Power distribution network distributed resource emergency voltage regulation method based on layered interactive multi-dimensional dynamic aggregation

Abstract

The invention discloses a power distribution network distributed resource emergency voltage regulation method based on hierarchical interactive multi-dimensional dynamic aggregation, and the method comprises the following steps: S1, obtaining the values of priority indexes of various distributed resources according to the voltage regulation cost and tracking response characteristics of the distributed resources in a power distribution network; s2, sorting the various distributed resources according to the values of the priority indexes of the various distributed resources; s3, performing a lower-layer aggregation strategy; carrying out self-organizing aggregation on various distributed resources according to a priority ranking result, responding to a voltage regulation demand after a formed aggregation unit receives a scheduling command, and continuously adjusting a distributed resource scheduling scheme of the aggregation unit; s4, performing an upper-layer adjustment strategy; performing optimization regulation and control on the voltage of the whole power grid according to the real-time data adjusted by the lower-layer aggregation strategy; and S5, feeding back the whole power grid real-time data obtained after the upper-layer adjustment strategy is carried out to S3, and carrying out the next round of lower-layer aggregation strategy to form a dynamic closed-loop nested regulation and control mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an emergency voltage regulation method for distributed resources in a distribution network based on hierarchical interactive multi-dimensional dynamic aggregation. Background Art

[0002] As the access scale of multi-source heterogeneous resources continues to increase, the distribution network has transformed from a traditional single power source and radial structure to a modern source-grid-load-storage deep coupling system. Due to the large number of distributed resources, uneven capacity, and scattered layout, the cost of direct dispatching through the power grid dispatch center is too high, and it is difficult to be directly dispatched by the power grid. At the same time, massive distributed resources are highly random, intermittent, and uncertain, which brings great operational challenges to the traditional distribution network dominated by centralized control, causing problems such as bidirectional power flow and reverse heavy overload in the distribution network, which can easily lead to sharp fluctuations in feeder power and voltage limit violations, and even affect the stability of distribution network operation in severe cases.

[0003] In this context, aggregated control can solve the problem that a large number of distributed resources are difficult to dispatch directly. At present, the aggregation methods of flexible resources mainly include Minkowski summation method, Chino polyhedron method, virtual battery model method, vertex enumeration method, etc. However, due to the uneven response performance of distributed resource scheduling, it is easy to cause disordered scheduling. Therefore, it is necessary to consider the voltage regulation cost and tracking response characteristics of distributed resources in the distribution network, and propose a distributed resource active-reactive multi-dimensional self-organization aggregation method based on comprehensive priority sorting. On the other hand, the power grid needs to have a rapid response capability, and the existing centralized control method often lags in response and is difficult to meet emergency needs. The current emergency voltage regulation research lacks hierarchical interactive collaboration and efficient scheduling of resources in the substation area. Summary of the invention

[0004] In view of this, the present invention provides a method for emergency voltage regulation of distributed resources in a distribution network based on hierarchical interactive multi-dimensional dynamic aggregation, which is used to at least solve the problem that emergency voltage regulation research in the prior art lacks hierarchical interactive collaboration and efficient scheduling of resources within the substation.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: A method for emergency voltage regulation of distributed resources in a distribution network based on hierarchical interactive multi-dimensional dynamic aggregation, comprising the following steps: S1. Obtain the values ​​of priority indicators of various types of distributed resources according to the voltage regulation cost and tracking response characteristics of distributed resources in the distribution network; S2. Sort various distributed resources according to the values ​​of the priority indicators of various distributed resources; S3. Carry out the lower-layer aggregation strategy; self-organize and aggregate various distributed resources according to the priority sorting results. The formed aggregation unit responds to the voltage regulation demand after receiving the scheduling command and continuously adjusts its distributed resource scheduling plan; S4. Carry out upper-level adjustment strategies; optimize and regulate the voltage of the entire power grid according to the real-time data adjusted by the lower-level aggregation strategy, including: long-time-scale scheduling of on-load voltage regulators and capacitor banks, and short-time-scale scheduling of aggregation units based on long-time-scale scheduling; S5. Feedback the real-time data of the overall power grid obtained after the upper-level adjustment strategy to S3, and carry out the next round of lower-level aggregation strategy to form a dynamic closed-loop nested control mechanism.

