Photovoltaic inverter power distribution network voltage / reactive power control comprehensive optimization method, system and equipment based on multi-parameter discretization

Abstract

The invention discloses a photovoltaic inverter power distribution network voltage / reactive power control comprehensive optimization method, system and equipment based on multi-parameter discretization, and the method comprises the steps: enabling the network loss cost, the inverter degradation cost and the average bus voltage deviation to be minimized through the centralized optimization of a reactive power set value and a Q-V control function of a photovoltaic inverter; a piecewise linearization method is adopted to accurately describe a general Q-V control model of a photovoltaic inverter, life degradation caused by inverter support voltage / reactive power is analyzed and quantified as voltage regulation cost, and the voltage regulation cost is taken into an optimization target. And a continuous variable discretization method is adopted to convert non-convex nonlinear constraints in the piecewise linear Q-V control model into linear constraints which can be effectively solved. According to the voltage control method, the operation cost of the power distribution network can be reduced, the economical efficiency is improved, the residual capacity of the photovoltaic inverter can be fully utilized to participate in voltage control, and the rapidity and flexibility of voltage regulation and control when photovoltaic and load output is unstable are ensured.

Description

Technical Field

[0001] The present invention relates to the field of distribution network voltage control, and in particular to a photovoltaic inverter distribution network voltage / reactive power control comprehensive optimization method, system and equipment based on multi-parameter discretization. Background Art

[0002] In recent years, photovoltaics have developed rapidly as a clean energy source. By the end of 2020, its installed capacity reached 253 million kilowatts. The high proportion of photovoltaic access in the distribution network has caused the voltage of the distribution network to fluctuate greatly or even exceed the limit, which has brought huge challenges to the voltage control of the distribution network. Photovoltaic inverters have fast and flexible reactive output functions and can provide reactive compensation for the power grid. As the proportion gradually increases, the voltage control of photovoltaic inverters has become an important means of regulation that cannot be ignored. Due to the low regulation cost, no additional regulation device is required, and the response to random voltage fluctuations is faster, it is of great significance to the safe and stable operation of the distribution network.

[0003] However, most of the existing photovoltaic inverter reactive voltage (QV) control models only optimize some parameters, without comprehensively considering the model parameters that affect the inverter voltage regulation performance (such as voltage dead zone position, dead zone range, control gain, etc.). In addition, as the number of parameters that need to be optimized in the model increases, the nonlinear terms in the model become more complex, which increases the computational burden. Summary of the invention

[0004] Purpose of the invention: The present invention aims to provide a comprehensive optimization method for voltage / reactive power control of photovoltaic inverter distribution network based on multi-parameter discretization, which minimizes network loss cost, inverter degradation cost and average bus voltage deviation; another object of the present invention is to provide a comprehensive optimization system and equipment for voltage / reactive power control of photovoltaic inverter distribution network based on multi-parameter discretization.

[0005] Technical solution: The photovoltaic inverter distribution network voltage / reactive power control comprehensive optimization method based on multi-parameter discretization of the present invention comprises the following steps:

[0006] S1. Based on the reactive power control characteristics of photovoltaic inverters, a general model of photovoltaic inverter QV control is established;

[0007] S2. Based on the voltage regulation characteristics of the inverter, establish the relationship between the voltage regulation cost and the life reduction of the inverter;

[0008] S3, according to the photovoltaic inverter QV control general model in step S1 and the relationship between the inverter voltage regulation cost and life reduction in step S2, considering the power flow constraints of the distribution network, with the minimum network loss cost, minimum inverter loss cost and minimum average bus voltage deviation of the distribution network as the goal, establish a comprehensive optimization model for distribution network voltage control;

[0009] S4. According to the comprehensive optimization model of distribution network voltage control in step S3, a multi-parameter discretization method is used to transform the non-convex nonlinear constraints in the piecewise linear QV control model into effectively solvable linear constraints, and a comprehensive optimization linear model of distribution network voltage control is obtained. By adjusting the accuracy level, the output result of the comprehensive optimization linear model of distribution network voltage control gradually approaches the optimal solution, and the optimal control strategy is obtained.

