Micro-grid power electronic dynamic voltage regulation and power mutual aid collaborative optimization method considering running state coupling

Abstract

The invention discloses a micro-grid power electronic dynamic voltage regulation and power mutual aid collaborative optimization method considering running state coupling. The method comprises the following steps: establishing a mathematical model considering running state coupling, and quantifying an incidence relation among distributed power supply output, an energy storage charge state, a power electronic device parameter, a voltage regulation target and a power mutual aid target; a collaborative optimization strategy based on running state coupling is provided, and the double-target optimization precision and response speed of voltage regulation and power mutual aid are improved; and a dynamic voltage regulation-power mutual aid cooperation feasible region model is further constructed, the safe operation boundary of the system is defined, and a clear decision basis is provided for operation scheduling of the micro-grid. According to the method, the operation stability and the resource utilization efficiency of the micro-grid in a complex scene are improved, and the effectiveness and the performability in the micro-grid scene are achieved.

Description

Technical Field

[0001] This invention belongs to the field of power system optimization and control technology, and in particular relates to a microgrid power electronic dynamic voltage regulation and power mutual assistance synergistic optimization method that considers the coupling of operating states. Background Technology

[0002] In the development of microgrids, dynamic voltage regulation and power balance, as key technologies to ensure stable system operation, have been widely researched and applied. Existing technologies optimize the regulation logic of power electronic devices through a hierarchical control approach, achieving basic voltage stabilization and power distribution functions when dealing with single-type disturbances such as load fluctuations and changes in distributed power output. Simultaneously, the integration of multi-objective optimization algorithms into microgrid dispatching provides a computational framework for balancing objectives such as voltage deviation and power deficit, promoting an initial improvement in control accuracy and, to some extent, enhancing the absorption efficiency of distributed energy sources such as wind, solar, and energy storage, while reducing power waste.

[0003] However, existing technologies still have significant shortcomings and are difficult to meet the needs of complex microgrid operation scenarios: First, there is a lack of characterization of the coupling relationship of operating states. Most solutions treat dynamic voltage regulation and power mutual assistance as independent control objectives, without fully considering the correlation between distributed power output, energy storage state of charge, and power electronic device operating parameters. This leads to conflicts in control strategies when multiple parameters are coordinated, reducing the overall system operating efficiency. Second, there is insufficient ability to quantify operating boundaries. Existing technologies lack a method for analyzing the feasible domain of coordinated dynamic voltage regulation range and power mutual assistance capability, making it impossible to clearly define the safe operating boundary of the system under dual-objective constraints. This makes microgrids prone to exceeding operating limits under extreme conditions or parameter fluctuations, affecting system stability and reliability.

[0004] In summary, there is an urgent need to propose a collaborative optimization method for dynamic voltage regulation and power mutual assistance in microgrid power electronics that considers the coupling of operating states, so as to provide support for the decision-making of application personnel. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic voltage regulation and power mutual assistance optimization method for microgrid power electronics that considers the coupling of operating states, which effectively improves the operational stability and resource utilization of microgrids and has the effectiveness and feasibility in microgrid scenarios.

[0006] To achieve the objective of this invention, this invention provides a microgrid power electronic dynamic voltage regulation and power mutual assistance synergistic optimization method considering operating state coupling, comprising the following steps:

[0007] S1. Real-time acquisition of core operating parameters of the microgrid's power source side, energy storage side, and grid side using sensors; quantification of coupling strength between parameters and identification of strongly coupled parameters through data preprocessing and correlation analysis.

[0008] S2. Using dynamic voltage regulation and power mutual assistance as the objective function, the identified strongly coupled parameters are correlated and quantized with the objective function to construct a multi-objective function optimization model, and the optimal control parameters of the system are obtained by using an improved multi-objective particle swarm algorithm.

[0009] S3. Construct a collaborative feasible region model of dynamic voltage regulation range and power mutual assistance capability by fitting the optimal control parameters through convex hull fitting, and establish a feasible region margin index to quantify the safe operation boundary of the system under dual objective constraints.

[0010] S4. The optimal control parameters that are determined to be in the safe and feasible region are issued for execution. By collecting operating data at fixed intervals, the optimal control parameters are dynamically corrected according to changes in the operating state, thus forming a control feedback adjustment mechanism.

