Whole-process lean management platform for power quality of distribution network

Abstract

This invention discloses a lean management platform for the entire process of power quality in distribution networks. It manages the power quality of high-penetration renewable energy generation systems connected to the distribution network. The platform includes a distribution network voltage quality analysis module, a distribution network high-voltage quality control module, and a distribution network voltage quality regulation and optimization module. The distribution network voltage quality analysis module establishes a structural model of the distribution network system with high-penetration renewable energy generation based on parameters of the medium- and low-voltage distribution network. This comprehensive lean management platform for power quality in distribution networks improves the quality and stability of the power delivered to the distribution network by performing lean management of the connected high-penetration renewable energy generation systems through these modules.

Description

Technical Field

[0001] This invention belongs to the field of power quality management technology for distribution networks, specifically relating to a lean management platform for the entire process of power quality in distribution networks. Background Technology

[0002] As the penetration rate of new energy sources continues to increase, the randomness, volatility, and intermittency of wind and solar power output have an increasingly significant impact on the safe and stable operation of the power system, greatly reducing the original power quality. High-penetration new energy grid-connected power generation has brought enormous challenges to the stability, dispatchability, and power quality of the power grid.

[0003] With the integration of a large amount of renewable energy into the distribution network, the energy flow has changed from unidirectional and single-path to multidirectional and multi-path, becoming a multi-source network system. This complicates power flow analysis and undoubtedly increases the difficulty of handling voltage quality issues inherent in the distribution network itself. On the other hand, because renewable energy generation systems, especially those with high penetration rates, are started and stopped by users according to their needs, the voltage in the distribution network is prone to fluctuations. The uncertainty of wind and solar resources causes fluctuations in the output power of renewable energy, leading to voltage quality problems such as voltage deviation, voltage sag, voltage surge, voltage fluctuation and flicker, harmonics, and voltage interruptions. As various types of renewable energy become the main source of power for local loads, resulting in high penetration rates in local distribution networks, changes in the timing and amplitude of output power fluctuations exacerbate voltage quality problems. Factors affecting voltage quality in the distribution network include the type, scale, capacity, and interface with the grid of high-penetration renewable energy generation, the size of the load, and voltage regulation of feeders.

[0004] The high penetration rate of renewable energy generation and the voltage quality problems caused by the distribution network itself have made the voltage quality problems of the distribution network increasingly prominent, and people's demands for improvement of voltage quality are becoming more and more urgent.

[0005] Therefore, further improvements will be made to address the aforementioned issues. Summary of the Invention

[0006] The main objective of this invention is to provide a lean management platform for the entire process of power quality in distribution networks. Through a distribution network voltage quality analysis module, a distribution network high voltage quality control module, and a distribution network voltage quality regulation and optimization module, the platform enables lean management of the entire process of high-penetration renewable energy power generation systems connected to the distribution network. This improves the quality and stability of the power delivered to the distribution network, thereby enhancing the reliable power supply to sensitive loads of power users and reducing equipment development costs.

[0007] To achieve the above objectives, this invention provides a lean management platform for the entire process of power quality in distribution networks, used to manage the power quality of high-penetration renewable energy generation systems connected to the distribution network. The platform includes a distribution network voltage quality analysis module, a distribution network high-voltage quality control module, and a distribution network voltage quality regulation and optimization module, wherein:

[0008] The distribution network voltage quality analysis module establishes a distribution network system structure model with high-penetration renewable energy generation based on medium and low voltage distribution network parameters. It analyzes the distribution network voltage fluctuation problem and the causes of voltage quality problems from the perspective of energy flow. By analyzing the changes in voltage at different penetration rates / load capacities under various conditions including grid connection, off-grid, three-phase faults and unbalanced loads, the module aims to improve the voltage quality of the distribution network.

[0009] The high-voltage quality control module of the distribution network selects the topology for power quality improvement, establishes the corresponding mathematical model, and analyzes the compensation principle of series and parallel grid connection according to different grid connection methods (series / parallel) of power converters to obtain the corresponding compensation control strategy for improving voltage quality. It comprehensively compares the parameters of control strategy, compensation effect, adverse factors and applicable occasions when handling different voltage quality problems by series and parallel methods, and then uses the voltage quality regulator to match and handle various voltage quality problems accordingly.

[0010] The distribution network voltage quality regulation optimization module establishes a power flow model for a high-penetration renewable energy power generation system with a composite power quality regulator. It obtains the changes in system power flow and the capacity requirements of series and parallel converters. Furthermore, it establishes an optimization compensation model for the composite power quality regulator, determines the economic objective function and technical constraints, and then outputs an optimization configuration strategy for the composite power quality regulator based on a genetic algorithm. This strategy optimizes the installation location and capacity of the composite power quality regulator and provides case studies.

[0011] As a further preferred technical solution to the above technical solution, for the distribution network voltage quality analysis module:

[0012] In voltage variation and power fluctuation analysis, the current I in the equivalent circuit when a high-penetration renewable energy power generation system is connected to the distribution network is expressed as:

[0013]

[0014] Therefore, the voltage difference between the distribution network and the voltage of the high-penetration renewable energy generation system is expressed as:

[0015]

[0016] Among them, U PCCP, Q, and U represent the output voltage, output active power, and output reactive power of a high-penetration renewable energy generation system (DG), respectively; G R is the grid voltage; R is the line equivalent resistance; X is the line equivalent reactance.

