Dynamic and static reactive power regulation photovoltaic power station SVG and capacitor joint optimization configuration method

Abstract

The invention belongs to the technical field of power distribution network voltage quality optimization, and particularly relates to a dynamic and static reactive regulation photovoltaic power station SVG and capacitor joint optimization configuration method, which comprises the following steps of: firstly, establishing an objective function of an SVG and capacitor joint optimization configuration model by taking SVG distribution capacity, capacitor quantity and single capacitor capacity as decision variables of the objective function; constructing constraint conditions of the target function; and solving the SVG and capacitor joint optimization configuration model through an improved particle swarm optimization algorithm to obtain the optimal SVG distribution capacity, the capacitor number and the single capacitor capacity, so that the annual comprehensive cost of SVG and capacitor configuration is minimum. The improved particle swarm optimization algorithm comprises inertia weight improvement of a standard particle swarm algorithm and learning factor improvement of the standard particle swarm algorithm.

Description

Technical Field

[0001] The present invention belongs to the technical field of voltage quality optimization of distribution networks, and in particular relates to a method for jointly optimizing the configuration of SVG and capacitors in a photovoltaic power station with dynamic and static reactive power regulation. Background Art

[0002] Photovoltaic power generation has been widely used in recent years due to its advantages such as abundant light resources, low cost and environmental protection. However, photovoltaic power generation has significant instability and uncontrollability, which brings new challenges to the stable operation of the power grid and increases the randomness and uncertainty of power output. With the large-scale access of photovoltaic power stations to the power grid, its impact on the regional power grid has become more and more significant, becoming a key issue restricting the efficient utilization of photovoltaic power generation and the stable operation of the power grid. Against the background of growing demand for green, low-carbon, energy-saving and environmental protection at home and abroad, large-scale photovoltaic power generation has become an important direction for energy transformation. However, this also puts forward new requirements for the current power system, especially in the process of power generation and transmission, the power grid needs to bear higher pressure to ensure the quality of power. Therefore, how to reduce the impact of photovoltaic power station grid connection is the key to the efficient utilization of photovoltaic power generation.

[0003] The existing technology mainly takes the impact of photovoltaic power stations on the system after being connected to the grid as the starting point, starts from the changes in power quality of grid nodes under different light intensities, analyzes and optimizes, and proposes some targeted methods to solve the coordination relationship between different types of reactive power optimization equipment (such as static VAR generators (SVG) and capacitors) and the reactive power demand of photovoltaic power sources.

[0004] However, these methods still face many limitations in practical applications. First, the existing technology is difficult to adapt to different grid structures and power consumption change scenarios. In addition, configuring SVG alone requires multiple capacitors, which is costly. Configuring capacitors alone has a high capacity cost. Therefore, the existing technology lacks a joint optimization configuration method for SVG and capacitors. How to achieve the joint optimization configuration of SVG and capacitors has become an urgent problem to be solved. Summary of the invention

[0005] In view of this, the present invention provides a method for jointly optimizing configuration of SVG and capacitors in a photovoltaic power station with dynamic and static reactive power control, so as to solve the technical problem of lack of a method for jointly optimizing configuration of SVG and capacitors in the prior art.

[0006] To achieve the above objectives, this application adopts the following scheme:

[0007] A method for jointly optimizing the configuration of SVG and capacitors in a photovoltaic power station with dynamic and static reactive power control, comprising the following steps:

[0008] S10. Taking SVG allocation capacity, number of capacitors, and single capacitor capacity as decision variables, an objective function of a joint optimization configuration model of SVG and capacitors is established;

[0009] S20. Constructing the constraint conditions of the objective function, the constraint conditions include power flow equation constraint, node voltage constraint, total capacitor reactive power constraint, static reactive power capacity constraint, dynamic reactive power capacity constraint and control variable constraint, and obtaining the joint optimization configuration model of SVG and capacitor according to the constraint conditions and the objective function;

[0010] S30. The SVG and capacitor joint optimization configuration model is solved by an improved particle swarm optimization algorithm to obtain the optimal SVG allocation capacity, the number of capacitors and the capacity of a single capacitor, so as to minimize the annual comprehensive cost of the SVG and capacitor configuration. The improved particle swarm optimization algorithm includes an improvement on the inertia weight of the standard particle swarm algorithm and an improvement on the learning factor of the standard particle swarm algorithm.

