Active power regulation and control method and system based on boundary constraint

Abstract

The invention relates to an active power regulation and control method and system based on boundary constraint, and the method comprises the following steps: constructing a boundary constraint condition model, and achieving the dynamic updating of the boundary constraint condition model through a visual maintenance tool; the load rate of the power grid equipment is monitored in real time, whether the power grid equipment is out of limit is judged according to the scene definition table, working condition classification is performed, and generation of a control strategy is triggered; a control strategy is generated based on sensitivity analysis and resource priority, and a comprehensive index calculation model is adopted to screen and adjust resources according to a strategy constraint definition table; sensitivity analysis obtains a sensitivity coefficient based on real-time topology of power grid equipment in combination with load flow calculation, and the resource priority is determined according to a resource rule definition table; and when a single type of resources cannot meet the regulation and control requirements, generating a comprehensive control strategy containing multi-type resource regulation variables, and executing closed-loop effect verification and strategy iteration through a strategy index definition table.

Description

Technical Field

[0001] The present application relates to the technical field of power system regulation and control, and more specifically, to an active power regulation method and system based on boundary constraints. Background Art

[0002] With the development of the new power system, the installed capacity of distributed power sources has gradually increased, and the reverse power transmission of distributed power sources has become the norm in power grid operation. In some local areas, due to the rapid growth of distributed power sources and insufficient planning, frequent reverse overload problems have occurred in the power grid. The power grid dispatching mode has gradually changed from "source following load" to "source-load interaction" mode, and the traditional automatic generation control mode of source following load has been broken. Especially in the face of heavy overload control of equipment in local areas, there is a lack of effective automatic control means, and it is urgent to incorporate distributed resources into the power grid regulation and control scope.

[0003] Distributed resources are numerous and widely distributed, and there are significant differences in the regulation performance, regulation rate, etc. of various types of resources. At the same time, for the regulation of load resources, due to factors such as capital subsidies, it is also necessary to comprehensively consider factors such as its regulation compensation mechanism and regulation cost, which are quite different from the traditional power source regulation strategy. It is necessary to comprehensively consider various factors such as the regulation amount required by the power grid regulation and the regulation speed requirement, and comprehensively generate a regulation control strategy.

[0004] The prior art, such as the Chinese patent application with the publication number "CN119448415A", discloses an aggregation method for the adjustable power domain of power grid demand-side resources. The method includes: S1: Construct an adjustable power domain for various demand-side resources including time-series coupling characteristic constraints, and use time-segmented linear boundary constraints to characterize the adjustable power domains of demand-side resources in adjacent time periods. S2: Establish a comprehensive probability model of demand-side resources containing various correlations based on the historical data of various demand-side resources. S3: Combine the linear boundary constraints and the comprehensive probability model to construct an aggregation model for the adjustable power domain of demand-side resources, and obtain the flexibility decision space of demand-side resources through model optimization.

[0005] The problems existing in the above prior art are that this method focuses on the probability model driven by historical data, does not involve real-time load monitoring and dynamic response to over-limit scenarios, and is difficult to cope with sudden equipment overload problems; it mainly focuses on the aggregation modeling of demand-side resources, but does not propose a collaborative optimization algorithm for cross-type resources, and it is difficult to achieve the optimal balance between regulation cost and effect; it does not design a closed-loop verification mechanism (such as effect evaluation, anomaly marking, and iterative optimization) after the strategy is executed, which may lead to the inability to correct in time when there is a deviation between the strategy and the actual working conditions. Summary of the Invention

[0006] To solve the above technical problems, the present invention proposes an active power regulation method and system based on boundary constraints.