[0011] Compared with the prior art, the significant progress of the present invention is as follows: (1) The present invention proposes an operation state coupling optimization model, which breaks through the limitations of the traditional technology of dynamic voltage regulation and power mutual assistance control, and improves the voltage-power coordinated response effect; (2) The present invention constructs a dynamic voltage regulation-power mutual assistance coordinated feasible domain and margin index, realizes the quantification of the system safety boundary under dual objective constraints, can intuitively reflect the system's anti-disturbance capability, and avoid the risk of exceeding the operation limit; (3) The present invention can effectively improve the operation stability and resource utilization of microgrids, and has the effectiveness and feasibility in microgrid scenarios.

[0012] To more clearly illustrate the functional characteristics and structural parameters of the present invention, further explanation is provided below in conjunction with the accompanying drawings and specific embodiments. Attached Figure Description

[0013] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0014] Figure 1 This is a flowchart of the steps of the present invention. Detailed Implementation

[0015] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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.

[0016] This invention provides a microgrid power electronic dynamic voltage regulation and power mutual assistance synergistic optimization method considering operating state coupling, combined with... Figure 1 This includes the following steps:

[0017] S1. Real-time acquisition of core operating parameters of the microgrid's power source side, energy storage side, and grid side using sensors; quantification of coupling strength between parameters and identification of strongly coupled parameters through data preprocessing and correlation analysis.

[0018] S2. Using dynamic voltage regulation and power mutual assistance as the objective function, the identified strongly coupled parameters are correlated and quantized with the objective function to construct a multi-objective function optimization model, and the optimal control parameters of the system are obtained by using an improved multi-objective particle swarm algorithm.

[0019] S3. Construct a collaborative feasible region model of dynamic voltage regulation range and power mutual assistance capability by fitting the optimal control parameters through convex hull fitting, and establish a feasible region margin index to quantify the safe operation boundary of the system under dual objective constraints.

[0020] S4. The optimal control parameters that are determined to be in the safe and feasible region are issued and executed. By collecting operating data at fixed intervals, the optimal control parameters are dynamically corrected according to changes in the operating state, thus forming a control feedback adjustment mechanism to ensure that the system is always in the optimal operating state.

[0021] S1 includes the following steps:

[0022] S11. Collect core operating parameters of the microgrid source side, storage side, and grid side using sensors. The source side parameters include photovoltaic and wind power output power and the corresponding power electronic inverter modulation ratio. The storage side parameters include the energy storage system state of charge, charging and discharging power, and converter switching frequency. The grid side parameters include the point of common coupling voltage, load power, and the power of the microgrid-main grid interconnection line.

[0023] S12. Use the sliding window trend analysis method to identify outliers in the data, and correct or remove the operating parameters according to the allowable parameter fluctuation range to ensure data quality.

[0024] S13. Based on the preprocessed operating parameter data, construct a coupled quantitative model of the operating state.

[0025] The sliding window trend analysis method in S12 sets the window size to k and determines the linear trend slope of the load within the window, as shown in the following formula:

[0026] ;

[0027] Where i represents time. Let i be the load value at time i within the window; when This was determined to be an abnormal trend.

[0028] The operational state coupling degree of S13 is defined as the correlation strength between the i-th operational parameter and the j-th operational parameter, and the operational state coupling quantification model is described. The specific formula is as follows:

[0029] ;

[0030] in, These are the weighting coefficients. Let i be the mutual information entropy between parameters i and j. The gray relational degree between parameter i and parameter j; the coupling degree takes values ​​in the range [0,1]. This set of parameters is determined to be strongly coupled, and their mutual influence needs to be carefully considered in subsequent models; when It is determined to be a weakly coupled parameter, which can be simplified in the model.

[0031] S2 specifically includes the following steps:

[0032] S21. Guided by the stability and economy of microgrid operation, construct a weighted normalized comprehensive objective function that includes dynamic voltage regulation and power mutual assistance objectives. ;

[0033] S22. Combining the performance and safe operation requirements of microgrid equipment, a multi-dimensional constraint system is constructed, which includes constraints on power electronic devices, distributed power sources, energy storage systems, and safe operation.

[0034] S21 is specifically shown in the following formula:

[0035] ;

[0036] in, For dynamic voltage regulation target, For the goal of power mutual assistance, , These are the weighting coefficients for the dynamic voltage regulation target and the power mutual assistance target, respectively.