[0017] A mathematical model is established to represent the relationship between penetration rate and voltage. In a simplified power flow model of a distribution network system with high-penetration renewable energy generation, P... DG +jQ DG For high-penetration renewable energy generation to input power into the distribution network, P L +jQ L The power required by the load;

[0018] Let P = P L -P DG ;

[0019] Q = Q L -Q DG ;

[0020] The voltage deviation is:

[0021] If ΔU = 0:

[0022] Then there is

[0023] When P≠0 and Q≠0, we can obtain Not valid;

[0024] This does not hold true when P≠0 or Q≠0;

[0025] When P = Q = 0, this holds true, and we obtain:

[0026]

[0027] Based on the load limit condition (no load), i.e., P L =Q L When = 0, then:

[0028] P DG =Q DG =0;

[0029] If ΔU=ΔU max =5%U PCC ;

[0030] Under unity power factor conditions, setting Q=0 yields the following (and in distribution networks, R / X is generally large):

[0031]

[0032] Then we can obtain,

[0033] Based on the load limit condition (no load), i.e., P L =Q L When = 0, then:

[0034]

[0035] When ΔU = 5%U PCC At that time, we obtained:

[0036]

[0037] As a further preferred technical solution to the above technical solution, for the high-voltage quality control module of the distribution network:

[0038] A composite power quality management device topology is established. When the grid voltage vs is normal, the series converter is bypassed. When the grid voltage vs drops, the series converter outputs a corresponding compensation voltage vc, thereby maintaining the voltage vL at the sensitive load end within the normal range and ensuring the normal operation of the sensitive load. The parallel converter is used to compensate for reactive power and absorb active power from the grid to maintain the DC bus voltage constant.

[0039] A mathematical model of a composite power quality regulator is established. (A composite power quality regulator is actually a comprehensive power quality compensator that integrates the functions of series and parallel compensators. Taking a composite power quality regulator with voltage and reactive power compensation as an example, it can be regarded as a DVR and DSTATCOM sharing a DC bus. Therefore, based on the research results of DVR and DSTATCOM above, the relevant mathematical model of the composite power quality regulator is derived.) Let the equivalent parallel resistance of the DC bus terminal loss be Rdc, and take the series inductors Rz and Lz as equivalent sensitive loads, then:

[0040]

[0041] The small-signal state equation of the series-parallel converter at a certain static operating point is obtained as follows:

[0042]

[0043]

[0044] Among them, state variables and Input and Output and They are respectively:

[0045]

[0046]

[0047] The expressions for each coefficient matrix are as follows:

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065] B 11 =[B 01 K1K p1 H1] T B 12 =[0 8×2 ];

[0066] B 21 =[-B'2K1K p1 0 2×2 ] T B 22 =[B 02 K2K p2 H1] T;

[0067] C 11 =[C 01 0 2×2 ], C 12 =[0 2×5 ], C 21 =[C kf 0 2×2 ], C 22 =[C 02 0 2×2 ];

[0068] Where m1d0, m1q0, m2d0, and m2q0 represent the relevant parameter values ​​of the pulse width modulation signals of the series and parallel converters at the system's quiescent operating point, respectively; i1d0, i1q0, icd0, and icq0 represent the inductor current values ​​of the two converters at the quiescent operating point; iLd0 and iLq0 represent the load current values ​​at the quiescent operating point; Vdc0 represents the DC bus voltage value of the parallel converter at the quiescent operating point; and the expressions for Ck1 and Ck2 are:

[0069]

[0070] A composite power quality regulator model is established, with intermediate variables vd, vq, id, and iq added as new state variables. Based on the mathematical model of the composite power quality regulator, the small-signal state equations that simultaneously include the (UPQC) model itself and the control model are obtained, namely:

[0071]

[0072]

[0073] Among them, state variables and Input and They are respectively:

[0074]

[0075]

[0076]

[0077] The transfer function of the four-input four-output system is obtained as follows:

[0078]

[0079] As a further preferred technical solution to the above technical solution, for the distribution network voltage quality regulation and optimization module:

[0080] Establish a mathematical model for optimized compensation of a composite power quality regulator:

[0081] Obtain the objective function:

[0082] (Objective function selection criteria: high economic benefits, low investment costs, and small equipment capacity. That is, [the objective function is defined as follows])

[0083] min[μ V S Vn +μ I S In +μ E (S Vn +S In )+C];

[0084] Where: μ V The unit capacity price of a series-connected composite power quality regulator unit, S Vn Capacity of the series unit of the composite power quality regulator; μ I The unit capacity price of a parallel unit of a composite power quality regulator, S In Capacity of the parallel unit of the composite power quality regulator; μ E C represents the unit capacity cost of the remaining parts of the composite power quality regulator, C represents the cost of the fixed parts, and n represents the node location.

[0085] Constraints:

[0086] Meets load voltage quality and stability requirements:

[0087]

[0088] Among them: U Δ U is the rate of change of voltage. n Let U be the voltage at node n. R For the rated voltage, C UR This is a limit value for the rate of voltage change;

[0089] To meet the harmonic requirements of the power grid current and reduce the total harmonic distortion rate of the current;

[0090]

[0091] Among them: THD in Let H be the total harmonic distortion of the current at node n, h be the harmonic order, H be the maximum harmonic order, and C be the total harmonic distortion of the current at node n. THDR This is the limit value for total harmonic distortion (THD).

[0092] The optimization configuration method uses a genetic algorithm to optimize the series and parallel compensation capacities of the composite power quality regulator. By performing multivariate optimization, the optimal solution is finally found, so that the objective function requirements are met while satisfying the constraints.