[0007] The technical solution of the present invention is as follows:

[0008] The present invention proposes an active power regulation method based on boundary constraints, including the following steps:

[0009] Step S1, construct a boundary constraint condition model, including: a scenario definition table, a resource rule definition table, a policy constraint definition table, and a policy index definition table, and realize the dynamic update of the boundary constraint condition model through a visual maintenance tool; the scenario definition table is used to construct a benchmark framework for the operation of the power grid system, and clarify the equipment operation boundary conditions and working condition classifications; the resource rule definition table is used to standardize the scope, control characteristics, and interaction logic of schedulable resources under different scenarios; the policy constraint definition table is used to set safety and economic boundary constraint conditions; the policy index definition table is used to quantitatively evaluate the effectiveness of the regulation strategy;

[0010] Step S2, monitor the load rate of power grid equipment in real time, and determine whether the power grid equipment exceeds the limit according to the power grid equipment operation boundary constraints in the scenario definition table, and perform working condition classification to trigger the generation of control strategies; the power grid equipment includes: transformers, lines, and sections;

[0011] Step S3, the control strategy is generated based on sensitivity analysis and resource priority. The resource priority obtains the scope and priority of schedulable resources according to the resource rule definition table, and uses a comprehensive index calculation model to screen adjustment resources according to the policy constraint definition table; the sensitivity analysis is based on the real-time topology of power grid equipment combined with power flow calculation to obtain sensitivity coefficients;

[0012] Step S4, when a single type of resource cannot meet the regulation requirements, generate a comprehensive control strategy including the adjustment amounts of multiple types of resources, and perform closed-loop effect verification and policy iteration through the policy index definition table.

[0013] As a preferred implementation manner, the dynamic update of the boundary constraint condition model is realized through a visual maintenance tool, and the visual editing information includes: creating an over-limit scenario, load rate limit value, optimized target load rate, whether to enable power grid security, new energy full absorption constraint, comprehensive index coefficient, available resource types and priorities.

[0014] As a preferred implementation manner, the power grid equipment includes: transformers, lines, and sections; among them:

[0015] The formula for calculating the real-time load rate of a transformer is as follows:

[0016]

[0017] Among them:

[0018]

[0019] Where: β b is the real-time load rate of the transformer; S is the real-time apparent power of the transformer; R b is the rated capacity of the transformer; P is the real-time active power of the high-voltage winding of the transformer; Q is the real-time reactive power of the high-voltage winding of the transformer;

[0020] The calculation formula for the real-time load rate of the line is as follows:

[0021]

[0022] Where: β x is the real-time load rate of the line; I is the real-time current value of the line; I lmt is the current limit of the line;

[0023] The calculation formula for the real-time load rate of the section is as follows:

[0024]

[0025] Where: β d is the real-time load rate of the section; P d is the real-time active power of the section; P lmt is the power limit of the section.

[0026] As a preferred embodiment, the control strategy is generated based on sensitivity analysis and resource priority, and specifically includes the following steps:

[0027] According to the real-time value of the equipment load rate and the scenario limit, determine the working condition classification and the optimized target load rate, and calculate the active power value of the out-of-limit equipment that needs to be adjusted to correct the equipment out-of-limit;

[0028] According to the equipment working condition classification, obtain the range of schedulable resources from the resource rule definition table;

[0029] According to the sensitivity data, search for the source, load, and storage resources connected to the nodes with sensitivity greater than the preset value, and perform secondary filtering according to the range of schedulable resources to form an initial available resource pool, and determine the currently used available resource type according to the priority of the resource types in the available resource pool from high to low;

[0030] The disposal strategy is load transfer. According to the currently used available resource type, form a load transfer plan, calculate the transferable load of the out-of-limit equipment, and update the adjusted sensitivity data and the data of the available resource pool;

[0031] If the disposal strategy is not load transfer, then calculate the comprehensive index of the available resource monomers in the currently used available resource type, and preferentially select the resources with high comprehensive index for allocation; after all the resources of the currently used available resource type are allocated, use the subsequent available resource types in order of priority;

[0032] At the end of the allocation of a certain type of available resource type, device heavy overload verification is performed according to the sensitivity data. When the verification fails, the overload quantity of the newly added over-limit device is calculated, and the associated available resource allocation quantity is adjusted in reverse.

