Figure 3 This is a schematic diagram of the structure of an evaluation system for high-proportion distributed photovoltaic access to a power grid proposed in one embodiment of the present invention.

[0066] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be understood by people with general skills in the field to which the present invention belongs. "Including" and similar words used in this article mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0068] like Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a method for evaluating high-proportion distributed photovoltaic access to a power grid, the method comprising steps S101 to S104, wherein:

[0069] Step S101: obtaining at least one first key parameter of a target distributed photovoltaic grid-connected system, and constructing a distributed photovoltaic grid-connected point voltage function according to the at least one first key parameter, so as to obtain an influencing factor related to voltage according to the distributed photovoltaic grid-connected point voltage function;

[0070] It should be noted that due to the photovoltaic output current I pv It is directly related to the intensity of sunlight. Generally speaking, the higher the intensity of sunlight, the greater the current generated by the photovoltaic cell. pv The effect of temperature on the output current I of photovoltaic cells pv The influence of temperature is relatively complex. Although to a certain extent, as the temperature increases, some performance parameters of photovoltaic cells (such as open circuit voltage V oc ) will decrease, but this does not mean that I pv will also drop directly. In fact, I pv The relationship with temperature depends on the combined effect of many factors, including the material, structure, manufacturing process and working environment of the photovoltaic cell. The expressions of photovoltaic output power and output current are as follows:

[0071] P pv =I pv υ pv (1)

[0072]

[0073] Among them, P pv is the photovoltaic output power, v pvis the photovoltaic output voltage, Ic is the short-circuit current of the photovoltaic cell under standard test conditions. S is the sunshine intensity, and Sref is the sunshine intensity reference value, usually 1000W / m 2 . T is the real-time temperature, T ref is the reference temperature, usually 25°C, k η It is the temperature coefficient of current, which describes the relative change in short-circuit current for every 1 degree change in temperature.

[0074] Based on the impact of photovoltaic sunshine, temperature and load on the voltage of distributed photovoltaic grid-connected nodes of VSG, the voltage and power of photovoltaic grid-connected nodes can be described as;

[0075] V s -i s (R g +jwL g )=V pcc (3)

[0076]

[0077] P s , Q s is the active and reactive power generated by the grid side, R g and L g is the equivalent resistance and inductance of the power grid, P pv and P ba is the active power output of photovoltaic and energy storage, Q VSG is the reactive power output by VSG, P L and Q L is the load active power and reactive power.

[0078] Based on formulas (1)-(4), the voltage of the distributed photovoltaic grid connection point can be calculated as:

[0079]

[0080] a=P L -P pv -P ba (6)

[0081] b=Q L -Q VSG (7)

[0082]

[0083] in, is the rated value of the grid voltage, k is the ratio of the actual amplitude of the grid voltage to the rated value, R g and L gare the equivalent resistance and inductance of the grid, a represents the difference between the load and the active power of photovoltaic and energy storage, b represents the difference between the reactive power of the load and the reactive power emitted by the VSG, and c is the square of the grid current value.

[0084] It can be seen from the formula that solar insolation, temperature, load, line parameters, and VSG output reactive power will have different degrees of influence on the VSG output voltage. Therefore, the VSG output voltage, that is, the photovoltaic grid-connected point voltage, can be improved by adjusting the above parameters. It is not difficult to see from formula (5) that the photovoltaic grid-connected point voltage is affected by uncertain factors such as photovoltaic output power, load power, and grid voltage amplitude.

[0085] Step S102: constructing a VSG-based distributed photovoltaic grid-connected point voltage model according to the influencing factors, and acquiring a second key parameter related to voltage stability according to the VSG-based distributed photovoltaic grid-connected point voltage model;

[0086] In this step, based on the voltage sensitivity factor extracted in step 1, a distributed photovoltaic grid-connected point voltage model of a virtual synchronous generator (VSG) incorporating this factor is constructed. This model integrates the internal control mechanism of the VSG with the operating conditions of the external power grid, and especially focuses on the dynamic effect analysis of the core parameters of the VSG on the grid-connected voltage. By exploring the response characteristics of the distributed photovoltaic grid-connected voltage based on the VSG when these key parameters change, the inherent mechanism of voltage regulation under the VSG control strategy can be deeply revealed. Specifically, with the help of the voltage sensitivity factor, the model accurately quantifies the sensitivity of different parameter changes to the grid-connected node voltage, thereby laying a solid theoretical foundation for achieving more accurate voltage regulation and optimizing the stability of distributed photovoltaic grid-connected systems.