[0011] Preferably, the objective function of the SVG and capacitor joint optimization configuration model is expressed as:

[0012] minF=C inv +C m +C Q

[0013] In the formula, C inv is the annual investment cost of capacitors and SVG; C m is the annual maintenance cost of capacitors and SVG; C Q Adjust costs for capacity.

[0014] Preferably, the annual investment cost of the capacitor and SVG is C inv for:

[0015] C inv =C(r,l c )(1-λ c )C c S c +C(r,l st )(1-λ st )C st S st

[0016] In the formula, C c and C st are the unit capacity investment costs of capacitors and SVG respectively; S c and S st are the installed capacity of capacitor machine and SVG respectively; c and λ st are the residual coefficients of capacitor and static synchronous compensator respectively; lc and l st They are capacity and SVG lifespan respectively.

[0017] Preferably, the annual maintenance cost of the equipment is m for:

[0018] C m =S c ×b c +S st ×b st

[0019] Where b c and b st are the maintenance cost coefficients of capacitors and SVG respectively.

[0020] Preferably, the capacity adjustment cost C Q for:

[0021]

[0022] In the formula, Q δT (k) is the sum of the adjustment costs of each capacitor of type k at time t on a typical day, N k It is the number of days in a year with a typical day of type k. The division of typical days is determined by the typical output type of PV.

[0023] Preferably, in S10, the constraints of the SVG and capacitor joint optimization configuration model are:

[0024] The expression of the power flow equation constraint is:

[0025]

[0026]

[0027] In the formula, G ij and are the elements of the i-th row and j-th column of the admittance matrix; B ij is the element in the i-th row and j-th column of the impedance matrix; and are respectively the active and reactive output of photovoltaic at node i at time t; With Q i d Distribution is the active and reactive load at node i at time t, is the reactive power injected by SVG at node i at time t; is the reactive power compensation of the capacitor at node i at time t; V i,t is the voltage at node i at time t;

[0028] The expression of the node voltage constraint is:

[0029]

[0030] In the formula, and are the voltage V at node i at time t i,t The lower and upper limits of

[0031] For a photovoltaic power station with a grid-connected voltage level of 10 kV to 35 kV, the expression for the total capacitor reactive power constraint is:

[0032]

[0033] In the formula, is the perceptual capacity of SVG; is the inductive capacity of the inverter; It is the charging reactive power of the line; is the inductive reactive power consumption of the line; Q max It is the reactive power calculated based on the power factor of 0.98 under the full active power output of the photovoltaic power station;

[0034]

[0035] In the formula, is the capacitor capacity, is the capacitance of SVG;

[0036] The inductive reactive power consumption of the acquisition line is:

[0037]

[0038] In the formula, I line is the line current, X line is the reactance of the line.

[0039] The capacitor charging power of the line is:

[0040]

[0041] In the formula, C line is the capacitance of the line;

[0042] The expression of the static reactive capacity constraint is:

[0043] K C ∈{0,1,2,...,K Cmax}

[0044] Q C0 ∈{Q C01 ,Q C02 ,...,Q C0max}

[0045] In the formula, K C is the number of capacitors configured, Q C0 To configure the single group capacity of capacitors;

[0046] The expression of the dynamic reactive capacity constraint is:

[0047] In order to ensure the continuity of steady-state reactive power regulation, the dynamic reactive capacity should meet the following requirements:

[0048]

[0049] In order to ensure the dynamic reactive power support capability when the grid voltage drops, the dynamic reactive power capacity should meet the following requirements:

[0050]

[0051] Where U N is the rated voltage of the photovoltaic power station connection point; I N is the rated current of the photovoltaic power station;

[0052] The control variable constraints include:

[0053] The variables included in the control variables include the number of input groups of capacitors in the photovoltaic power station and the reactive compensation power of SVG, where the number of input groups of capacitors in the photovoltaic power station is limited to:

[0054] 0≤k Cin,t ≤k C

[0055] In the formula, k Cin,t k is the number of capacitors put into operation during period t; C is the upper limit of the number of capacitors put into operation within period t;

[0056] SVG regulation operation includes constant reactive power, constant voltage or constant power factor mode. The reactive power output during operation shall not exceed the capacity limit of the SVG equipment:

[0057]

[0058] In the formula, S SVG SVG capacity configured for PV power plants.

