A resource optimization allocation method for maximizing dynamic frequency feasible region area

Abstract

The application discloses a resource optimization allocation method for maximizing dynamic frequency feasible region area, which comprises the following steps: taking system virtual inertia support strength and system frequency regulation strength as coordinates, and establishing a dynamic frequency feasible region scanning space; judging whether each point in the scanning space meets preset constraints, and if yes, the point is a feasible point, and the dynamic frequency feasible region is composed of all feasible points; constructing a dynamic frequency feasible region area maximization objective function, and solving inertia support correction coefficients and frequency regulation support correction coefficients of each resource; and based on the two kinds of correction coefficients of each resource obtained by solving, respectively optimizing inertia allocation weights and frequency modulation allocation weights of each resource, so as to determine the virtual inertia support strength and the frequency regulation strength of each resource. The application can improve the utilization efficiency of existing support resources, expand the frequency safety feasible region, reduce the resource overrun risk, and improve the frequency safety margin in multi-period rolling operation without increasing the total capacity of resources.

Description

Technical Field

[0001] This application relates to the field of novel power system frequency security control technology, and in particular to a resource optimization allocation method that maximizes the area of ​​the dynamic frequency feasible domain. Background Technology

[0002] As the proportion of new energy power generation, energy storage systems, flexible DC transmission, and power electronic converters in power systems continues to increase, the capacity of traditional synchronous machines is gradually decreasing, the system's equivalent inertia is declining, the frequency change rate after disturbances is increasing, the frequency minimum point is decreasing, and the issue of system frequency security is becoming increasingly prominent. Traditional power systems mainly rely on the rotor inertia of synchronous machines and primary frequency regulation by speed governors to maintain frequency stability. However, in high-proportion new energy systems, wind power, photovoltaics, energy storage, flexible DC transmission, and grid-connected converters are gradually becoming important resources for frequency support.

[0003] Existing methods for allocating frequency support resources often employ fixed-ratio allocation, capacity-ratio allocation, or experience-based frequency regulation parameter tuning, which fail to adequately consider the differences in frequency support capabilities, energy margins, response speeds, reserve capacity, energy storage state of charge, and node frequency weaknesses among various resources. For example, the same system-level virtual inertia support strength and frequency regulation strength, if allocated to different resources, may lead to completely different frequency response results and resource over-limit states. Resources with larger capacities are not necessarily located at the weakest point in frequency dynamics, and resources with higher reserve capacity may not necessarily have strong inertia support capabilities. Therefore, simply allocating support tasks according to capacity ratios may result in some resources being overloaded and others underutilized, limiting the feasible space for system frequency security.

[0004] In reality, the combination of frequency security support that the system can achieve varies dynamically depending on different disturbance intensities, time periods, energy storage SOCs, the online ratio of synchronous machines, and resource deployment / retreat states. If it can be achieved... and A dynamic frequency feasible region is constructed for the coordinates, and the resource allocation strategy is further optimized with the goal of maximizing the area of ​​the feasible region. By optimizing the proportion of different types of resources to bear virtual inertia and frequency adjustment intensity, the frequency safety feasible region is expanded, and the system's adaptability to disturbances and frequency safety margin are improved. Summary of the Invention

[0005] The purpose of this invention is to propose a resource optimization allocation method that maximizes the area of ​​the dynamic frequency feasible domain, in order to solve the problems of insufficient consideration of resource capability differences, unclear frequency security boundaries, high risk of resource overruns, and difficulty in adapting to rolling operating conditions in existing frequency support resource allocation methods.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a resource optimization allocation method for maximizing the area of ​​the dynamic frequency feasible domain, comprising: collecting current operating state parameters of the power system, establishing initial inertia allocation weights and initial frequency regulation allocation weights for each resource, and defining an inertia support correction coefficient for each type of resource. and frequency adjustment support correction coefficient Strength supported by system virtual inertia and system frequency regulation intensity Using coordinates, establish a dynamic frequency feasible domain scan space; determine the position of each point within the scan space. If the preset node RoCoF constraints, minimum frequency point constraints, and resource boundary constraints are met, the point is determined as a feasible point, and all feasible points form a dynamic frequency feasible region. An objective function to maximize the area of ​​the dynamic frequency feasible region is constructed, and a global heuristic optimization algorithm is used to solve for the inertia support correction coefficient and frequency regulation support correction coefficient of each resource. Based on the solved inertia support correction coefficient and frequency regulation support correction coefficient of each resource, the inertia allocation weight and frequency regulation allocation weight of each resource are optimized to determine the virtual inertia support strength and frequency regulation strength of each resource.