[0087] Among them, in step 2, the VSG voltage model is a two-level VSG control model based on VSC, which consists of BESS, DC-DC bidirectional converter (BDC) and VSC. VSC is connected to the common point (PCC) coupling through a step-up transformer and line impedance. BESS regulates the DC bus voltage through the voltage and current control loop through BDC. The control signal of BDC DC bus voltage control is calculated using the following formula:

[0088]

[0089] I Bref (t) = K pd ΔV D +K id ∫ΔV D dt (10)

[0090]

[0091] D B =Kpb ΔI B +K ib ∫ΔI B dt (12)

[0092] Among them, V D is the actual DC bus voltage passing through the low-pass filter (LPF). The cut-off frequency is f cf . V Dref is the reference DC bus voltage, ΔV D K is the change in DC bus voltage. pd and K id are the proportional gain constant and integral gain constant of the external voltage control loop respectively. Bref The reference battery current and I calculated by the outer voltage control loop B Actual battery current. ΔI B The difference between the reference current and the actual battery current. K pb and K ib are the proportional gain constant and integral gain constant of the inner current control loop respectively. B is the duty cycle of the bidirectional DC-DC converter.

[0093] During grid-connected operation, the VSC operates in grid-following mode and controls the active power flow to maintain the charge state of the BESS. The proposed VSG control is equivalent to the characteristics of the SG. Based on the swing equation, the frequency of the VSG is controlled by the equation. Assuming the viscous damping (N Z ) is opposed to the synchronous machine's rotating shaft system and the proportional-integral (PI) controller that regulates the system frequency.

[0094]

[0095]

[0096]

[0097] In the formula, w VSG and P VSG K is the angular frequency and active power of VSG. PF and K IF is the proportional gain and integral gain of VSG frequency control. pcc and w ref is the Pcc angular frequency and the reference angular frequency. D is the active power droop coefficient. j is the mechanical power of the VSG. Y is the inertia constant of the VSG in seconds. Δω is the angular frequency change of the VSG.

[0098] The terminal voltage and reactive power control of VSG are generated by the following equations.

[0099] E ref =V ref -Q D Q VSG +K pv (Q ref -Q VSG )+K iv ∫(Q ref -Q VSG )dt (16)

[0100] Where K pv ,K iv are the proportional gain and integral gain of VSG voltage control respectively. VSG are the reactive reference value and reactive power of VSG respectively. ref V is the d-axis voltage reference value in the dq synchronous rotating coordinate system. ref is the VSG output voltage reference value, which can be directly given as V according to the photovoltaic grid-connected point voltage in formula (1): p .Q D is the reactive power droop coefficient.

[0101] It can be seen that the voltage stability of the grid connection point of the virtual synchronous generator (VSG) is closely related to its multiple key control parameters, including the reactive power droop coefficient Q D , the reactive power Q generated by VSG VSG , proportional gain K of the voltage control loop pv And the integral gain K iv Among them, Q D It directly affects the voltage amplitude regulation of the VSG grid connection point. D The size of Q can effectively control the voltage amplitude of VSG when it is connected to the grid, thereby supporting or regulating the grid voltage. VSG The reactive power generated by VSG is closely related to the operating status of VSG, the output power of PV system, the active and reactive power requirements of load and the impedance of grid. The fluctuation of PV output, the change of load and the change of grid parameters will lead to the change of Q VSG The change of VSG grid connection point will affect the stability of voltage.

[0102] Therefore, in order to improve and enhance the stability and regulation capability of the VSG node voltage, this can be achieved by adjusting the above parameters. Specifically, Q can be dynamically adjusted according to changes in photovoltaic output, load demand, and grid status. D , Q VSG , K pv and K ivThe size of the voltage regulator can be adjusted to optimize the VSG grid-connected voltage control. This adjustment method not only helps to improve the adaptability of VSG to the grid, but also effectively enhances the voltage stability and reliability of the grid as a whole.