[0033] As a preferred implementation, the comprehensive index calculation model is used to screen the adjustment resources according to the policy constraint definition table. The comprehensive index of the available resource monomer is calculated as follows:

[0034]

[0035] In the formula: y i is the comprehensive index value of the i-th available resource monomer; w j is the weight of the j-th index component; x j is the dimensionless value after the j-th index component is normalized; w α , w ε , w δ , w θ are the weights of the sensitivity index, cost index, adjustment speed index, and adjustment ability index respectively; α i ′ , ε i ′ , δ ′ ′ , θ i ′ are the dimensionless values after the sensitivity index, cost index, adjustment speed index, and adjustment ability index of the i-th available resource monomer are normalized respectively; m is the total number of indexes.

[0036] As a preferred implementation, when a single type of resource cannot meet the regulation requirements, a comprehensive control strategy including the adjustment quantities of multiple types of resources is generated, and resource combination is performed through a cross-resource collaborative optimization algorithm, specifically:

[0037] min∑(C k ·ΔP k ) + λ * |ΔP total - ΔP required |;

[0038] s.t.∑ΔP k =ΔP required ;

[0039] ΔP k,min ≤ΔP k ≤ΔP k,max ;

[0040] In the formula: C k is the unit adjustment cost of resource k; ΔP kThe actual regulated power quantity for resource k; λ is the penalty coefficient for the regulation quantity deviation; ΔP total is the actual total regulated power quantity; ΔP required is the total regulated power quantity to be achieved; ΔP k,min and ΔP k,max are respectively the minimum and maximum adjustable power quantities of resource k.

[0041] As a preferred implementation manner, the execution of the closed-loop effect verification includes:

[0042] Within T time after the strategy is executed, T is the strategy response time. If the decrease in the device load rate is less than 90% of the preset target value, then:

[0043] Mark the scenario constraint condition as an abnormal state;

[0044] When starting secondary regulation, preferentially call the standby resource pool;

[0045] Generate optimization suggestions for the constraint conditions and send them to the visualization maintenance tool..

[0046] On the other hand, the present invention also provides an active power regulation system based on boundary constraints, including:

[0047] Construct a boundary constraint condition model, including: a scenario definition table, a resource rule definition table, a strategy constraint definition table, and a strategy index definition table, and realize the dynamic update of the boundary constraint condition model through a visualization maintenance tool; the scenario definition table is used to construct the benchmark framework for the operation of the power grid system, clarify the equipment operation boundary conditions and working condition classifications; the resource rule definition table is used to standardize the scope, control characteristics, and interaction logic of dispatchable resources under different scenarios; the strategy constraint definition table is used to set safety and economic boundary constraint conditions; the strategy index definition table is used to quantitatively evaluate the effectiveness of the regulation strategy;

[0048] A monitoring and strategy triggering module, which monitors the load rate of power grid equipment in real time, determines whether the power grid equipment exceeds the limit according to the grid equipment operation boundary constraints in the scenario definition table, and performs working condition classification to trigger the generation of control strategies; the power grid equipment includes: transformers, lines, and sections;

[0049] A regulation resource analysis module, the control strategy is generated based on sensitivity analysis and resource priority. The resource priority obtains the scope and priority of dispatchable resources according to the resource rule definition table, and uses a comprehensive index calculation model to screen the regulation resources according to the strategy constraint definition table; the sensitivity analysis is based on the real-time topology of the power grid equipment combined with the power flow calculation to obtain the sensitivity coefficient;

[0050] The policy execution and verification module generates a comprehensive control policy including the adjustment amounts of multiple types of resources when a single type of resource cannot meet the regulation requirements, and performs closed-loop effect verification and policy iteration through the policy index definition table.