[0059] Preferably, in S30, “solving the SVG and capacitor joint optimization configuration model by using an improved particle swarm optimization algorithm” specifically includes:

[0060] S31. Initialize the population size and the number of single particle updates n, n = 1;

[0061] S32. Initialize SVG configuration capacity, number of capacitors, capacity of a single capacitor, position and speed;

[0062] S33. Initialize the grid structure, line equipment, load characteristics, cost indicators and other simulation scenario parameters, calculate relevant electrical indicators, and determine whether the calculation results meet the constraints. If not, jump to S32. If the constraints are met, proceed to the next step;

[0063] S34. Calculate the particle fitness, update the individual optimal solution and the group optimal solution according to the particle fitness, update the speed and position of a single particle, set n=n+1, and determine whether n is greater than the population size. If n is greater than the population size, output the optimal SVG allocation capacity, the number of capacitors, and the single capacitor capacity. Otherwise, jump to S33.

[0064] Preferably, the iteration formula of the standard particle swarm algorithm is:

[0065]

[0066] In the formula, is the global optimum, is the individual extreme value, Speed ​​update formula, Position update formula, w is the inertia factor, c1 and c2 are called acceleration constants, the value range is c1, c2∈[0,4], and the value range of r1 and r2 is a random value between [0,1].

[0067] Preferably, the improved expression of the inertia weight of the standard particle swarm algorithm is:

[0068]

[0069]

[0070]

[0071] Where t is the number of iterations; w max and w min are the maximum and minimum values ​​of the inertia weight coefficient respectively; l1(t) is a nonlinear function; l2(t) is a linear function; w d is the initial inertia weight after the initial search, t max is the maximum number of iterations.

[0072] Preferably, the improved expression of the learning factor of the standard particle swarm algorithm is:

[0073]

[0074]

[0075] In the formula, c 1_max and c 2_max Respectively represent the maximum value of the learning factor; c 1_min and c 2_min They represent the minimum learning factor respectively; in the initial stage of the search, c1 is greater than c2, and each particle pays attention to its own historical information to ensure diversity; but in the later stage, c1 decreases and c2 increases; particles pay more attention to the overall information of the group and maintain rapid convergence.

[0076] In the above-mentioned method for jointly optimizing the configuration of SVG and capacitors in a photovoltaic power station with dynamic and static reactive power control, firstly, the objective function of the joint optimization configuration model of SVG and capacitors is established with the SVG allocation capacity, the number of capacitors and the capacity of a single capacitor as the decision variables of the objective function, and then, the constraints of the objective function are constructed, namely, the power flow equation constraint, the node voltage constraint, the total capacitor reactive power constraint, the static reactive power capacity constraint, the dynamic reactive power capacity constraint and the control variable constraint. After the establishment of the objective function and the constraints, the joint optimization configuration model of SVG and capacitors is obtained, and the joint optimization configuration model of SVG and capacitors is solved by an improved particle swarm optimization algorithm. The improved particle swarm optimization algorithm includes an improvement on the inertia weight of the standard particle swarm algorithm. In the initial search stage, the inertia weight coefficient decreases nonlinearly, which makes The algorithm achieves stronger overall search capability at this stage and enters local search as soon as possible. After multiple iterations, the inertia weight coefficient begins to drop linearly, which enables the algorithm to stably find the optimal solution. The learning factor of the standard particle swarm algorithm is improved to better balance the global and local searches, so that the optimal SVG allocation capacity, the number of capacitors and the capacity of a single capacitor can be quickly solved. Compared with the existing technology, the number of SVG configurations and the SVG allocation capacity are reduced, so that the annual comprehensive cost of SVG and capacitor configuration is minimized, which has high economy. In addition, in this method, while meeting the reactive power demand of photovoltaic power stations, it can reduce the equipment configuration cost, reduce the maintenance cost of SVG and capacitors, improve the stability and economy of the regional power grid, and achieve reactive power optimization under static and dynamic conditions, thereby realizing the joint optimal configuration of SVG and capacitors. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a flow chart of the present invention.

[0078] Figure 2 This is a flowchart for solving the problem in the present invention.

[0079] Figure 3 This is a network topology framework diagram of a case in an embodiment of the present invention. DETAILED DESCRIPTION

[0080] In order to facilitate the understanding of the present application, the present application will be described more comprehensively below in conjunction with the accompanying drawings. And the preferred implementation of the present application is given. However, the present application can be implemented in many different forms and is not limited to the implementation described herein. On the contrary, the purpose of providing these implementations is to make the disclosure of the present application more thoroughly and comprehensively understood.