[0006] Preferably, the priority indicators of each type of distributed resources in S1 include voltage sensitivity, voltage regulation cost and tracking response coefficient; (1) Voltage sensitivity: , In the formula, and Node For Node Active and reactive voltage sensitivity; is the line starting voltage; and Node With Node resistance and reactance between (2) The voltage regulation costs for each type include distributed photovoltaic equipment, energy storage equipment, electric vehicle, and SVG equipment; 1) Voltage regulation cost of distributed photovoltaic equipment: Total cost of photovoltaic discharge voltage regulation for: , In the formula, is the depreciation cost of the photovoltaic equipment; is the unit power maintenance cost of the photovoltaic equipment; is the active power loss cost per unit power generated by the photovoltaic device; Then the node PV equipment for nodes Unit reactive voltage regulation cost That is, the voltage regulation cost of distributed photovoltaic equipment is: , In the formula, For Node The voltage change before and after voltage regulation; Reactive power output by photovoltaic equipment; 2) Voltage regulation cost of energy storage equipment: Total cost of reactive voltage regulation power of energy storage equipment for: , In the formula, The loss degree of energy storage battery life under different depths of charge and discharge; is the total effective discharge capacity of the energy storage battery; The initial investment cost of the energy storage battery; The maintenance cost per unit power of energy storage; is the active power loss cost per unit power; Then the node Energy storage devices for nodes Unit reactive voltage regulation cost That is, the voltage regulation cost of energy storage equipment is: , In the formula, Reactive power output by the energy storage device; 3) Electric vehicle voltage regulation cost: Total cost of voltage regulation for electric vehicles for: , In the formula, the unit cost of charging an electric vehicle; Charging efficiency for electric vehicles; Charging power for electric vehicles; For electric vehicles at the node Charging time ; The energy loss in each charging cycle of electric vehicles; Then the node Electric vehicles at the node Unit active voltage regulation cost That is, the voltage regulation cost of electric vehicles is: , In the formula, It is the active power adjusted by the electric vehicle due to voltage regulation requirements; 4) SVG equipment voltage regulation cost: Total cost of SVG equipment participating in grid voltage regulation for: , In the formula, is the initial investment cost of the SVG equipment; For operating costs; For maintenance costs; For energy consumption cost; Then the node SVG device pair node Unit reactive voltage regulation cost That is, the voltage regulation cost of SVG equipment is: , In the formula, Reactive power output by SVG equipment; (3) Tracking response coefficient: During the scheduling period, the output of distributed resources is expressed in per-unit form as: , In the formula, Individuals of distributed resources In the period The power per unit value; For individuals In the period The power value; For individuals The maximum power value; is the number of time periods in the scheduling period; Power signal and load signal exist Consistency index for time periods for: , In the formula, For the period Load signal The power per unit value; By tracking the response coefficient To measure Individuals in a period Power signal and load signal Consistency between: , In the formula, The smaller the individual Power signal and load signal The more consistent.

[0007] Preferably, the specific content of S2 includes: All priority indicators are normalized, and according to the normalized indicator values, radar charts are constructed for different types of distributed resources with voltage sensitivity, voltage regulation cost, and response speed as vertices. Select the area and perimeter of each radar chart as two feature vectors to construct the evaluation function H, and calculate the evaluation value of each radar chart in turn H ; According to each evaluation value H The size of the .

[0008] Preferably, the specific contents of normalization include: The value of each priority indicator Normalized to the interval [0,1]: , In the formula, Indicates the minimum value of the priority indicator; Indicates the maximum value of the priority index; The area and perimeter of each radar chart are: , In the formula, and is the area and perimeter of the radar chart, For the The length of the side formed by the normalized index values; For the The angle between the indicators; Calculate the evaluation value of each radar chart in turn H The specific content is: .

[0009] Preferably, the specific content of S3 includes: Characterize the total aggregate power based on the current aggregate unit The feasible domain of For the period Active power of the aggregation unit, For the period Aggregate the reactive power of the unit and obtain the elliptical feasible region , using the time-decoupled elliptic feasible region : , In the formula, is the center of the ellipse; is a two-dimensional positive semidefinite matrix, representing the rotation and stretching transformation of the ellipse; is a variable subject to the uncertainty set U: , In the formula, For all time periods A collection of; The feasible domain As an uncertainty set, As uncertainty set Distributed resource scheduling scheme under uncertain variables It is an adaptive variable that changes dynamically with the uncertainty variable. An active-reactive multi-dimensional dynamic aggregation model is established. The goal is to maximize the total aggregation flexibility of active-reactive power in different time periods, and the active-reactive flexibility is optimally aggregated. The active-reactive multi-dimensional dynamic aggregation model is: , , , , , In the formula, the elliptical feasible region changes as Dynamically adjust according to changes, and obtain the corresponding distributed resource power dispatch plan ; Representation Matrix The determinant is equal to the feasible region of the ellipse in the period area; and All represent the power constraint parameters of the elliptical feasible region; and represent the apparent power capacity constraint parameters of photovoltaic equipment and energy storage equipment respectively; and denote other power constraints and network constraint parameters of distributed resources respectively; The active-reactive multi-dimensional dynamic aggregation model is solved to obtain the optimal flexibility space of the optimal distributed resources so as to maximize the elliptical feasible domain.

