Method and system for representing credible adjustment capability of flexibility resources, terminal and medium

Abstract

The invention discloses a method, a system, a terminal and a medium for representing credible adjustment capability of flexibility resources, and the method comprises the steps: obtaining key parameters of each flexibility resource, and constructing a constraint condition of each flexibility resource; obtaining parameter data of the power distribution system, and constructing a power flow constraint condition of the power distribution system; according to a preset target curve of a system operation demand and an expected adjustment effect, constructing a total power curve adjustment constraint condition of outward transmission of the flexible resource cluster; and a second-order cone optimization model is obtained by taking the total energy transmitted outwards by the maximized and minimized flexible resources as an objective function, and then the second-order cone power flow model of the power distribution system is solved by using a solver to obtain the upper and lower boundaries of the credible regulation capability of the flexible resource cluster. The method can solve the problem that the coupling of the adjustment target in the continuous time period cannot be fully considered in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, system, terminal and medium for characterizing the trusted regulation capability of flexibility resources. Background Art

[0002] To achieve the goals of carbon peak and carbon neutrality, building a modern power system dominated by renewable energy has become a core task of energy transformation. The intermittent and volatile nature of renewable energy requires the power system to fully leverage the demand-side regulation potential and achieve coordinated interaction between power generation, grid, load, and storage to support the large-scale integration of renewable energy.

[0003] However, demand-side flexibility resources are numerous, small in capacity, and diverse in type. Effectively integrating these diverse flexibility resources and providing reliable and precise regulation capabilities for the power system is a key technical challenge in achieving demand response.

[0004] Current research on the aggregation of flexible resources generally ignores the regulation objectives of resource groups, which can lead to deviations between the aggregated results and actual demand. In particular, given the temporal dependence of most flexible resources, the construction of their aggregation feasible domain must also consider temporal coupling. Existing methods often employ simplifications, such as describing the feasible domain by considering only the linear weighting of the sum of active and reactive power. This effectively ignores the temporal characteristics of resource regulation capabilities. Even if regulation objectives are set within such simplified boundaries, their feasibility is difficult to guarantee. This uncertainty in the aggregation results directly impacts the feasibility of bidirectional source-load scheduling.

[0005] In addition, existing technologies also have limitations in dealing with heterogeneous resources and multi-level resource aggregation. Most studies tend to use a unified model to describe the final aggregation results. Although this method performs well in the aggregation of homogeneous resources, it is difficult to determine an applicable universal model when faced with heterogeneous resources with significant differences in parameters and characteristics. Some studies have attempted to characterize aggregates through energy storage or generator models, but the parameters of such models are difficult to effectively reflect the differences between resources, which limits the scope of application and accuracy of the model. When considering the aggregation of resources at different topological nodes and levels, network constraints need to be introduced to ensure the feasibility of the results. However, existing studies mostly use linearized power flow models, and this simplified treatment further increases the aggregation error.

[0006] Therefore, it is urgent to develop a method that can fully consider the coupling of regulation targets in continuous time periods. Summary of the Invention

[0007] The present invention provides a method system, terminal and medium for characterizing the trusted regulation capability of flexibility resources. The method can solve the problem that the existing technology cannot fully consider the coupling of regulation targets within continuous time periods, and can accurately and reliably characterize the regulation capability of flexibility resources to support the efficient operation of the power system and the deep integration of renewable energy.

[0008] In a first aspect, the present invention provides a method for characterizing the trustworthy regulation capability of flexible resources, comprising:

[0009] Obtain key parameters of each flexibility resource and establish constraints for each flexibility resource; the key parameters include power limit, energy limit, charge and discharge efficiency, etc. of each flexibility resource;

[0010] Obtaining parameter data of the power distribution system and constructing power flow constraints of the power distribution system; the parameter data of the power distribution system includes the voltage of each node, the power of each node, the power of each branch, the line capacity, the line impedance of the branch, transformer parameters, node connection relationships, etc.;

[0011] According to the preset target curve of system operation requirements and expected regulation effect, the total power curve regulation constraint conditions of the flexible row resource cluster are established;

[0012] Based on the constraints of each flexibility resource, the flow constraints of the distribution system, and the total power curve regulation constraints transmitted outward by the flexibility resource cluster, and with maximizing and minimizing the total energy transmitted outward by the flexibility resources as the objective function, a second-order cone optimization model of the distribution system is obtained. Then, the second-order cone flow model of the distribution system is solved using a solver to obtain the upper and lower bounds of the credible regulation capability of the flexible resource cluster.