[0081] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0082] Please see Figure 1 This embodiment provides a method for optimizing the configuration of a photovoltaic power station SVG (static var generator) and a capacitor for dynamic and static reactive power control, comprising the following steps:

[0083] S10. Taking SVG allocation capacity, number of capacitors, and single capacitor capacity as decision variables, an objective function of a joint optimization configuration model of SVG and capacitors is established;

[0084] S20. Constructing the constraint conditions of the objective function, the constraint conditions include power flow equation constraint, node voltage constraint, total capacitor reactive power constraint, static reactive power capacity constraint, dynamic reactive power capacity constraint and control variable constraint, and obtaining the joint optimization configuration model of SVG and capacitor according to the constraint conditions and the objective function;

[0085] S30. The SVG and capacitor joint optimization configuration model is solved by an improved particle swarm optimization algorithm to obtain the optimal SVG allocation capacity, the number of capacitors and the capacity of a single capacitor, so as to minimize the annual comprehensive cost of the SVG and capacitor configuration. The improved particle swarm optimization algorithm includes an improvement on the inertia weight of the standard particle swarm algorithm and an improvement on the learning factor of the standard particle swarm algorithm.

[0086] After the establishment of the objective function and the constraints, the joint optimization configuration model of SVG and capacitors is obtained, and the joint optimization configuration model of SVG and capacitors is solved by an improved particle swarm optimization algorithm. The improved particle swarm optimization algorithm includes an improvement on the inertia weight of the standard particle swarm algorithm. In the initial search stage, the inertia weight coefficient decreases nonlinearly, which enables the algorithm to achieve stronger overall search capabilities at this stage and enter the local search as soon as possible. After multiple iterations, the inertia weight coefficient begins to drop linearly, which enables the algorithm to stably find the optimal solution. The learning factor of the standard particle swarm algorithm is improved to better balance the global and local searches, so that the optimal SVG allocation capacity, the number of capacitors and the capacity of a single capacitor can be quickly solved, so that the annual comprehensive cost of SVG and capacitor configuration is minimized, with high economy.

[0087] Furthermore, the objective function of the joint optimization configuration model of SVG and capacitor is expressed as:

[0088] minF=C inv +C m +C Q

[0089] In the formula, C inv is the annual investment cost of capacitors and SVG; C m is the annual maintenance cost of capacitors and SVG; C Q Adjust the cost for capacity (SVG capacity).

[0090] Specifically, the annual investment cost C of capacitors and SVG can be calculated using the following formula: inv :

[0091] C inv =C(r,l c )(1-λ c )C c S c +C(r,l st )(1-λ st )C st S st

[0092] In the formula, C c and C st are the unit capacity investment costs of capacitors and SVG respectively; S c and S st are the installed capacity of capacitor machine and SVG respectively; c and λ st are the residual coefficients of capacitor and static synchronous compensator respectively; l c and l stare the capacity and the useful life of SVG respectively, where the expression of the capital equivalent annual value coefficient C(r,l) is:

[0093]

[0094] Where r is the discount rate and l is the useful life of the equipment.

[0095] Specifically, the annual maintenance cost C of capacitors and SVG can be calculated using the following formula: m :

[0096] C m =S c ×b c +S st ×b st

[0097] Where b c and b st are the maintenance cost coefficients of capacitors and SVG respectively.

[0098] Specifically, the capacity adjustment cost C can be calculated using the following formula: Q :

[0099]

[0100] In the formula, Q δT (k) is the sum of the adjustment costs of each capacitor of type k at time t on a typical day, N k is the number of days in a year with a typical day of type k, and the division of typical days is determined by the typical output type of PV;

[0101]

[0102] Among them, N q is the total number of capacitors; Q cj (k, t) is the switching state of the jth capacitor of type k at time t, ΔC Q is the unit regulation cost of the capacitor.

[0103] Furthermore, the boundary conditions of the joint optimization configuration model of SVG and capacitors are constructed. The boundary conditions of the model specifically include power flow equation constraints, node voltage constraints, total capacitance / inductance reactive power constraints, static reactive capacity constraints, dynamic reactive capacity constraints and control variable constraints, among which:

[0104] (1) The power flow equation constraint can be expressed as follows:

[0105]

[0106]

[0107] In the formula, G ij and are the elements of the i-th row and j-th column of the admittance matrix; B ij is the element in the i-th row and j-th column of the impedance matrix; and are respectively the active and reactive output of photovoltaic at node i at time t; P i d ,t With Q i d Distribution is the active and reactive load at node i at time t, is the reactive power injected by SVG at node i at time t; Q i c ,t is the reactive power compensation of the capacitor at node i at time t; V i,t is the voltage at node i at time t.

