Power quality regulation method and system of transformer area energy storage system based on double closed loop

Abstract

The present application relates to a power quality regulation method and system of a double closed loop based transformer area energy storage system, wherein the method comprises: obtaining power quality data of the energy storage system; constructing a double closed loop dynamic control model comprising a voltage inner loop control submodel and a power outer loop control submodel; the voltage inner loop control submodel adopts a fuzzy proportional-integral controller; the power outer loop control submodel adopts a fuzzy logic controller; determining whether a voltage out-of-limit event occurs based on the power quality data; if the voltage out-of-limit event occurs, starting voltage inner loop control and generating a first regulation instruction using the voltage inner loop control submodel; if the voltage out-of-limit event does not occur, starting power outer loop control and generating a second regulation instruction using the power outer loop control submodel; controlling the energy storage system in the transformer area to execute corresponding instructions based on the first regulation instruction and the second regulation instruction, thereby realizing power quality regulation of the transformer area energy storage system.

Description

Technical Field

[0001] This invention relates to the field of transformer substation control technology, and mainly to a power quality control method and system based on a dual-closed-loop transformer substation energy storage system. Background Technology

[0002] Distributed photovoltaic (PV) power, as a crucial support for energy structure transformation, is seeing its penetration rate in distribution networks continue to rise. However, the volatility of PV output and the discreteness of inverter connection have led to a series of power quality problems in traditional low-voltage distribution areas, such as frequent reverse power flow, severe voltage exceedances, and increased line losses. These issues have become key bottlenecks restricting the high-quality consumption of renewable energy and the stable operation of distribution areas. Especially in typical high-penetration distribution areas where PV installed capacity accounts for more than 80%, voltage disturbances persist, and power flow direction changes dynamically. Traditional centralized and time-delayed control methods are unable to respond promptly to fluctuations in both load and output, and the disconnect between control capabilities and actual operation is becoming increasingly severe.

[0003] Chinese invention patent application CN 107947171A discloses a dual-loop composite control method for a unified power quality regulator. The technical solution includes (1) establishing a mathematical model in a synchronous rotating coordinate system using a three-phase three-wire UPQC topology diagram; (2) UPQC dual closed-loop voltage control: using a series-type three-phase converter circuit as the voltage source, outputting a compensation voltage that is equal in magnitude and opposite in direction to the difference between the load voltage and the grid voltage, and obtaining a sinusoidal load voltage through dual closed-loop PI control; (3) using a parallel-type three-phase converter circuit as the current source, outputting a compensation current that is equal in magnitude and opposite in direction to the difference between the grid current and the load current; (4) designing a current loop PI, and simultaneously adding a zero-order hold; (5) designing a repetitive controller: establishing the transfer function of the repetitive controller; (6) combining the inner loop of the PI control and the outer loop of the repetitive controller to perform complex control. The above-mentioned technical solution uses a combination of traditional fixed-parameter proportional-integral (PI) control and a repetitive controller. However, the PI parameters cannot be dynamically adjusted according to real-time fluctuations in the power grid / load. In complex scenarios such as distribution transformers where load and photovoltaic output change frequently, overshoot and regulation lag are likely to occur. At the same time, the voltage and current loops of the above-mentioned technical solution are in a coordinated compensation mode that starts simultaneously. This can only achieve passive compensation when power quality problems occur, and cannot perform differentiated regulation based on whether the power quality is abnormal. Under normal voltage / current conditions, compensation continues to be performed, resulting in redundant operation of the equipment.

[0004] Therefore, there is an urgent need for a control method that can adapt to the operating characteristics of the energy storage system in the distribution area and achieve tiered control based on the power quality status. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes a power quality control method and system based on a dual-closed-loop distribution area energy storage system.