[0093] Optimize the configuration of the composite power quality conditioner:

[0094] The capacity of the composite power quality regulator is undetermined.

[0095] For different installation points, select one or more installation locations for the composite power quality regulator based on actual needs (mainly including the importance of the load, the convenience of the installation point, etc.). If a certain installation location still cannot be determined after selection, calculate the capacity requirement for maximum full compensation at each location through simulation to finally determine the optimal installation location for the composite power quality regulator.

[0096] Given a fixed installation location, and under the premise of meeting constraints, a genetic algorithm is used to optimize the selection of the required capacity for voltage sag / boost, current harmonics, and reactive power compensation, thereby achieving configuration optimization for the selection of series and parallel converter capacity. (Note that if the capacity requirement has already been calculated when selecting the installation location, a secondary capacity optimization is required at this time.)

[0097] The capacity of the composite power quality regulator is already determined:

[0098] When the capacities of both series and parallel converters are determined, first determine the installation location (the specific method is the same as when the capacity of the composite power quality regulator is not determined). Then, under the premise of meeting the constraints, rationally configure the capacity of the composite power quality regulator required for voltage sags / surges, current harmonics, and reactive power compensation, so as to give full play to the role of the composite power quality regulator and achieve the best compensation effect.

[0099] To achieve the above objectives, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the lean management platform for power quality of distribution networks as described in any one of claims 1 to 4.

[0100] To achieve the above objectives, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the lean management platform for power quality of distribution networks as described in any one of claims 1 to 4.

[0101] The beneficial effects of this invention are as follows:

[0102] (1) It helps power supply users to formulate reasonable and effective service plans and strategies, clarify the development direction of high-quality power value-added services, and enhance their own market competitiveness and obtain greater profits.

[0103] (2) It helps power users to weigh tolerance characteristics and governance costs when making equipment selection and power quality management investments, thereby improving the return on investment.

[0104] (3) It helps reduce the investment of all parties in power quality management, optimize the cost and maximize the benefits of power quality management, and improve the quality of user products, production efficiency, corporate profits and user market competitiveness.

[0105] (4) The results are in line with the State Grid Corporation's low-carbon and green development strategy, adapt to the actual needs of national power demand-side management and energy conservation and emission reduction, and play an important role in the company's construction of a highly resilient power grid. Attached Figure Description

[0106] Figure 1 This is a schematic diagram of the lean management platform for the entire process of power quality in power distribution networks according to the present invention.

[0107] Figure 2 This is the equivalent circuit diagram of the distribution network voltage quality analysis module of the lean management platform for the entire process of power quality in distribution networks according to the present invention.

[0108] Figure 3 This is a voltage deviation vector diagram of the distribution network voltage quality analysis module of the lean management platform for the entire process of power quality in the distribution network, which is based on the present invention.

[0109] Figure 4 This is a simplified power flow model diagram of the distribution network system power flow of the distribution network voltage quality analysis module in the lean management platform for the entire process of power quality in distribution networks of the present invention.

[0110] Figure 5 This is a schematic diagram of the composite power quality management device of the high-voltage quality control module of the distribution network in the present invention, which is part of the lean management platform for the entire process of power quality in the distribution network. Detailed Implementation

[0111] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0112] In the preferred embodiments of the present invention, those skilled in the art should note that the management of high-penetration renewable energy power generation systems and distribution networks involved in the present invention can be considered as prior art.

[0113] Preferred embodiments can be found in Tables 3-5.

[0114] Preferred embodiment.

[0115] This invention discloses a lean management platform for the entire process of power quality in distribution networks, used to manage the power quality of high-penetration renewable energy generation systems connected to the distribution network. It includes a distribution network voltage quality analysis module, a distribution network high-voltage quality control module, and a distribution network voltage quality regulation and optimization module, wherein:

[0116] The distribution network voltage quality analysis module establishes a distribution network system structure model with high-penetration renewable energy generation based on medium and low voltage distribution network parameters. It analyzes the distribution network voltage fluctuation problem and the causes of voltage quality problems from the perspective of energy flow. By analyzing the changes in voltage at different penetration rates / load capacities under various conditions including grid connection, off-grid, three-phase faults and unbalanced loads, the module aims to improve the voltage quality of the distribution network.

[0117] The high-voltage quality control module of the distribution network selects the topology for power quality improvement, establishes the corresponding mathematical model, and analyzes the compensation principle of series and parallel grid connection according to different grid connection methods (series / parallel) of power converters to obtain the corresponding compensation control strategy for improving voltage quality. It comprehensively compares the parameters of control strategy, compensation effect, adverse factors and applicable occasions when handling different voltage quality problems by series and parallel methods, and then uses the voltage quality regulator to match and handle various voltage quality problems accordingly.

[0118] The distribution network voltage quality regulation optimization module establishes a power flow model for a high-penetration renewable energy power generation system with a composite power quality regulator. It obtains the changes in system power flow and the capacity requirements of series and parallel converters. Furthermore, it establishes an optimization compensation model for the composite power quality regulator, determines the economic objective function and technical constraints, and then outputs an optimization configuration strategy for the composite power quality regulator based on a genetic algorithm. This strategy optimizes the installation location and capacity of the composite power quality regulator and provides case studies.