[0103] Step S103: analyzing the relationship between the VSG frequency change and the inertia constant of the VSG-based distributed photovoltaic grid-connected point voltage model after the power disturbance, so as to construct a multi-objective optimization function with the goal of minimizing the inertia constant and the frequency change rate according to the analysis results;

[0104] It is assumed that a high value of line filter including large power electronic VSCs results in a high X / R ratio of the microgrid. Such a high value of X / R ratio results in the inductive nature of the microgrid, so the well-known P / X and Q / V droop control strategies can be considered in the proposed work to regulate the voltage of the microgrid. The active and reactive droop coefficients can be calculated as;

[0105]

[0106]

[0107] Where (f max -f min ) is the acceptable frequency variation range of VSC. VSGmax is the maximum required active power. Similarly, (V max -V min ) is the acceptable voltage variation range of the VSC. For the current working state, a 10% voltage variation is considered.

[0108] The instantaneous f after any power disturbance occurs in the system B The relationship between the value (Δ<0.05) and the inertia constant (Y) can be expressed as;

[0109]

[0110] For practical situations, it is necessary to choose the inertia constant (Y) reasonably so that f B To obtain the optimum instantaneous f B The two main objectives are to minimize the inertia constant Y and the frequency change rate f. B . These two objectives are mathematically expressed as follows:

[0111] Ming 1 (x) = minY (20)

[0112] Ming 2 (x) = minf B (twenty one)

[0113] Subject to the following constraints

[0114] f B min ≤f B ≤f B max (twenty two)

[0115] Y min ≤Y≤Y max (twenty three)

[0116]

[0117] In the formula, (f B / Y) min and (f B / Y) max Yes B and the reference maximum and minimum values ​​of Y, P 0 and f 0 is the power and frequency of the system. △P depends on the real-time P pv and P L , which has inherent uncertainty.

[0118] Step S104: construct power constraints for fluctuating photovoltaics and uncertain loads for the multi-objective optimization function, and solve the multi-objective optimization function and the power constraints to obtain the optimal inertia constant and the optimal frequency change rate under the current operating conditions, so as to update the second key parameter according to the optimal inertia constant and the optimal frequency change rate.

[0119] In this step, in addition to considering the constraints in the above formula, the impact of fluctuating photovoltaics and random uncertain loads should also be considered, where the output active power of VSG is;

[0120]

[0121] P pv is the photovoltaic output active power, P ba The energy storage battery outputs active power, P L is the load power, P loss is the system loss; P j Output active power for VSG.

[0122] P pv The short lead time uncertainty can be accurately expressed by the probability distribution function (PDF)

[0123]

[0124] Where υ′ and ω′ are the shape parameters of the probability distribution function, N ω is the normalization factor, y is Ppv Similarly, the random uncertainty of load occurrence can be accurately represented by the normal probability distribution function, as shown below.

[0125]

[0126] Among them, μ l and l are the mean and standard deviation of load forecast, respectively.

[0127] In addition, a stochastic programming model is introduced to minimize the expected value of the objective function in the presence of uncertainty. Let the objective function be expressed as g i (y i ):y i ∈G i (x,u),x∈X,μ∈U. Therefore, the objective function is mathematically reformulated as

[0128]

[0129] Among them, u / U and x / X are the uncertain decision and input variable sets respectively. i (x,u) and E[Y i (x, u)] represent the optimal value and expected value of the uncertain target, respectively.

[0130] For the solution of the stochastic programming model, it is formulated as a deterministic equivalent, assuming that the uncertainty occurs in a finite number of scenarios, each of which is associated with a probability of occurrence. Accordingly, the expression of the expectation function is as follows.

[0131]

[0132] As the number of uncertainty scenarios increases, the accuracy of the expectation function increases, but the computational complexity also increases. Therefore, in order to minimize the computational complexity while ensuring the accuracy of the solution, an appropriate trade-off needs to be made. Therefore, a finite number of uncertainty scenarios are generated using Monte Carlo simulation and appropriately reduced to a smaller number (N), each of which is associated with a probability of occurrence ρ x , using the backward reduction technique to accurately represent the expected uncertainty. Therefore, the expected functions of the two objectives are as follows.