[0006] The technical solution of the present invention is as follows: On the one hand, this invention proposes a power quality control method for a transformer substation energy storage system based on a dual closed-loop system, the method comprising: Obtain power quality data from energy storage systems within the transformer area; A dual-closed-loop dynamic control model is constructed, comprising an inner-loop voltage control sub-model and an outer-loop power control sub-model. The inner-loop voltage control sub-model employs a fuzzy proportional-integral controller to achieve power correction of the energy storage system, while the outer-loop power control sub-model employs a fuzzy logic controller to formulate the charging and discharging power regulation plan of the energy storage system. Based on power quality data, determine whether a voltage over-limit event has occurred; If a voltage over-limit event occurs, the voltage inner loop control is activated, and the first regulation command is generated using the voltage inner loop control sub-model. If no voltage over-limit event occurs, power outer loop control is initiated, and a second regulation command is generated using the power outer loop control sub-model. Based on the first and second control commands, the energy storage system in the control area executes the corresponding commands to realize the power quality control of the energy storage system in the control area.

[0007] Preferably, the power quality data includes at least voltage amplitude, voltage deviation, current, photovoltaic output power, and the state of charge of the energy storage system.

[0008] Preferably, the first control command is generated using the voltage inner loop control sub-model, and the specific steps are as follows: The fuzzy proportional-integral controller uses the deviation between the actual voltage on the user side and the preset voltage target value as the input variable, and fuzzifies the deviation into multiple first fuzzy subsets. Based on the preset fuzzy control rules, fuzzy inference is performed on the first fuzzy subset to obtain the proportional coefficient correction amount and the integral coefficient correction amount. Based on the proportional coefficient correction and integral coefficient correction, the preset proportional-integral baseline parameters are dynamically adjusted to obtain the adjusted proportional coefficient and integral coefficient. The power adjustment amount is obtained by performing proportional-integral calculation on the deviation based on the adaptive proportional coefficient and the adaptive integral coefficient, and is used as the first control command.

[0009] Preferably, the second control command is generated using the power outer loop control sub-model. The specific steps are as follows: The fuzzy logic controller takes time period, voltage level and state of charge of energy storage system as input variables; and divides the input variables into multiple second fuzzy subsets. Map the input variable at the current moment to the second fuzzy subset to obtain the corresponding membership degree; The membership degree is mapped to a preset fuzzy rule base to obtain the fuzzy output of the charging and discharging power of the energy storage system; The fuzzy output is defuzzified using the centroid method or the maximum membership method to obtain the charging and discharging power value, which is then used as the second control command.

[0010] Preferably, based on power quality data, the determination of whether a voltage over-limit event has occurred involves the following steps: The real-time voltage amplitude at each monitoring point is compared with the preset upper voltage threshold and lower voltage threshold, respectively. If the voltage amplitude at any monitoring point exceeds the upper voltage threshold or falls below the lower voltage threshold, and the duration of this over-limit state exceeds the preset over-limit tolerance time, then a voltage over-limit event is determined to have occurred, and the over-limit type and over-limit location are recorded.

[0011] Preferably, the configuration also includes an energy storage system within the transformer substation area, with the following specific steps: Construct a transformer area simulation platform; Construct simulation evaluation functions, including sub-functions for evaluating the effectiveness of voltage over-limit mitigation, line loss reduction, and return on investment. The simulation evaluation function values ​​corresponding to multiple candidate configuration schemes are calculated on the transformer area simulation platform. The candidate configuration schemes include different energy storage power, capacity and deployment locations of the energy storage system. The candidate configuration scheme corresponding to the maximum simulation evaluation function value is selected as the target configuration scheme, and the energy storage system in the transformer area is optimized.

[0012] Preferably, it also includes collecting the operating data of the transformer substation after regulation, and optimizing the parameters of the dual closed-loop dynamic control model based on the operating data of the transformer substation after regulation.

[0013] On the other hand, the present invention also provides a power quality control system based on a dual-closed-loop distribution area energy storage system, the system comprising: The data acquisition module acquires power quality data of the energy storage system within the transformer area; The dual-closed-loop control module incorporates a dual-closed-loop dynamic control model, including an inner voltage control sub-model and an outer power control sub-model. The inner voltage control sub-model employs a fuzzy proportional-integral controller to achieve power correction of the energy storage system, while the outer power control sub-model employs a fuzzy logic controller to formulate the charging and discharging power adjustment plan of the energy storage system. The control command generation module determines whether a voltage over-limit event has occurred based on power quality data; If a voltage over-limit event occurs, the voltage inner loop control is activated, and the first regulation command is generated using the voltage inner loop control sub-model. If no voltage over-limit event occurs, power outer loop control is initiated, and a second regulation command is generated using the power outer loop control sub-model. The control command execution module controls the distributed energy storage system in the distribution area to execute corresponding commands based on the first and second control commands, thereby realizing the power quality control of the distributed energy storage system in the distribution area.