[0013] Furthermore, the constraints of each flexibility resource are specifically as follows:

[0014]

[0015] Where, is the feasible region of the i-th flexibility resource of the K-th category; is the decision variable; P i K,chT Charging power column vector for flexibility resources; P i K,disT is the column vector of the discharge power of the flexibility resource; is the upper bound of charging power; is the charging power; is the upper bound of charging power; is the discharge power; is the lower bound of discharge power; is the lower bound of discharge power; is the maximum downward climbing rate; is the external power; is the maximum upward climbing rate; The lower bound of energy state; is the energy state; is the upper bound of the energy state; T is the time interval

[0016] Furthermore, the power flow constraint conditions of the power distribution system are specifically as follows:

[0017]

[0018]

[0019] Where p j,t is the active power of node j at time t; P ij,t is the active power of branch ij at time t; r ij is the resistance of branch ij; l ij.t is the square of the current in branch ij at time t; q j,t is the reactive power of node j at time t; Q jk,t is the reactive power of branch jk at time t; x ij is the branch ij reactance; v i,t is the square of the voltage at node i at time t; V i is the lower bound of the voltage at node i; is the upper bound of the voltage at node i; is the lower bound of the active power of the generator at node i; is the upper bound of the active power of the generator at node i; is the generator power at node i at time t; is the lower bound of the reactive power of the generator at node i; is the upper bound of the reactive power of the generator at node i; is the reactive power of the generator at node i at time t; is the upper bound of the branch ij current; is the node set; T is the time interval; ε is the branch set.

[0020] Furthermore, the total power curve adjustment constraint condition for outward transmission of the flexible row resource cluster is specifically:

[0021]

[0022] in, is the total power of flexibility resources; P pcc is the power at the point of common connection; is the base load power; E is the total energy of the flexibility resources; is the total power of the flexible resource at time t; τ is the final time; I is the unit matrix; W is the coefficient matrix; σ is a given constant; ε is a given threshold; 1 is the unit column vector; Δt is the time interval; L goal,τ is the target shape curve value at time τ.

[0023] In a second aspect, the present invention provides a flexible resource trustworthy regulation capability characterization system, comprising:

[0024] Constraint Acquisition Module: This module is used to obtain the key parameters of each flexible resource and establish the constraints for each flexible resource. It is also used to obtain parameter data of the power distribution system and establish the power flow constraints of the power distribution system. Based on the preset target curve of the system operation requirements and the expected regulation effect, it establishes the regulation constraints for the total power curve transmitted outward by the flexible resource cluster.

[0025] Flexible resource trusted regulation capability acquisition module: used to obtain the second-order cone optimization model of the distribution system based on the constraints of each flexible resource, the flow constraints of the distribution system, and the total power curve regulation constraints transmitted outward by the flexible resource cluster, and to maximize and minimize the total energy transmitted outward by the flexible resource as the objective function, and then use the solver to solve the second-order cone flow model of the distribution system to obtain the upper and lower boundaries of the trusted regulation capability of the flexible resource cluster.

[0026] In a third aspect, the present invention provides an electronic terminal comprising a processor and a memory, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the above method.

[0027] In a fourth aspect, the present invention provides a readable storage medium storing a computer program, which is used by a processor to execute the steps of the method described above.

[0028] Beneficial effects

[0029] The present invention proposes a method, system, terminal, and medium for characterizing the trusted regulation capability of flexible resources, wherein the method has the following specific advantages:

[0030] 1. Overall power curve analysis: Unlike traditional methods that focus on a single moment or simplify the model, this method studies the entire power curve. This global perspective allows us to more accurately capture the dynamic regulation characteristics of resources.

[0031] 2. Consideration of temporal coupling characteristics: The method fully considers the mutual influence of flexibility resources on a long time scale, which is crucial for accurately assessing the overall regulatory potential of the system.

[0032] 3. Regulation goal orientation: The innovative incorporation of regulation goals into the optimization model enables precise aggregation for specific regulation needs. This greatly improves the practicality and effectiveness of the aggregation results.