[0133]

[0134] The normal boundary intersection (NB I) algorithm is then used to solve the multi-objective optimization problem. The NBI algorithm generates a shift benefit matrix M, whose nth column can be expressed as

[0135]

[0136] Furthermore, the convex hull of the individual minimum is generated {φδ:δ∈Δ}, where δ is a convex combination vector and Δ is a set of δ. Next, the NBI algorithm generates the normal of the convex hull And identify the intersection of the normal line and the Pareto front. Finally, the algorithm searches for the intersection point closest to the origin, which is the "knee" solution. The knee solution is the optimal trade-off between two opposing objectives and is considered the optimal solution point of Pareto.

[0137] In summary, the present invention first analyzes the specific influence of environmental factors such as photovoltaic sunshine intensity, temperature and key parameters such as load parameters on the voltage of distributed photovoltaic grid-connected nodes based on virtual synchronous generators (VSGs), and then extracts voltage sensitivity factors (influencing factors). On this basis, a VSG grid-connected point voltage model incorporating voltage sensitivity factors is constructed to further explore the influence of changes in key parameters of VSG on distributed photovoltaic grid-connected voltage. Then, using the constructed VSG output voltage model, the correlation between VSG frequency change and inertia constant after power disturbance is analyzed, and a multi-objective optimization problem aimed at minimizing inertia constant Y and frequency change rate fB is established accordingly. In addition, the power constraints brought by fluctuating photovoltaics and uncertain loads are fully considered. Finally, based on the above optimization objectives and constraints, a random programming model and a normal boundary intersection (NBI) solution algorithm are used to obtain the optimal inertia constant and frequency change rate fB under random working conditions. This result can be used to adaptively update VSG parameters to ensure the voltage stability of VSG grid-connected points. When dealing with practical problems such as voltage instability caused by high-proportion distributed photovoltaic access to the power grid, it shows significant advantages.

[0138] like Figure 3 As shown, an embodiment of the present invention further provides an evaluation system for high-proportion distributed photovoltaic access to a power grid, the system comprising:

[0139] The influencing factor analysis module 10 is used to obtain at least one first key parameter of the target distributed photovoltaic grid-connected, and construct a distributed photovoltaic grid-connected point voltage function according to the at least one first key parameter, so as to obtain an influencing factor related to voltage according to the distributed photovoltaic grid-connected point voltage function;

[0140] A model building module 20, configured to build a VSG-based distributed photovoltaic grid-connected point voltage model according to the influencing factors, and to obtain a second key parameter related to voltage stability according to the VSG-based distributed photovoltaic grid-connected point voltage model;

[0141] The objective function construction module 30 is used to analyze the relationship between the VSG frequency change and the inertia constant of the distributed photovoltaic grid-connected point voltage model based on the VSG after the power disturbance, so as to construct the inertia constant Y and the frequency change rate f according to the analysis results.B is the multi-objective optimization function of the target;

[0142] The solution module 40 is used to construct power constraints on fluctuating photovoltaics and uncertain loads for the multi-objective optimization function, and solve the multi-objective optimization function and the power constraints to obtain the optimal inertia constant and the optimal frequency change rate under the current operating conditions, so as to update the second key parameter according to the optimal inertia constant and the optimal frequency change rate.

[0143] On the other hand, the present invention further provides a storage medium on which one or more programs are stored, and when the programs are executed by a processor, the above-mentioned evaluation method for high-ratio distributed photovoltaic access to the power grid is implemented.

[0144] On the other hand, the present invention further proposes an electronic device, including a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the above-mentioned evaluation method for high-proportion distributed photovoltaic access to the power grid.

[0145] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0146] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0147] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0148] Although the embodiments of the present invention are described in detail above, it is obvious to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as described in the claims. Moreover, the present invention described herein may have other embodiments and may be implemented or realized in a variety of ways.