[0010] Preferably, the specific contents of the long-time-scale hourly scheduling model in the upper layer of S4 include: The distribution network layer comprehensively considers the operation times of the on-load voltage regulator OLTC and the capacitor bank CB, constructs the OLTC and CB coordinated voltage regulation action strategy with the goal of minimizing the system network loss, and completes the hourly optimization. The objective function is: , In the formula, and The time periods Branch Road resistance and current; is the number of time periods in a scheduling cycle; is the duration of each time period; The constraints include: OLTC constraints and CB constraints; among them, The OLTC constraints are: , , , , In the formula, For Node In the period Voltage; For the period The on-load voltage regulator ratio; It is the number of gears of the on-load voltage regulator; For Node In the period The on-load voltage regulator is in gear The square of the reference voltage at ; and The time periods Branch Road Active power, reactive power and reactance; is any positive number; Indicates time period Is the on-load voltage regulator Binary variable for gear position; The maximum number of transformer switching times in a dispatching cycle; The CB constraints are: , , , , In the formula, The capacitor bank switched in the time period Output value; For the period The number of groups of switched capacitors; is the reactive power compensation of a single capacitor; and It is the upper and lower limits of the output of the switched capacitor bank; The specific number of capacitor groups to be installed; For the period The upper limit of the number of capacitor switching groups; is the maximum switching times of the capacitor; is a sign function. When a is greater than 0, its value is 1, and when a is equal to 0, its value is 0.

[0011] Preferably, the specific contents of the short-time scale minute-level control model in the lower layer of S4 include: After the upper-layer long-time-scale hourly dispatch model determines the switching action of the on-load voltage regulator and capacitor bank, the preset dispatch interval is used to perform deterministic optimization on the active and reactive power of various distributed resources at the substation level; Construct a short-time optimization objective function with the minimum system node voltage deviation: , In the formula, is the number of distribution network nodes; Representative Node In the period Voltage; Representative node in the period Rated voltage.

[0012] It can be seen from the above technical solution that, compared with the prior art, the present invention discloses a method for emergency voltage regulation of distributed resources in a distribution network based on hierarchical interactive multi-dimensional dynamic aggregation, which has the following beneficial effects: By considering the comprehensive regulation characteristics of distributed resources, the present invention proposes a distributed resource active-reactive multi-dimensional self-organizing aggregation method based on comprehensive priority sorting. Compared with the existing aggregation method, it solves the problems of low efficiency of distributed resource scheduling, inaccurate voltage regulation effect, and lack of interactive coordination mechanism in the existing distribution network. Compared with the traditional voltage regulation technology, the proposed distributed resource emergency voltage regulation strategy based on hierarchical interactive dynamic aggregation nesting decomposes the emergency voltage regulation problem at multiple levels. Through scheduling control at different time scales, it has a faster response speed and higher accuracy for fault voltage recovery, ensuring the precise control and efficient regulation of distributed resources in large-scale applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 It is a flow chart of a method for emergency voltage regulation of distributed resources in a distribution network based on hierarchical interactive multi-dimensional dynamic aggregation disclosed by the present invention; Figure 2 An architecture diagram of distributed resource emergency voltage regulation based on hierarchical interactive dynamic aggregation nesting provided in an embodiment of the present invention; Figure 3 A radar chart of comprehensive priority indicators of different resources at a certain moment provided by an embodiment of the present invention, Figure 3 (a) is a radar chart of comprehensive priority indicators of energy storage equipment. Figure 3 (b) is a radar chart of comprehensive priority indicators of photovoltaic equipment. Figure 3 (c) is a radar chart of comprehensive priority indicators for electric vehicles. Figure 3 (d) is a radar chart of SVG comprehensive priority indicators. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] The present invention provides a method for emergency voltage regulation of distributed resources in a distribution network based on hierarchical interactive multi-dimensional dynamic aggregation, such as Figure 1 As shown, the following steps are included: S1. Obtain the values ​​of priority indicators of various types of distributed resources according to the voltage regulation cost and tracking response characteristics of distributed resources in the distribution network; S2. Sort various distributed resources according to the values ​​of the priority indicators of various distributed resources; S3. Carry out the lower-layer aggregation strategy; self-organize and aggregate various distributed resources according to the priority sorting results. The formed aggregation unit responds to the voltage regulation demand after receiving the scheduling command and continuously adjusts its distributed resource scheduling plan; S4. Carry out upper-level adjustment strategies; optimize and regulate the voltage of the entire power grid according to the real-time data adjusted by the lower-level aggregation strategy, including: long-time-scale scheduling of on-load voltage regulators and capacitor banks, and short-time-scale scheduling of aggregation units based on long-time-scale scheduling; S5. Feedback the real-time data of the overall power grid obtained after the upper-level adjustment strategy to S3, and carry out the next round of lower-level aggregation strategy to form a dynamic closed-loop nested control mechanism.

[0017] It should be noted that: In this embodiment, the voltage regulation cost and tracking response characteristics of distributed resources such as photovoltaic equipment, energy storage equipment, electric vehicles, and static VAR generators (SVG) in the distribution network are considered to obtain accurate indicators for sorting adjustable resources and optimizing flexibility space.