[0051] On the other hand, the present invention also provides an electronic device with a computer program stored thereon. When the computer program is executed by a processor, it implements a method for regulating active power based on boundary constraints as described in any embodiment of the present invention.

[0052] On the other hand, the present invention also provides a computer-readable medium for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement a method for regulating active power based on boundary constraints as described in any embodiment of the present invention.

[0053] The present invention has the following beneficial effects:

[0054] 1. By real-time monitoring the load rates of main transformers, lines, and sections, and combining with the scenario definition table to quickly identify equipment over-limit situations and trigger dynamic control policies, the response speed is effectively improved.

[0055] 2. A boundary model including multi-dimensional constraints such as safety, economy, and new energy consumption is constructed and dynamically updated through a visualization tool, enhancing the flexibility and adaptability of the policy.

[0056] 3. When a single resource is insufficient, a comprehensive index calculation model (such as sensitivity, cost, and adjustment speed) is used to screen resources, and cross-resource coordination is achieved through a mathematical optimization model to obtain the optimal combination of multiple types of resources, improving the regulation efficiency.

[0057] 4. After the policy is executed, the effect is verified by the decrease in the load rate. In case of anomalies, secondary adjustment is automatically started to form a closed-loop feedback mechanism, ensuring the effectiveness and robustness of the policy. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0059] Figure 1 It is a schematic flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0061] It should be understood that the step numbers used in the text are only for convenient description and do not limit the execution order of the steps.

[0062] It should be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0063] The terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0064] The term "and / or" refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0065] Embodiment 1:

[0066] To make the purpose, technical solution and advantages of the present invention clearer, the following will combine specific embodiments of the present application and refer to the appended Figure 1 , and clearly and completely describe the technical solution of the present invention.

[0067] To solve the problems of the prior art, the present invention provides an active power regulation method based on boundary constraints, including the following steps:

[0068] Step S1, constructing a boundary constraint condition model, including: a scenario definition table, a resource rule definition table, a policy constraint definition table and a policy index definition table, and realizing the dynamic update of the boundary constraint condition model through a visualization maintenance tool; the scenario definition table is used to construct a benchmark framework for the operation of the power grid system, and clarify the equipment operation boundary conditions and working condition classifications; the resource rule definition table is used to standardize the scope, control characteristics and interaction logic of schedulable resources under different scenarios; the policy constraint definition table is used to set safety and economic boundary constraint conditions; the policy index definition table is used to quantitatively evaluate the effectiveness of the regulation strategy;

[0069] In this embodiment, specific examples of the scenario definition table, resource rule definition table, policy constraint definition table, and policy metric definition table are shown in Tables 1-4.

[0070] Table 1 Scenario Definition Table

[0071] Serial Number Chinese Description English Name Data Type Primary Key Remarks 1 Scene Number no int Yes Starting from 1 2 Scene Name sceneName varchar(256) No 3 Scene Type sceneType int No Refer to IESLAB_APC_SCENE_TYPE 4 Equipment Type equipType int No Refer to IESLAB_APC_MONITORRES_TYPE 5 Equipment ID equipId ulonglong No Reserved for use when specifying equipment 6 Equipment Name equipName varchar(256) No 7 Upper Limit Value upLmt float No Default value is 0 8 Lower Limit Value dnLmt float No Default value is 0 9 Target Limit Value targetLmt float No 10 Enable Flag useFlag int No

[0072] Table 2 Resource Rule Definition Table

[0073] Serial Number Chinese Description English Name Data Type Primary Key Remarks 1 Belonging Scene Number secneNo int No 2 Resource Type resType int No Refer to IESLAB_APC_RESCALC_TYPE 3 Resource Priority resPriority int No

[0074] Table 3 Policy Constraint Definition Table

[0075] Serial Number Chinese Description English Name Data Type Primary Key Remarks 1 Belonging Scene Number secneNo int No 2 Constraint Type boundType int No Refer to IESLAB_APC_BOUND_TYPE