Claims

1. A method for evaluating high-proportion distributed photovoltaic access to a power grid, characterized in that: The method comprises: Acquire at least one first key parameter of the target distributed photovoltaic grid-connected, and construct a distributed photovoltaic grid-connected point voltage function according to the at least one first key parameter, so as to obtain an influencing factor related to voltage according to the distributed photovoltaic grid-connected point voltage function; Constructing a VSG-based distributed photovoltaic grid-connected point voltage model according to the influencing factors, and acquiring a second key parameter related to voltage stability according to the VSG-based distributed photovoltaic grid-connected point voltage model; The relationship between the VSG frequency change and the inertia constant of the VSG-based distributed photovoltaic grid-connected point voltage model after power disturbance is analyzed, and a model is constructed based on the analysis results to minimize the inertia constant Y and the frequency change rate f. B is the multi-objective optimization function of the target; Power constraints on fluctuating photovoltaics and uncertain loads are constructed for the multi-objective optimization function, and the multi-objective optimization function and the power constraints are solved to obtain the optimal inertia constant and the optimal frequency change rate under the current operating conditions, so as to update the second key parameter according to the optimal inertia constant and the optimal frequency change rate.

2. The evaluation method for high-proportion distributed photovoltaic access to the grid according to claim 1 is characterized in that: The step of obtaining at least one first key parameter of the target distributed photovoltaic grid-connected, and constructing a distributed photovoltaic grid-connected point voltage function according to the at least one first key parameter, so as to obtain an influencing factor related to voltage according to the distributed photovoltaic grid-connected point voltage function comprises: The voltage function of the distributed photovoltaic grid-connected point is constructed according to the following formula: in, represents the rated value of the grid voltage, k represents the ratio of the actual grid voltage amplitude to the rated value, R g and L g represents the equivalent resistance and inductance of the grid, a represents the difference between the load and the active power of photovoltaic and energy storage, b represents the difference between the reactive power of the load and the reactive power emitted by the VSG, c represents the square of the grid current value, L g represents the equivalent inductance of the power grid, w represents the angular frequency, The influencing factors include photovoltaic output power, load power, and grid voltage amplitude.

3. The evaluation method for high-proportion distributed photovoltaic access to the grid according to claim 2 is characterized in that: The VSG-based distributed photovoltaic grid-connected point voltage model includes a BESS, a DC-DC bidirectional converter and a VSC. The steps of constructing a VSG-based distributed photovoltaic grid-connected point voltage model according to the influencing factors and acquiring a second key parameter related to voltage stability according to the VSG-based distributed photovoltaic grid-connected point voltage model include: The VSC is connected to the common point coupling through the step-up transformer and line impedance. The BESS adjusts the DC bus voltage through the voltage and current control loop through the BDC. The control signal of the BDC DC bus voltage control is as follows: I Bref (t)= kp dΔV D +K id ∫ΔV D dt D B =K pb Δi B +k ib ∫ΔI B dt Among them, V D is the actual DC bus voltage passing through the low-pass filter, with a cut-off frequency of f cf , V Dref is the reference DC bus voltage, ΔV D is the change in DC bus voltage, K pd and K id are the proportional gain constant and integral gain constant of the outer voltage control loop, I Bref is the reference battery current calculated by the outer voltage control loop, I B is the actual battery current, ΔI B The difference between the reference current and the actual battery current, K pb and K ib are the proportional gain constant and integral gain constant of the inner current control loop, D B is the duty cycle of the bidirectional DC-DC converter, s represents the complex frequency domain signal, I Bref (t) represents the variation of the reference battery current over time t; The terminal voltage and reactive power control of VSG are generated by the following equation: E ref =V ref -Q D Q VSG +K pv (Q ref -Q VSG )+K iv ∫(Q ref -Q VSG )dt Among them, K pv ,K iv are the proportional gain and integral gain of VSG voltage control, Q ref and Q VSG are the reactive reference value and reactive power of VSG, E ref is the d-axis voltage reference value in the dq synchronous rotating coordinate system, V ref is the VSG output voltage reference value, Q D is the reactive power droop coefficient; The second key parameter includes the reactive power droop coefficient Q D , the reactive power Q generated by VSG VSG , proportional gain K of the voltage control loop pv And the integral gain K iv .