[0018] The core idea of ​​the distributed resource emergency voltage regulation strategy based on hierarchical interactive dynamic aggregation and nesting is to make full use of the flexibility and adjustability of various distributed resources in the emergency voltage regulation of the power grid through the hierarchical interactive structure and dynamic aggregation and nesting mechanism to optimize the voltage regulation effect. The architecture diagram is as follows: Figure 2 shown.

[0019] This strategy uses a bottom-up order, and the lower layer self-organizes and aggregates multiple distributed resources according to the priority sorting results. After receiving the scheduling command, the formed aggregation unit responds to the voltage regulation demand based on local information, and continuously adjusts the scheduling strategy and power allocation of its internal resources. These local adjustment results and feedback will be transmitted back to the upper layer in real time, thereby providing a basis for the upper layer's decision-making. The upper layer optimizes and regulates the voltage of the entire power grid by integrating feedback information from different levels, and adjusts and guides the lower layer to form a dynamic closed-loop nested regulation mechanism. Through this interactive hierarchical aggregation nesting, it can ensure that when an emergency occurs in the power grid, resources at all levels can be flexibly allocated according to real-time needs, quickly respond to power grid voltage fluctuations, ensure the stability and safety of the power grid, and improve the overall resource utilization efficiency and scheduling flexibility.

[0020] In order to further implement the above technical solution, the priority indicators of each type of distributed resources in S1 include voltage sensitivity, voltage regulation cost and tracking response coefficient; (1) Voltage sensitivity: Voltage sensitivity is an important parameter to measure the voltage regulation effect of active and reactive power of each node. The traditional calculation method relies on the real-time power of the whole network, which is usually calculated by a centralized control server. However, in order to realize the application of voltage sensitivity in distributed control strategy, the present invention approximates the voltage sensitivity by using the line impedance of the distribution network: , In the formula, and Node For Node Active and reactive voltage sensitivity; is the line starting voltage; and Node With Node resistance and reactance between (2) The voltage regulation costs for each type include distributed photovoltaic equipment, energy storage equipment, electric vehicle, and SVG equipment; 1) Voltage regulation cost of distributed photovoltaic equipment: Photovoltaic equipment is generally operated by operators. In order to ensure the power generation income, the output of active power is usually not reduced. At the same time, the capacity of photovoltaic equipment is usually greater than the rated capacity. Therefore, reactive power regulation can be performed without affecting the active power output of photovoltaic equipment. The total cost of photovoltaic discharge voltage regulation is for: , In the formula, is the depreciation cost of the photovoltaic equipment; is the unit power maintenance cost of the photovoltaic equipment; is the active power loss cost per unit power generated by the photovoltaic device; Then the node PV equipment for nodes Unit reactive voltage regulation cost That is, the voltage regulation cost of distributed photovoltaic equipment is: , In the formula, For Node The voltage change before and after voltage regulation; Reactive power output by photovoltaic equipment; 2) Voltage regulation cost of energy storage equipment: In the process of voltage regulation, energy storage devices usually adjust reactive power first, and then adjust active power as needed. The present invention mainly considers the reactive power regulation of energy storage devices. Frequent charging and discharging operations, high-power operation, and deep discharge of energy storage systems will cause great losses to the equipment and reduce its service life, thereby increasing the system operating cost during the voltage regulation process. The total cost of reactive voltage regulation power of energy storage devices is for: , In the formula, The loss degree of energy storage battery life under different depths of charge and discharge; is the total effective discharge capacity of the energy storage battery; The initial investment cost of the energy storage battery; The maintenance cost per unit power of energy storage; is the active power loss cost per unit power; Then the node Energy storage devices for nodes Unit reactive voltage regulation cost That is, the voltage regulation cost of energy storage equipment is: , In the formula, Reactive power output by the energy storage device; 3) Electric vehicle voltage regulation cost: The voltage regulation cost of electric vehicles can usually be expressed as the economic cost per unit of power regulation generated by the electric vehicle when participating in voltage regulation. for: , In the formula, the unit cost of charging an electric vehicle; Charging efficiency for electric vehicles; Charging power for electric vehicles; For electric vehicles at the node Charging time ; The energy loss in each charging cycle of electric vehicles; Then the node Electric vehicles at the node Unit active voltage regulation cost That is, the voltage regulation cost of electric vehicles is: , In the formula, It is the active power adjusted by the electric vehicle due to voltage regulation requirements; 4) SVG equipment voltage regulation cost: The SVG device sends out compensation drive signals through the control chip, and the power electronic inverter circuit sends out reactive power compensation current, thereby achieving the purpose of dynamic reactive power compensation and realizing dynamic voltage regulation. for: , In the formula, is the initial investment cost of the SVG equipment; The operating costs include the monitoring, control and operation costs of the SVG equipment during operation; Maintenance costs refer to the costs of regular equipment inspections and replacement of parts; Energy consumption cost refers to the electricity consumption cost caused by internal loss when the SVG equipment is running; Then the node SVG device pair node Unit reactive voltage regulation cost That is, the voltage regulation cost of SVG equipment is: , In the formula, Reactive power output by SVG equipment; (3) Tracking response coefficient: In order to evaluate the consistency between the output level of distributed resources and load changes, the load tracking coefficient is defined. During the scheduling period, the output of distributed resources is expressed in per-unit form as: , In the formula, Individuals of distributed resources In the period The power per unit value; For individuals In the period The power value; For individuals The maximum power value can generally be taken as the installed capacity; is the number of time periods in the scheduling period; Power signal and load signal exist Consistency index for time periods for: , In the formula, Load signal The power per unit value and individual The difference in the power per unit value indicates that the two consistency, For the period Load signal The power per unit value; By tracking the response coefficient To measure Individuals in a period Power signal and load signal Consistency between: , In the formula, The smaller the individual Power signal and load signal The more consistent.