[0037] The dynamic voltage regulation target is defined as minimizing the voltage deviation at the point of common coupling, reflecting the accuracy of voltage regulation.

[0038] The dynamic voltage regulation target is defined as follows:

[0039] ;

[0040] in, For the scheduling period, The actual voltage at the point of common coupling. Rated voltage;

[0041] The power balance target is defined as minimizing the power deficit of the microgrid, reflecting the power balance capability.

[0042] The power mutual assistance target is defined as follows:

[0043] ;

[0044] in, For distributed power supply output power, The charging and discharging power of the energy storage system (discharging is positive, charging is negative). For load power, The power of the main microgrid interconnection line (power supplied by the microgrid to the main grid is positive, and power purchased from the main grid is negative).

[0045] S22 is specifically shown in the following formula:

[0046] The power electronic device constraints are defined as restrictions on the operating parameters of core devices such as inverters to ensure that the devices operate within a safe range.

[0047] Define the constraints of the power electronic device:

[0048] ;

[0049] in, The inverter modulation ratio;

[0050] The distributed power source constraints are defined as output limits for wind power and photovoltaic power to ensure that the model conforms to actual operating conditions.

[0051] Define the distributed power source constraints as follows:

[0052] ;

[0053] in, These are the upper limits for photovoltaic and wind power output, respectively.

[0054] The constraints of the energy storage system are defined as avoiding overcharging and discharging of the energy storage, extending its service life, and ensuring its regulation capability.

[0055] Define the constraints of the energy storage system:

[0056] ;

[0057] ;

[0058] ;

[0059] ;

[0060] ;

[0061] in, , These refer to the energy storage and charging, and the energy release power of energy storage devices, respectively. , These represent the charging and discharging states of the energy storage device, respectively. For energy storage device capacity; , These refer to the charging and discharging efficiencies of energy storage devices, respectively. , These represent the maximum and minimum capacity states of the energy storage device, respectively.

[0062] The safety operation constraints are defined as ensuring the overall stable operation of the microgrid and avoiding damage to equipment and loads.

[0063] Define the safety operation constraints as follows:

[0064] ;

[0065] in, Limitation of voltage deviation rate at the point of common coupling;

[0066] ;

[0067] in, Power limitation of the main microgrid interconnect line.

[0068] S3 includes the following steps:

[0069] S31, with dynamic voltage regulation range With power mutual support capability Construct a feasible region for two-dimensional coordinates; the feasible region satisfies the comprehensive objective function. All The region composed of combinations, in which To achieve the overall target expected value; by analyzing the results of multiple optimization sets... The points are fitted with a convex hull to generate the feasible region boundary.

[0070] S32. Establish a feasible region margin evaluation index, wherein the feasible region margin is defined as the shortest distance from the current running point to the boundary of the feasible region.

[0071] The shortest distance of the feasible region boundary of S32 The specific formula is as follows:

[0072] ;

[0073] in, Let be the equation of the k-th boundary line of the feasible region; the farther the current running point is from the boundary of the feasible region, the stronger the system's ability to resist disturbances.

[0074] The control feedback adjustment mechanism of S4 is defined as repeating S1 at fixed intervals, collecting the actual operating parameters of the microgrid once, calculating the coupling degree and the deviation of the comprehensive objective function under the current operating state, and executing different adjustment strategies according to the magnitude of the deviation: when the deviation of the comprehensive objective function does not exceed 5%, the current control parameters can still meet the system optimization requirements, the coupling relationship has not changed significantly, and the current control parameters remain unchanged; when the deviation of the comprehensive objective function exceeds 5%, it indicates that the coupling relationship of the operating state has changed significantly (such as a sudden drop in the output of distributed power sources or a sudden change in load), and the current control parameters can no longer meet the optimization requirements. S2 is repeated to solve for the optimal control parameters, and after verification by the feasible domain in S3, the parameters are updated and issued to ensure that the system quickly returns to the optimal operating state.

[0075] Example

[0076] This embodiment selects a typical microgrid scenario, which includes wind power, photovoltaic, energy storage systems and integrated loads.

[0077] S1: Collect typical moment data on wind and solar power output, energy storage charge / discharge status and power, and load power. Calculate strongly coupled parameters based on a coupled quantitative model. Parameters with a coupling degree greater than 0.7 are considered strongly coupled. According to the calculation results, photovoltaic power output and inverter modulation ratio, energy storage state of charge and charge / discharge power, and point of common coupling voltage and load power are considered strongly coupled parameters.