[0014] In another aspect, 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 method described in the present invention.

[0015] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the present invention.

[0016] The present invention has the following beneficial effects: 1. This invention adopts a dual-closed-loop hierarchical control principle, constructing a dual-closed-loop dynamic control model with an inner voltage loop and an outer power loop. Hierarchical regulation is achieved based on the occurrence of voltage over-limit events: the inner voltage loop activates in emergency situations involving voltage over-limits, while the outer power loop activates in normal operating conditions with normal voltage. This ensures that the energy storage system's regulation actions are highly matched to the actual operating state of the distribution area, avoiding the rigidity of a single control mode. The inner voltage loop is responsible for dynamic voltage correction, quickly resolving the core issue of voltage over-limits; the outer power loop is responsible for setting charging and discharging power plans, ensuring the normalized stability of power quality in the distribution area, enhancing the real-time performance and effectiveness of power quality regulation, and improving the accuracy of voltage stability control. 2. In the dual-closed-loop dynamic control model provided by this invention, the voltage inner loop adopts a fuzzy proportional-integral controller, which uses voltage deviation as input for fuzzification and fuzzy inference, dynamically adjusts the proportional-integral coefficients and generates power adjustment commands. This solves the parameter adaptability problem caused by fluctuations in transformer load and photovoltaic output, improves the adaptive capability of voltage correction, and can dynamically adjust the control strategy according to the actual voltage deviation, allowing the voltage to recover to the target value faster and with higher accuracy. The power outer loop adopts a fuzzy logic controller, which uses time period, voltage level, and energy storage state of charge as input. It generates charging and discharging power commands through fuzzy subset partitioning, membership degree calculation, fuzzy rule inference, and defuzzification. This fully considers the multi-dimensional influencing factors of transformer operation, making the charging and discharging behavior of the energy storage system more consistent with the peak and valley loads, voltage state, and the operating characteristics of the energy storage itself. 3. This invention also constructs a simulation evaluation function that includes three sub-functions: voltage over-limit management, line loss reduction, and return on investment. Combined with a transformer area simulation platform, it quantitatively evaluates different power, capacity, and deployment location schemes for energy storage and selects the optimal configuration scheme. This principle takes into account both the technical effect of power quality regulation and the economic effect of energy storage investment, improves the rationality and economy of energy storage system configuration, avoids resource waste or insufficient regulation capabilities caused by blind configuration, and improves the effect of reducing line losses and the ability to manage voltage over-limits in the transformer area, so that the investment in energy storage system can maximize technical and economic benefits. Attached Figure Description

