Multi-power-grid cooperative regulation and control method and system based on multi-level resource aggregation decoupling

Abstract

The invention discloses a multi-power-grid cooperative regulation and control method and system based on multi-level resource aggregation decoupling, and belongs to the technical field of intelligent power grid operation control. The method comprises the following steps: determining a time-varying feasible boundary of a microgrid in a power regulation domain of a common connection point by acquiring multivariate state information of heterogeneous resources of the microgrid; determining a power bidirectional interaction boundary constraint between the micro-grid and the power distribution network through the common connection point, and constructing a micro-grid power distribution optimization model according to the power bidirectional interaction boundary constraint and the electric energy characteristics of the power distribution network; dividing resource adjustment levels corresponding to the heterogeneous resources to obtain a multi-level adjustment sequence; when the micro-grid power distribution optimization model outputs a scheduling instruction, the scheduling instruction is decomposed into optimal scheduling strategies corresponding to the levels according to the multi-level adjustment sequence, and the optimal scheduling strategies are issued to the levels in sequence, so that the adjustment instruction issued by the power distribution network is decomposed in sequence according to the adjustable characteristics of heterogeneous resources, and the optimal scheduling strategies are obtained. The resource adjustment efficiency is improved; and the response cost is optimized.

Description

Technical Field

[0001] This application relates to the field of smart grid operation and control technology, and in particular to a multi-grid collaborative control method and system based on multi-level resource aggregation and decoupling. Background Technology

[0002] With the large-scale integration of high-proportion renewable energy and diverse flexible resources into microgrids, the energy interaction and coupling between distribution networks and microgrids has been significantly enhanced. In the context of building new power systems, microgrids have gradually evolved from traditional, single-function electricity consumers into active participants in bidirectional power interaction with the upper-level distribution network.

[0003] However, the heterogeneous and flexible resources within microgrids (such as distributed energy storage, flexible loads, and electric vehicle clusters) and the distribution network and microgrids have significant differences in regulation capacity, response speed, and control components. Using a unified equivalent aggregation model would prevent the rapid response advantage and flexible potential of resources in microgrids from being fully released, resulting in the inability to reasonably coordinate and allocate new heterogeneous resources composed of distribution networks and microgrids. Summary of the Invention

[0004] The main purpose of this application is to provide a multi-grid coordinated control method and system based on multi-level resource aggregation and decoupling, which aims to solve the technical problem of the inability to reasonably coordinate and allocate new heterogeneous resources composed of distribution networks and microgrids.

[0005] To achieve the above objectives, this application provides a multi-grid coordinated control method based on multi-level resource aggregation and decoupling, the method comprising the following steps: The system acquires multivariate state information of heterogeneous resources within the microgrid and determines the time-varying feasible boundary of the power regulation domain of the microgrid at the point of common coupling based on the multivariate state information. Based on the time-varying feasible boundary, the power bidirectional interaction boundary constraints between the microgrid and the distribution network through the point of common coupling are determined, and a microgrid distribution optimization model is constructed based on the power bidirectional interaction boundary constraints and the power characteristics of the distribution network. The multidimensional dynamic characteristics of the heterogeneous resources are obtained, and the resource adjustment levels corresponding to the heterogeneous resources are divided according to the multidimensional dynamic characteristics to obtain a multi-level adjustment sequence. When the microgrid distribution optimization model outputs a global power optimization scheduling instruction, the scheduling instruction is decomposed into the optimal scheduling strategy corresponding to each level according to the multi-level adjustment sequence, and the optimal scheduling strategy is issued to each level in sequence.

[0006] In one embodiment, the step of determining the time-varying feasible boundary of the power regulation domain of the microgrid at the point of common coupling based on the multivariate state information includes: Based on the multi-state information, the regulation characteristics of the photovoltaic inverter, distributed energy storage system and building air conditioning load of the microgrid are determined respectively, and the constraint conditions of the power regulation domain operation boundary point of the microgrid are determined based on the regulation characteristics. Based on the constraints, the boundary point samples of the power regulation domain operating boundary points are determined with the active power and reactive power at the common connection point of the microgrid and the distribution network as the objective function. By using a preset dynamic interval envelope algorithm, convex hull fitting is performed on the boundary point samples to generate the time-varying feasible boundary of the power regulation domain of the microgrid.

[0007] In one embodiment, the step of determining the constraint conditions for the operating boundary point of the power regulation domain of the microgrid based on the regulation characteristics includes: Based on the aforementioned regulation characteristics, the inverter operation constraints of the photovoltaic inverter, the energy storage operation constraints of the distributed energy storage system, and the load operation constraints of the building air conditioning load are determined respectively. Based on the inverter operation constraints, the energy storage operation constraints, and the load operation constraints, an operation constraint model for heterogeneous resources is constructed. Based on the operational constraint model, determine the constraint conditions of the power regulation domain operating boundary points of the microgrid; The inverter operation constraint is that the sum of the squares of its active power output and reactive power output does not exceed the square of its rated capacity, and its operating power factor is not lower than the minimum value in the preset power range; the energy storage operation constraint is that its charging and discharging power does not exceed the preset maximum allowable power, the charging and discharging power is related to the change in its stored capacity, and the change in stored capacity is maintained between the upper and lower limits of the preset rated capacity; the load operation constraint is based on relevant parameters such as outdoor temperature, building heat dissipation coefficient, air conditioning thermal conductivity and operating duty cycle, constraining the building indoor temperature within a preset temperature range.

[0008] In one embodiment, the step of determining the boundary point sample of the power regulation domain operating boundary point based on the constraints, using the active power and reactive power at the common connection point of the microgrid and the distribution network as the objective function, includes: Based on the operational constraints, an operational boundary point search model is constructed with the active power and reactive power at the microgrid common connection point and the minimum distribution network as objective functions. Based on the operating boundary point search model, the search direction corresponding to the linear combination of active power and reactive power at the common connection point is determined, and the boundary point samples of the power regulation domain operating boundary points in the search direction are obtained.

[0009] In one embodiment, the step of generating a time-varying feasible boundary of the power regulation domain of the microgrid by performing convex hull fitting on the boundary point samples using a preset dynamic interval envelope algorithm includes: By using a preset dynamic interval envelope algorithm, convex hull fitting is performed on the boundary point samples to generate a convex polygon region composed of several linear segments. The nonlinear convex polygon region is piecewise linearized, and based on the results of the piecewise linearization, a corresponding feasible boundary is generated for each segment to obtain the time-varying feasible boundary of the power regulation domain of the microgrid.

[0010] In one embodiment, the step of constructing a microgrid distribution optimization model based on the bidirectional power interaction boundary constraints and the power characteristics of the distribution network includes: Based on the bidirectional power interaction boundary constraints and the power characteristics of the distribution network, the following sub-objectives are determined: transmission loss based on the resistance of each branch and the total network loss of power flow; node potential balance based on the statistical calculation of the voltage deviation between each node and the standard voltage within the distribution network; and operation energy efficiency cost based on the cost of purchasing electricity from the main grid, the cost of energy storage scheduling within the grid, and the cost of transactions with each microgrid. The transmission loss sub-objective, the node potential balance sub-objective, and the operating energy efficiency cost sub-objective are combined into a comprehensive objective function using a weighted summation method. Based on the comprehensive objective function, a microgrid distribution optimization model is constructed.

[0011] In one embodiment, the step of determining the power bidirectional interaction boundary constraints between the microgrid and the distribution network through the point of common coupling based on the time-varying feasible boundary includes: Obtain the power regulation domain of the microgrid at the point of common coupling, and determine the active and reactive power vectors of the microgrid interacting with the distribution network through the point of common coupling; By constraining the power interaction vector to lie within the feasible range corresponding to the power regulation domain, a two-way power interaction boundary constraint is obtained.