[0033] 4. Network constraint integration: By introducing network topology and power flow constraints, it ensures that the optimization results meet the physical limitations of the actual power grid, enhancing the practicality of the method.

[0034] 5. Complementary utilization of resources: The method design fully considers the characteristic differences of different types of flexibility resources and maximizes the complementary effects between resources through optimized aggregation.

[0035] 6. Improved system regulation capability: Numerical experimental results show that this method can significantly improve the overall regulation capability of the system, providing the possibility for more efficient use of flexibility resources.

[0036] 7. Adaptability and scalability: Although the method is complex, its framework has good adaptability and can be adjusted and expanded according to specific application scenarios.

[0037] 8. Decision support capability: By providing a more accurate representation of regulation capability, this method provides strong support for system operators' decision-making, helping to improve the accuracy and efficiency of scheduling. That is, the assessed energy range can be used by higher-level dispatchers to make direct decisions to meet energy requirements without having to consider multi-time power balance issues. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0039] Figure 1 Schematic diagram of a flow chart of a method for characterizing the trustworthy adjustment capability of flexible resources provided by an embodiment of the present invention;

[0040] Figure 2 Schematic diagram of the upper and lower bounds of the aggregated total power curve provided by an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of the upper bounds of various flexible row resource cluster powers provided by an embodiment of the present invention;

[0042] Figure 4 This is a schematic diagram of lower bounds corresponding to various flexible row resource cluster powers provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other implementations obtained by those of ordinary skill in the art without inventive effort are within the scope of protection of the present invention.

[0044] Example 1

[0045] like Figure 1 As shown, this embodiment provides a method for characterizing the trustworthy adjustment capability of flexible resources, including:

[0046] S1: Obtain key parameters of each flexibility resource and construct constraints for each flexibility resource, wherein the key parameters include power limit, energy limit, charge and discharge efficiency, etc. of each flexibility resource.

[0047] Specifically, a topological network constructed by obtaining information of each node in the power distribution system is constructed, and constraints are constructed on the flexibility resources of each node. The flexible resource cluster includes a variety of different flexibility resources. The type of flexibility resources can be selected according to actual needs and is not limited. This embodiment is specifically implemented by taking thermostatically controlled load (TCL), energy storage (ES), electric vehicle (EV), transferable load (TL), photovoltaic (PV), microturbine (MT), etc. as examples for explanation.

[0048] Flexibility resources are included in node power, and node injection power is the generated power minus the consumed power. Different types of flexibility resources have corresponding individual characteristics. In order to simplify the complex and changeable problems brought about by individual characteristics, this embodiment uses a virtual battery model to uniformly characterize the constraints of various types of flexibility resources, thereby converting the individual characteristics of different types of resources into a standardized linear inequality constraint group. Specifically, the key parameters such as power limit, energy limit, ramp limit, charge and discharge efficiency of each flexibility resource are obtained, and then the constraints of each flexibility resource are constructed as follows:

[0049]

[0050] Where, is the feasible domain of the i-th flexibility resource of the K-th category (where K category includes photovoltaic, generator, etc.); is the decision variable; P i K,chTCharging power column vector for flexibility resources; P i K,disT is the column vector of the discharge power of the flexibility resource; is the upper bound of charging power; is the charging power; is the upper bound of charging power; is the discharge power; is the lower bound of discharge power; is the lower bound of discharge power; is the maximum downward climbing rate; is the external power; is the maximum upward climbing rate; The lower bound of energy state; is the energy state; is the upper bound of the energy state; T is the time interval.

[0051] By adopting the virtual battery model to uniformly characterize each flexibility resource, the charging and discharging power constraints, ramp constraints, and energy state constraints are described, and the feasible domain Ω of the K-th category i-th flexibility resource is further defined. i K Written in matrix form:

[0052]

[0053] Where, F i K is the parameter matrix corresponding to the matrix transformation; is the decision variable; is the coefficient matrix.

[0054] By uniformly representing the constraints of various flexibility resources, standardized prerequisites are provided for the subsequent solution of the optimized second-order cone tidal flow model, which helps to simplify problem processing and improve the applicability and computational efficiency of the model.

[0055] S2: Obtain parameter data of the power distribution system and construct power flow constraints of the power distribution system. The parameter data of the power distribution system includes the voltage of each node, the power of each node, the power of each branch, the line capacity, the line impedance of the branch, transformer parameters, and node connection relationships.