[0017] Figure 1 This is a detailed flowchart of an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0020] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0021] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0022] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0023] Example 1: See Figure 1 This invention provides a power quality control method for a transformer substation energy storage system based on a dual closed-loop system, the method comprising: S1. Obtain power quality data of the energy storage system within the transformer area; The power quality data includes at least voltage amplitude, voltage deviation, current, photovoltaic output power, and the state of charge of the energy storage system. S2. Construct a dual-closed-loop dynamic control model including a voltage inner-loop control sub-model and a power outer-loop control sub-model. The voltage inner-loop control sub-model adopts a fuzzy proportional-integral controller to realize the power correction of the energy storage system; the power outer-loop control sub-model adopts a fuzzy logic controller to formulate the charging and discharging power adjustment plan of the energy storage system. S3. Based on power quality data, determine whether a voltage over-limit event has occurred; The real-time voltage amplitude at each monitoring point is compared with the preset upper voltage threshold and lower voltage threshold, respectively. If the voltage amplitude at any monitoring point exceeds the upper voltage threshold or falls below the lower voltage threshold, and the duration of this over-limit state exceeds the preset over-limit tolerance time, then a voltage over-limit event is determined to have occurred, and the over-limit type and over-limit location are recorded. S31. If a voltage over-limit event occurs, activate the voltage inner loop control and generate the first regulation command using the voltage inner loop control sub-model. The specific steps are as follows: S311. The fuzzy proportional-integral controller uses the deviation between the actual voltage on the user side and the preset voltage target value as the input variable, and fuzzifies the deviation into multiple first fuzzy subsets. The deviation is calculated as follows: ; In the formula, express The deviation at time; This indicates the preset target voltage value; express Actual voltage on the user side at any given time; In this embodiment, the specific process of blurring is as follows: The physical domain of the deviation is [ 10,10]V is divided into 5 fuzzy subsets: {negative large NB, negative small NS, zero ZO, positive small PS, positive large PB}; The physical domain of the change in deviation is [ 5,5]V is divided into 5 fuzzy subsets: {negative large NB, negative small NS, zero ZO, positive small PS, positive large PB}; S312. Based on the preset fuzzy control rules, perform fuzzy inference on the first fuzzy subset to obtain the proportional coefficient correction amount and the integral coefficient correction amount; In this embodiment, the fuzzy control rule is, for example: If the deviation is PB and the change in deviation is NB, then the proportional coefficient correction is PS and the integral coefficient correction is ZO. If the deviation is NB and the change in deviation is NB, then the proportional coefficient correction is PB and the integral coefficient correction is NS. S313. Based on the proportional coefficient correction amount and the integral coefficient correction amount, the preset proportional-integral reference parameters are dynamically adjusted to obtain the adjusted proportional coefficient and integral coefficient. S314. Perform proportional-integral calculations on the deviation based on the adaptive proportional coefficient and the adaptive integral coefficient to obtain the power adjustment amount, which serves as the first control command. The calculation method for the power adjustment amount is as follows: ; In the formula, express The amount of power adjustment at any given time; express The adjusted scaling factor for the time period; express The time-adjusted integral coefficient; Represents the integral variable; S32. If no voltage over-limit event occurs, initiate power outer loop control and generate a second regulation command using the power outer loop control sub-model. The specific steps are as follows: S321, The fuzzy logic controller uses time period, voltage level and state of charge of energy storage system as input variables; and divides the input variables into multiple second fuzzy subsets; In this embodiment, the 24-hour period is divided into multiple fuzzy subsets of time periods, including at least the period when the total load of the transformer area is less than 60% of the daily average load as the load trough period, the period when the total load of the transformer area is between 60% and 85% of the daily average load as the load level period, and the period when the total load of the transformer area exceeds 85% of the daily average load as the load peak period. when and When it is the off-peak period, and When it is the load flat period, when and and This is the peak load period; where, indicates time; The membership degree corresponding to the load trough period is specifically when... When the membership degree is 1; when When the membership degree is ;when or At that time, the membership degree is 0; The membership degree corresponding to the load flat period is specifically when or When the membership degree is 0; when When the membership degree is ;when When the membership degree is 1; when When the membership degree is ;when At that time, the membership degree is 0; The membership degree corresponding to the peak load period is specifically when When the membership degree is 0; when When the membership degree is ;when When the membership degree is 1; when When the membership degree is ; The voltage level is divided into multiple fuzzy subsets of voltage levels, including at least low voltage level, normal voltage level and high voltage level; The membership degree corresponding to the low voltage level is specifically when When the membership degree is 1; when When the membership degree is ;when When the membership degree is 0, then, Indicates the per-unit voltage value; The membership degree corresponding to the normal voltage level is specifically when or When the membership degree is 0; when When the membership degree is ;when When the membership degree is 1; when When the membership degree is ; The membership degree corresponding to the high voltage level is specifically when When the membership degree is 0; when When the membership degree is ;when At that time, the membership degree is 1; The state of charge (SOC) of the energy storage system is divided into multiple fuzzy subsets of SOC, including at least low SOC, medium SOC and high SOC. The membership degree corresponding to the low SOC state is specifically when When the membership degree is 1; when When the membership degree is ;when When the membership degree is 0, then, This indicates the state of charge (SOC) value of the energy storage system. The membership degree corresponding to a medium SOC state is specifically when or When the membership degree is 0; when When the membership degree is ;when When the membership degree is 1; when When the membership degree is ; The membership degree corresponding to a high SOC state is specifically when When the membership degree is 0; when When the membership degree is ;when At that time, the membership degree is 1; For example, input (During the 6-8 period) (Per-unit value, normal voltage) Membership calculation at (medium SOC); The time period corresponds to a low load period of (8-7) / 2=0.5, and a load level period of (7-6) / 2=0.5; The voltage level corresponds to a normal voltage of 