[0012] In one embodiment, the step of dividing the heterogeneous resources into resource adjustment levels according to the multi-dimensional dynamic characteristics to obtain a multi-level adjustment sequence includes: Based on the multidimensional dynamic characteristics, a dynamic feature matrix of the heterogeneous resources is constructed, wherein the rows of the dynamic feature matrix correspond to the resource type, and the columns correspond to the evaluation index composed of unit adjustment cost, response margin and response speed. The dynamic feature matrix is ​​normalized, and based on the processing results, the positive and negative ideal solutions of each evaluation index are determined. Based on the positive and negative ideal solutions, the objective weights of each evaluation index are calculated using the entropy weight method, and the relative proximity between the heterogeneous resources and the positive and negative ideal solutions are calculated based on the objective weights. The heterogeneous resources are sorted in descending order of relative proximity to obtain a multi-level adjustment sequence.

[0013] In one embodiment, before the step of decomposing the scheduling instruction into optimal scheduling strategies corresponding to each level according to the multi-level adjustment sequence and sequentially issuing the optimal scheduling strategies to each level when the global power optimization scheduling instruction is output by the microgrid distribution optimization model, the method further includes: Set priority response ratio parameters for each level of the heterogeneous resources; When the priority response ratio parameter is a first preset value, the highest level of the heterogeneous resources shall take on all adjustment requirements. When the adjustment margin of the highest level is exhausted, the next level of resources in the heterogeneous resources shall take on the remaining instructions. When the priority response ratio parameter is a second preset value, each resource in the heterogeneous resources is used to receive scheduling instructions.

[0014] Furthermore, to achieve the above objectives, this application also provides a multi-grid coordinated control system, the system comprising: The acquisition module is used to acquire multi-dimensional state information of heterogeneous resources within the microgrid, and determine the time-varying feasible boundary of the power regulation domain of the microgrid at the point of common coupling based on the multi-dimensional state information. The determination module is used to determine the power bidirectional interaction boundary constraints between the microgrid and the distribution network through the point of common coupling based on the time-varying feasible boundary, and to construct a microgrid distribution optimization model based on the power bidirectional interaction boundary constraints and the power characteristics of the distribution network. The partitioning module is used to obtain the multi-dimensional dynamic characteristics of the heterogeneous resources, and to partition the resource adjustment levels corresponding to the heterogeneous resources according to the multi-dimensional dynamic characteristics to obtain a multi-level adjustment sequence. The scheduling module is used to decompose the scheduling instruction for global power optimization output by the microgrid distribution optimization model into the optimal scheduling strategy for each level according to the multi-level adjustment sequence, and issue the optimal scheduling strategy to each level in sequence.

[0015] The one or more technical solutions proposed in this application have at least the following technical effects: acquiring multi-dimensional state information of heterogeneous resources within a microgrid, and determining the time-varying feasible boundary of the power regulation domain of the microgrid at the point of common coupling based on the multi-dimensional state information; determining the bidirectional power interaction boundary constraint between the microgrid and the distribution network through the point of common coupling based on the time-varying feasible boundary, and constructing a microgrid distribution optimization model based on the bidirectional power interaction boundary constraint and the power characteristics of the distribution network; acquiring the multi-dimensional dynamic characteristics of the heterogeneous resources, and dividing the corresponding resource regulation levels of the heterogeneous resources based on the multi-dimensional dynamic characteristics to obtain a multi-level regulation sequence; when the global power optimization scheduling command is output by the microgrid distribution optimization model, decomposing the scheduling command into optimal scheduling strategies corresponding to each level based on the multi-level regulation sequence, and sequentially issuing the optimal scheduling strategies to each level; by establishing the time-varying feasible boundary of the microgrid power regulation domain and combining it with the multi-level regulation sequence of heterogeneous resources, achieving coordinated control from global optimization to hierarchical execution, effectively solving the complexity and uncertainty problems in distributed resource regulation. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating an embodiment of the multi-grid coordinated control method based on multi-level resource aggregation and decoupling provided in this application. Figure 2 This is a flowchart illustrating Embodiment 2 of the multi-grid coordinated control method based on multi-level resource aggregation and decoupling provided in this application.

[0019] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0021] Reference Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the multi-grid coordinated control method based on multi-level resource aggregation and decoupling in this application.

[0022] In this embodiment, a multi-grid coordinated control method based on multi-level resource aggregation and decoupling is proposed. The method includes the following steps: S10, acquire multi-state information of heterogeneous resources within the microgrid, and determine the time-varying feasible boundary of the power regulation domain of the microgrid at the point of common connection based on the multi-state information. S20, Based on the time-varying feasible boundary, determine the power bidirectional interaction boundary constraints between the microgrid and the distribution network through the point of common coupling, and construct a microgrid distribution optimization model based on the power bidirectional interaction boundary constraints and the power characteristics of the distribution network; S30, obtain the multi-dimensional dynamic features of the heterogeneous resources, and divide the resource adjustment levels corresponding to the heterogeneous resources according to the multi-dimensional dynamic features to obtain a multi-level adjustment sequence; S40, when the microgrid distribution optimization model outputs a global power optimization scheduling instruction, the scheduling instruction is decomposed into the optimal scheduling strategy corresponding to each level according to the multi-level adjustment sequence, and the optimal scheduling strategy is issued to each level in sequence.

[0023] In this embodiment, by establishing a time-varying feasible boundary for the microgrid power regulation domain and combining it with a multi-level regulation sequence of heterogeneous resources, collaborative control from global optimization to hierarchical execution is achieved, effectively solving the complexity and uncertainty problems in distributed resource regulation.

[0024] Understandably, a comprehensive system status monitoring system needs to be established to provide accurate basic data for subsequent optimization and control. By collecting the operating status of various resources within the microgrid in real time, a comprehensive understanding of the overall system operation can be formed, and multi-dimensional status information of heterogeneous resources within the microgrid can be obtained, including real-time data such as photovoltaic output, energy storage charge status, load demand, and network topology.

[0025] Furthermore, based on the multivariate state information, the time-varying feasible boundary of the power regulation domain of the microgrid at the point of common coupling is determined. This time-varying feasible boundary defines the range of power that the microgrid can provide to or absorb from the distribution network at a specific time. Based on the time-varying feasible boundary, the power bidirectional interaction boundary constraint between the microgrid and the distribution network through the point of common coupling is determined to ensure that the power exchange between the microgrid and the distribution network is within a safe range.

[0026] Furthermore, based on the data obtained above, an optimization model that comprehensively considers technical and economic indicators can be established to provide the optimal decision-making basis for system operation. This reduces the difficulty of solving the problem by transforming the complex operation optimization problem into a solvable mathematical programming problem through mathematical modeling. Specifically, a microgrid distribution optimization model is constructed based on the bidirectional power interaction boundary constraints and the power characteristics of the distribution network.

[0027] This model comprehensively considers multiple optimization objectives, including network loss, voltage quality, and operating costs.

[0028] In this process, by solving the optimization model, the globally optimal operating scheme of the system is obtained, forming a global power optimization scheduling instruction. This scheduling instruction needs to be decomposed and divided according to the hierarchy corresponding to the heterogeneous resources. A hierarchical control system is established based on the dynamic characteristics of the resources to realize differentiated management and orderly use of resources, thereby ensuring the efficiency and reliability of the control process.