[0007] Furthermore, the feasible region of the dynamic frequency is defined as:

[0008]

[0009] In the formula, t is the current running time. The set of correction coefficients; the support strength scanning region D(t) corresponding to t, and the RoCoF constraints of the nodes. Minimum frequency constraint Resource boundary constraints They are defined as follows:

[0010]

[0011]

[0012]

[0013]

[0014] In the formula, This represents the upper limit of the system's virtual inertia support strength scan. This represents the upper limit of the system frequency adjustment intensity scan. For the limit of the rate of change of frequency, For the maximum frequency change rate, The lowest frequency point, The minimum frequency limit. The effective spare capacity of the i-th resource. Let be the peak power command of the i-th resource during the frequency support process. Further, the initial inertia allocation weights are constructed based on the node frequency change rate:

[0015]

[0016] In the formula, For the first The rate of change of the frequency of each resource node Indicates all Total number of resources;

[0017] The initial frequency regulation allocation weights are constructed based on resource energy margin, energy storage state of charge, and reserve capacity:

[0018]

[0019] In the formula, Let i be the energy margin of the i-th resource. In the state of energy storage charge, For effective reserve capacity.

[0020] Furthermore, frequency support resources are categorized into several types based on resource control patterns and energy attributes, including grid-connected wind power resources, grid-connected energy storage resources, grid-connected flexible DC power resources, grid-built wind power resources, and grid-built energy storage resources; for each type of resource... Define the inertia support correction factor respectively and frequency adjustment support correction coefficient .

[0021] Furthermore, the corrected inertia allocation weights and frequency modulation allocation weights are as follows:

[0022]

[0023]

[0024] In the formula, For the category to which the i-th resource belongs, This is the set of correction coefficients.

[0025] Furthermore, the virtual inertia and frequency modulation intensity of each resource satisfy:

[0026]

[0027]

[0028] And satisfy resource boundary constraints:

[0029]

[0030]

[0031] In the formula, Let i be the maximum virtual inertia capability of the i-th resource. The maximum frequency regulation strength capability of the i-th resource.

[0032] Furthermore, under a discrete scanning grid, the target area of ​​the dynamic frequency feasible domain in the period at time t is defined as:

[0033]

[0034] In the formula, , Set the step size for the scan grid. As a feasibility indicator variable, it is defined as:

[0035]

[0036] In the formula, Let t be the dynamic frequency feasible region, and t be the current running time. This is the set of correction coefficients.

[0037] Furthermore, the objective function is:

[0038]

[0039] In the formula, This is the average stability margin at feasible points, used to select a safer option when the areas are similar. To correct the coefficients and avoid excessive concentration of results, This is the stability margin weighting coefficient.

[0040] Furthermore, for a single scan point stability margin Defined as the minimum of three constraint margins: frequency change rate margin, frequency minimum point margin, and resource reserve margin.

[0041]

[0042] The three terms in the formula are the frequency change rate margin, the frequency minimum point margin, and the resource reserve margin, respectively. This is the system's rated power.

[0043] average stability margin at feasible points :

[0044]

[0045] Correction coefficient offset penalty term The expression is:

[0046]

[0047] In the formula, , These represent the proportion of virtual inertia support and the proportion of frequency regulation intensity borne by the g-th type of resources after optimization, respectively. and These represent the initial inertia load ratio and the initial frequency modulation load ratio of the g-th resource before optimization, respectively. Here, G represents the weighting coefficient for the offset penalty term, and G is the number of resource categories.

[0048] Furthermore, the method is used for rolling time-domain operation, updating the load level, disturbance intensity, renewable energy availability, energy storage state of charge, reserve capacity, online ratio of synchronous machines and online status of resources at each rolling moment, and recalculating the dynamic frequency feasible domain, inertia support correction coefficient, frequency regulation support correction coefficient, inertia allocation weight and frequency regulation allocation weight.

[0049] The correction coefficients are optimized using a global heuristic algorithm. and The optimization process is performed to obtain the optimal correction coefficient, and the results are then distributed to the support resources of each frequency. Subsequently, the correction coefficient and resource allocation weights are updated and distributed according to the set rolling update cycle.