[0010] Furthermore, the photovoltaic inverter QV control general model is:

[0011]

[0012] w i,t,1,r ≤y i,t,1,r ,w i,t,6,r ≤y i,t,5,r (3)

[0013] w i,t,k,r ≤y i,t,k,r +y i,t,k-1,r ,k=2,3,4,5 (4)

[0014] w i,t,k,r ≥0,y i,t,k,r ∈{0,1} (5)

[0015]

[0016] Among them, V i,t,r is the voltage amplitude of the ith node in the rth scenario within the tth scheduling interval; w i,t,k,r is a continuous variable; is the voltage amplitude corresponding to the kth point on the QV control curve; is the reactive power output by the inverter, Q i,t,k is the reactive power corresponding to the kth point on the QV control curve, y i,t,k,r is a binary variable.

[0017] Furthermore, the relationship between the inverter voltage regulation cost and the life reduction is quantified by quantifying the photovoltaic inverter voltage regulation cost and the reactive power output by the inverter. And inverter unit voltage regulation cost The relationship is expressed as follows:

[0018]

[0019] Among them, η LR,0 , η LR,1 and η LR,2 is a constant.

[0020] Furthermore, considering the power flow constraints of the distribution network, with the minimum network loss cost, inverter loss cost and average bus voltage deviation of the distribution network as the goal, the comprehensive optimization model of distribution network voltage control is:

[0021]

[0022] st(1)—(6)(9)

[0023]

[0024]

[0025] Where obj is the network loss cost; w is the weight coefficient, w∈[0,1]; C loss is the network loss cost, C LR is the reactive power cost caused by the PV inverter participating in voltage regulation, is the average node voltage offset of the rth scenario in the tth scheduling interval, and R is the number of scenarios for each scheduling interval; is the time-of-use electricity price; P ij,t,r and Q ij,t,r They represent the active power and reactive power on the transmission line respectively; V0 represents the rated voltage of the line; is the rated capacity of the PV inverter; is the active power output of photovoltaic; P hi,t,r is the active power flowing into all lines of node i; Q hi,t,r is the reactive power of all lines flowing into node i; V j,t,r is the voltage amplitude of the jth node in the rth scenario within the tth scheduling interval; r ij and x ij They represent the resistance and reactance of the line respectively; V i and Respectively represent the lower and upper limits of node voltage; Indicates the power capacity of the transmission line.

[0026] Furthermore, in step S4, the bilinear term in equation (1) Linearization is performed as follows:

[0027] Introducing auxiliary variables Redescribe

[0028]

[0029] in, is a continuous variable; Substituting formula (20) into And introduce auxiliary variables get:

[0030]

[0031] Will After discretization, it is expressed as follows:

[0032]

[0033] Among them, p is a negative integer, which is used to represent Accuracy level; g is Values ​​at different quantiles; is a binary variable; ensure Get any value in the domain;

[0034] Introducing auxiliary variables Substituting it into formula (21), we get:

[0035]

[0036] Introducing continuous variables have to:

[0037]

[0038] Among them, the nonlinear term Relaxation is performed using the McCormick envelope, w i,t,k,r ∈[0,1], Let the variable The relaxation model is as follows:

[0039]

[0040] Multiply both sides of formula (24) by w i,t,k,r The following formula can be obtained:

[0041]

[0042] Bilinear term It is expressed as a mixed integer linear constraint by equations (20), (22)-(25) and (27)-(33).

[0043] Furthermore, in step S4, w in formula (2) i,t,k,r Q i,t,k After linearization, the linear model is as follows:

[0044]

[0045] Furthermore, the comprehensive optimization linear model of distribution network voltage control obtained in step S4 is:

[0046]

[0047] st(9)-(20),(22)-(25),(27)-(45)(47).