[0029] Specifically, by acquiring the multi-dimensional dynamic characteristics of the heterogeneous resources, including key indicators such as adjustment cost, response speed, and adjustment accuracy, the resource adjustment levels corresponding to the heterogeneous resources are divided according to these multi-dimensional dynamic characteristics, resulting in a multi-level adjustment sequence. Resources with similar characteristics are grouped into the same level to form an ordered calling sequence, and global optimization instructions are transformed into specific executable control strategies to ensure the accurate realization of the control intention, thereby avoiding drastic system fluctuations and improving the stability of the control process.

[0030] Specifically, when the microgrid distribution optimization model outputs a global power optimization scheduling instruction, the scheduling instruction is decomposed into the optimal scheduling strategy corresponding to each level according to the multi-level adjustment sequence, and the optimal scheduling strategy is issued to each level in sequence. The control instruction is executed in the order of the resource level to ensure the orderly progress of the regulation process.

[0031] Heterogeneous resources refer to distributed resources of different types and characteristics in a microgrid, including power generation resources, energy storage resources, and load resources. These resources differ significantly in terms of response speed, regulation accuracy, and operating costs, requiring differentiated management strategies. For example, photovoltaic inverters have fast response speeds but limited regulation capabilities, energy storage systems have high regulation accuracy but high costs, and air conditioning loads have great regulation potential but are subject to temperature constraints.

[0032] Among them, multi-dimensional state information refers to a set of multi-dimensional measurement data that reflects the operating status of a microgrid, including electrical quantities (voltage, current, power), state quantities (temperature), environmental quantities (light, temperature), etc. Through this information, the operating status of the system can be fully understood.

[0033] The point of common coupling (PCC) is the physical node where the microgrid connects to the distribution network and exchanges power. It is usually located at the outlet of the microgrid and is the only channel for energy interaction between the microgrid and the external power grid. Its power exchange needs to meet specific technical constraints.

[0034] The power regulation domain refers to the feasible range of power regulation capabilities that a microgrid can provide at the point of common coupling. It is a two-dimensional region (active-reactive plane) that varies with time, reflecting the regulation potential of the microgrid at a specific moment and serving as the basis for the microgrid's participation in distribution network regulation.

[0035] Among them, the time-varying feasible boundary refers to the boundary curve of the power regulation domain as it changes over time.

[0036] Technical Explanation: Due to the time-varying nature of renewable energy output and load demand, the regulation capability of microgrids also changes accordingly, requiring real-time updates of boundary information.

[0037] Among them, the power bidirectional interaction boundary constraints refer to the technical restrictions on power exchange between the microgrid and the distribution network, including upper and lower limits of active power, upper and lower limits of reactive power, and power factor constraints, to ensure the safety and stability of the power exchange process.

[0038] Among them, multidimensional dynamic characteristics refer to multidimensional performance indicators that describe the regulation characteristics of heterogeneous resources, including indicators such as unit regulation cost (economic efficiency), response margin (regulation capacity), and response speed (dynamic performance), which are used to evaluate the regulation value of resources.

[0039] Among them, the resource regulation level refers to the regulation priority level divided according to the resource characteristics. Resources with similar characteristics are divided into the same level to form an orderly regulation sequence, which is usually divided according to response speed and regulation cost.

[0040] Among them, the multi-level regulation sequence refers to the orderly arrangement of resource regulation levels, which clarifies the order of resource allocation at each level in the regulation process, and ensures the economy and reliability of the regulation process.

[0041] Among them, the microgrid distribution optimization model refers to the mathematical model that describes the optimization problem of the coordinated operation of microgrids and distribution networks. It usually includes an objective function and constraints. The objective function considers multiple indicators such as economy and safety, while the constraints include power balance, equipment capacity and voltage safety.

[0042] Global power optimization refers to power scheduling optimization from the perspective of the entire system, taking into account the overall network operation status, and making optimization decisions with the goal of achieving optimal overall system operation.

[0043] The optimal scheduling strategy refers to the optimal control scheme formulated for a specific resource, including specific information such as control objectives, control timing, and control quantities, to ensure the accurate realization of the control intention.

[0044] In this embodiment, the step of constructing a microgrid distribution optimization model based on the bidirectional power interaction boundary constraints and the power characteristics of the distribution network includes: Based on the bidirectional power interaction boundary constraints and the power characteristics of the distribution network, the following sub-objectives are determined: transmission loss based on the resistance of each branch and the total network loss of power flow; node potential balance based on the statistical calculation of the voltage deviation between each node and the standard voltage within the distribution network; and operation energy efficiency cost based on the cost of purchasing electricity from the main grid, the cost of energy storage scheduling within the grid, and the cost of transactions with each microgrid. The transmission loss sub-objective, the node potential balance sub-objective, and the operating energy efficiency cost sub-objective are combined into a comprehensive objective function using a weighted summation method. Based on the comprehensive objective function, a microgrid distribution optimization model is constructed.

[0045] In this embodiment, by analyzing the power characteristics of the distribution network, a multi-objective optimization function is constructed, which includes transmission loss, node potential balance, and operating energy efficiency cost. The weighted summation method is used to achieve unified optimization of the multiple objectives. Specifically, it includes the process of multi-objective analysis and sub-objective construction, as well as multi-objective integration and model construction, and constructs the corresponding model.

[0046] Specifically, an optimization target system is established from multiple dimensions, including technical and economic aspects. Each sub-target reflects different requirements of system operation and needs to be considered in a coordinated manner. The transmission loss sub-target is determined based on the resistance of each branch of the distribution network and the total network loss of power flow, so as to reduce the loss in the energy transmission process. The node potential balance sub-target is determined based on the statistical calculation of the voltage deviation of each node in the distribution network from the standard voltage, so as to improve the power supply quality. At the same time, the operating energy efficiency cost sub-target is determined for the cost of purchasing electricity from the main grid, the cost of energy storage dispatch within the grid, and the cost of transactions with each microgrid, so as to optimize the operating economy.

[0047] Furthermore, by scientifically allocating weights, the multi-objective problem is transformed into a single-objective optimization problem. The weight allocation reflects the importance of different objectives in the decision-making process. The weighted summation method can be used to merge the transmission loss sub-objective, the node potential balance sub-objective, and the operating energy efficiency cost sub-objective into a comprehensive objective function. Based on the comprehensive objective function, a microgrid distribution optimization model is constructed to form a complete optimization problem model.

[0048] Among them, the transmission loss sub-objective refers to the optimization function aimed at minimizing network power loss, which usually reflects network transmission efficiency.

[0049] Among them, the nodal potential balance sub-objective refers to an optimization function aimed at improving voltage quality, which usually reflects the degree of voltage deviation.

[0050] Among them, the sub-objective of operating energy efficiency cost refers to the optimization function aimed at minimizing operating costs, including economic indicators such as electricity purchase cost, energy storage operation and maintenance cost, and transaction cost.

[0051] The weighted summation method is a scalarization method commonly used in multi-objective optimization. By assigning weights to each sub-objective, it transforms a multi-objective problem into a single-objective problem.