[0108] (2) Node voltage constraint can be expressed as follows:

[0109]

[0110] In the formula, and are the voltage V at node i at time t i,t The lower and upper limits of .

[0111] (3) Total capacitive / inductive reactive power constraint: The total amount of capacitive / inductive reactive power constraint varies according to the voltage level of the grid connection.

[0112] For photovoltaic power stations with grid-connected voltage levels of 10kV to 35kV, the formula must be met: In the formula, is the perceptual capacity of SVG; is the inductive capacity of the inverter; It is the charging reactive power of the line; is the inductive reactive power consumption of the line;

[0113] In the above formula, Q max The reactive power calculated at a power factor of 0.98 under full active power output of the photovoltaic power station must satisfy the formula In the formula, is the capacitor capacity, is the capacitance of SVG.

[0114] Specifically, the inductive reactive power consumption of the acquisition line in the above formula can be calculated using the formula Indicates that, where I lineis the line current, X line is the reactance of the line.

[0115] Specifically, the capacitor charging power of the circuit in the above formula can be expressed as In the formula, C line is the capacitance of the line.

[0116] (4) Static reactive capacity constraint can be expressed as follows:

[0117] K C ∈{0,1,2,...,K Cmax}

[0118] Q C0 ∈{Q C01 ,Q C02 ,...,Q C0max}

[0119] In the formula, K C is the number of capacitors configured, Q C0 It is the single group capacity of the configuration capacitor.

[0120] (5) When the dynamic reactive capacity is constrained, in order to ensure the continuity of steady-state reactive power regulation, the dynamic reactive capacity should satisfy the following formula:

[0121]

[0122] In order to ensure the dynamic reactive power support capability when the grid voltage drops, the dynamic reactive power capacity should satisfy the following formula:

[0123]

[0124] Where U N is the rated voltage of the photovoltaic power station connection point; I N is the rated current of the PV power station.

[0125] (6) When the control variables are constrained, the variables included in the control variables include the number of input groups of capacitors in the PV power station and the reactive compensation power of SVG. The limit on the number of input groups of capacitors can be expressed by the following formula:

[0126] 0≤k Cin,t ≤k C

[0127] In the formula, k Cin,t is the number of capacitors put into operation during period t, k C is the upper limit of the number of capacitors put into operation during period t;

[0128] SVG regulation operation includes constant reactive power, constant voltage or constant power factor mode. The reactive power output during operation shall not exceed the capacity limit of the SVG equipment, which can be expressed by the following formula:

[0129]

[0130] In the formula, S SVG SVG capacity configured for PV power plants.

[0131] Please see Figure 2 Further, in S30, “solving the SVG and capacitor joint optimization configuration model by improving the particle swarm optimization algorithm” specifically includes:

[0132] S31. Initialize the population size and the number of single particle updates n, n = 1;

[0133] S32. Initialize SVG configuration capacity, number of capacitors, capacity of a single capacitor, position and speed;

[0134] S33. Initialize the grid structure, line equipment, load characteristics, cost indicators and other simulation scenario parameters, calculate relevant electrical indicators, and determine whether the calculation results meet the constraints. If not, jump to S32. If the constraints are met, proceed to the next step;

[0135] S34. Calculate the particle fitness, update the individual optimal solution and the group optimal solution according to the particle fitness, update the speed and position of a single particle, set n=n+1, and determine whether n is greater than the population size. If n is greater than the population size, output the optimal SVG allocation capacity, the number of capacitors, and the single capacitor capacity. Otherwise, jump to S33.

[0136] The improved particle swarm algorithm (PSO) is specifically: the standard particle swarm algorithm (PSO) is a global dynamic optimization calculation method based on particle iteration to find the optimal solution of the space. and individual extreme values To continuously adjust its position and speed, the iterative formula of the standard particle swarm algorithm is:

[0137]

[0138] In the formula, is the global optimum, is the individual extreme value, Speed ​​update formula, Position update formula, w is the inertia factor, c1 and c2 are called acceleration constants, the value range is c1, c2∈[0,4], and the value range of r1 and r2 is a random value between [0,1].