[0078] S2: Based on the objective function and constraints of the multi-objective optimization model, the basic parameters for the implementation example are set as follows:

[0079] Weighting coefficients for dynamic voltage regulation target and power balance target , Both are 0.5, rated voltage The voltage is 380V, and the charging and discharging efficiency of the energy storage device is... , Both are 0.9, representing the maximum and minimum capacity states of the energy storage device. , The values ​​are 0.8 and 0.2 respectively.

[0080] Taking into account the requirements of the implementation scenario and system balance, a dynamic voltage regulation control target is set. Power mutual support control target Based on this, the multi-objective optimization model was solved, and the dynamic voltage regulation target corresponding to the optimal objective function value F was 0.8%, and the power mutual assistance target was 1.5%, both of which met the requirements.

[0081] S3: In this embodiment, 100 sets of optimization results at different times are taken, and their respective results are obtained. Points are used to fit the feasible region, obtaining the coordinates of each vertex. The equations of the boundary lines of the feasible region are then derived. The point corresponding to the current optimization time is selected. Substitute the values ​​into the feasible region margin assessment index calculation formula, calculate the distance from the running point to each boundary of the feasible region, and take the minimum value to obtain the margin index at the current time.

[0082] Set a margin indicator The threshold for the safe operation state of the system in this embodiment is calculated. The minimum distance from the running point at the optimized time to the boundary of the feasible region is 0.09. Therefore, the system is determined to be in a safe operation state at the current time.

[0083] S4: The optimal control parameters obtained at the current time are sent to the system. At the next optimization time in the next cycle, actual operating parameters are collected, and the deviation of the comprehensive objective function is calculated. In this embodiment, the deviation of the comprehensive objective function is 2.5%, which does not exceed 5%, so the current control parameters are maintained unchanged.

[0084] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0085] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A microgrid power electronic dynamic voltage regulation and power mutual assistance synergistic optimization method considering operating state coupling, characterized in that, Includes the following steps: S1. Real-time acquisition of core operating parameters of the microgrid's power source side, energy storage side, and grid side using sensors; quantification of coupling strength between parameters and identification of strongly coupled parameters through data preprocessing and correlation analysis. S2. Using dynamic voltage regulation and power mutual assistance as the objective function, the identified strongly coupled parameters are correlated and quantized with the objective function to construct a multi-objective function optimization model, and the optimal control parameters of the system are obtained by using an improved multi-objective particle swarm algorithm. S3. Construct a collaborative feasible region model of dynamic voltage regulation range and power mutual assistance capability by fitting the optimal control parameters through convex hull fitting, and establish a feasible region margin index to quantify the safe operation boundary of the system under dual objective constraints. S4. The optimal control parameters that are determined to be in the safe and feasible region are issued for execution. By collecting operating data at fixed intervals, the optimal control parameters are dynamically corrected according to changes in the operating state, thus forming a control feedback adjustment mechanism.

2. The microgrid power electronic dynamic voltage regulation and power mutual assistance synergistic optimization method considering operating state coupling as described in claim 1, characterized in that, S1 includes the following steps: S11. Collect core operating parameters of the microgrid source side, storage side, and grid side using sensors. The source side parameters include photovoltaic and wind power output power and the corresponding power electronic inverter modulation ratio. The storage side parameters include the energy storage system state of charge, charging and discharging power, and converter switching frequency. The grid side parameters include the point of common coupling voltage, load power, and the power of the microgrid-main grid interconnection line. S12. Use the sliding window trend analysis method to identify outliers in the data, and correct or remove the operating parameters according to the allowable parameter fluctuation range. S13. Based on the preprocessed operating parameter data, construct a coupled quantitative model of the operating state.

3. The microgrid power electronic dynamic voltage regulation and power mutual assistance synergistic optimization method considering operating state coupling as described in claim 2, characterized in that, The sliding window trend analysis method in S12 sets the window size to k and determines the linear trend slope of the load within the window, as shown in the following formula: ； in, For a moment, For the first in the window The load value at each moment; when This was determined to be an abnormal trend.