[0076] Table 4 Policy Metric Definition Table

[0077] Serial Number Chinese Description English Name Data Type Primary Key Remarks 1 Belonging Scene Number secneNo int No 2 Quota Type quotaType int No Refer to IESLAB_APC_QUOTA_TYPE 3 Quota Coefficient quotaCoeff float No Default value is 0

[0078] Step S2: Monitor the load rate of grid equipment in real time. According to the operating boundary constraints of grid equipment in the scenario definition table, determine whether the grid equipment is overloaded, classify the operating conditions, and trigger the generation of control strategies. The grid equipment includes transformers, lines, and sections.

[0079] The grid equipment includes main transformers, lines, and sections. Among them:

[0080] The calculation formula for the real-time load rate of the main transformer is as follows:

[0081]

[0082] Where:

[0083]

[0084] In the formula: β b is the real-time load rate of the transformer; S is the real-time apparent power of the transformer; R b is the rated capacity of the transformer; P is the real-time active power of the high-voltage winding of the transformer; Q is the real-time reactive power of the high-voltage winding of the transformer. The calculation formula for the real-time load rate of the line is as follows:

[0085]

[0086] In the formula: β x is the real-time load rate of the line; I is the real-time current value of the line; I lmt is the current limit of the line.

[0087] The calculation formula for the real-time load rate of the section is as follows:

[0088]

[0089] where: β d is the real-time load rate of the section; P d is the real-time active power of the section; P lmt is the power limit of the section.

[0090] Step S3, the control strategy is generated based on sensitivity analysis and resource priority. The resource priority obtains the range and priority of schedulable resources according to the resource rule definition table, and filters the regulating resources using a comprehensive index calculation model according to the policy constraint definition table; the sensitivity analysis obtains the sensitivity coefficient based on the real-time topology of grid equipment combined with power flow calculation;

[0091] The control strategy is generated based on sensitivity analysis and resource priority, and specifically includes the following steps:

[0092] According to the real-time value of the equipment load rate and the scenario limit, determine the working condition classification and the optimized target load rate, and calculate the active power value of the over-limit equipment that needs to be adjusted to correct the equipment over-limit;

[0093] According to the equipment working condition classification, obtain the range of schedulable resources from the resource rule definition table;

[0094] According to the sensitivity data, find the source, load, and storage resources connected to the nodes with sensitivity greater than the preset value, and perform secondary filtering according to the range of schedulable resources to form an initial available resource pool, and determine the currently used available resource type according to the priority of resource types in the available resource pool from high to low;

[0095] The disposal strategy is load transfer. According to the currently used available resource type, form a load transfer plan, calculate the transferable load of the over-limit equipment, and update the adjusted sensitivity data and available resource pool data;

[0096] If the disposal strategy is not load transfer, then calculate the comprehensive index of the available resource monomers in the currently used available resource type, and preferentially select the resources with high comprehensive index for allocation; after all the resources of the currently used available resource type are allocated, use the subsequent available resource types in order of priority;

[0097] When screening the regulating resources using a comprehensive index calculation model according to the policy constraint definition table, the comprehensive index of the available resource monomer, the specific calculation formula is as follows:

[0098]

[0099] where: y i is the comprehensive index value of the i-th available resource monomer; w j is the weight of the j-th index component; x j is the dimensionless value after the j-th index component is normalized; w α 、wε , w δ , w θ are the sensitivity index weight, cost index weight, regulation speed index weight, and regulation ability index weight respectively; α i ′ , ε i ′ , δ ′ ′ , θ i ′ are the dimensionless values after the normalization processing of the sensitivity index, cost index, regulation speed index, and regulation ability index of the i-th available resource monomer respectively; m is the total number of indices.

[0100] Step S4: When a single type of resource cannot meet the regulation demand, generate a comprehensive control strategy including the regulation amounts of multiple types of resources, and perform closed-loop effect verification and strategy iteration through the strategy index definition table.