[0139] The improved expression of inertia weight of standard particle swarm algorithm is:

[0140]

[0141]

[0142]

[0143] Where t is the number of iterations; w max and w min are the maximum and minimum values ​​of the inertia weight coefficient respectively; l1(t) is a nonlinear function; l2(t) is a linear function; w d is the initial inertia weight after the initial search, t max is the maximum number of iterations.

[0144] In order to obtain the diversity of particles in the initial search stage and converge to the global optimal solution as soon as possible in the later stage, by analyzing the impact of the change in the learning factor, the cosine function is used to dynamically adjust the parameters c1 and c2 to better balance the global and local searches. The expression of the learning factor improvement (cosine function) of the standard particle swarm algorithm is:

[0145]

[0146]

[0147] In the formula, c 1_max and c 2_max Respectively represent the maximum value of the learning factor; c 1_min and c 2_min They represent the minimum learning factor respectively; in the initial stage of the search, c1 is greater than c2, and each particle pays attention to its own historical information to ensure diversity; but in the later stage, c1 decreases and c2 increases; particles pay more attention to the overall information of the group and maintain rapid convergence.

[0148] The following specific experimental examples are used to further illustrate the technical solutions and technical effects of the present invention. It should be noted that the following experimental examples are only for further explaining the present invention and do not limit the technical solutions of the present invention.

[0149] Example

[0150] This example verifies the combined optimization configuration method of SVG and capacitors in photovoltaic power plants with dynamic and static reactive power control. Figure 3 The network topology framework provided for this example connects the branch lines under the photovoltaic power station to the Figure 3 On node 2 in the network, node 1 is the balance node.

[0151] The process of reactive power is: first switch the capacitor, if the capacitor cannot meet the demand of reactive power compensation, then use SVG for reactive power regulation.

[0152] Condition setting: The overall photovoltaic distribution of the power grid is at each PQ node. It is assumed that the ratio of sunny days to cloudy days in a year is set to 4:1, 3:1 and 2:1. The capacity range of SVG reactive power configuration is 0-300kvar. The capacity specifications of a single group of optional capacitors are 100kvar, 200kvar, 300kvar, 400kvar and 500kvar. Among them, the unit investment cost of capacitors is 100 yuan / kvar, the unit investment cost of SVG is 150 yuan / kvar, the unit adjustment cost of capacitors is 4 yuan / time, the service life of capacitors and SVGs are both 10 years, the residual value coefficient of capacitors and static synchronous compensators is 0.5, the discount rate is 0.1, and the maintenance cost coefficient of capacitors and SVGs is 0.02.

[0153] Based on the above conditions, the improved particle swarm optimization algorithm is solved to obtain the optimal SVG allocation capacity, the number of capacitors and the capacity of a single capacitor, and the annual investment cost of capacitors and SVG, the annual maintenance cost of capacitors and SVG, the capacitor adjustment fee and the annual comprehensive cost of the single capacitor capacity are calculated. The results are shown in Table 1.

[0154] Comparative Example

[0155] The difference between this comparative example and the above embodiment is that the genetic algorithm is used to solve the problem in this comparative example, and its constraint conditions do not include static reactive capacity constraint and dynamic reactive capacity constraint, but are limited by traditional reactive capacity constraint. The algorithm is used to solve the annual investment cost of capacitors and SVG, the annual maintenance cost of capacitors and SVG, capacitor adjustment fee and annual comprehensive cost, and the results are shown in Table 1:

[0156] Table 1 Comparison of results of embodiments and comparative examples

[0157]

[0158] As can be seen from Table 1 above, compared with the strategy in the comparative example, the strategy proposed in the embodiment has a lower economic cost. For different annual sunny / cloudy ratios, the SVG configuration capacity in the embodiment is lower. For the configuration of capacitors, the number of configurations obtained by the optimization model is 3. When the sunny / cloudy ratio is 4:1 and 3:1, a set (3 units) of capacitors with a capacity of 500kvar can meet the demand. Under this topological structure, the annual comprehensive cost is mainly composed of the annual investment cost and maintenance cost of capacitors and SVG. The total capacity of the 3 capacitors is configured to be 1500kvar, which is much larger than the capacity of the SVG configuration. This is mainly because in actual engineering, the unit cost of SVG is higher than that of capacitors. After the main reactive power compensation is realized by configuring capacitors, the SVG configuration (1 unit) is only to supplement a small part of the reactive power demand. On the other hand, for different day ratios, only the adjustment cost of the capacitor is different, which is mainly affected by the difference in adjustment time under different weather conditions. However, when the comprehensive cost is mainly affected by the investment cost, the impact of weather factors on the configuration scheme is relatively small.