[0021] In order to further implement the above technical solution, the specific contents of S2 include: All priority indicators are normalized, and according to the normalized indicator values, radar charts are constructed for different types of distributed resources with voltage sensitivity, voltage regulation cost, and response speed as vertices. Select the area and perimeter of each radar chart as two feature vectors to construct the evaluation function H , and calculate the evaluation value of each radar chart in turn H ; According to each evaluation value H The size of the .

[0022] In order to further implement the above technical solution, the specific contents of normalization include: The value of each priority indicator Normalize to the interval [0,1] to ensure comparability between indicators: , In the formula, Indicates the minimum value of the priority indicator; Indicates the maximum value of the priority indicator. After the indicator is normalized, the closer its value is to 1, the better the indicator effect of the resource is. A comprehensive priority indicator radar chart of different resources is constructed based on multiple indicators.

[0023] The area and perimeter of the radar chart are selected as two feature vectors to construct a priority evaluation system for different resources. The present invention uses the average area and average perimeter to calculate the function value. The area and perimeter of each radar chart are: , In the formula, and is the area and perimeter of the radar chart, For the The length of the side formed by the normalized index values; For the The angle between the indicators; Calculate the evaluation value of each radar chart in turn H The specific content is: .

[0024] In order to further implement the above technical solutions, the specific contents of S3 include: Aggregate total power The exact feasible domain of the total power is due to the existence of a large number of distributed resources and power flow network relationships. The exact feasible domain of is complex and difficult to obtain or use, so internal approximation is usually performed to effectively characterize the feasible region of the total power.

[0025] The elliptical feasible region is an effective method to characterize the region of active-reactive flexibility for each aggregation. Therefore, the feasible region is parameterized by the time-decoupled ellipse. .

[0026] Characterize the total aggregate power based on the current aggregate unit The feasible domain of For the period Active power of the aggregation unit, For the period Aggregate the reactive power of the unit and obtain the elliptical feasible region , using the time-decoupled elliptic feasible region : , In the formula, is the center of the ellipse; is a two-dimensional positive semidefinite matrix, representing the rotation and stretching transformation of the ellipse; is a variable subject to the uncertainty set U: , In the formula, For all time periods A collection of; In the two-stage adaptive robust optimization aggregation, the feasible domain As an uncertainty set, As uncertainty set Distributed resource scheduling scheme under uncertain variables It is an adaptive variable that can be determined after the uncertainty variables are revealed. Compared with static variables, the introduction of adaptive variables can significantly enhance the optimality of the robust solution.

[0027] Approximate feasible region There are two desirable properties: 1) Clustering optimality: is the optimal inner approximation of the largest exact feasible region; 2) Disaggregation feasibility: Without violating the operation constraints, Any one within The aggregate power value can be achieved through distributed scheduling.

[0028] In order to achieve these two important characteristics, an active-reactive multi-dimensional dynamic aggregation model is established. The active-reactive flexibility is optimally aggregated with the goal of maximizing the total aggregation flexibility of active-reactive power in different time periods. The active-reactive multi-dimensional dynamic aggregation model is: , , , , , In the formula, the elliptical feasible region changes as Dynamically adjust according to changes, and obtain the corresponding distributed resource power dispatch plan ; Representation Matrix The determinant is equal to the feasible region of the ellipse in the period area; and Both represent the power constraint parameters of the elliptical feasible region. The elliptical feasible region power constraint ensures the feasibility of the decomposition, indicating that for For any total power within the range, there must be a corresponding distributed resource scheduling solution under the condition that all operation constraints are met. to achieve; and represent the apparent power capacity constraint parameters of photovoltaic equipment and energy storage equipment respectively; and denote other power constraints and network constraint parameters of distributed resources respectively; The active-reactive multi-dimensional dynamic aggregation model is solved to obtain the optimal flexibility space of the optimal distributed resources so as to maximize the elliptical feasible domain.