[0056] Specifically, the voltage of each node, power of each node, power of each branch, line capacity, line impedance of each branch, transformer parameters, and node connection relationship of the distribution system are obtained to establish the key equations of the power flow model:

[0057] Voltage transfer balance equation:

[0058] V i,t -V j,t =z ij Iij,t

[0059] Where V i,t is the voltage of node i at time t; V j,t is the voltage of node j at time t; z ij is the impedance of branch ij at time t; I ij,t is the current in branch ij at time t. The voltage transfer balance equation can be used to describe the voltage relationship between adjacent nodes in the distribution system, taking into account the impact of line impedance on voltage.

[0060] Branch power flow equation:

[0061]

[0062] Where S ij,t is the power of branch ij at time t; is the conjugate of the current in branch ij at time t. The branch power flow equation can describe the active and reactive power at the head end of the branch, reflecting the power transmission characteristics in the network.

[0063] Node power balance constraints:

[0064]

[0065] Where S jk,t is the power of branch jk at time t; I ij,t is the current of branch ij at time t; s j,t is the injected power of node j at time t. The node power balance constraint ensures that the power injection and outflow of each node are balanced, which is the embodiment of the energy conservation principle in the power system.

[0066] Node voltage limit:

[0067]

[0068] Where V i is the lower bound of the voltage amplitude at node i; The node voltage limit specifies the allowable range of the voltage amplitude at each node to ensure the stable operation of the system and the quality of power consumption.

[0069] Power generation equipment output constraints:

[0070]

[0071] Where, is the generator power at node i at time t; s i g is the lower bound of the generator power at node i at time t; is the upper bound of the generator power at node i at time t. The output constraints of power generation equipment can limit the active and reactive power output range of the generator set, reflecting the physical limitations of power generation resources.

[0072] Line capacity constraints:

[0073]

[0074] Where, I ij,t is the current of branch ij at time t; is the upper bound of the branch ij current. By limiting the line current amplitude, the line load is ensured not to exceed its rated capacity, thus ensuring equipment safety.

[0075] In order to reduce the computational complexity, the current and voltage information is removed and only the amplitude is retained; l is introduced ij,t =|I ij,t | 2 , v i,t =|V i,t | 2 , and then transform the node injection power constraint and voltage amplitude constraint into linear form; combined with the application of second-order cone relaxation to the branch power constraint, the power flow constraint of the distribution system is obtained as follows:

[0076]

[0077]

[0078] Where p j,t is the active power of node j at time t; P ij,t is the active power of branch ij at time t; r ij is the resistance of branch ij; l ij.t is the square of the current in branch ij at time t; q j,t is the reactive power of node j at time t; Q jk,t is the reactive power of branch jk at time t; x ij is the branch ij reactance; v i,t is the square of the voltage at node i at time t; V i is the lower bound of the voltage at node i; is the upper bound of the voltage at node i; is the lower bound of the active power of the generator at node i; is the upper bound of the active power of the generator at node i; is the generator power at node i at time t; is the lower bound of the reactive power of the generator at node i; is the upper bound of the reactive power of the generator at node i; is the reactive power of the generator at node i at time t; is the upper bound of the branch ij current; is the node set; T is the time interval; ε is the branch set.

[0079] S3: Based on the preset target curve of the system operation requirements and expected regulation effect, the total power curve regulation constraint conditions for the flexible row resource cluster to be transmitted outward are constructed.

[0080] Based on the target curve, a second-order cone shape constraint for the regulation target curve is constructed. There are two ways to preset the target curve based on the system operation requirements and the expected regulation effect: (1) The load criterion can be directly used as the aggregated regulation target. (2) Following the load baseline concept, the regulation target curve can be formed by calculating the baseline and the expected reduction amount, and then normalized to obtain the shape.