[0048] Further, in step S4, the accuracy level is adjusted as follows: the accuracy level is first set to p = -1, and the distribution network voltage control comprehensive optimization linear model is solved to obtain the optimal value of each decision variable, which is used as the initial point of the distribution network voltage control comprehensive optimization model, and the target optimal value obj of the distribution network voltage control comprehensive optimization model and the target optimal value obj of the power grid voltage control comprehensive optimization linear model are solved respectively. * Compare, when constraint C is satisfied, the decision is optimal, otherwise set p = p-1, repeat the above process until constraint C is satisfied and the iteration ends, and finally the optimal control strategy of the distribution network is obtained;

[0049] The constraint C is

[0050]

[0051] Where ε is a preset threshold, which is a small positive number.

[0052] The photovoltaic inverter distribution network voltage and reactive power control comprehensive optimization system based on multi-parameter discretization of the present invention comprises:

[0053] The model building module is used to build a general model of photovoltaic inverter QV control based on the reactive power control characteristics of photovoltaic inverters; based on the general model of photovoltaic inverter QV control and the relationship between the inverter voltage regulation cost and life reduction, considering the power flow constraints of the distribution network, with the minimum network loss cost, the minimum inverter loss cost and the minimum average bus voltage deviation of the distribution network as the goals, to establish a comprehensive optimization model of distribution network voltage control; based on the comprehensive optimization model of distribution network voltage control, using a multi-parameter discretization method, the non-convex nonlinear constraints in the piecewise linear QV control model are converted into effectively solvable linear constraints, and a comprehensive optimization linear model of distribution network voltage control is constructed;

[0054] The decision-making solution module is used to obtain the optimal control strategy by adjusting the accuracy level so that the output result of the distribution network voltage control comprehensive optimization linear model gradually approaches the optimal solution.

[0055] The computer device of the present invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the steps of the above method are implemented when the processor executes the computer program.

[0056] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: 1. The present invention minimizes network loss costs, inverter degradation costs and average bus voltage deviation by centrally optimizing the reactive setting value and QV control function of the photovoltaic inverter; 2. The present invention takes into account the influence of the additional reactive power support provided by the inverter when participating in voltage / reactive power control on its life, analyzes the relationship between the reduced inverter life and the voltage regulation cost, analyzes the life degradation caused by the inverter supporting voltage / reactive power control and quantifies it as voltage regulation cost, and incorporates it into the optimization target; 3. The present invention adopts a continuous variable discretization method to convert the non-convex nonlinear constraints in the piecewise linear QV control model into linear constraints that can be effectively solved, and tests the proposed method on a 33-bus distribution system, and the simulation results verify its effectiveness; 4. In order to balance the solution speed and model accuracy, the present invention adopts an adaptive model accuracy solution method. By gradually adjusting the accuracy, the result of the linearized model gradually approaches the optimal solution, thereby realizing rapid regulation and precise response of the distribution network voltage. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a flow chart of the present invention;

[0058] Figure 2 is the relationship curve between the unit reactive power cost of the inverter and the output reactive power;

[0059] Figure 3 This is the topology diagram of the 33-node power distribution system;

[0060] Figure 4 This is a voltage comparison diagram between the present invention and the non-control method;

[0061] Figure 5 This is a comparison chart of the solution results of the comprehensive optimization linear model for distribution network voltage control. DETAILED DESCRIPTION

[0062] The present invention will be further described below in conjunction with the accompanying drawings.

[0063] like Figure 1 As shown, the photovoltaic inverter distribution network voltage / reactive power comprehensive optimization method based on multi-parameter discretization of the present invention comprises the following steps:

[0064] S1. Reactive power constraint of photovoltaic inverter

[0065]

[0066] in, is the maximum reactive power output / absorption of the photovoltaic inverter, is the inverter capacity, is the photovoltaic active output with uncertainty;

[0067] General model of QV control of photovoltaic inverter:

[0068]

[0069] Among them, index t represents a scheduling interval in the entire optimization cycle, r represents a preset scenario within the scheduling interval, i index represents the node number, V i,t,r represents the node voltage amplitude, is the upper limit of the inverter reactive power output, It represents the reactive power value output by the inverter when the node voltage is within the dead zone, that is, the reactive power setting value of the central layer, where Indicates the horizontal coordinates of the six points that make up the QV control curve.