[0159] In summary, by optimizing the joint configuration of SVG and capacitors, the economic cost of the photovoltaic power station grid connection process is effectively reduced. By reasonably configuring the equipment to ensure that the reactive power demand is met under different sunny and cloudy ratios, the optimized solution not only improves the stability of the power grid, but also reduces equipment investment and maintenance costs. Although weather factors have some impact on the adjustment costs, the overall cost is mainly dominated by the equipment investment cost. The optimization strategy still maintains high economic benefits and adaptability under different climatic conditions, and has strong practicality and sustainability.

[0160] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or substitutions within the technical scope disclosed by the present invention, and these modifications or substitutions should be included in the protection scope of the present invention.

Claims

1. A method for joint optimization configuration of SVG and capacitors in a photovoltaic power station with dynamic and static reactive power control, characterized in that: The following steps are involved: S10. Taking SVG allocation capacity, number of capacitors, and single capacitor capacity as decision variables, an objective function of a joint optimization configuration model of SVG and capacitors is established; S20. Constructing the constraint conditions of the objective function, the constraint conditions include power flow equation constraint, node voltage constraint, total capacitor reactive power constraint, static reactive power capacity constraint, dynamic reactive power capacity constraint and control variable constraint, and obtaining the joint optimization configuration model of SVG and capacitor according to the constraint conditions and the objective function; S30. The SVG and capacitor joint optimization configuration model is solved by an improved particle swarm optimization algorithm to obtain the optimal SVG allocation capacity, the number of capacitors and the capacity of a single capacitor, so as to minimize the annual comprehensive cost of the SVG and capacitor configuration. The improved particle swarm optimization algorithm includes an improvement on the inertia weight of the standard particle swarm algorithm and an improvement on the learning factor of the standard particle swarm algorithm.

2. The method for joint optimization configuration of SVG and capacitors in a photovoltaic power station with dynamic and static reactive power control according to claim 1 is characterized in that: The objective function of the joint optimization configuration model of SVG and capacitor is expressed as: minF=C inv +C m +C Q In the formula, C inv is the annual investment cost of capacitors and SVG; C m is the annual maintenance cost of capacitors and SVG; C Q Adjust costs for capacity.

3. The method for joint optimization configuration of SVG and capacitors in a photovoltaic power station with dynamic and static reactive power control according to claim 2 is characterized in that: The annual investment cost of the capacitor and SVG is C inv for: C inv =C(r,l c )(1-λ c )C c S c +C(r,l st )(1-λ st )C st S st In the formula, C c and C st are the unit capacity investment costs of capacitors and SVG respectively; S c and S st are the installed capacity of capacitor machine and SVG respectively; c and λ st are the residual coefficients of capacitor and static synchronous compensator respectively; l c and l st They are capacity and SVG lifespan respectively.

4. The method for joint optimization configuration of SVG and capacitors in a photovoltaic power station with dynamic and static reactive power control according to claim 3 is characterized in that: The annual maintenance cost of the equipment is C m for: C m =S c ×b c +S st ×b st Where b c and b st are the maintenance cost coefficients of capacitors and SVG respectively.

5. The method for joint optimization configuration of SVG and capacitors in a photovoltaic power station with dynamic and static reactive power control according to claim 4 is characterized in that: The capacity adjustment cost C Q for: In the formula, Q δT (k) is the sum of the adjustment costs of each capacitor of type k at time t on a typical day, N k It is the number of days in a year with a typical day of type k. The division of typical days is determined by the typical output type of PV.