4. The evaluation method for high-proportion distributed photovoltaic access to the grid according to claim 3 is characterized in that: The VSG-based distributed photovoltaic grid-connected point voltage model is analyzed to minimize the inertia constant Y and the frequency change rate f according to the analysis results. B The steps of multi-objective optimization function for target include: The active and reactive droop coefficients can be calculated as; Among them, (f max -f min ) is the acceptable frequency variation range of VSC, P VSGmax is the maximum required active power, (V max -V min ) is the acceptable voltage variation range of VSC; The instantaneous f after any power disturbance occurs in the system B The relationship between the value and the inertia constant Y is expressed as; 5. The evaluation method for high-proportion distributed photovoltaic access to the grid according to claim 4 is characterized in that: The VSG-based distributed photovoltaic grid-connected point voltage model is analyzed to minimize the inertia constant Y and the frequency change rate f according to the analysis results. B The steps of the multi-objective optimization function for the target also include: The multi-objective optimization function is constructed according to the following formula: ming1(x)=minY ming2(x)=minf B Among them, (f B / Y) min and (f B / Y) max Yes B and the reference maximum and minimum values ​​of Y, P0 and f0 are the power and frequency of the system, and △P depends on the real-time P pv and P L .

6. The evaluation method for high-proportion distributed photovoltaic access to the grid according to claim 5 is characterized in that: The step of constructing power constraints on the fluctuating photovoltaic power and the uncertain load for the multi-objective optimization function comprises: The output active power of VSG is; P j =P s -P pv -P ba -P L P loss P pv The short lead time uncertainty is expressed using a probability distribution function: The random uncertainty of load occurrence is as follows: Among them, P pv is the photovoltaic output active power, P ba The energy storage battery outputs active power, P L is the load power, P loss is the system loss; P j is the VSG output active power, v′ and ω′ are the shape parameters of the probability distribution function, N ω is the normalization factor, y is P pv The occurrence of uncertainty in μ l and l are the mean and standard deviation of load forecast, respectively.

7. The evaluation method for high-proportion distributed photovoltaic access to the grid according to claim 5 is characterized in that: The step of solving the multi-objective optimization function and the power constraint condition to obtain the optimal inertia constant and the optimal frequency change rate under the current working condition, and updating the second key parameter according to the optimal inertia constant and the optimal frequency change rate comprises: A stochastic programming model is used to minimize the expected value of the objective function under uncertainty, where the objective function is expressed as: g i (and i ):and i ∈G i (x, u), x∈X, u∈U The objective function is mathematically reformulated as: Among them, x / X and μ / U are the input variable set and uncertain decision respectively, Y i (x,u) and E[Y i (x, u)] represent the optimal value and expected value of the uncertain target, respectively.

8. An evaluation system for high-proportion distributed photovoltaic access to the power grid, characterized in that: The system comprises: An influencing factor analysis module, used to obtain at least one first key parameter of the target distributed photovoltaic grid-connected, and to construct a distributed photovoltaic grid-connected point voltage function according to the at least one first key parameter, so as to obtain an influencing factor related to voltage according to the distributed photovoltaic grid-connected point voltage function; A model building module, used to build a VSG-based distributed photovoltaic grid-connected point voltage model according to the influencing factors, and obtain a second key parameter related to voltage stability according to the VSG-based distributed photovoltaic grid-connected point voltage model; The objective function building module is used to analyze the relationship between the VSG frequency change and the inertia constant of the VSG-based distributed photovoltaic grid-connected point voltage model after power disturbance, so as to minimize the inertia constant Y and the frequency change rate f according to the analysis results. B is the multi-objective optimization function of the target; A solution module is used to construct power constraints on fluctuating photovoltaics and uncertain loads for the multi-objective optimization function, and solve the multi-objective optimization function and the power constraints to obtain the optimal inertia constant and the optimal frequency change rate under the current operating conditions, so as to update the second key parameter according to the optimal inertia constant and the optimal frequency change rate.

9. A storage medium, characterized in that: The storage medium stores one or more programs, which, when executed by a processor, implement the evaluation method for high-proportion distributed photovoltaic access to the power grid as described in any one of claims 1-7.

10. An electronic device, comprising a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, it implements the evaluation method for high-proportion distributed photovoltaic access to the power grid as described in any one of claims 1-7.

Images