[0119] Specifically, for the distribution network voltage quality analysis module:

[0120] like Figure 2 (equivalent circuit) Figure 3 As shown in the voltage deviation vector, voltage variation and power fluctuation analysis are performed. In the equivalent circuit when a high-penetration renewable energy power generation system is connected to the distribution network, the current I is expressed as:

[0121]

[0122] Therefore, the voltage difference between the distribution network and the voltage of the high-penetration renewable energy generation system is expressed as:

[0123]

[0124] Among them, U PCC P, Q, and U represent the output voltage, output active power, and output reactive power of a high-penetration renewable energy generation system (DG), respectively; G R is the grid voltage; R is the line equivalent resistance; X is the line equivalent reactance.

[0125] A mathematical model is established to show the relationship between penetration rate and voltage. (When high-penetration renewable energy generation is connected to the distribution network, it will cause voltage fluctuations in the PCC. There are certain requirements for the capacity of high-penetration renewable energy generation connected to a specific distribution network, that is, there is a limitation on its penetration rate. The following analysis shows the relationship between the penetration rate (capacity) and voltage of high-penetration renewable energy generation.) Figure 4 (Simplified power flow model for distribution network system with high penetration of renewable energy generation) In the simplified power flow model of distribution network system with high penetration of renewable energy generation, P DG +jQ DG For high-penetration renewable energy generation to input power into the distribution network, P L +jQ L The power required by the load;

[0126] Let P = P L -P DG ;

[0127] Q = Q L -Q DG ;

[0128] The voltage deviation is:

[0129] If ΔU = 0:

[0130] Then there is

[0131] When P≠0 and Q≠0, we can obtain Not valid;

[0132] This does not hold true when P≠0 or Q≠0;

[0133] When P = Q = 0, this holds true, and we obtain:

[0134]

[0135] Based on the load limit condition (no load), i.e., P L =Q L When = 0, then:

[0136] P DG =Q DG =0;

[0137] If ΔU=ΔU max =5%U PCC ;

[0138] Under unity power factor conditions, setting Q=0 yields the following (and in distribution networks, R / X is generally large):

[0139]

[0140] Then we can obtain,

[0141] Based on the load limit condition (no load), i.e., P L =Q L When = 0, then:

[0142]

[0143] When ΔU = 5%U PCC At that time, we obtained:

[0144]

[0145] Therefore, it can be concluded that the power output P of high-penetration renewable energy generation DG There is a certain upper limit, influenced by voltage deviation ΔU, PCC voltage UPCC, and line equivalent resistance R. When ΔU is limited, the power input to the distribution network by the DG must be matched accordingly. When the distribution network system capacity PS and R remain constant, increasing the PDG increases the penetration rate λ (λ = PDG / PS), and ΔU increases accordingly; that is, an increase in the penetration rate λ will affect the voltage quality level of the PCC. Reducing the line series impedance can improve the penetration rate of the DG, but at the same time, it increases the short-circuit current, affecting protection operation. In addition, the power input to the distribution network by the DG cannot exceed the current carrying capacity of the lines and transformers.

[0146] More specifically, for the high-voltage quality control module of the distribution network:

[0147] In medium- and low-voltage distribution networks with high penetration of renewable energy generation, energy flow fluctuations from renewable energy generation systems can cause voltage quality problems. Distribution network faults and other issues can also lead to voltage quality problems. Furthermore, increasing nonlinear and unbalanced loads can cause current harmonics and imbalances, further impacting voltage quality. Power quality improvement devices using power converters / inverters (DGs) can address various power quality issues in distribution networks.

[0148] By selecting the topology of a power quality improvement device, a corresponding mathematical model is established to study control technologies for improving voltage quality in distribution networks. For different grid connection methods (series / parallel) of power converters, the compensation principles for series and parallel grid connections are analyzed, and corresponding compensation control strategies and methods for improving voltage quality are proposed and studied through simulation and experiments. By comprehensively comparing the control strategies, compensation effects, adverse factors, and applicable scenarios of series and parallel connections when handling different voltage quality problems, a voltage quality regulator is designed to flexibly handle various voltage quality issues. The topology of the voltage quality regulator is determined, and principle analysis and simulation studies are conducted to analyze the performance of the voltage quality regulator in improving voltage quality.

[0149] like Figure 5 As shown, a composite power quality management device topology is established, where is and iL represent the grid-side and load-side currents, respectively; vs and vL represent the grid-side and load-side voltages, respectively; L1, R1, and C1 represent the filter inductance, equivalent inductance resistance, and filter capacitor of the series converter, respectively; L2 and R2 represent the connecting reactor and equivalent resistance of the parallel converter, respectively; i1 and ic represent the inductor currents of the series and parallel converters, respectively; and Cdc represents the DC bus capacitor shared by the two converters. When the grid-side voltage vs is normal, the series converter is bypassed; when the grid-side voltage vs drops, the series converter outputs a corresponding compensation voltage vc, thereby maintaining the sensitive load-side voltage vL within the normal range and ensuring the normal operation of the sensitive load. The parallel converter is used to compensate for reactive power and absorb active power from the grid to maintain a constant DC bus voltage.