[0052] Specifically, the relevant formulas for calculating the objective function are as follows: ; ; In the formula, The goal is to minimize a multi-objective function (representing how, under various constraints, the overall operating cost of the system is minimized through coordinated scheduling of various resources). This refers to the transmission loss of the distribution network during the scheduling period; The potential balance of nodes in each time period within the scheduling cycle; The energy efficiency cost of power distribution network operation; , , The results are as follows: when the objective functions are optimal transmission loss of the distribution network, maximum node potential balance, and minimum operating energy efficiency cost, the scheduling model is optimized. , , These are the weight coefficients for each item, and ; The scheduling period is 24. This refers to the number of branches in the distribution network. branch road The resistance; and They are respectively Flowing through the side road at all times Active power and reactive power; for Flowing through the side road at all times The voltage; This refers to the number of nodes in the distribution network. for Time Node The voltage; Standard voltage; and They are respectively The electricity price and volume purchased by the distribution network from the main grid at all times; for The scheduling price of user-side energy storage at any given time; and Distribution network Energy storage at nodes The charging and discharging power at any given moment; for The transaction price of electricity for microgrids at any time; and They are respectively The power of interaction with the microgrid at all times.

[0053] In this embodiment, the step of determining the power bidirectional interaction boundary constraints between the microgrid and the distribution network through the point of common coupling based on the time-varying feasible boundary includes: Obtain the power regulation domain of the microgrid at the point of common coupling, and determine the active and reactive power vectors of the microgrid interacting with the distribution network through the point of common coupling; By constraining the power interaction vector to lie within the feasible range corresponding to the power regulation domain, a two-way power interaction boundary constraint is obtained.

[0054] In this embodiment, mathematical constraints on the power interaction vector are established based on the feasible range of the power regulation domain to ensure the safe and reliable power exchange between the microgrid and the distribution network. The power regulation domain and the interaction vector are determined first to establish boundary constraints.

[0055] Understandably, by obtaining the power regulation domain of the microgrid at the point of common coupling, the range of the microgrid's power regulation capability is clarified, and the active and reactive power vectors of the microgrid interacting with the distribution network through the point of common coupling are determined. A mathematical representation of power interaction is established, that is, by accurately describing the power interaction capability of the microgrid at the point of common coupling, and based on the definition of the power regulation domain, a geometric intuition and mathematical basis are provided for constraint establishment. At the same time, the geometric form of the power regulation domain is transformed into mathematical constraint conditions, and it is ensured that the power interaction vector established by the constraint always falls within the feasible range. By constraining the power interaction vector to be within the feasible range corresponding to the power regulation domain, strict mathematical constraints are established, and finally, the bidirectional power interaction boundary constraints are obtained, which are usually expressed as a series of linear or nonlinear inequality constraints.

[0056] Among them, the power interaction vector refers to a two-dimensional vector that describes the power exchange between the microgrid and the distribution network.

[0057] The feasible range refers to the region where the power interaction vector is allowed to take values.

[0058] Among them, the power bidirectional interaction boundary constraint refers to the mathematical constraint that restricts the value of the power interaction vector.

[0059] Specifically, the relevant calculation formulas for active and reactive power involved in the above process are as follows: ; ; In the formula, , They are respectively Time-of-use distribution network to microgrid The active and reactive power transmitted; , They are respectively Time-of-use microgrids The active and reactive power received by the PCC , These are the power regulation domains between the distribution network and the microgrid, respectively. , These are the active power and reactive power at the k-th vertex that constitutes the boundary of the power regulation domain, respectively.

[0060] In this embodiment, the step of dividing the resource adjustment levels corresponding to the heterogeneous resources according to the multi-dimensional dynamic characteristics to obtain a multi-level adjustment sequence includes: Based on the multidimensional dynamic characteristics, a dynamic feature matrix of the heterogeneous resources is constructed, wherein the rows of the dynamic feature matrix correspond to the resource type, and the columns correspond to the evaluation index composed of unit adjustment cost, response margin and response speed. The dynamic feature matrix is ​​normalized, and based on the processing results, the positive and negative ideal solutions of each evaluation index are determined. Based on the positive and negative ideal solutions, the objective weights of each evaluation index are calculated using the entropy weight method, and the relative proximity between the heterogeneous resources and the positive and negative ideal solutions are calculated based on the objective weights. The heterogeneous resources are sorted in descending order of relative proximity to obtain a multi-level adjustment sequence.

[0061] In this embodiment, the effect of hierarchical division of heterogeneous resources is achieved. Specifically, a dynamic feature matrix is ​​constructed, a multi-attribute decision method is used to calculate the relative proximity of resources, and a multi-level adjustment sequence is generated based on the proximity ranking. The specific process includes feature matrix construction and preprocessing, ideal solution calculation and weight determination, and proximity calculation and hierarchical ranking.

[0062] Specifically, based on the multidimensional dynamic characteristics, a dynamic feature matrix of the heterogeneous resources is constructed, where the rows correspond to resource types and the columns correspond to evaluation indicators composed of unit adjustment cost, response margin, and response speed. The dynamic feature matrix is ​​normalized to eliminate the influence of dimensions, thereby systematically describing the adjustment characteristics of resources. This feature matrix provides structured input data for subsequent multi-attribute decision-making. At the same time, based on the processing results, the positive and negative ideal solutions of each evaluation indicator are determined. Based on the positive and negative ideal solutions, the entropy weight method is used to calculate the objective weights of each evaluation indicator, establishing a reference benchmark and indicator importance weight for decision-making, thereby providing a clear comparison benchmark for resource ranking.

[0063] Furthermore, based on the objective weights, the relative proximity between the heterogeneous resources and the positive ideal solution and the negative ideal solution are calculated respectively. The heterogeneous resources are then sorted in descending order of relative proximity to obtain a multi-level adjustment sequence. This allows for the quantification of the relative proximity between the resources and the ideal solution, and the relative proximity comprehensively reflects the overall performance level of the resources.

[0064] The dynamic feature matrix refers to an index matrix that describes the resource regulation characteristics. Rows represent resources and columns represent evaluation indicators, providing input data for multi-attribute decision-making.

[0065] Among them, the positive ideal solution refers to the virtual reference point composed of the optimal values ​​of each evaluation indicator, which reflects the decision-maker's optimal expectation level for each indicator.

[0066] Among them, the negative ideal solution refers to the virtual reference point composed of the worst values ​​of each evaluation indicator, which reflects the decision-maker's worst acceptance level of each indicator.

[0067] Among them, the entropy weight method refers to an objective weight determination method based on the principle of information entropy. It automatically determines the weight according to the degree of variation of each indicator data, and the greater the degree of variation, the higher the weight.

[0068] Among them, relative proximity refers to a comprehensive measure of the relative distance between resources and positive and negative ideal solutions.

[0069] Specifically, based on the multidimensional dynamic feature matrix, the calculation of the relative distance between heterogeneous resources and positive and negative ideals is as follows: ; ; In the formula, The dynamic feature matrix of heterogeneous resources is... matrix; This represents the number of heterogeneous resource types contained in the microgrid; The number of evaluation indicators for resources; For the first Class resources in The dynamic feature vector at any given time; , , The first Class resources in Unit adjustment cost, response margin, and response rate at any given time.

[0070] The dynamic feature matrix is ​​normalized to eliminate dimensional differences. The normalized adjustment cost, response margin, and response speed are expressed as follows: , , By combining the dynamic feature matrix of resources, the differences between various heterogeneous resource scheduling schemes are comprehensively evaluated, and scheduling levels for heterogeneous resource identification are defined.

[0071] This includes determining the positive and negative ideal values ​​for each indicator, that is, the minimum and maximum values ​​of each resource adjustment indicator, specifically including: ; In the formula, The matrix represents the positive ideal solution. The negative interpretation matrix.

[0072] Finally, the objective weights of each indicator are calculated using the entropy weight method, and then the relative proximity of various heterogeneous resources is calculated. Based on the relative proximity, the scheduling levels of resources are divided, specifically including: ; ; ; In the formula, , The first The distance between heterogeneous resources and positive and negative ideal values; For the first The first heterogeneous resource Item; For the first The weighting coefficients of each indicator are determined by the entropy weighting method. No. The relative proximity of heterogeneous resources determines the scheduling level of resources. Resources with higher relative proximity are given priority in scheduling instructions.