[0081] Through power flow calculation, under the condition that power flow constraints are met, the total power transmitted outward by the flexible row resource cluster must meet certain constraints on the shape of the adjustment curve. This embodiment adjusts the shape of the total power curve transmitted outward by each flexible row resource cluster so that the adjusted total power curve transmitted outward by the flexible row resource cluster has the time coupling characteristics of the output power after aggregation. In order to more accurately capture the dynamic characteristics of resource regulation capabilities, the core lies in performing aggregation along the normal vector of the hyperplane of the total power curve transmitted outward by the flexible row resource cluster, and maximizing the aggregation accuracy in the direction of the normal vector. In order to ensure that the similarity between the actual total power curve transmitted outward by the flexible row resource cluster after aggregation and the preset target curve is within the preset error range, the adjustment constraint conditions for the total power curve transmitted outward by the flexible row resource cluster are constructed, specifically:

[0082]

[0083] Where, is the total power of flexibility resources; P pcc is the power at the point of common connection; is the base load power; E is the total energy of the flexibility resources; is the total power of the flexibility resource at time t; τ is the final time; σ is a given constant; d is the normalized total power curve and the target shape curve L goal The second norm of; ε is the given threshold; L goal is the target shape curve vector; is the curve similarity. The bold letters are vectors, and the non-bold letters are scalars.

[0084] However, this constraint is non-convex in its original form, which is not conducive to direct solution. Therefore, it is transformed into an equivalent second-order cone constraint, that is, the total power curve adjustment constraint condition for the flexible row resource cluster to transmit outward is as follows:

[0085]

[0086] Where I is the identity matrix; W is the coefficient matrix; is the total power of the flexibility resource; σ is a given constant; ε is a given threshold; 1 is a unit column vector; Δt is the time interval; L goal,τ is the target shape curve value at time τ.

[0087] S4: Based on the constraints of each flexible resource, the flow constraints of the distribution system, and the total power curve adjustment constraints transmitted outward by the flexible row resource cluster, and with maximizing and minimizing the total energy transmitted outward by the flexible resources as the objective function, a second-order cone optimization model of the distribution system is obtained. Then, the second-order cone flow model of the distribution system is solved using a solver to obtain the upper and lower bounds of the credible adjustment capability of the flexible row resource cluster.

[0088] Specifically, the corresponding second-order cone optimization model in this embodiment is:

[0089] max E and min E

[0090]

[0091]

[0092] Specifically, the solver can be selected according to actual needs. In this embodiment, the Gurobi solver is selected to obtain the upper and lower bounds of the overall trustworthy adjustment capability of the flexible row resource cluster, such as Figure 2 As shown, the upper bounds of the power of various flexible row resource clusters calculated in this embodiment are as follows: Figure 3 As shown in the figure, the lower bounds of the power of various flexible row resource clusters are as follows: Figure 4 As can be seen from the figure, the upper limit of the total energy that can be transmitted externally is 2611kWh, and the lower limit is 1552kWh. The ES cluster and the EV cluster complement each other in the timing of discharge and charging, effectively balancing the supply and demand relationship. At the same time, the adjustment of the TCL cluster frees up power adjustment space for subsequent clusters, enhancing overall coordination. The superior dispatch center can make direct decisions based on the assessed energy range and meet the energy requirements without having to consider the issue of multi-time power balance.

[0093] Example 2

[0094] This embodiment provides a system for characterizing the trusted regulation capability of flexible resources, including:

[0095] Constraint Acquisition Module: This module is used to obtain the key parameters of each flexible resource and establish the constraints for each flexible resource. It is also used to obtain parameter data of the power distribution system and establish the power flow constraints of the power distribution system. Based on the preset target curve of the system operation requirements and the expected regulation effect, it establishes the regulation constraints for the total power curve transmitted outward by the flexible resource cluster.

[0096] Flexible resource trusted regulation capability acquisition module: used to obtain the second-order cone optimization model of the distribution system based on the constraints of each flexible resource, the flow constraints of the distribution system, and the total power curve regulation constraints transmitted outward by the flexible resource cluster, and to maximize and minimize the total energy transmitted outward by the flexible resource as the objective function, and then use the solver to solve the second-order cone flow model of the distribution system to obtain the upper and lower boundaries of the trusted regulation capability of the flexible resource cluster.

[0097] Example 3

[0098] This embodiment provides an electronic terminal, which includes a processor and a memory, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the above method.