[0150] A mathematical model of a composite power quality regulator is established. (A composite power quality regulator is actually a comprehensive power quality compensator that integrates the functions of series and parallel compensators. Taking a composite power quality regulator with voltage and reactive power compensation as an example, it can be regarded as a DVR and DSTATCOM sharing a DC bus. Therefore, based on the research results of DVR and DSTATCOM above, the relevant mathematical model of the composite power quality regulator is derived.) Let the equivalent parallel resistance of the DC bus terminal loss be Rdc, and take the series inductors Rz and Lz as equivalent sensitive loads, then:

[0151]

[0152] The small-signal state equation of the series-parallel converter at a certain static operating point is obtained as follows:

[0153]

[0154]

[0155] Among them, state variables and Input and Output and

[0156] They are respectively:

[0157]

[0158]

[0159] The expressions for each coefficient matrix are as follows:

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166]

[0167]

[0168]

[0169]

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176]

[0177] B 11 =[B 01 K1K p1 H1] T B 12 =[0 8×2];

[0178] B 21 =[-B'2K1K p1 0 2×2 ] T B 22 =[B 02 K2K p2 H1] T ;

[0179] C 11 =[C 01 0 2×2 ], C 12 =[0 2×5 ], C 21 =[C kf 0 2×2 ], C 22 =[C 02 0 2×2 ];

[0180] Where m1d0, m1q0, m2d0, and m2q0 represent the relevant parameter values ​​of the pulse width modulation signals of the series and parallel converters at the system's quiescent operating point, respectively; i1d0, i1q0, icd0, and icq0 represent the inductor current values ​​of the two converters at the quiescent operating point; iLd0 and iLq0 represent the load current values ​​at the quiescent operating point; Vdc0 represents the DC bus voltage value of the parallel converter at the quiescent operating point; and the expressions for Ck1 and Ck2 are:

[0181]

[0182] A composite power quality regulator model is established, with intermediate variables vd, vq, id, and iq added as new state variables. Based on the mathematical model of the composite power quality regulator, the small-signal state equations that simultaneously include the (UPQC) model itself and the control model are obtained, namely:

[0183]

[0184]

[0185] Among them, state variables and Input and They are respectively:

[0186]

[0187]

[0188]

[0189] The transfer function of the four-input four-output system is obtained as follows:

[0190]

[0191] Furthermore, for the distribution network voltage quality regulation and optimization module:

[0192] Establish a mathematical model for optimized compensation of a composite power quality regulator:

[0193] Obtain the objective function:

[0194] (Objective function selection criteria: high economic benefits, low investment costs, and small equipment capacity. That is, [the objective function is defined as follows])

[0195] min[μ V S Vn +μ I S In +μ E (S Vn +S In )+C];

[0196] Where: μ V The unit capacity price of a series-connected composite power quality regulator unit, S Vn Capacity of the series unit of the composite power quality regulator; μ I The unit capacity price of a parallel unit of a composite power quality regulator, S In Capacity of the parallel unit of the composite power quality regulator; μ E C represents the unit capacity cost of the remaining parts of the composite power quality regulator, C represents the cost of the fixed parts, and n represents the node location.

[0197] Constraints:

[0198] Meets load voltage quality and stability requirements:

[0199]

[0200] Among them: U Δ U is the rate of change of voltage. n Let U be the voltage at node n. R For the rated voltage, C UR This is a limit value for the rate of voltage change;

[0201] To meet the harmonic requirements of the power grid current and reduce the total harmonic distortion rate of the current;

[0202]

[0203] Among them: THD in Let H be the total harmonic distortion of the current at node n, h be the harmonic order, H be the maximum harmonic order, and C be the total harmonic distortion of the current at node n.THDR This is the limit value for total harmonic distortion (THD).

[0204] Optimization allocation methods, particularly multi-objective intelligent optimization algorithms, are introduced into power systems to address the optimization allocation problems related to high-penetration renewable energy generation and reactive power compensation. Optimization algorithms are rapidly evolving and numerous; generally, an appropriate algorithm is selected based on the specific circumstances. Among these, genetic algorithms are a simple, effective, and highly practical method that searches for the optimal solution by simulating a natural evolutionary process.

[0205] The basic idea of ​​genetic algorithms originates from Darwin's theory of evolution and Mendel's theory of heredity. Darwin's theory of evolution posits that each species becomes increasingly adapted to its environment through continuous development. The basic characteristics of each individual are inherited by their offspring, but offspring are not entirely identical to their parents. These new variations, if adapted to the environment, are preserved. In a given environment, those traits that are better adapted are retained—this is the principle of survival of the fittest. Mendel's theory of heredity states that heredity is encapsulated as instructions in each cell, contained in chromosomes in the form of genes. Each gene has a specific location and controls a specific property. Each gene produces individuals with a certain degree of adaptability to the environment. Gene hybridization and gene mutation may produce offspring with even greater environmental adaptability. Through natural selection, highly adaptive gene structures are preserved.

[0206] Genetic algorithms represent the solution to a problem as chromosomes, thus forming a group of chromosomes. These chromosomes are placed in the problem environment, and according to the principle of survival of the fittest, chromosomes that are adapted to the environment are selected and replicated. Then, through two gene operations, crossover and mutation, a new generation of chromosomes that are more adapted to the environment is generated. This process continues to evolve generation after generation, eventually converging on an individual that is most adapted to the environment, thus obtaining the optimal solution to the problem.

[0207] The main steps of a genetic algorithm:

[0208] ① Determine the coding strategy based on the decision variables and randomly generate the initial population; the coding strategies mainly include binary coding, real number coding, symbol coding, etc.

[0209] ② Select the fitness function; the main selection methods include fitness ratio method, elite individual retention strategy, tournament selection method, and ranking selection method.

[0210] ③ Decode the initial population and calculate the corresponding fitness values.

[0211] ④ Repeat the selection, crossover, and mutation operations until the optimal population is generated; the main crossover methods include uniform crossover, simulated binary crossover, single-point crossover, and two-point crossover; the main mutation methods include uniform mutation, non-uniform mutation, and polynomial mutation.