[0073] In this embodiment, before the step of decomposing the scheduling instruction into optimal scheduling strategies corresponding to each level according to the multi-level adjustment sequence and sequentially issuing the optimal scheduling strategies to each level when the global power optimization scheduling instruction is output by the microgrid distribution optimization model, the method further includes: Set priority response ratio parameters for each level of the heterogeneous resources; When the priority response ratio parameter is a first preset value, the highest level of the heterogeneous resources shall take on all adjustment requirements. When the adjustment margin of the highest level is exhausted, the next level of resources in the heterogeneous resources shall take on the remaining instructions. When the priority response ratio parameter is a second preset value, each resource in the heterogeneous resources is used to receive scheduling instructions.

[0074] In this embodiment, the specific scheduling strategy for heterogeneous resources to participate in regulation is to establish differentiated resource calling rules by setting priority response ratio parameters, so as to achieve a reasonable allocation of regulation needs among resources at all levels. Specifically, by setting response ratio parameters, the effect of formulating differentiated scheduling rules can be achieved.

[0075] Specifically, during parameter setting, priority response ratio parameters are set for each level of the heterogeneous resources to clarify the calling priority of each level of resources. This allows for flexible setting of resource calling strategies based on system operating status and control needs. The response ratio parameters determine the allocation of control commands among resources at each level. When the priority response ratio parameter is at a first preset value, the highest-level heterogeneous resource prioritizes handling all control needs, fully leveraging the control role of high-quality resources. This establishes resource calling rules adaptable to different scenarios. Differentiated scheduling achieves a balance between economy and reliability in the control process. When the control margin of the highest level is exhausted, the next level of resources in the heterogeneous resources takes over the remaining commands, ensuring complete satisfaction of control needs. When the priority response ratio parameter is at a second preset value, all resources in the heterogeneous resources are used to receive scheduling commands, achieving balanced resource utilization.

[0076] Among them, the priority response ratio parameter refers to the parameter that controls the priority of resources at all levels participating in regulation and control. The parameter value determines whether a priority scheduling or balanced scheduling strategy is adopted.

[0077] The first preset value refers to the parameter threshold that triggers priority scheduling. It is usually set to 1, which indicates that a strict priority scheduling strategy is adopted.

[0078] The second preset value refers to the parameter threshold that triggers balanced scheduling. It is usually set to 0, which indicates that a fully balanced scheduling strategy is adopted.

[0079] Among them, the adjustment margin refers to the measure of the remaining adjustable capacity of resources, which is usually expressed as the ratio of the current adjustment amount to the maximum adjustment capacity, reflecting the available adjustment potential of resources.

[0080] The highest level refers to the resource level that ranks first in the multi-level adjustment sequence, which usually includes high-quality resources with low adjustment costs and fast response speed.

[0081] The next level of resources refers to the set of resources in the multi-level adjustment sequence that is below the current level, and provides supplementary adjustment capabilities when the current level of resources is exhausted.

[0082] Specifically, the relevant constraint formulas for setting the hierarchical rules based on preset values ​​are as follows: ; In the formula, , These represent the maximum active power regulation margins for heterogeneous resources at the first and second tiers, respectively. It is a real number with a relatively large value; , This indicates the response status of heterogeneous resources at the second and third tiers. The priority response ratio is [0,1]. At that time, there is no hierarchical division between heterogeneous resources (in the case of the second preset value). In this case, the next level of resources will only participate in scheduling when the higher-level resources reach the maximum active power adjustment margin (in the case of the first preset value).

[0083] This embodiment acquires multi-dimensional state information of heterogeneous resources within a microgrid and determines the time-varying feasible boundary of the power regulation domain of the microgrid at the point of common coupling (PCC) based on this information. Based on the time-varying feasible boundary, it determines the bidirectional power interaction boundary constraints between the microgrid and the distribution network through the PCC. Then, based on these constraints and the power characteristics of the distribution network, it constructs a microgrid distribution optimization model. It acquires the multi-dimensional dynamic characteristics of the heterogeneous resources and, based on these characteristics, divides the resource regulation levels corresponding to the heterogeneous resources to obtain a multi-level regulation sequence. When the microgrid distribution optimization model outputs a global power optimization scheduling command, it decomposes the scheduling command into optimal scheduling strategies for each level based on the multi-level regulation sequence and sequentially issues these optimal scheduling strategies to each level. By establishing the time-varying feasible boundary of the microgrid power regulation domain and combining it with the multi-level regulation sequence of heterogeneous resources, it achieves coordinated control from global optimization to hierarchical execution, effectively solving the complexity and uncertainty problems in distributed resource regulation.

[0084] like Figure 2 As shown, based on the first embodiment, a second embodiment of the multi-grid coordinated control method based on multi-level resource aggregation and decoupling is proposed in this application. In this embodiment, the method further includes: S11. Based on the multi-state information, determine the regulation characteristics of the photovoltaic inverter, distributed energy storage system and building air conditioning load corresponding to the microgrid, and determine the constraint conditions of the power regulation domain operation boundary point of the microgrid based on the regulation characteristics. S12, Based on the constraints, using the active power and reactive power at the common connection point of the microgrid and the distribution network as the objective function, determine the boundary point sample of the power regulation domain operating boundary point; S13, by using a preset dynamic interval envelope algorithm, convex hull fitting is performed on the boundary point samples to generate the time-varying feasible boundary of the power regulation domain of the microgrid.

[0085] In this embodiment, a specific calculation method for the time-varying feasible boundary of the power regulation domain is proposed. This method mainly involves analyzing the regulation characteristics of various resources, establishing a search model for the operating boundary points, and using convex hull fitting technology to generate the time-varying feasible boundary, thereby providing an accurate quantitative description of the power regulation capability of the microgrid.

[0086] In the process of regulating characteristic analysis and constraint establishment, the regulating characteristics of the photovoltaic inverter, distributed energy storage system and building air conditioning load of the microgrid are determined according to the multi-state information. Based on the regulating characteristics, the constraint conditions of the power regulation domain operation boundary point of the microgrid are determined. These constraint conditions constitute the feasible space for resource regulation, thereby deeply analyzing the operation characteristics of various resources and establishing an accurate mathematical model to describe their operation constraints.

[0087] In the boundary point search and sample generation process, the objective function is set as follows: the active and reactive power at the common connection point between the microgrid and the distribution network are used as the objective function to establish the optimization direction of the boundary search and determine the boundary point samples of the power regulation domain. Through optimization calculations in multiple search directions, dense sampling of boundary points is obtained, and the system searches for the boundary points of the power regulation domain within the feasible space defined by the constraints. Obtaining dense sampling of boundary points through multi-directional search provides sufficient data for subsequent boundary fitting.

[0088] In the process of convex hull fitting and boundary generation, a preset dynamic interval envelope algorithm is used to perform convex hull fitting on the boundary point samples, generating a convex polygon region and a time-varying feasible boundary for the power regulation domain of the microgrid. This boundary accurately describes the power regulation capability of the microgrid at a specific time, allowing computational geometry methods to fit discrete boundary points into a continuous feasible boundary. Convex hull fitting ensures the convexity of the boundary, facilitating the solution of subsequent optimization problems.

[0089] Among them, regulation characteristics refer to the technical characteristics that describe the resource power regulation capability, including indicators such as regulation range, regulation speed, and regulation accuracy, which reflect the resource's ability to participate in system regulation.