[0101] Use the available resource types according to the priority. When a single type of resource cannot meet the regulation demand, generate a comprehensive control strategy including the regulation amounts of multiple types of resources, and perform resource combination through the cross-resource collaborative optimization algorithm. Specifically:

[0102] min∑(C k ·ΔP k ) + λ * |ΔP total - ΔP required |;

[0103] s.t.∑ΔP k = ΔP required ;

[0104] ΔP k,min ≤ΔP k ≤ΔP k,max ;

[0105] In the formula: C k is the unit regulation cost of resource k; ΔP k is the actual regulation power amount of resource k; λ is the penalty coefficient for the regulation amount deviation; ΔP total is the actual total regulation power amount; ΔP required is the total regulation power amount to be achieved; ΔP k,min , ΔP k,max are the minimum and maximum adjustable power amounts of resource k respectively.

[0106] The execution of the closed-loop effect verification includes:

[0107] Within T time after the strategy is executed, T is the strategy response time. If the decrease in the device load rate is less than 90% of the preset target value, then:

[0108] Mark the scene constraint condition as an abnormal state;

[0109] When starting secondary regulation, preferentially call the backup resource pool;

[0110] Generate optimization suggestions for constraint conditions and send them to the visualization maintenance tool.

[0111] Embodiment 2:

[0112] This embodiment provides an active power regulation system based on boundary constraints, including:

[0113] Construct a boundary constraint condition model, including: a scene definition table, a resource rule definition table, a policy constraint definition table, and a policy index definition table, and realize the dynamic update of the boundary constraint condition model through a visualization maintenance tool; the scene definition table is used to construct a benchmark framework for the operation of the power grid system, and clarify the equipment operation boundary conditions and operating conditions classification; the resource rule definition table is used to standardize the scope, control characteristics, and interaction logic of schedulable resources under different scenarios; the policy constraint definition table is used to set safety and economic boundary constraint conditions; the policy index definition table is used to quantitatively evaluate the effectiveness of the regulation strategy;

[0114] A monitoring and policy triggering module, which monitors the load rate of power grid equipment in real time, determines whether the power grid equipment exceeds the limit according to the power grid equipment operation boundary constraints in the scene definition table, and conducts operating condition classification to trigger the generation of control strategies; the power grid equipment includes: transformers, lines, and sections;

[0115] A regulation resource analysis module, the control strategy is generated based on sensitivity analysis and resource priority, the resource priority obtains the scope and priority of schedulable resources according to the resource rule definition table, and filters the regulation resources according to the comprehensive index calculation model in the policy constraint definition table; the sensitivity analysis is based on the real-time topology of the power grid equipment combined with power flow calculation to obtain the sensitivity coefficient;

[0116] A policy execution and verification module, when a single type of resource cannot meet the regulation requirements, generates a comprehensive control strategy including the regulation amounts of multiple types of resources, and performs closed-loop effect verification and policy iteration through the policy index definition table.

[0117] Embodiment 3:

[0118] This embodiment provides an electronic device, on which a computer program is stored, and when the computer program is executed by a processor, it implements an active power regulation method based on boundary constraints as described in any embodiment of the present invention.

[0119] Embodiment 4:

[0120] This embodiment provides a computer-readable medium for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement a method for regulating active power based on boundary constraints as described in any embodiment of the present invention.

[0121] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent the cases of A existing alone, A and B existing simultaneously, and B existing alone. Where A and B may be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, and c may represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c may be single or multiple.

[0122] Those of ordinary skill in the art can realize that the units and algorithm steps described in the embodiments disclosed herein can be implemented by a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0123] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0124] In several embodiments provided by the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (hereinafter referred to as ROMs), random access memories (hereinafter referred to as RAMs), magnetic disks, or optical discs that can store program codes.