[0029] It should be noted that: By drawing and calculating the radar chart, the evaluation values ​​of different distributed resources are obtained. H The size of the distributed resources is prioritized, and high-priority resources can be aggregated first. Such priority sorting ensures that the power grid can select the most appropriate resources for adjustment under different operating conditions, thereby reducing the overall cost of system operation and improving the stability and flexibility of the power grid. The self-organizing aggregation strategy allows distributed resources to automatically adjust their participation and scheduling behavior according to preset rules and real-time feedback without central control, so that resources can maximize their effectiveness while ensuring system stability. In addition, this strategy also avoids over-reliance on a single resource by coordinating the collaboration between various resources, thereby optimizing resource utilization and intelligent power grid operation.

[0030] Power aggregation is essentially the The high-dimensional feasible domain of is projected into a low-dimensional aggregate power space In the above, the apparent power capacity constraints of photovoltaic equipment and energy storage equipment, other power constraints of distributed resources and network constraints constitute The high-dimensional feasible domain , the elliptical feasible region power constraint describes arrive The mapping relationship, matrix and The constraint ensures that the feasible set U is in the aggregate power space The projection in is exact and this relationship is reflected in the model through adaptive variables.

[0031] The model establishes two specific two-stage optimization aggregation models: one calculates the optimal feasible interval of time decoupled active-reactive power aggregation, and the other solves the optimal elliptical feasible region of active-reactive aggregation domain. The feasible region Z can be selected as any convex set that can be appropriately parameterized, such as a time-coupled ellipsoid or polyhedron.

[0032] Since the active-reactive multidimensional aggregation model involves a two-stage adaptive robust optimization problem, the present invention adopts a widely used column constraint generation algorithm to solve it. According to the difference between the two-stage decisions, the original model is decomposed into a main problem and a sub-problem, and then the main-sub-iterative process is performed to obtain the optimal solution. The main problem and the sub-problem can be effectively solved using the Gurobi optimizer.

[0033] In order to further implement the above technical solutions, the specific contents of the long-term hourly scheduling model in the upper layer of S4 include: The distribution network layer comprehensively considers the number of on-load voltage regulator (OLTC) and capacitor bank (CB) operations, constructs an OLTC and CB coordinated voltage regulation action strategy with the goal of minimizing system network losses, completes hourly optimization, and the objective function of the long-term scale stage is: , In the formula, and The time periods Branch Road resistance and current; is the number of time periods in a scheduling cycle; is the duration of each time period; The constraints include: OLTC constraints and CB constraints; among them, OLTC equipment is a discrete control device, and the constraints need to be convexly transformed. The OLTC constraints are: , , , , In the formula, For Node In the period Voltage; For the period The on-load voltage regulator ratio; It is the number of gears of the on-load voltage regulator; For Node In the period The on-load voltage regulator is in gear The square of the reference voltage at ; and The time periods Branch Road Active power, reactive power and reactance; is any positive number; Indicates time period Is the on-load voltage regulator Binary variable for gear position; The maximum number of transformer switching times in a dispatching cycle; CB is a reactive compensation device that is switched according to the number of groups and is also a discrete control device. The CB constraint is: , , , , In the formula, The capacitor bank switched in the time period Output value; For the period The number of groups of switched capacitors; is the reactive power compensation of a single capacitor; and It is the upper and lower limits of the output of the switched capacitor bank; The specific number of capacitor groups to be installed; For the period The upper limit of the number of capacitor switching groups; is the maximum switching times of the capacitor; is a sign function. When a is greater than 0, its value is 1, and when a is equal to 0, its value is 0.

[0034] In order to further implement the above technical solutions, the specific contents of the short-time scale minute-level control model in the lower layer of S4 include: After the upper-layer long-time-scale hourly dispatch model determines the switching action of the on-load voltage regulator and capacitor bank, the preset dispatch interval is used to perform deterministic optimization on the active and reactive power of various distributed resources at the substation level; Construct a short-time optimization objective function with the minimum system node voltage deviation: , In the formula, is the number of distribution network nodes; Representative Node In the period Voltage; Representative node in the period Rated voltage.

[0035] It should be noted that: Based on the one-hour-ahead forecast value, in this embodiment, ten minutes is used as a scheduling interval to perform deterministic optimization on the active and reactive power of photovoltaic equipment, energy storage equipment, electric vehicles, and SVG equipment at the substation level.

[0036] The present invention is further described below through simulation experiments: The model and method of the present invention are used for emergency voltage regulation of distributed resources in the distribution network with hierarchical interactive multi-dimensional dynamic aggregation. Figure 1 The process shown is implemented in Python. The radar chart of the comprehensive priority index of distributed resources at a certain moment is as follows: Figure 3As shown in the figure, from the simulation results, it can be obtained that the priority order of different resources is SVG (H=262), photovoltaic equipment (H=246), energy storage equipment (H=243), and electric vehicles (H=236).