[0090] Among them, constraints refer to the technical conditions that limit the operation of resources, including equipment capacity constraints, operational safety constraints, user comfort constraints, etc., to ensure that resources operate within a safe and reliable range.

[0091] In this context, the objective function refers to the mathematical function that needs to be maximized or minimized in the optimization problem. In boundary search, the objective function defines the search direction, such as maximizing active power output or minimizing reactive power absorption.

[0092] Among them, the boundary point sample refers to the set of discrete points on the boundary of the power regulation domain. The boundary points obtained through multi-directional optimization search are the basis for constructing continuous boundaries.

[0093] Among them, the dynamic interval envelope algorithm refers to an efficient computational method for generating convex hull boundaries. It can quickly process the convex hull calculation of large-scale point sets and adapt to the needs of real-time applications.

[0094] Among them, convex hull fitting refers to the computational geometry method for calculating the convex hull of a point set. The convex hull is the smallest convex polygon that contains all points, has good mathematical properties, and is easy to optimize.

[0095] In this embodiment, the step of determining the constraint conditions of the power regulation domain operating boundary point of the microgrid based on the regulation characteristics includes: Based on the aforementioned regulation characteristics, the inverter operation constraints of the photovoltaic inverter, the energy storage operation constraints of the distributed energy storage system, and the load operation constraints of the building air conditioning load are determined respectively. Based on the inverter operation constraints, the energy storage operation constraints, and the load operation constraints, an operation constraint model for heterogeneous resources is constructed. Based on the operational constraint model, determine the constraint conditions of the power regulation domain operating boundary points of the microgrid; The inverter operation constraint is that the sum of the squares of its active power output and reactive power output does not exceed the square of its rated capacity, and its operating power factor is not lower than the minimum value in the preset power range; the energy storage operation constraint is that its charging and discharging power does not exceed the preset maximum allowable power, the charging and discharging power is related to the change in its stored capacity, and the change in stored capacity is maintained between the upper and lower limits of the preset rated capacity; the load operation constraint is based on relevant parameters such as outdoor temperature, building heat dissipation coefficient, air conditioning thermal conductivity and operating duty cycle, constraining the building indoor temperature within a preset temperature range.

[0096] In this embodiment, specific operational constraints for various heterogeneous resources are specified. The aim is to establish precise mathematical models to describe the operational limitations of photovoltaic inverters, distributed energy storage systems, and building air conditioning loads, based on their different technical characteristics. This provides strict constraints for determining the power regulation domain. Specifically, this includes the following: constraint modeling for photovoltaic inverters, constraint modeling for distributed energy storage systems, constraint modeling for building air conditioning loads, and integration of operational constraint models.

[0097] Specifically, the sum of the squares of its active and reactive power outputs is constrained to not exceed the square of its rated capacity, ensuring that the inverter operates within a safe capacity range and that its operating power factor is not lower than the minimum value of the preset power range, thus meeting the power grid's technical requirements for power factor. This achieves the establishment of a joint active and reactive power regulation model that considers the inverter's capacity limitations and power factor requirements, treating the inverter as a rapid regulation resource, and ensuring that its constraint modeling needs to take into account both regulation capability and equipment safety.

[0098] Furthermore, the charging and discharging power is constrained to not exceed a preset maximum allowable power to prevent overload operation. The charging and discharging power is correlated with changes in the stored capacity, and these changes are maintained between the upper and lower limits of the preset rated capacity to avoid overcharging and over-discharging. Based on this, an energy-power joint constraint model for the energy storage system can be established to ensure safe operation. Constraint modeling of the energy storage system needs to consider both power limitations and energy state.

[0099] In addition, based on relevant parameters such as outdoor temperature, building heat dissipation coefficient, air conditioning thermal conductivity and operating duty cycle, a building thermal dynamics model is established to constrain the building's indoor temperature within a preset temperature range, ensuring users' thermal comfort requirements. Based on this, an air conditioning load adjustment model considering thermal comfort requirements is established. The constraint modeling of this air conditioning load needs to balance adjustment capacity and user comfort.

[0100] In summary, based on the inverter operation constraints, energy storage operation constraints, and load operation constraints, an operation constraint model for heterogeneous resources is constructed. Furthermore, based on this model, the constraint conditions for the power regulation domain operation boundary points of the microgrid are determined, thereby integrating the constraint models of various resources into a unified operation constraint model. This model integration forms a complete constraint system, providing a foundation for subsequent optimization.

[0101] Among them, inverter operation constraints refer to the technical limitations on the operation of photovoltaic inverters, including apparent capacity constraints and power factor constraints, to ensure the safe operation of inverters and meet grid requirements.

[0102] Among them, energy storage operation constraints refer to the technical limitations on the operation of distributed energy storage systems, including power constraints and energy constraints, to ensure that the energy storage system operates in a safe state.

[0103] Among them, load operation constraints refer to the technical limitations of building air conditioning load regulation. Temperature constraints are established based on thermal dynamic equations to explore regulation potential while ensuring user comfort.

[0104] Among them, the operational constraint model refers to the integrated mathematical model that describes the operational constraints of all resources, and expresses the constraints of various resources in a unified mathematical inequality, forming a complete constraint system.

[0105] The building heat dissipation coefficient is a physical parameter that characterizes the heat dissipation capacity of a building envelope. It reflects the intensity of heat exchange between the building and the external environment and affects the dynamic changes in indoor temperature.

[0106] Among them, the thermal conductivity of air conditioning refers to the physical parameter that characterizes the cooling / heating capacity of an air conditioning system. It reflects the ability of the air conditioner to regulate indoor temperature and determines the load regulation potential.

[0107] The duty cycle refers to the proportion of time an air conditioner operates within a control cycle. By adjusting the duty cycle, the power consumption of the air conditioner can be changed, thus achieving continuous load regulation.

[0108] Specifically, the formulas for the inverter operating constraints are as follows: ; In the formula: and They are respectively Inverter The active power output and reactive power output; for PV inverter Rated capacity; This is the minimum power factor of the inverter. is a given constant.

[0109] Specifically, the formulas for the operational constraints of distributed energy storage are as follows: ; ; ; ; In the formula, , They are respectively Maximum charging and discharging power; , They are respectively exist Indicators of charging and discharging status at all times; for exist The amount of electricity stored at any given time; , They are respectively The charging and discharging efficiency; , They are respectively The upper and lower limits of energy storage capacity.

[0110] Specifically, the formulas for the building air conditioning operation constraints are as follows: ; In the formula, , They represent and Indoor temperature over a period of time; , They represent and Outdoor temperature over a given time period; The heat dissipation coefficient is set to 0.96. This refers to the duty cycle of air conditioner energy efficiency. The thermal conductivity of the air conditioner is taken as 0.18; For air conditioning exist The rated energy consumption of the air conditioner at any given time.

[0111] In this embodiment, the step of determining the boundary point sample of the power regulation domain operating boundary point based on the constraints, using the active power and reactive power at the common connection point of the microgrid and the distribution network as the objective function, includes: Based on the operational constraints, an operational boundary point search model is constructed with the active power and reactive power at the microgrid common connection point and the minimum distribution network as objective functions. Based on the operating boundary point search model, the search direction corresponding to the linear combination of active power and reactive power at the common connection point is determined, and the boundary point samples of the power regulation domain operating boundary points in the search direction are obtained.

[0112] In this embodiment, a specific implementation method for power regulation domain boundary point search is proposed. Specifically, by establishing and running a boundary point search model, the boundary of the power regulation domain is systematically explored in multiple search directions to obtain sufficient boundary point samples, providing high-quality input data for subsequent convex hull fitting. This includes the construction of the running boundary point search model, as well as the determination of search directions and boundary point search based on the model.