[0212] The series and parallel compensation capacities of the composite power quality regulator are optimized using a genetic algorithm. By performing multivariate optimization, the optimal solution is finally found, so that the objective function requirements are met while satisfying the constraints.

[0213] Optimize the configuration of the composite power quality conditioner:

[0214] The capacity of the composite power quality regulator is undetermined.

[0215] For different installation points, select one or more installation locations for the composite power quality regulator based on actual needs (mainly including the importance of the load, the convenience of the installation point, etc.). If a certain installation location still cannot be determined after selection, calculate the capacity requirement for maximum full compensation at each location through simulation to finally determine the optimal installation location for the composite power quality regulator.

[0216] Given a fixed installation location, and under the premise of meeting constraints, a genetic algorithm is used to optimize the selection of the required capacity for voltage sag / boost, current harmonics, and reactive power compensation, thereby achieving configuration optimization for the selection of series and parallel converter capacity. (Note that if the capacity requirement has already been calculated when selecting the installation location, a secondary capacity optimization is required at this time.)

[0217] The capacity of the composite power quality regulator is already determined:

[0218] When the capacities of both series and parallel converters are determined, first determine the installation location (the specific method is the same as when the capacity of the composite power quality regulator is not determined). Then, under the premise of meeting the constraints, rationally configure the capacity of the composite power quality regulator required for voltage sags / surges, current harmonics, and reactive power compensation, so as to give full play to the role of the composite power quality regulator and achieve the best compensation effect.

[0219] In this scenario, optimized configuration is based on maximizing the capacity of the composite power quality regulator, while coordinating the capacity of series and parallel converters and the energy of the DC energy storage stage. A genetic algorithm is used to rationally allocate the capacity required for each harmonic and reactive power response in the parallel section, prioritizing harmonic requirements to ensure compliance with at least national standards. When there are no voltage quality issues in the grid, the DC energy storage stage can provide the energy required by the parallel converters, and the series converters can also share the reactive power compensation function of the parallel converters. When there are voltage sags in the grid, the energy allocated by the DC energy storage stage to the series and parallel converters is determined based on the degree of the sag (required capacity). When there are voltage spikes in the grid, the series converters can absorb power from the grid and supply energy to the DC energy storage stage and the parallel converters.

[0220] Optimized configuration example:

[0221] The rated voltage of the common coupling point PCC is 380V, and the allowable voltage fluctuation range is 361V to 399V (i.e., (1±5%)U). N The permissible harmonic content injected into the PCC should comply with the requirements of Table 3 (Harmonic current order and corresponding allowable value injected into the PCC when the rated voltage is 380V and the reference short-circuit capacity is 10MVA), and the total current harmonic distortion rate should be less than 5%. Assuming that the maximum voltage sag that the PCC may generate is 20%UN (76V), the harmonic order and value of the current injected into the PCC under actual operating conditions are shown in Table 4 (coding and conversion relationship of relevant current harmonic content when the system short-circuit capacity is 1MVA).

[0222] Taking the case where the capacity of a composite power quality conditioner is undetermined as an example, this paper optimizes the configuration of the composite power quality conditioner. First, the installation location of the composite power quality conditioner is determined: since all parameters in this example impose requirements on the voltage and current of the PCC, based on the actual situation of the project, the composite power quality conditioner is installed at the PCC location. The capacities of the series and parallel converters of the composite power quality conditioner are determined using a genetic algorithm. The detailed steps are as follows:

[0223] Using a binary encoding method, 6 bits represent the PCC voltage, and every 4 bits represent the harmonic current. The voltage range of 361V to 399V corresponds to binary numbers 0 to 63. Taking Ub = 000000 to 111111, the decoding conversion relationship is as follows:

[0224]

[0225] In the formula, λ represents the number of bits, Un is the actual value of the PCC voltage, Umax is the maximum allowable value of the PCC voltage, and Umin is the minimum allowable value of the PCC voltage. Harmonic current encoding and decoding are shown in Table 4.

[0226] The binary codes for voltage and harmonic currents are 22 bits long, divided into 5 segments: Ub, Ib3, Ib5, Ib7, and Ib9. Based on the aforementioned conversion relationships, the actual values ​​of each decision variable—Un, I3, I5, I7, and I9—can be obtained respectively. The more bits selected in the code, the more accurate the decision variable values, but the more calculations are required. Binary coding can be used to randomly generate an initial population.

[0227] The requirements for voltage and harmonic current have been incorporated into the coding process. That is, the constraints required for optimized configuration and the allowable values ​​of harmonic current have been met. Therefore, the total harmonic distortion rate of current is selected as the fitness function here, which can be calculated.

[0228]

[0229] Where h = 3, 5, 7, 9; Ih is the harmonic value after compensation. Assuming Iah is the harmonic value before compensation, then (Iah - Ih) is the harmonic current value compensated by the composite power quality regulator. Calculate SIn (SIn = Schunt); (U n -kU o The formula (4-24) is used to calculate SVn (SVn = Sseries). Under a fixed load, the series compensation capacity is affected by the sag ratio k, i.e., the sag voltage; the parallel compensation capacity is affected by Shar, i.e., the load harmonic capacity, which can be calculated using the following formula. Therefore, the key parameters SVn and SIn in the optimization objective function can be obtained.