[0037] In order to highlight the advantages of the method proposed in the present invention, the following simulation comparison schemes are used: 1) Scheme 1 is a voltage regulation method that does not consider priority sorting; 2) Scheme 2 is a voltage regulation method that does not consider hierarchical aggregation and nesting; 3) Scheme 3 is the method proposed in the present invention. The network loss and voltage comparison results under different methods are shown in Table 1. Analysis shows that the system network loss of the method proposed in the present invention is reduced by 7.03% and 3.98% compared with Schemes 1 and 2, respectively, and the voltage level is also significantly improved, effectively improving the power quality.

[0038] Table 1 Comparison of network loss and voltage under different methods .

[0039] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for emergency voltage regulation of distributed resources in a distribution network based on hierarchical interactive multi-dimensional dynamic aggregation, characterized in that: The following steps are involved: S1. Obtain the values ​​of priority indicators of various types of distributed resources according to the voltage regulation cost and tracking response characteristics of distributed resources in the distribution network; S2. Sort various distributed resources according to the values ​​of the priority indicators of various distributed resources; S3. Carry out the lower-layer aggregation strategy; self-organize and aggregate various distributed resources according to the priority sorting results. The formed aggregation unit responds to the voltage regulation demand after receiving the scheduling command and continuously adjusts its distributed resource scheduling plan; S4. Perform upper-level adjustment strategies; Based on the real-time data adjusted by the lower-level aggregation strategy, the voltage of the entire power grid is optimized and regulated, including: long-time-scale scheduling of on-load voltage regulators and capacitor banks, and short-time-scale scheduling of aggregation units based on long-time-scale scheduling; S5. Feedback the real-time data of the overall power grid obtained after the upper-level adjustment strategy to S3, and carry out the next round of lower-level aggregation strategy to form a dynamic closed-loop nested control mechanism.

2. According to claim 1, a method for emergency voltage regulation of distributed resources in a distribution network based on hierarchical interactive multi-dimensional dynamic aggregation is characterized in that: The priority indicators of each type of distributed resources in S1 include voltage sensitivity, voltage regulation cost and tracking response coefficient; (1) Voltage sensitivity: ， In the formula, and Node For Node Active and reactive voltage sensitivity; is the line starting voltage; and Node With Node resistance and reactance between (2) The voltage regulation costs for each type include distributed photovoltaic equipment, energy storage equipment, electric vehicle, and SVG equipment; 1) Voltage regulation cost of distributed photovoltaic equipment: Total cost of photovoltaic discharge voltage regulation for: ， In the formula, is the depreciation cost of the photovoltaic equipment; is the unit power maintenance cost of the photovoltaic equipment; is the active power loss cost per unit power generated by the photovoltaic device; Then the node PV equipment for nodes Unit reactive voltage regulation cost That is, the voltage regulation cost of distributed photovoltaic equipment is: ， In the formula, For Node The voltage change before and after voltage regulation; Reactive power output by photovoltaic equipment; 2) Voltage regulation cost of energy storage equipment: Total cost of reactive voltage regulation power of energy storage equipment for: ， In the formula, The loss degree of energy storage battery life under different depths of charge and discharge; is the total effective discharge capacity of the energy storage battery; The initial investment cost of the energy storage battery; The maintenance cost per unit power of energy storage; is the active power loss cost per unit power; Then the node Energy storage devices for nodes Unit reactive voltage regulation cost That is, the voltage regulation cost of energy storage equipment is: ， In the formula, Reactive power output by the energy storage device; 3) Electric vehicle voltage regulation cost: Total cost of voltage regulation for electric vehicles for: ， In the formula, the unit cost of charging an electric vehicle; Charging efficiency for electric vehicles; Charging power for electric vehicles; For electric vehicles at the node Charging time ; The energy loss in each charging cycle of electric vehicles; Then the node Electric vehicles at the node Unit active voltage regulation cost That is, the voltage regulation cost of electric vehicles is: ， In the formula, It is the active power adjusted by the electric vehicle due to voltage regulation requirements; 4) SVG equipment voltage regulation cost: Total cost of SVG equipment participating in grid voltage regulation for: ， In the formula, is the initial investment cost of the SVG equipment; For operating costs; For maintenance costs; For energy consumption cost; Then the node SVG device pair node Unit reactive voltage regulation cost That is, the voltage regulation cost of SVG equipment is: ， In the formula, Reactive power output by SVG equipment; (3) Tracking response coefficient: During the scheduling period, the output of distributed resources is expressed in per-unit form as: ， In the formula, Individuals of distributed resources In the period The power per unit value; For individuals In the period The power value; For individuals The maximum power value; is the number of time periods in the scheduling period; Power signal and load signal exist Consistency index for time periods for: ， In the formula, For the period Load signal The power per unit value; By tracking the response coefficient To measure Individuals in a period Power signal and load signal Consistency between: ， In the formula, The smaller the individual Power signal and load signal The more consistent.