[0070] It can be seen from formula (2) that the second and fourth segments of the QV control curve have relatively complex nonlinear terms, which are difficult to linearize. In order to relax the nonlinear terms, the QV control curve is accurately modeled by piecewise linearization. i,t,k,r (k=1,2,…,6) and binary variable y i,t,k,r (k=1,2,…5), the QV control curve can be transformed into the following model:

[0071]

[0072] w i,t,1,r ≤y i,t,1,r ,w i,t,6,r ≤y i,t,5,r (6)

[0073] w i,t,k,r ≤y i,t,k,r +y i,t,k-1,r ,k=2,3,4,5 (7)

[0074] w i,t,k,r ≥0,y i,t,k,r ∈{0,1} (8)

[0075]

[0076] In formula (5), Q i,t,k (k=1,2,…,6) are the ordinates of the six points that make up the QV control curve, and the auxiliary variable y i,t,k,r Limit w i,t,k,r The value of V i,t,r The interval where w i,t,k,r The original function used to accurately describe the interval. S2. The reduction in life due to the inverter participating in QV control can be calculated by the following formula:

[0077]

[0078] In the formula, OL P is the working life of the inverter when it only outputs active power, OL Q is the working life of the inverter when it outputs both active and reactive power, Q represents the reactive power output by the inverter, v, θ c , μ, λ c are the inverter related working parameters in time period c.

[0079] To quantify the inverter life loss, it is necessary to establish the relationship between the inverter life loss LR and the incremental LI of the levelized cost of electricity of the photovoltaic system. According to the calculation formula of the levelized cost of electricity of the photovoltaic system, the following relationship is fitted:

[0080]

[0081] Where η LI,A0 , η LI,A1 , η LI,B0 , η LI,B1 is a constant.

[0082] Multiply both sides of formula (11) by That is, the average active power of the photovoltaic system in time period c during its life cycle. Therefore, the reactive power cost caused by the inverter life loss is as follows:

[0083]

[0084] Where c c Q,LR Reactive power costs due to inverter life loss, is the reactive power generated by the inverter in time period c. Due to the correlation between the reactive power output by the inverter and the inverter life, equations (11) and (12) can be simplified into a single variable polynomial equation as shown below by fitting method:

[0085]

[0086] Where η LR,0 , η LR,1 and η LR,2 is a constant. Finally, the relationship between the reactive power output of the photovoltaic inverter and the regulation cost is as follows: Figure 2 shown.

[0087] S3. The comprehensive optimization problem of distribution network voltage control is modeled as follows:

[0088]

[0089] st(4)-(9)(15)

[0090]

[0091] The objective function (14) aims to minimize the network loss cost, inverter voltage regulation cost and average node voltage deviation, where C loss represents the network loss cost, C LR Represents the reactive power cost caused by the PV inverter participating in voltage regulation, represents the average node voltage offset of all preset scenarios, and R represents the number of scenarios for each scheduling interval. w is the weight coefficient, w∈[0,1]; Equations (16)-(18) respectively give the calculation methods of network loss cost, inverter voltage regulation cost and node average voltage deviation in the objective function.

[0092] Equations (19) and (20) are the reactive power capacity constraints of the inverter. Equations (21) and (22) are the node power balance equations. The voltage relationship between nodes can be expressed as a linear model shown in equation (23). Equation (24) is the node voltage constraint. Equation (25) is the line active power transmission capacity constraint.

[0093] S4. According to the comprehensive optimization model of distribution network voltage control in step S3, a multi-parameter discretization method is used to transform the non-convex nonlinear constraints in the piecewise linear QV control model into effectively solvable linear constraints, and a comprehensive optimization linear model of distribution network voltage control is obtained. By adjusting the accuracy level, the output result of the comprehensive optimization linear model of distribution network voltage control gradually approaches the optimal solution, and the optimal control strategy is obtained.