6. The method for joint optimization configuration of SVG and capacitors in a photovoltaic power station with dynamic and static reactive power control according to claim 1 is characterized in that: In S10, the constraints of the SVG and capacitor joint optimization configuration model are: The expression of the power flow equation constraint is: In the formula, G ij and are the elements of the i-th row and j-th column of the admittance matrix; B ij is the element in the i-th row and j-th column of the impedance matrix; and are respectively the active and reactive output of photovoltaic at node i at time t; and Distribution is the active and reactive load at node i at time t, is the reactive power injected by SVG at node i at time t; is the reactive power compensation of the capacitor at node i at time t; V i,t is the voltage at node i at time t; The expression of the node voltage constraint is: In the formula, and are the voltage V at node i at time t i,t The lower and upper limits of For a photovoltaic power station with a grid-connected voltage level of 10 kV to 35 kV, the expression for the total capacitor reactive power constraint is: In the formula, is the perceptual capacity of SVG; is the inductive capacity of the inverter; It is the charging reactive power of the line; is the inductive reactive power consumption of the line; Q max It is the reactive power calculated based on the power factor of 0.98 under the full active power output of the photovoltaic power station; In the formula, is the capacitor capacity, is the capacitance of SVG; The inductive reactive power consumption of the acquisition line is: In the formula, I line is the line current, X line is the reactance of the line. The capacitor charging power of the line is: In the formula, C line is the capacitance of the line; The expression of the static reactive capacity constraint is: K C ∈{0,1,2,...,K Cmax } Q C0 ∈{Q C01 ,Q C02 ,...,Q C0max } In the formula, K C is the number of capacitors configured, Q C0 To configure the single group capacity of capacitors; The expression of the dynamic reactive capacity constraint is: In order to ensure the continuity of steady-state reactive power regulation, the dynamic reactive capacity should meet the following requirements: In order to ensure the dynamic reactive power support capability when the grid voltage drops, the dynamic reactive power capacity should meet the following requirements: Where U N is the rated voltage of the photovoltaic power station connection point; I N is the rated current of the photovoltaic power station; The control variable constraints include: The variables included in the control variables include the number of input groups of capacitors in the photovoltaic power station and the reactive compensation power of SVG, where the number of input groups of capacitors in the photovoltaic power station is limited to: 0≤k Cin,t ≤k C In the formula, k Cin,t is the number of capacitors put into operation during period t; k C is the upper limit of the number of capacitors put into operation during period t; SVG regulation operation includes constant reactive power, constant voltage or constant power factor mode. The reactive power output during operation shall not exceed the capacity limit of the SVG equipment: In the formula, S SVG SVG capacity configured for PV power plants.

7. The method for joint optimization configuration of SVG and capacitors in a photovoltaic power station with dynamic and static reactive power control according to claim 1 is characterized in that: In the S30, "solving the SVG and capacitor joint optimization configuration model by using an improved particle swarm optimization algorithm" specifically includes: S31. Initialize the population size and the number of single particle updates n, n = 1; S32. Initialize SVG configuration capacity, number of capacitors, capacity of a single capacitor, position and speed; S33. Initialize the grid structure, line equipment, load characteristics, cost indicators and other simulation scenario parameters, calculate relevant electrical indicators, and determine whether the calculation results meet the constraints. If not, jump to S32. If the constraints are met, proceed to the next step; S34. Calculate the particle fitness, update the individual optimal solution and the group optimal solution according to the particle fitness, update the speed and position of a single particle, set n=n+1, and determine whether n is greater than the population size. If n is greater than the population size, output the optimal SVG allocation capacity, the number of capacitors, and the single capacitor capacity. Otherwise, jump to S33.

8. The method for joint optimization configuration of SVG and capacitors in a photovoltaic power station with dynamic and static reactive power control according to claim 1 is characterized in that: The iteration formula of the standard particle swarm algorithm is: In the formula, is the global optimum, is the individual extreme value, Speed ​​update formula, Position update formula, w is the inertia factor, c1 and c2 are called acceleration constants, the value range is c1, c2∈[0,4], and the value range of r1 and r2 is a random value between [0,1].

9. The method for joint optimization configuration of SVG and capacitors in a photovoltaic power station with dynamic and static reactive power control according to claim 1, characterized in that: The improved expression of inertia weight of standard particle swarm algorithm is: Where t is the number of iterations; w max and w min are the maximum and minimum values ​​of the inertia weight coefficient respectively; l1(t) is a nonlinear function; l2(t) is a linear function; w d is the initial inertia weight after the initial search, t max is the maximum number of iterations.

10. The method for joint optimization configuration of SVG and capacitors in a photovoltaic power station with dynamic and static reactive power control according to claim 1, characterized in that: The improved expression of the learning factor of the standard particle swarm algorithm is: In the formula, c 1_max and c 2_max Respectively represent the maximum value of the learning factor; c 1_min and c 2_min Respectively represent the minimum value of learning factor; In the initial stage of the search, c1 is greater than c2, and each particle pays attention to its own historical information to ensure diversity; but in the later stage, c1 decreases and c2 increases; particles pay more attention to the overall information of the group and maintain rapid convergence.

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