[0230]

[0231] Objective function parameters: μ V =0.3 million yuan / kVA, μ I =0.2 million yuan / kVA, μ E =0.1 million yuan / kVA, C =0.5 million yuan. The maximum total output capacity of renewable energy power generation is 45kVA, with a load of 35kW / 20kVar, then Io = 61.25A. From the maximum voltage sag condition, k = 0.8. The optimal configuration of the composite power quality regulator is implemented using a genetic algorithm programmed in MATLAB. The parameters used in the genetic algorithm are: crossover probability 0.8, mutation probability 0.01, population size of 20 per generation, and 100 iterations.

[0232] The population was repeatedly subjected to selection, crossover, and mutation operations. After a certain number of iterations, the optimal solution was obtained, namely the PCC voltage value and the values ​​of each harmonic current. Table 5 shows a set of relevant data obtained after the genetic algorithm calculation. Based on the obtained data, the following calculations were performed: series compensation capacity 3.2 kVA; parallel compensation capacity 15.2 kVA; voltage change rate 5%; total current harmonic distortion rate 4.8%; objective function investment cost 62,000 yuan. Analysis of the optimized configuration results shows that the voltage change rate and total current harmonic distortion rate meet the requirements, and the capacities of series and parallel compensation were determined, resulting in optimal investment cost.

[0233] The present invention also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to realize the lean management platform for the entire process of power quality in the power distribution network.

[0234] The present invention also discloses a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the lean management platform for the entire process of power quality in the power distribution network.

[0235] It is worth mentioning that the technical features of managing high-penetration renewable energy power generation systems and distribution networks involved in this patent application should be regarded as prior art. The specific structure, working principle, and possible control methods and spatial arrangement of these technical features can be adopted using conventional choices in the field, and should not be regarded as the inventive point of this patent. This patent will not be further elaborated in detail.

[0236] For those skilled in the art, modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the protection scope of this invention.

[0237] Table 3. Allowable values ​​of harmonic current injected into the point of common coupling.

[0238] Harmonic number 2 3 4 5 6 7 8 9 10 Harmonic current allowable value / A 78 62 39 62 26 44 19 21 16 Harmonic number 11 12 13 14 15 16 17 18 19 Harmonic current allowable value / A 28 13 24 11 12 9.7 18 8.6 16

[0239] Table 4 Harmonic Current Encoding and Decoding

[0240] Harmonic number n Actual value / A Allowed value / A Encoding / Ibn Conversion Relationship 3 8.9 0～6.2 0000～1111 <![CDATA[I3=0.413I b3 ]]> 5 8.5 0～6.2 0000～1111 <![CDATA[I5=0.413I b5 ]]> 7 6.2 0～4.4 0000～1111 <![CDATA[I7=0.293I b7 ]]> 9 3.5 0～2.1 0000～1111 <![CDATA[I9=0.14I b9 ]]>

[0241] Table 5 Results of the Genetic Algorithm

[0242] Encoding result Decision variables <![CDATA[U b =000000]]> <![CDATA[U n =361V]]> <![CDATA[I b3 =0100]]> <![CDATA[I3=1.652A]]> <![CDATA[I b5 =0100]]> <![CDATA[I5=1.652A]]> <![CDATA[I b7 =0110]]> <![CDATA[I7=1.465A]]> <![CDATA[I b9 =1000]]> <![CDATA[I9=1.12A]]>

Claims

1. A lean management platform for the entire process of power quality in distribution networks, used to manage the power quality of high-penetration renewable energy generation systems connected to the distribution network, characterized in that, It includes a distribution network voltage quality analysis module, a distribution network high voltage quality control module, and a distribution network voltage quality regulation and optimization module, wherein: The distribution network voltage quality analysis module establishes a distribution network system structure model with high-penetration renewable energy generation based on medium and low voltage distribution network parameters. It analyzes the distribution network voltage fluctuation problem and the causes of voltage quality problems from the perspective of energy flow. By analyzing the changes in voltage at different penetration rates / load capacities under various conditions including grid connection, off-grid, three-phase faults and unbalanced loads, the module aims to improve the voltage quality of the distribution network. The high-voltage quality control module of the distribution network selects the topology for power quality improvement, establishes the corresponding mathematical model, and analyzes the series and parallel grid connection compensation principle according to the different grid connection methods of the power converter. It obtains the corresponding compensation control strategy for improving voltage quality problems, and comprehensively compares the parameters of series and parallel connection methods when dealing with different voltage quality problems, including control strategies, compensation effects, adverse factors and applicable occasions. Then, it uses the voltage quality regulator to match and handle various voltage quality problems accordingly. The distribution network voltage quality regulation optimization module establishes a power flow model for a high-penetration renewable energy power generation system with a composite power quality regulator. It obtains the changes in system power flow and the capacity requirements of series and parallel converters. Furthermore, it establishes an optimization compensation model for the composite power quality regulator, determines the economic objective function and technical constraints, and then outputs an optimization configuration strategy for the composite power quality regulator based on a genetic algorithm. This strategy optimizes the installation location and capacity of the composite power quality regulator and provides case studies.