3. The method for emergency voltage regulation of distributed resources in distribution network based on hierarchical interactive multi-dimensional dynamic aggregation according to claim 1 is characterized in that: The specific contents of S2 include: All priority indicators are normalized, and according to the normalized indicator values, radar charts are constructed for different types of distributed resources with voltage sensitivity, voltage regulation cost, and response speed as vertices. Select the area and perimeter of each radar chart as two feature vectors to construct the evaluation function H , and calculate the evaluation value of each radar chart in turn H ; According to each evaluation value H The size of the .

4. The method for emergency voltage regulation of distributed resources in a distribution network based on hierarchical interactive multi-dimensional dynamic aggregation according to claim 3 is characterized in that: The specific contents of normalization include: The value of each priority indicator Normalized to the interval [0,1]: ， In the formula, Indicates the minimum value of the priority indicator; Indicates the maximum value of the priority indicator; The area and perimeter of each radar chart are: ， In the formula, and is the area and perimeter of the radar chart, For the The length of the side formed by the normalized index values; For the The angle between the indicators; Calculate the evaluation value of each radar chart in turn H The specific content is: 。 5. The method for emergency voltage regulation of distributed resources in distribution network based on hierarchical interactive multi-dimensional dynamic aggregation according to claim 1 is characterized in that: The specific contents of S3 include: Characterize the total aggregate power based on the current aggregate unit The feasible domain of For the period Active power of the aggregation unit, For the period Aggregate the reactive power of the unit and obtain the elliptical feasible region , using the time-decoupled elliptic feasible region : ， In the formula, is the center of the ellipse; is a two-dimensional positive semidefinite matrix, representing the rotation and stretching transformation of the ellipse; is a variable constrained by the uncertainty set U: ， In the formula, For all time periods A collection of; The feasible domain As an uncertainty set, As uncertainty set Distributed resource scheduling scheme under uncertain variables It is an adaptive variable that changes dynamically with the uncertainty variable. An active-reactive multi-dimensional dynamic aggregation model is established. The goal is to maximize the total aggregation flexibility of active-reactive power in different time periods, and the active-reactive flexibility is optimally aggregated. The active-reactive multi-dimensional dynamic aggregation model is: ， ， ， ， ， In the formula, the elliptical feasible region changes as Dynamically adjust according to changes, and obtain the corresponding distributed resource power dispatch plan ; Representation Matrix The determinant is equal to the feasible domain of the ellipse in the period area; and All represent the power constraint parameters of the elliptical feasible region; and represent the apparent power capacity constraint parameters of photovoltaic equipment and energy storage equipment respectively; and denote other power constraints and network constraint parameters of distributed resources respectively; The active-reactive multi-dimensional dynamic aggregation model is solved to obtain the optimal flexibility space of the optimal distributed resources so as to maximize the elliptical feasible domain.

6. The method for emergency voltage regulation of distributed resources in a distribution network based on hierarchical interactive multi-dimensional dynamic aggregation according to claim 1, characterized in that: The specific contents of the long-term hourly scheduling model in the upper layer of S4 include: The distribution network layer comprehensively considers the operation times of the on-load voltage regulator OLTC and the capacitor bank CB, constructs the OLTC and CB coordinated voltage regulation action strategy with the goal of minimizing the system network loss, and completes the hourly optimization. The objective function is: ， In the formula, and The time periods Branch Road resistance and current; is the number of time periods in a scheduling cycle; is the duration of each time period; The constraints include: OLTC constraints and CB constraints; among them, The OLTC constraints are: ， ， ， ， In the formula, For Node In the period Voltage; For the period The on-load voltage regulator ratio; It is the number of gears of the on-load voltage regulator; For Node In the period The on-load voltage regulator is in gear The square of the reference voltage at ; and The time periods Branch Road Active power, reactive power and reactance; is any positive number; Indicates time period Is the on-load voltage regulator Binary variable for gear position; The maximum number of transformer switching times in a dispatching cycle; The CB constraints are: ， ， ， ， In the formula, The capacitor bank switched in the time period Output value; For the period The number of groups of switched capacitors; is the reactive power compensation of a single capacitor; and It is the upper and lower limits of the output of the switched capacitor bank; The specific number of capacitor groups to be installed; For the period The upper limit of the number of capacitor switching groups; is the maximum switching times of the capacitor; is a sign function. When a is greater than 0, its value is 1, and when a is equal to 0, its value is 0.

7. The method for emergency voltage regulation of distributed resources in a distribution network based on hierarchical interactive multi-dimensional dynamic aggregation according to claim 1, characterized in that: The specific contents of the short-time scale minute-level control model in the lower layer of S4 include: After the upper-layer long-time-scale hourly dispatch model determines the switching action of the on-load voltage regulator and capacitor bank, the preset dispatch interval is used to perform deterministic optimization on the active and reactive power of various distributed resources at the substation level; Construct a short-time optimization objective function with the minimum system node voltage deviation: ， In the formula, is the number of distribution network nodes; Representative Node In the period Voltage; Representative node in the period Rated voltage.

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