[0094] For bilinear terms and w i,t,k,r Q i,t,k (k=2,3,4,5) are linearized respectively:

[0095] First, introduce auxiliary variables Redescribe

[0096]

[0097] Substitute equation (26) into the original nonlinear term and introduce auxiliary variables The following formula is obtained:

[0098]

[0099] Will The discretization is as follows:

[0100]

[0101] Introducing auxiliary variables Substituting it into formula (27) we get:

[0102]

[0103] Introducing new continuous variables therefore

[0104]

[0105] The newly generated nonlinear term in formula (34) The McCormick envelope is used for relaxation, where w i,t,k,r ∈[0,1], Let the variable The relaxation model is shown below.

[0106]

[0107] Multiply both sides of formula (30) by w i,t,k,r The following formula can be obtained:

[0108]

[0109] Therefore, the bilinear term It is expressed as a mixed integer linear constraint by equations (26), (28)-(31) and (33)-(39).

[0110] Similarly i,t,k,r Q i,t,k (k=2,3,4,5) can also be linearized by the above method, and its linear model is as follows:

[0111]

[0112]

[0113] Finally, the original optimization model is restated as follows

[0114]

[0115] st(14)-(26),(28)-(31),(33)-(51)(53)

[0116] However, according to the above derivation process, it can be found that the optimal solution of the reconstructed model is not necessarily the optimal solution of the original problem, but only provides a lower bound for the optimal solution of the original problem. This is mainly due to and The precision of p is insufficient, that is, p cannot be an infinitesimal negative integer. To solve this problem, this method first takes the lowest precision p = -1, solves the reconstructed optimization model to obtain the optimal value of each decision variable as the initial point of the original model, and then solves the original model to obtain its target optimal value obj and the target optimal value obj of the reconstructed model. * By comparison, when the constraint of formula (54) is satisfied, the decision is optimal, otherwise p=p-1 is set and the above process is continued until the iteration ends when formula (54) is satisfied.

[0117]

[0118] In order to verify the effect of the present invention, the following experiment is given in the embodiment of the present invention:

[0119] like Figure 3 The network topology and PV location of the test system are shown in Figure 1. The rated active power and inverter capacity of the PV are shown in Table 1.

[0120] Table 1

[0121]

[0122] The voltage of the test system is controlled by the method of the present invention, such as Figure 4 The figure shows the voltage deviation comparison between using this method and not using the control method.

[0123] The present invention also proposes to relax the bilinear terms by using a multi-parameter decomposition method and proposes an adaptive precision solution algorithm. Figure 5 The process of solving the optimization problem.

[0124] It can be seen that the use of this method to control the voltage of the distribution network can effectively reduce voltage deviation and improve economic efficiency. When the reactive power output of the inverter is too high, it will seriously affect the life of the inverter. However, when the output power of the inverter is small, the life of the inverter is little affected by this. Therefore, it is necessary to quantify the life loss caused by the inverter participating in voltage regulation as a cost. In addition, for the non-convex nonlinear terms in the QV control model, the proposed multi-parameter solution method can continuously iterate and approximate the original optimal solution according to different accuracy requirements, which also ensures the accuracy of the model solution while improving the solution speed.

[0125] The photovoltaic inverter distribution network voltage and reactive power control comprehensive optimization system based on multi-parameter discretization of the present invention comprises:

[0126] The model building module is used to build a general model of photovoltaic inverter QV control based on the reactive power control characteristics of photovoltaic inverters; based on the general model of photovoltaic inverter QV control and the relationship between the inverter voltage regulation cost and life reduction, considering the power flow constraints of the distribution network, with the minimum network loss cost, the minimum inverter loss cost and the minimum average bus voltage deviation of the distribution network as the goals, to establish a comprehensive optimization model of distribution network voltage control; based on the comprehensive optimization model of distribution network voltage control, using a multi-parameter discretization method, the non-convex nonlinear constraints in the piecewise linear QV control model are converted into effectively solvable linear constraints, and a comprehensive optimization linear model of distribution network voltage control is constructed;

[0127] The decision-making solution module is used to obtain the optimal control strategy by adjusting the accuracy level so that the output result of the distribution network voltage control comprehensive optimization linear model gradually approaches the optimal solution.