[0125] The above are only embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for regulating active power based on boundary constraints, characterized in that It includes the following steps: Step S1, construct a boundary constraint condition model, including: a scenario definition table, a resource rule definition table, a policy constraint definition table, and a policy index definition table, and realize the dynamic update of the boundary constraint condition model through a visualization maintenance tool; the scenario definition table is used to construct a benchmark framework for the operation of the power grid system, and clarify the equipment operation boundary conditions and operating condition classifications; the resource rule definition table is used to standardize the scope, control characteristics, and interaction logic of dispatchable resources under different scenarios; the policy constraint definition table is used to set safety and economic boundary constraint conditions; the policy index definition table is used to quantitatively evaluate the effectiveness of the control strategy; Step S2, monitor the load rate of power grid equipment in real time, determine whether the power grid equipment is over-limit according to the power grid equipment operation boundary constraints in the scenario definition table, and perform operating condition classification to trigger the generation of control strategies; the power grid equipment includes: transformers, lines, and sections; Step S3, the control strategy is generated based on sensitivity analysis and resource priority. The resource priority obtains the scope and priority of dispatchable resources according to the resource rule definition table, and uses a comprehensive index calculation model to screen the adjustment resources according to the policy constraint definition table; the sensitivity analysis is based on the real-time topology of the power grid equipment combined with the power flow calculation to obtain the sensitivity coefficient; Step S4, when a single type of resource cannot meet the control requirements, generate a comprehensive control strategy including the adjustment amounts of multiple types of resources, and perform closed-loop effect verification and policy iteration through the policy index definition table.

2. The active power regulation method based on boundary constraints according to claim 1, wherein: The dynamic update of the boundary constraint condition model is realized through the visualization maintenance tool, where the visual editing information includes: creating an over-limit scenario, load rate limit value, optimized target load rate, whether to enable power grid safety, new energy full consumption constraint, comprehensive index coefficient, available resource type and priority.

3. A method for regulating active power based on boundary constraints according to claim 1, characterized in that: The power grid equipment includes: transformers, lines, and sections; among which: The calculation formula for the real-time load rate of the transformer is as follows: Where: where: β b is the real-time load rate of the transformer; S is the real-time apparent power of the transformer; R b is the rated capacity of the transformer; P is the real-time active power of the high-voltage winding of the transformer; Q is the real-time reactive power of the high-voltage winding of the transformer; The calculation formula for the real-time load rate of the line is as follows: Where: β x is the real-time line load rate; I is the real-time line current value; I lmt is the line current limit value; The calculation formula for the real-time load rate of the section is as follows: Where: β d is the real-time load rate of the section; P d is the real-time active power of the section; P lmt is the power limit of the section.

4. A method for regulating active power based on boundary constraints according to claim 1, characterized in that: The control strategy is generated based on sensitivity analysis and resource priority, and specifically includes the following steps: According to the real-time value of the equipment load rate and the scenario limit value, determine the operating condition classification and the optimized target load rate, and calculate the active power value of the over-limit equipment that needs to be adjusted to correct the equipment over-limit; According to the equipment operating condition classification, obtain the scope of dispatchable resources from the resource rule definition table; According to the sensitivity data, find the source, load, and storage resources connected to the nodes with sensitivity greater than the preset value, and perform secondary filtering according to the scope of dispatchable resources to form an initial available resource pool, and determine the currently used available resource type according to the priority of the resource types in the available resource pool from high to low; If the disposal strategy is load transfer, form a load transfer plan according to the currently used available resource type, calculate the transferable load amount of the over-limit equipment, and update the adjusted sensitivity data and available resource pool data; If the disposal strategy is not load transfer, calculate the comprehensive index of the available resource monomers in the currently used available resource type, and preferentially select the resources with high comprehensive index for allocation; after all the resources of the currently used available resource type are allocated, use the subsequent available resource types in order of priority; When the allocation of a certain type of available resource type ends, device overloading and underloading verification is performed according to sensitivity data. When the verification fails, the overlimit amount is calculated for the newly added overlimit devices, and the allocation amount of the related available resources is adjusted in reverse.