[0113] Specifically, based on the operational constraints, and using the active and reactive power at the microgrid's point of common coupling and the minimized distribution network as objective functions, a mathematical foundation for boundary search is established. Based on this, an operational boundary point search model is constructed, formulating the boundary search problem as a constrained optimization problem. This establishes a mathematical model that accurately describes the boundary search problem. This model needs to comprehensively consider the operational constraints of various resources and explore the limit boundaries of the power regulation domain in different search directions. Furthermore, it is necessary to determine the search direction corresponding to the linear combination of active and reactive power at the point of common coupling. Typically, multiple search directions evenly distributed within the 0-360 degree range are used, and boundary point samples of the power regulation domain's operational boundary points are obtained along these search directions. Optimization calculations are performed in each search direction to find the farthest feasible point in that direction. This systematic approach to setting search directions ensures that sample points comprehensively cover the power regulation domain boundary. The selection of the corresponding search directions directly affects the completeness and accuracy of the boundary point samples.

[0114] Among them, the running boundary point search model refers to the optimization model used to search for the boundary points of the power regulation domain. Under the premise of satisfying all running constraints, the model maximizes or minimizes the objective function along a specific search direction to find the boundary points.

[0115] Linear combination refers to the weighted combination of active power and reactive power.

[0116] The search direction refers to the direction vector of the boundary search. Each search direction corresponds to a specific power regulation target, such as maximum active power output or minimum reactive power absorption.

[0117] Here, boundary point samples refer to the set of discrete boundary points obtained through boundary search. These points are distributed on the boundary of the power adjustment domain, and the complete feasible boundary can be reconstructed through convex hull fitting.

[0118] ; In the formula, and The search direction for the boundary points has been determined; To ensure the search direction meets the minimum value required to adjust the target, Total number of search directions; The step size for the search angle change, and This is related to the total number of search directions.

[0119] The calculation model for the operating boundary point of the microgrid power regulation domain is minimized by adjusting the search direction. and , obtain Time period The microgrid is obtained by using the convex hull fitting method at several boundary points. Time-of-use power regulation domain.

[0120] In this embodiment, the step of generating the time-varying feasible boundary of the power regulation domain of the microgrid by performing convex hull fitting on the boundary point samples using a preset dynamic interval envelope algorithm includes: By using a preset dynamic interval envelope algorithm, convex hull fitting is performed on the boundary point samples to generate a convex polygon region composed of several linear segments. The nonlinear convex polygon region is piecewise linearized, and based on the results of the piecewise linearization, a corresponding feasible boundary is generated for each segment to obtain the time-varying feasible boundary of the power regulation domain of the microgrid.

[0121] In this embodiment, an algorithm for generating continuous feasible boundaries from discrete boundary points is specifically defined. The main feature is that the discrete boundary points are transformed into convex polygon regions by using convex hull fitting technology, and time-varying feasible boundaries are generated through piecewise linearization to facilitate optimization calculations. The corresponding regions are generated by fitting polygons with convex hulls, and the corresponding boundaries are generated by piecewise linearization.

[0122] Specifically, a convex hull fitting is performed on the boundary point samples using a preset dynamic interval envelope algorithm to generate a convex polygon region. This region, composed of several linear segments, accurately describes the geometry of the power regulation domain. This allows for the construction of a minimum convex polygon containing all boundary points using computational geometry methods. This convex hull fitting effectively handles the distribution characteristics of boundary points, generating smooth boundary curves. Simultaneously, the nonlinear convex polygon region is piecewise linearized, discretizing the continuous boundary into a series of linear segments. Based on the piecewise linearization results, a corresponding feasible boundary is generated for each segment, resulting in the time-varying feasible boundary of the microgrid's power regulation domain. This transforms the nonlinear convex polygon boundary into a piecewise linear representation, facilitating direct use in the optimization model. Piecewise linearization significantly improves computational efficiency while maintaining accuracy.

[0123] Among them, the dynamic interval envelope algorithm refers to an efficient computational method for generating convex hulls. It can quickly process the convex hull calculation of large-scale point sets and adapt to the needs of real-time applications.

[0124] A convex polygon region refers to a polygon region in which all interior angles are less than 180 degrees. Convex polygons have good mathematical properties, and their boundaries can be described by a set of linear inequalities, which makes them easy to handle in optimization problems.

[0125] Piecewise linearization refers to the technique of approximating a nonlinear curve as multiple linear segments. By selecting appropriate segmentation points and the number of segments, a good balance can be achieved between accuracy and computational complexity.

[0126] The feasible boundary refers to the boundary curve of the power regulation domain, which describes the limit range of power regulation capability that a microgrid can provide at a specific time.

[0127] Specifically, the relevant calculation formulas for determining the time-varying feasible boundary of the power regulation domain of a microgrid are as follows: ; ; In the formula, for The power regulation domain of a microgrid at any given time is the power operation boundary point. The convex hull surface obtained by the convex hull fitting method; The number of vertices in the convex hull; For the first Each piecewise coefficient; and They are respectively Momentary power operating boundary point The active power component and reactive power component.

[0128] This embodiment determines the regulation characteristics of the photovoltaic inverter, distributed energy storage system, and building air conditioning load of the microgrid based on the multi-dimensional state information, and determines the constraint conditions of the power regulation domain operating boundary points of the microgrid based on the regulation characteristics. Based on the constraint conditions, the active power and reactive power at the common connection point of the microgrid and the distribution network are used as the objective function to determine the boundary point samples of the power regulation domain operating boundary points. The boundary point samples are fitted with a convex hull using a preset dynamic interval envelope algorithm to generate the time-varying feasible boundary of the power regulation domain of the microgrid. By analyzing the regulation characteristics of various resources, a search model for the operating boundary points is established, and the time-varying feasible boundary is generated using convex hull fitting technology, providing an accurate quantitative description of the power regulation capability of the microgrid.

[0129] Furthermore, to achieve the above objectives, this embodiment also proposes a multi-grid coordinated control system, the system comprising: The acquisition module is used to acquire multi-dimensional state information of heterogeneous resources within the microgrid, and determine the time-varying feasible boundary of the power regulation domain of the microgrid at the point of common coupling based on the multi-dimensional state information. The determination module is used to determine the power bidirectional interaction boundary constraints between the microgrid and the distribution network through the point of common coupling based on the time-varying feasible boundary, and to construct a microgrid distribution optimization model based on the power bidirectional interaction boundary constraints and the power characteristics of the distribution network. The partitioning module is used to obtain the multi-dimensional dynamic characteristics of the heterogeneous resources, and to partition the resource adjustment levels corresponding to the heterogeneous resources according to the multi-dimensional dynamic characteristics to obtain a multi-level adjustment sequence. The scheduling module is used to decompose the scheduling instruction for global power optimization output by the microgrid distribution optimization model into the optimal scheduling strategy for each level according to the multi-level adjustment sequence, and issue the optimal scheduling strategy to each level in sequence.