[0128] The computer device of the present invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the steps of the above method are implemented when the processor executes the computer program.

[0129] The present invention minimizes network loss costs, inverter degradation costs and average bus voltage deviations by centrally optimizing the reactive power set value and QV control function of the photovoltaic inverter. A universal QV control model for photovoltaic inverters is established, and the model is accurately described by a piecewise linearization method. The life degradation caused by the inverter support voltage / reactive power is analyzed and quantified as a voltage regulation cost, which is then included in one of the optimization goals. On this basis, a continuous variable discretization method is used to convert the non-convex nonlinear constraints in the piecewise linear QV control model into linear constraints that can be effectively solved. The voltage control method in the present invention can reduce the operating cost of the distribution network, improve economy, and make full use of the remaining capacity of the photovoltaic inverter to participate in voltage control, ensuring the rapidity and flexibility of voltage regulation when the photovoltaic and load outputs are unstable.

Claims

1. A photovoltaic inverter distribution network voltage / reactive power control comprehensive optimization method based on multi-parameter discretization, characterized in that: The following steps are involved: S1. Based on the reactive power control characteristics of photovoltaic inverters, a general model of photovoltaic inverter QV control is established; S2. Based on the voltage regulation characteristics of the inverter, establish the relationship between the voltage regulation cost and the life reduction of the inverter; S3, according to the photovoltaic inverter QV control general model in step S1 and the relationship between the inverter voltage regulation cost and life reduction in step S2, considering the power flow constraints of the distribution network, with the minimum network loss cost, minimum inverter loss cost and minimum average bus voltage deviation of the distribution network as the goal, establish a comprehensive optimization model for distribution network voltage control; S4. According to the comprehensive optimization model of distribution network voltage control in step S3, a multi-parameter discretization method is used to transform the non-convex nonlinear constraints in the piecewise linear QV control model into effectively solvable linear constraints, and a comprehensive optimization linear model of distribution network voltage control is obtained. By adjusting the accuracy level, the output result of the comprehensive optimization linear model of distribution network voltage control gradually approaches the optimal solution, and the optimal control strategy is obtained.

2. The photovoltaic inverter distribution network voltage / reactive power control comprehensive optimization method based on multi-parameter discretization according to claim 1 is characterized in that: The general model of photovoltaic inverter QV control is: Among them, V i,t,r is the voltage amplitude of the ith node in the rth scenario within the tth scheduling interval; w i,t,k,r is a continuous variable; is the voltage amplitude corresponding to the kth point on the QV control curve; is the reactive power output by the inverter, Q i,t,k is the reactive power corresponding to the kth point on the QV control curve, y i,t,k,r is a binary variable.

3. The photovoltaic inverter distribution network voltage / reactive power control comprehensive optimization method based on multi-parameter discretization according to claim 2 is characterized in that: The relationship between the voltage regulation cost of the inverter and the reduction of its life span is quantified by quantifying the voltage regulation cost of the photovoltaic inverter and the reactive power output by the inverter. And inverter unit voltage regulation cost The relationship is expressed as follows: Among them, η LR,0 , η LR,1 and η LR,2 is a constant.

4. The photovoltaic inverter distribution network voltage / reactive power control comprehensive optimization method based on multi-parameter discretization according to claim 1 is characterized in that: Considering the power flow constraints of the distribution network, with the minimum network loss cost, inverter loss cost and average bus voltage deviation as the goal, the comprehensive optimization model of distribution network voltage control is: st(1)—(6)(9) Where obj is the optimization target; w is the weight coefficient, w∈[0,1]; C loss is the network loss cost, C LR is the reactive power cost caused by the PV inverter participating in voltage regulation, is the average node voltage offset of the rth scenario in the tth scheduling interval, and R is the number of scenarios for each scheduling interval; is the time-of-use electricity price; P ij,t,r and Q ij,t,r They represent the active power and reactive power on the transmission line respectively; V0 represents the rated voltage of the line; is the rated capacity of the PV inverter; The active power output of photovoltaic and Respectively represent the load active power and reactive power; P hi,t,r is the active power flowing into all lines of node i; Q hi,t,r is the reactive power of all lines flowing into node i; V j,t,r is the voltage amplitude of the jth node in the rth scenario within the tth scheduling interval; r ij and x ij Respectively represent the resistance and reactance of the line; V i and Respectively represent the lower and upper limits of node voltage; Indicates the power capacity of the transmission line.