[0099] It should be understood that in the embodiments of the present invention, the processor referred to may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The memory may include a read-only memory and a random access memory, and provides instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0100] The readable storage medium is a computer-readable storage medium, which may be an internal storage unit of the controller described in any of the aforementioned embodiments, such as a hard disk or memory of the controller. The readable storage medium may also be an external storage device of the controller, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the controller. Furthermore, the readable storage medium may also include both an internal storage unit of the controller and an external storage device. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0101] Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned readable storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0102] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0103] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for characterizing the trustworthy regulation capability of flexible resources, characterized by: include: Obtain key parameters of each flexibility resource and construct constraints for each flexibility resource; Obtain parameter data of the power distribution system and construct power flow constraints of the power distribution system; According to the preset target curve of system operation requirements and expected regulation effect, the total power curve regulation constraint conditions of the flexible row resource cluster are established; Based on the constraints of each flexibility resource, the flow constraints of the distribution system, and the total power curve regulation constraints transmitted outward by the flexibility resource cluster, and with maximizing and minimizing the total energy transmitted outward by the flexibility resources as the objective function, a second-order cone optimization model of the distribution system is obtained. Then, the second-order cone flow model of the distribution system is solved using a solver to obtain the upper and lower bounds of the credible regulation capability of the flexible resource cluster.

2. The method for characterizing the trustworthy regulation capability of flexible resources according to claim 1 is characterized in that: The constraints of each flexibility resource are as follows: Where, is the feasible region of the i-th flexibility resource of the K-th category; is the decision variable; Charging power column vector for flexibility resources; is the column vector of the discharge power of the flexibility resource; is the upper bound of charging power; is the charging power; is the upper bound of charging power; is the discharge power; is the lower bound of discharge power; is the lower bound of discharge power; is the maximum downward climbing rate; is the external power; is the maximum upward climbing rate; The lower bound of energy state; is the energy state; is the upper bound of the energy state; T is the time interval.

3. The method for characterizing the trustworthy regulation capability of flexible resources according to claim 1 is characterized in that: The power flow constraints of the power distribution system are specifically as follows: Where p j,t is the active power of node j at time t; P ij,t is the active power of branch ij at time t; r ij is the resistance of branch ij; l ij.t is the square of the current in branch ij at time t; q j,t is the reactive power of node j at time t; Q jk,t is the reactive power of branch jk at time t; x ij is the branch ij reactance; v i,t is the square of the voltage at node i at time t; V i is the lower bound of the voltage at node i; is the upper bound of the voltage at node i; is the lower bound of the active power of the generator at node i; is the upper bound of the active power of the generator at node i; is the generator power at node i at time t; is the lower bound of the reactive power of the generator at node i; is the upper bound of the reactive power of the generator at node i; is the reactive power of the generator at node i at time t; is the upper bound of the branch ij current; is the node set; T is the time interval; ε is the branch set.

4. The method for characterizing the trustworthy regulation capability of flexible resources according to claim 1 is characterized in that: The specific constraint conditions for adjusting the total power curve transmitted outward by the flexible row resource cluster are: in, is the total power of flexibility resources; P pcc is the power at the point of common connection; is the base load power; E is the total energy of the flexibility resources; is the total power of the flexible resource at time t; τ is the final time; I is the unit matrix; W is the coefficient matrix; σ is a given constant; ε is a given threshold; 1 is the unit column vector; Δt is the time interval; L goal,τ is the target shape curve value at time τ.

5. A flexible resource trustworthy regulation capability characterization system, characterized by: include: Constraint condition acquisition module: used to obtain key parameters of each flexibility resource and construct the constraint conditions of each flexibility resource; Used to obtain parameter data of the power distribution system and construct power flow constraint conditions of the power distribution system; According to the preset target curve of system operation requirements and expected regulation effect, the total power curve regulation constraint conditions of the flexible row resource cluster are established; Flexible resource trusted regulation capability acquisition module: used to obtain the second-order cone optimization model of the distribution system based on the constraints of each flexible resource, the flow constraints of the distribution system, and the total power curve regulation constraints transmitted outward by the flexible resource cluster, and to maximize and minimize the total energy transmitted outward by the flexible resource as the objective function, and then use the solver to solve the second-order cone flow model of the distribution system to obtain the upper and lower boundaries of the trusted regulation capability of the flexible resource cluster.

6. An electronic terminal, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the method according to any one of claims 1 to 4.

7. A readable storage medium, characterized in that: A computer program is stored, and when the computer program is called by a processor, it is used to execute: the steps of the method according to any one of claims 1 to 4.

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