2. The lean management platform for power quality in distribution networks according to claim 1, characterized in that, For the power distribution network voltage quality analysis module: In voltage variation and power fluctuation analysis, the current I in the equivalent circuit when a high-penetration renewable energy power generation system is connected to the distribution network is expressed as: Therefore, the voltage difference between the distribution network and the voltage of the high-penetration renewable energy generation system is expressed as: wherein, U PCC , P, Q represent output voltage, output active power, output reactive power of high penetration rate renewable energy power generation system respectively; U G is distribution network voltage; R is line equivalent resistance, and X is line equivalent reactance; A mathematical model is established to relate the permeability and voltage. In the simplified model of power flow in the distribution network system with high permeability renewable energy power generation, P DG +jQ DG is the input power of the distribution network with high permeability renewable energy power generation L +jQ L is the required power of the load Let P = P L - P DG ; Q = Q L - Q DG ; The voltage deviation is: If ΔU = 0: Then there is When P≠0 and Q≠0, we can obtain Not valid; This does not hold true when P≠0 or Q≠0; When P = Q = 0, this holds true, and we obtain: According to the limit case of the load, i.e. P L = Q L = 0, then there is: P DG = Q DG = 0; If ΔU=ΔU max =5%U PCC ; With unity power factor, setting Q = 0 yields: Then we can obtain, Based on the load limit, i.e., P L =Q L When = 0, then: When ΔU = 5%U PCC At that time, we obtained:

3. The lean management platform for the entire process of power quality in distribution networks according to claim 2, characterized in that, For high-voltage quality control modules in distribution networks: A composite power quality management device topology is established. When the grid voltage vs is normal, the series converter is bypassed. When the grid voltage vs drops, the series converter outputs a corresponding compensation voltage vc, thereby maintaining the voltage vL at the sensitive load end within the normal range and ensuring the normal operation of the sensitive load. The parallel converter is used to compensate for reactive power and absorb active power from the grid to maintain the DC bus voltage constant. A mathematical model of a composite power quality regulator is established. Let Rdc be the equivalent parallel resistance of the DC bus terminal loss, and let the series-type inductors Rz and Lz be equivalent to sensitive loads. Then: The small-signal state equation of the series-parallel converter at a certain static operating point is obtained as follows: Among them, state variables and Input and Output and They are respectively: The expressions for each coefficient matrix are as follows: B 11 =[B 01 K1K p1 H1] T ,B 12 =[0 8×2 ]; B 21 =[-B2'K1K p1 0 2×2 ] T ,B 22 =[B 02 K2K p2 H1] T ; C 11 =[C 01 0 2×2 ],C 12 =[0 2×5 ],C 21 =[C kf 0 2×2 ],C 22 =[C 02 0 2×2 ]; Where m1d0, m1q0, m2d0, and m2q0 represent the relevant parameter values ​​of the pulse width modulation signals of the series and parallel converters at the system's quiescent operating point, respectively; i1d0, i1q0, icd0, and icq0 represent the inductor current values ​​of the two converters at the quiescent operating point; iLd0 and iLq0 represent the load current values ​​at the quiescent operating point; Vdc0 represents the DC bus voltage value of the parallel converter at the quiescent operating point; and the expressions for Ck1 and Ck2 are: A composite power quality regulator model is established, with intermediate variables vd, vq, id, and iq added as new state variables. Based on the mathematical model of the composite power quality regulator, the small-signal state equation, which simultaneously includes its own model and the control model, is obtained, namely: Among them, state variables and Input and They are respectively: The transfer function of the four-input four-output system is obtained as follows:

4. The lean management platform for the entire process of power quality in distribution networks according to claim 3, characterized in that, For the distribution network voltage quality regulation and optimization module: Establish a mathematical model for optimized compensation of a composite power quality regulator: Obtain the objective function: min[μ V S Vn +m I S In +m E (S Vn +S In )+C]; Where: μ V The unit capacity price of a series-connected composite power quality regulator unit, S Vn Capacity of the series unit of the composite power quality regulator; μ I The unit capacity price of a parallel unit of a composite power quality regulator, S In Capacity of the parallel unit of the composite power quality regulator; μ E C represents the unit capacity cost of the remaining parts of the composite power quality regulator, C represents the cost of the fixed parts, and n represents the node location. Constraints: Meets load voltage quality and stability requirements: Among them: U Δ U is the rate of change of voltage. n Let U be the voltage at node n. R For the rated voltage, C UR This is a limit value for the rate of voltage change; To meet the harmonic requirements of the power grid current and reduce the total harmonic distortion rate of the current; Among them: THD in Let H be the total harmonic distortion of the current at node n, h be the harmonic order, H be the maximum harmonic order, and C be the total harmonic distortion of the current at node n. THDR This is a limit value for the total harmonic distortion (THD). The optimization configuration method uses a genetic algorithm to optimize the series and parallel compensation capacities of the composite power quality regulator. By performing multivariate optimization, the optimal solution is finally found, so that the objective function requirements are met while satisfying the constraints. Optimize the configuration of the composite power quality conditioner: The capacity of the composite power quality regulator is undetermined. For different installation points, select one or more installation locations for the composite power quality conditioner according to actual needs. If a certain installation location still cannot be determined after selection, calculate the capacity requirement for maximum full compensation at each location through simulation to finally determine the optimal installation location for the composite power quality conditioner. Given the determined installation location, and under the premise of meeting the constraints, a genetic algorithm is used to optimize the selection of the capacity required for voltage sag / boost, current harmonics, and reactive power compensation, thereby achieving configuration optimization for the selection of series and parallel converter capacity. The capacity of the composite power quality regulator is already determined: When the capacities of both series and parallel converters are determined, the installation location is first determined. Then, under the premise of meeting the constraints, the capacity of the composite power quality regulator required for voltage sag / surge, current harmonics, and reactive power compensation is reasonably configured to give full play to the role of the composite power quality regulator and achieve the best compensation effect.

5. An electronic 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 program, it implements the lean management platform for power quality of the distribution network as described in any one of claims 1 to 4.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the lean management platform for power quality of the distribution network as described in any one of claims 1 to 4.

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