5. The photovoltaic inverter distribution network voltage / reactive power control comprehensive optimization method based on multi-parameter discretization according to claim 4 is characterized in that: In step S4, the bilinear term in equation (1) is Linearization is performed as follows: Introducing auxiliary variables Redescribe in, is a continuous variable; Substituting formula (20) into And introduce auxiliary variables get: Will After discretization, it is expressed as follows: Among them, p is a negative integer, which is used to represent Accuracy level; g is Values ​​at different quantiles; is a binary variable; ensure Get any value in the domain; Introducing auxiliary variables Substituting it into formula (21), we get: Introducing continuous variables have to: Among them, the nonlinear term Relaxation is performed using the McCormick envelope, w i,t,k,r ∈[0,1], Let the variable The relaxation model is as follows: Multiply both sides of formula (24) by w i,t,k,r The following formula can be obtained: Bilinear term It is expressed as a mixed integer linear constraint by equations (20), (22)-(25) and (27)-(33).

6. The photovoltaic inverter distribution network voltage / reactive power control comprehensive optimization method based on multi-parameter discretization according to claim 5 is characterized in that: In step S4, w in equation (2) is i,t,k,r Q i,t,k After linearization, the linear model is as follows:

7. The photovoltaic inverter distribution network voltage / reactive power control comprehensive optimization method based on multi-parameter discretization according to claim 6 is characterized in that: The comprehensive optimization linear model of distribution network voltage control obtained in step S4 is: st(9)-(20),(22)-(25),(27)-(45)(47).

8. The photovoltaic inverter distribution network voltage and reactive power control comprehensive optimization method based on multi-parameter discretization according to claim 7 is characterized in that: In step S4, the accuracy level is adjusted as follows: the accuracy level is first set to p = -1, and the distribution network voltage control comprehensive optimization linear model is solved to obtain the optimal value of each decision variable, which is used as the initial point of the distribution network voltage control comprehensive optimization model, and the target optimal value obj of the distribution network voltage control comprehensive optimization model and the target optimal value obj of the power grid voltage control comprehensive optimization linear model are solved respectively. * Compare, when constraint C is satisfied, the decision is optimal, otherwise set p = p-1, repeat the above process until constraint C is satisfied and the iteration ends, and finally the optimal control strategy of the distribution network is obtained; Among them, the constraint C is C: Where ε is a preset threshold, which is a small positive number.

9. A photovoltaic inverter distribution network voltage / reactive power control integrated optimization system based on multi-parameter discretization, characterized in that: include: The model building module is used to build a general model of photovoltaic inverter QV control based on the reactive power control characteristics of photovoltaic inverters; based on the general model of photovoltaic inverter QV control and the relationship between the inverter voltage regulation cost and life reduction, considering the power flow constraints of the distribution network, with the minimum network loss cost, the minimum inverter loss cost and the minimum average bus voltage deviation of the distribution network as the goals, to establish a comprehensive optimization model of distribution network voltage control; based on the comprehensive optimization model of distribution network voltage control, using a multi-parameter discretization method, the non-convex nonlinear constraints in the piecewise linear QV control model are converted into effectively solvable linear constraints, and a comprehensive optimization linear model of distribution network voltage control is constructed; The decision-making solution module is used to obtain the optimal control strategy by adjusting the accuracy level so that the output result of the distribution network voltage control comprehensive optimization linear model gradually approaches the optimal solution.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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