1; The state of charge (SOC) of an energy storage system corresponds to 1 in the high SOC state. S322. Map the input variables at the current moment to the second fuzzy subset to obtain the corresponding membership degree; S323. Map the membership degree to the preset fuzzy rule base to obtain the fuzzy output of the charging and discharging power of the energy storage system; The fuzzy rule base contains multiple fuzzy control rules expressed in "IF-THEN" form, simultaneously considering time period characteristics, voltage state, and SOC state, used to determine the charging and discharging behavior of the energy storage system; the fuzzy rule base includes at least the following rule types: When IF is in a peak load period AND low voltage level AND high SOC state, THEN controls the energy storage system to discharge with a first discharge power, wherein the first discharge power is calculated based on the rated power of the energy storage system, the current SOC value and the voltage deviation. When IF is in a peak load period AND low voltage level AND medium SOC state, THEN controls the energy storage system to discharge with a second discharge power, which is less than the first discharge power, wherein the second discharge power is calculated based on the rated power of the energy storage system and the current SOC value; When the system is in a peak load period AND normal voltage level AND high SOC state, THEN controls the energy storage system to discharge at a third discharge power, which is less than the second discharge power. The third discharge power is calculated based on the product of the rated power of the energy storage system and a preset peak shaving factor. When IF is in a period of low load AND high voltage level AND low SOC state, THEN controls the energy storage system to charge at a first charging power, wherein the first charging power is calculated based on the rated power of the energy storage system, the current SOC value and the voltage deviation. When the energy storage system is in a period of low load, high voltage level and medium SOC state, THEN controls the energy storage system to charge at a second charging power, which is less than the first charging power. The second charging power is calculated based on the functional relationship between the rated power of the energy storage system and the current SOC value. When IF is in a flat load period AND high voltage level AND low SOC state, THEN controls the energy storage system to charge with a third charging power, which is less than the second charging power. The third charging power is calculated based on the product of the rated power of the energy storage system and the preset valley filling coefficient. When IF is in a flat load period AND low voltage level AND high SOC state, THEN controls the energy storage system to discharge with a fourth discharge power, wherein the fourth discharge power is calculated based on the product of the rated power of the energy storage system and the preset voltage regulation coefficient. When the IF is in a high SOC state AND a high voltage level, the energy storage system is controlled to charge at the maximum available power regardless of the time period, wherein the maximum available charging power is calculated based on the product of the rated power of the energy storage system and the maximum charging rate allowed by the current SOC. When the IF is in a low SOC state AND a low voltage level, the energy storage system is controlled to discharge at the maximum available power regardless of the time period, wherein the maximum available discharge power is calculated based on the product of the rated power of the energy storage system and the maximum discharge rate allowed by the current SOC. S324. Defuzzify the fuzzy output using the centroid method or the maximum membership method to obtain the charging and discharging power value, which is used as the second control command. S4. Based on the first and second control commands, control the energy storage system in the control area to execute the corresponding commands to realize the power quality control of the energy storage system in the control area. S5 also includes configuring the energy storage system within the distribution area. The specific steps are as follows: Construct a transformer area simulation platform; A simulation evaluation function is constructed, including a sub-function for evaluating the effectiveness of voltage over-limit mitigation to quantify the ability of the configuration scheme to suppress voltage over-limit events, a sub-function for evaluating the effectiveness of line loss reduction to quantify the degree of improvement of the line loss rate of the transformer substation, and a sub-function for evaluating the return on investment to assess the economic feasibility of the configuration scheme. The calculation method is as follows: ; In the formula, Represents the simulation evaluation function; This represents a sub-function for evaluating the effectiveness of voltage over-limit mitigation measures. This represents a sub-function for evaluating the effectiveness of line loss reduction; This represents the sub-function for evaluating investment returns; This indicates the weight of the sub-function for evaluating the effectiveness of voltage over-limit mitigation; This indicates the weight of the sub-function evaluating the effectiveness of line loss reduction; This indicates the weight of the investment return evaluation sub-function; Indicates the rated power of the energy storage system; Indicates the rated capacity of the energy storage system; This indicates the location of the energy storage system within the distribution area; in, The relative importance of voltage regulation effectiveness, line loss reduction effectiveness, and return on investment; In this embodiment, the calculation methods for the voltage over-limit mitigation effect evaluation sub-function, the line loss reduction effect evaluation sub-function, and the return on investment evaluation sub-function are as follows: ; ; ; In the formula, This indicates the duration of total voltage exceeding the limit under typical operating scenarios when no energy storage system is configured within the transformer area; This indicates the total voltage over-limit duration under typical operating scenarios after the energy storage system in the transformer area is configured; wherein the typical operating scenarios include at least sunny high-sunlight scenario, cloudy fluctuating scenario, and peak load scenario. This indicates the bus loss rate under typical operating scenarios when no energy storage system is configured within the distribution area; This indicates the bus loss rate under typical operating scenarios after the energy storage system is configured within the distribution area; This indicates the return on investment for the current candidate configuration. This indicates the preset target rate of return on investment; This indicates the preset minimum acceptable rate of return on investment; The simulation evaluation function values ​​corresponding to multiple candidate configuration schemes are calculated on the transformer area simulation platform. The candidate configuration schemes include different energy storage power, capacity and deployment locations of the energy storage system. The candidate configuration scheme corresponding to the maximum simulation evaluation function value is selected as the target configuration scheme, and the energy storage system in the transformer area is optimized. S6 also includes collecting and adjusting the operating data of the transformer substations, and optimizing the parameters of the dual-closed-loop dynamic control model based on the adjusted operating data of the transformer substations.