4. The microgrid power electronic dynamic voltage regulation and power mutual assistance synergistic optimization method considering operating state coupling as described in claim 3, characterized in that, The operational state coupling degree of S13 is defined as the correlation strength between the i-th operational parameter and the j-th operational parameter, and the operational state coupling quantification model is described. The specific formula is as follows: ； in, These are the weighting coefficients. Let i be the mutual information entropy between parameters i and j. The gray relational degree between parameter i and parameter j; the coupling degree takes values ​​in the range [0,1]. The parameters in this set are determined to be strongly coupled parameters; when It is determined to be a weakly coupled parameter.

5. The microgrid power electronic dynamic voltage regulation and power mutual assistance synergistic optimization method considering operating state coupling as described in claim 4, characterized in that, S2 specifically includes the following steps: S21. Construct a weighted normalized comprehensive objective function that includes dynamic voltage regulation target and power mutual assistance target. ; S22. Combining the performance and safe operation requirements of microgrid equipment, a multi-dimensional constraint system is constructed, which includes constraints on power electronic devices, distributed power sources, energy storage systems, and safe operation.

6. The microgrid power electronic dynamic voltage regulation and power mutual assistance synergistic optimization method considering operating state coupling as described in claim 5, characterized in that, S21 is specifically shown in the following formula: ； in, For dynamic voltage regulation target, For the goal of power mutual assistance, , These are the weighting coefficients for the dynamic voltage regulation target and the power mutual assistance target, respectively. The dynamic voltage regulation target is defined as follows: ； in, For the scheduling period, The actual voltage at the point of common coupling. Rated voltage; The power mutual assistance target is defined as follows: ； in, For distributed power supply output power, For the charging and discharging power of the energy storage system, For load power, This refers to the power of the main microgrid interconnect line.

7. The microgrid power electronic dynamic voltage regulation and power mutual assistance synergistic optimization method considering operating state coupling as described in claim 6, characterized in that, S22 is specifically shown in the following formula: Define the constraints of the power electronic device: ； in, The inverter modulation ratio; Define the distributed power source constraints as follows: ； in, These are the upper limits for photovoltaic and wind power output, respectively. Define the constraints of the energy storage system: ； ； ； ； ； in, , These refer to the energy storage and charging, and the energy release power of energy storage devices, respectively. , These represent the charging and discharging states of the energy storage device, respectively. For energy storage device capacity; , These refer to the charging and discharging efficiencies of energy storage devices, respectively. , These represent the maximum and minimum capacity states of the energy storage device, respectively. Define the safety operation constraints as follows: ； in, Limitation of voltage deviation rate at the point of common coupling; ； in, Power limitation of the main microgrid interconnect line.

8. The microgrid power electronic dynamic voltage regulation and power mutual assistance synergistic optimization method considering operating state coupling as described in claim 7, characterized in that, S3 includes the following steps: S31, with dynamic voltage regulation range With power mutual support capability Construct a feasible region for two-dimensional coordinates; the feasible region satisfies the comprehensive objective function. All The region composed of combinations, in which To achieve the overall target expected value; through the analysis of multiple sets of optimization results... The points are fitted with a convex hull to generate the feasible region boundary. S32. Establish a feasible region margin evaluation index, wherein the feasible region margin is defined as the shortest distance from the current running point to the boundary of the feasible region.

9. The microgrid power electronic dynamic voltage regulation and power mutual assistance synergistic optimization method considering operating state coupling as described in claim 8, characterized in that, The shortest distance of the feasible region boundary of S32 The specific formula is as follows: ； in, Let be the equation of the k-th boundary line of the feasible region.

10. The microgrid power electronic dynamic voltage regulation and power mutual assistance synergistic optimization method considering operating state coupling as described in claim 1, characterized in that, The control feedback adjustment mechanism of S4 is defined as repeating S1 at a fixed period, collecting the actual operating parameters of the microgrid once, calculating the coupling degree and the deviation of the comprehensive objective function under the current operating state, and executing different adjustment strategies according to the magnitude of the deviation: when the deviation of the comprehensive objective function does not exceed 5%, the current control parameters can still meet the system optimization requirements, the coupling relationship has not changed significantly, and the current control parameters remain unchanged; When the deviation of the comprehensive objective function exceeds 5%, it indicates that the coupling relationship of the operating state has changed significantly and the current control parameters can no longer meet the optimization requirements. The optimal control parameters are solved by repeating step S2 and updated and issued after passing the feasible domain verification in step S3, so as to ensure that the system quickly returns to the optimal operating state.

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