[0130] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0131] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0132] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0133] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A multi-grid collaborative regulation method based on multi-level resource aggregation decoupling, characterized in that, The method comprises the following steps: acquiring multi-element state information of heterogeneous resources inside a micro-grid, and determining a time-varying feasible boundary of a power regulation domain of the micro-grid at a point of common coupling according to the multi-element state information; determining a power two-way interaction boundary constraint between the micro-grid and a power distribution network through the point of common coupling according to the time-varying feasible boundary, and constructing a micro-grid power distribution optimization model according to the power two-way interaction boundary constraint and power characteristics of the power distribution network; acquiring multi-dimensional dynamic characteristics of the heterogeneous resources, and dividing a corresponding resource regulation level of the heterogeneous resources according to the multi-dimensional dynamic characteristics to obtain a multi-level regulation sequence; when a global power optimization scheduling instruction is output by the micro-grid power distribution optimization model, the scheduling instruction is decomposed into optimal scheduling strategies corresponding to each level according to the multi-level regulation sequence, and the optimal scheduling strategies are sequentially issued to the levels.

2. The method of claim 1, wherein, The step of determining the time-varying feasible boundary of the power regulation domain of the micro-grid at the point of common coupling according to the multi-element state information comprises: determining regulation characteristics of a photovoltaic inverter, a distributed energy storage system and a building air conditioning load corresponding to the micro-grid respectively according to the multi-element state information, and determining constraint conditions of a power regulation domain operating boundary point of the micro-grid according to the regulation characteristics; determining a boundary point sample of the power regulation domain operating boundary point with active power and reactive power at the point of common coupling between the micro-grid and the power distribution network as an objective function according to the constraint conditions; generating the time-varying feasible boundary of the power regulation domain of the micro-grid by convex hull fitting on the boundary point sample through a preset dynamic interval envelope algorithm.

3. The method of claim 2, wherein, The step of determining the constraint conditions of the power regulation domain operating boundary point of the micro-grid according to the regulation characteristics comprises: determining an inverter operation constraint of the photovoltaic inverter, an energy storage operation constraint of the distributed energy storage system and a load operation constraint of the building air conditioning load respectively according to the regulation characteristics; constructing an operation constraint model of the heterogeneous resources according to the inverter operation constraint, the energy storage operation constraint and the load operation constraint; determining the constraint conditions of the power regulation domain operating boundary point of the micro-grid according to the operation constraint model; wherein the inverter operation constraint is that the sum of squares of active output power and reactive output power is not more than the square of rated capacity, and the operation power factor is not less than the minimum value in a preset power range; the energy storage operation constraint is that the charge and discharge power cannot exceed a preset maximum allowed power, the charge and discharge power is associated with a change in storage capacity, and the change in storage capacity is maintained between upper and lower limits of a preset rated capacity; and the load operation constraint is that, based on related parameters of outdoor temperature, building heat dissipation coefficient, air conditioning heat dissipation coefficient and operation duty cycle, the indoor temperature of the building is constrained within a preset temperature interval.

4. The method of claim 2, wherein, The step of determining the boundary point sample of the power regulation domain operating boundary point with active power and reactive power at the point of common coupling between the micro-grid and the power distribution network as an objective function according to the constraint conditions comprises: According to the operation constraint, a search model of an operation boundary point is constructed, with the micro-grid public connection point and minimization of active power and reactive power at the power distribution network as an objective function; According to the search model of the operation boundary point, a search direction corresponding to a linear combination of active power and reactive power at the public connection point is determined, and a boundary point sample of the operation boundary point in the search direction is searched.

5. The method of claim 2, wherein, The step of generating a time-varying feasible boundary of the power regulation domain of the micro-grid by performing convex hull fitting on the boundary point sample through a preset dynamic interval envelope algorithm, comprises: The convex hull fitting is performed on the boundary point sample through a preset dynamic interval envelope algorithm to generate a convex polygon region composed of a plurality of linear segments; The nonlinear convex polygon region is segmented and linearized, and according to the segmented linearization result, a corresponding feasible boundary is generated for each segment to obtain the time-varying feasible boundary of the power regulation domain of the micro-grid.

6. The method of claim 1, wherein, The step of constructing a micro-grid power distribution optimization model according to the power bidirectional interaction boundary constraint and the power characteristics of the power distribution network, comprises: According to the power bidirectional interaction boundary constraint and the power characteristics of the power distribution network, a transmission loss sub-target based on the total network loss of each branch resistance and power flow of the power distribution network is determined, a node potential balance degree sub-target based on the statistical calculation of the voltage deviation of each node in the power distribution network from the standard voltage is determined, and an operation energy efficiency cost sub-target of the power distribution network to the upper-level main network, the power distribution network, and the transaction cost of each micro-grid is determined; The transmission loss sub-target, the node potential balance degree sub-target, and the operation energy efficiency cost sub-target are combined into a comprehensive objective function by using a weighted summation method, and a micro-grid power distribution optimization model is constructed according to the comprehensive objective function.

7. The method of claim 1, wherein, The step of determining the power bidirectional interaction boundary constraint between the micro-grid and the power distribution network through the public connection point according to the time-varying feasible boundary, comprises: The power regulation domain of the micro-grid at the public connection point is obtained, and the active power and reactive power vectors of the micro-grid interacting with the power distribution network through the public connection point are determined; The power interaction vector is constrained to be within the feasible range corresponding to the power regulation domain to obtain the power bidirectional interaction boundary constraint.

8. The method of claim 1, wherein, The step of dividing the resource regulation hierarchy corresponding to the heterogeneous resources according to the multi-dimensional dynamic characteristics to obtain a multi-level regulation sequence, comprises: According to the multi-dimensional dynamic characteristics, a dynamic characteristic matrix of the heterogeneous resources is constructed, wherein the rows of the dynamic characteristic matrix correspond to resource types, and the columns correspond to evaluation indexes composed of unit regulation cost, response margin, and response speed; The dynamic characteristic matrix is normalized, and according to the processing result, the positive ideal solution and the negative ideal solution of each evaluation index are determined; According to the positive ideal solution and the negative ideal solution, the objective weights of the evaluation indexes are calculated by using an entropy weight method, and the relative closeness of the heterogeneous resources to the positive ideal solution and the negative ideal solution is calculated according to the objective weights. The heterogeneous resources are sorted according to the relative proximity from large to small, and a multi-level adjustment sequence is obtained.

9. The method of claim 1, wherein, Before the step of outputting the scheduling instruction of the global power optimization of the micro-grid power distribution optimization model, the method further comprises: a priority response proportion parameter corresponding to each level of the heterogeneous resources is set; when the priority response proportion parameter is a first preset value, all adjustment demands are borne by the highest level of the heterogeneous resources, and when the adjustment margin of the highest level is used up, the next level of resources of the highest level of the heterogeneous resources undertakes the remaining instructions; when the priority response proportion parameter is a second preset value, each resource of the heterogeneous resources is used to undertake the scheduling instruction.

10. A multi-grid coordinated control system based on the method of any of claims 1-9, characterized in that, The system comprises: an acquisition module configured to acquire multi-element state information of heterogeneous resources in a micro-grid, and determine a time-varying feasible boundary of a power adjustment domain of the micro-grid at a point of common coupling according to the multi-element state information; a determination module configured to determine a power two-way interaction boundary constraint between the micro-grid and a power distribution network through the point of common coupling according to the time-varying feasible boundary, and construct a micro-grid power distribution optimization model according to the power two-way interaction boundary constraint and an electrical energy characteristic of the power distribution network; a division module configured to acquire multi-dimensional dynamic characteristics of the heterogeneous resources, and divide resource adjustment levels corresponding to the heterogeneous resources according to the multi-dimensional dynamic characteristics, and obtain a multi-level adjustment sequence; a scheduling module configured to, when a scheduling instruction of global power optimization of the micro-grid power distribution optimization model is output, decompose the scheduling instruction into optimal scheduling strategies corresponding to each level according to the multi-level adjustment sequence, and successively issue the optimal scheduling strategies to each level.

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