[0024] Example 2: This embodiment provides a power quality optimization system for distribution transformer areas based on a dual closed-loop system. The system includes: The data acquisition module acquires power quality data of the energy storage system within the transformer area; The dual-closed-loop control module incorporates a dual-closed-loop dynamic control model, including an inner voltage control sub-model and an outer power control sub-model. The inner voltage control sub-model employs a fuzzy proportional-integral controller to achieve power correction of the energy storage system, while the outer power control sub-model employs a fuzzy logic controller to formulate the charging and discharging power adjustment plan of the energy storage system. The control command generation module determines whether a voltage over-limit event has occurred based on power quality data; If a voltage over-limit event occurs, the voltage inner loop control is activated, and the first regulation command is generated using the voltage inner loop control sub-model. If no voltage over-limit event occurs, power outer loop control is initiated, and a second regulation command is generated using the power outer loop control sub-model. The control command execution module controls the distributed energy storage system in the distribution area to execute corresponding commands based on the first and second control commands, thereby realizing the power quality control of the distributed energy storage system in the distribution area.

[0025] Example 3: This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the power quality control method for a distribution area energy storage system based on a dual closed loop as described in any one of Embodiment 1.

[0026] Example 4: This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the power quality control method for a distribution area energy storage system based on a dual closed loop as described in any one of Embodiment 1.

[0027] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0028] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0029] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0030] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0031] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A power quality control method based on a dual-closed-loop distribution network energy storage system, characterized in that, The method includes: Obtain power quality data from energy storage systems within the transformer area; A dual-closed-loop dynamic control model is constructed, comprising an inner-loop voltage control sub-model and an outer-loop power control sub-model. The inner-loop voltage control sub-model employs a fuzzy proportional-integral controller to achieve power correction of the energy storage system, while the outer-loop power control sub-model employs a fuzzy logic controller to formulate the charging and discharging power regulation plan of the energy storage system. Based on power quality data, determine whether a voltage over-limit event has occurred; If a voltage over-limit event occurs, the voltage inner loop control is activated, and the first regulation command is generated using the voltage inner loop control sub-model. If no voltage over-limit event occurs, power outer loop control is initiated, and a second regulation command is generated using the power outer loop control sub-model. Based on the first and second control commands, the energy storage system in the control area executes the corresponding commands to realize the power quality control of the energy storage system in the control area.

2. The power quality control method for a distribution area energy storage system based on a dual closed loop according to claim 1, characterized in that, The power quality data includes at least voltage amplitude, voltage deviation, current, photovoltaic output power, and the state of charge of the energy storage system.