5. A method for regulating active power based on boundary constraints according to claim 1, characterized in that: The comprehensive index calculation model is used to screen the adjustment resources according to the policy constraint definition table. The comprehensive index of the available resource unit is calculated as follows: where: y i is the comprehensive index value of the i-th available resource monomer; w j is the weight of the j-th index component; x j is the dimensionless value after the j-th index component is normalized; w α , w ε , w δ , w θ are the weights of the sensitivity index, cost index, adjustment speed index, and adjustment ability index respectively; α i ′ , ε i ′ , δ i ′ , θ i ′ are the dimensionless values after the sensitivity index, cost index, adjustment speed index, and adjustment ability index of the i-th available resource monomer are normalized respectively; m is the total number of indexes.

6. The active power regulation method based on boundary constraints according to claim 1, wherein: When a single type of resource cannot meet the regulation requirements, a comprehensive control strategy including the adjustment amounts of multiple types of resources is generated, and resource combination is performed through a cross-resource collaborative optimization algorithm. Specifically: min∑(C k ·ΔP k )+λ*|ΔP total -ΔP required |; s.t.∑ΔP k =ΔP required ; ΔP k,min ≤ΔP k ≤ΔP k,max ; Where: C k is the unit adjustment cost of resource k; ΔP k is the actual adjustment power quantity of resource k; λ is the penalty coefficient for the adjustment quantity deviation; ΔP total is the actual total adjustment power quantity; ΔP required is the total adjustment power quantity to be achieved; ΔP k,min , ΔP k,max are respectively the minimum and maximum adjustable power quantities of resource k.

7. The active power regulation system based on boundary constraints according to claim 1, characterized in that: The execution of the closed-loop effect verification includes: Within T time after the strategy is executed, T is the strategy response time. If the decrease in the device load rate is less than 90% of the preset target value, then: Mark the scene constraint condition as an abnormal state; When starting secondary adjustment, give priority to calling the standby resource pool; Generate optimization suggestions for the constraint conditions and send them to the visualization maintenance tool.

8. A real power regulation system based on boundary constraints, characterized in that, Including: Construct a boundary constraint condition model, including a scene definition table, a resource rule definition table, a policy constraint definition table, and a policy index definition table, and realize the dynamic update of the boundary constraint condition model through a visualization maintenance tool; the scene definition table is used to construct the benchmark framework for the operation of the power grid system, and clarify the device operation boundary conditions and working condition classifications; the resource rule definition table is used to standardize the scope, control characteristics, and interaction logic of dispatchable resources in different scenarios; the policy constraint definition table is used to set the safety and economic boundary constraint conditions; the policy index definition table is used to quantitatively evaluate the effectiveness of the regulation strategy; A monitoring and policy triggering module that monitors the load rate of power grid devices in real time, determines whether the power grid devices are overlimit according to the power grid device operation boundary constraints in the scene definition table, and performs working condition classification to trigger the generation of control strategies; the power grid devices include transformers, lines, and sections; A regulation resource analysis module, the control strategy is generated based on sensitivity analysis and resource priority. The resource priority obtains the scope and priority of dispatchable resources according to the resource rule definition table, and screens the adjustment resources by using a comprehensive index calculation model according to the policy constraint definition table; the sensitivity analysis is based on the real-time topology of the power grid device combined with power flow calculation to obtain the sensitivity coefficient; A policy execution and verification module, when a single type of resource cannot meet the regulation requirements, generates a comprehensive control strategy including the adjustment amounts of multiple types of resources, and performs closed-loop effect verification and policy iteration through the policy index definition table.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a method for regulating active power based on boundary constraints according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements a method for regulating active power based on boundary constraints according to any one of claims 1 to 7.

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