3. The power quality control method for a distribution area energy storage system based on a dual closed loop according to claim 1, characterized in that, The first control command is generated using the voltage inner loop control sub-model. The specific steps are as follows: The fuzzy proportional-integral controller uses the deviation between the actual voltage on the user side and the preset voltage target value as the input variable, and fuzzifies the deviation into multiple first fuzzy subsets. Based on the preset fuzzy control rules, fuzzy inference is performed on the first fuzzy subset to obtain the proportional coefficient correction amount and the integral coefficient correction amount. Based on the proportional coefficient correction and integral coefficient correction, the preset proportional-integral baseline parameters are dynamically adjusted to obtain the adjusted proportional coefficient and integral coefficient. The power adjustment amount is obtained by performing proportional-integral calculation on the deviation based on the adaptive proportional coefficient and the adaptive integral coefficient, and is used as the first control command.

4. The power quality control method for a distribution area energy storage system based on a dual closed loop according to claim 1, characterized in that, The second control command is generated using the power outer loop control sub-model. The specific steps are as follows: The fuzzy logic controller uses time period, voltage level and state of charge of energy storage system as input variables; The input variables are divided into multiple second fuzzy subsets; Map the input variable at the current moment to the second fuzzy subset to obtain the corresponding membership degree; The membership degree is mapped to a preset fuzzy rule base to obtain the fuzzy output of the charging and discharging power of the energy storage system; The fuzzy output is defuzzified using the centroid method or the maximum membership method to obtain the charging and discharging power value, which is then used as the second control command.

5. The power quality control method for a distribution area energy storage system based on a dual closed loop according to claim 1, characterized in that, Based on power quality data, the specific steps to determine whether a voltage over-limit event has occurred are as follows: The real-time voltage amplitude at each monitoring point is compared with the preset upper voltage threshold and lower voltage threshold, respectively. If the voltage amplitude at any monitoring point exceeds the upper voltage threshold or falls below the lower voltage threshold, and the duration of this over-limit state exceeds the preset over-limit tolerance time, then a voltage over-limit event is determined to have occurred, and the over-limit type and over-limit location are recorded.

6. The power quality control method for a distribution area energy storage system based on a dual closed loop according to claim 1, characterized in that, This also includes configuring the energy storage system within the distribution area, with the specific steps as follows: Construct a transformer area simulation platform; Construct simulation evaluation functions, including sub-functions for evaluating the effectiveness of voltage over-limit mitigation, line loss reduction, and return on investment. The simulation evaluation function values ​​corresponding to multiple candidate configuration schemes are calculated on the transformer area simulation platform. The candidate configuration schemes include different energy storage power, capacity and deployment locations of the energy storage system. The candidate configuration scheme corresponding to the maximum simulation evaluation function value is selected as the target configuration scheme, and the energy storage system in the transformer area is optimized.

7. The power quality control method for a distribution area energy storage system based on a dual closed loop according to claim 1, characterized in that, It also includes collecting and adjusting the operating data of the transformer substations, and optimizing the parameters of the dual-closed-loop dynamic control model based on the adjusted operating data of the transformer substations.

8. A power quality control system based on a dual-closed-loop distribution transformer energy storage system, characterized in that, The system includes: The data acquisition module acquires power quality data of the energy storage system within the transformer area; The dual-closed-loop control module incorporates a dual-closed-loop dynamic control model, including an inner voltage control sub-model and an outer power control sub-model. The inner voltage control sub-model employs a fuzzy proportional-integral controller to achieve power correction of the energy storage system, while the outer power control sub-model employs a fuzzy logic controller to formulate the charging and discharging power adjustment plan of the energy storage system. The control command generation module determines whether a voltage over-limit event has occurred based on power quality data; If a voltage over-limit event occurs, the voltage inner loop control is activated, and the first regulation command is generated using the voltage inner loop control sub-model. If no voltage over-limit event occurs, power outer loop control is initiated, and a second regulation command is generated using the power outer loop control sub-model. The control command execution module controls the distributed energy storage system in the distribution area to execute corresponding commands based on the first and second control commands, thereby realizing the power quality control of the distributed energy storage system in the distribution area.

9. 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 method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 7.

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