[0050] The global heuristic optimization algorithm is one or more of the following: particle swarm optimization, genetic algorithm, Bayesian optimization algorithm, covariance matrix adaptive evolution strategy, or simulated annealing algorithm.

[0051] The beneficial effects of this invention are as follows: Compared with traditional fixed-parameter allocation methods, this invention guides resource allocation parameter optimization by maximizing the feasible region area, enabling more inertial support to be allocated to frequency-dynamically weak regions and more frequency modulation support to resources with sufficient energy margin and reserve capacity. Furthermore, this invention employs global heuristic optimization methods such as particle swarm optimization to solve for the correction coefficients of various resources, achieving a near-globally optimal resource allocation strategy with high efficiency. In each set rolling update cycle, the correction coefficients, final allocation weights, and the responsibilities of each resource are recalculated. and The system layer then distributes the control information to each resource controller, achieving a closed-loop control process of "state awareness—feasibility domain assessment—correction coefficient optimization—resource allocation distribution—frequency support execution." Therefore, this invention can improve the utilization efficiency of existing support resources, reduce resource overruns and support gaps, enhance frequency security margin and disturbance rejection capabilities, and strengthen the adaptive frequency support capability of high-proportion renewable energy systems under multi-period rolling operation without increasing the total resource capacity.

[0052] This invention constructs a dynamic frequency feasible domain using system-level virtual inertia support strength and frequency regulation strength as coordinates, which can intuitively characterize the range of frequency security support capabilities that the system can achieve under current operating conditions. Simultaneously, it establishes differentiated resource allocation weights by comprehensively considering factors such as node frequency change rate, resource reserve capacity, and energy storage state of charge. Furthermore, it optimizes the proportion of inertia support and frequency regulation support undertaken by various resources through resource type correction coefficients, making the support tasks more aligned with the dynamic response capabilities and energy boundaries of different resources. Compared to fixed parameter allocation methods, this invention can improve the utilization efficiency of existing frequency support resources without increasing the total system resource capacity, reduce resource overruns and unmet support needs, expand the feasible domain area that satisfies RoCoF, minimum frequency point, and resource boundary constraints, and improve the system's frequency security margin and disturbance resistance. Moreover, this method is applicable to rolling operation scenarios, dynamically updating the feasible domain and resource allocation strategy according to changes in load, renewable energy output, energy storage SOC, reserve capacity, and synchronous machine online status, thereby improving the adaptability, coordination, and operational safety of frequency support in high-proportion renewable energy power systems. Attached Figure Description

[0053] Figure 1 This is a schematic flowchart of a resource optimization allocation method for maximizing the area of ​​the dynamic frequency feasible domain, provided in an embodiment of the present invention.

[0054] Figure 2 This is a schematic diagram of an electronic device structure provided in an embodiment of the present invention. Detailed Implementation

[0055] The present invention will now be further described with reference to the accompanying drawings.

[0056] This invention provides a resource optimization allocation method for maximizing the dynamic frequency feasible domain area. This method is applicable to power systems containing various resource types, such as synchronous machines, grid-connected converters, grid-linked converters, energy storage, wind power, and flexible DC transmission. Figure 1 As shown, it includes the following steps:

[0057] 1) Collect power system operating status parameters, including system rated frequency, base capacity, disturbance power, online capacity or online ratio of synchronous machines, system damping, synchronous machine governor parameters, capacity of each converter resource, reserve power, energy storage state of charge, energy margin, node frequency change rate, resource online status, and maximum virtual inertia and maximum frequency regulation intensity of resources; and classify resources into several types according to resource control methods and energy attributes: grid-connected wind power resources, grid-connected energy storage resources, grid-connected flexible DC resources, grid-built wind power resources, and grid-built energy storage resources;

[0058] 2) Establish a system-level frequency support parameter space, using the system's virtual inertia to support strength. and system frequency regulation intensity Establish a two-dimensional scanning space using coordinates;

[0059] 3) Construct a dynamic frequency feasible region criterion for each frequency in the scan space in step 2). The maximum rate of frequency change was obtained through frequency response simulation. Lowest frequency point and peak power commands for each resource If the set node RoCoF constraints are met, If resource boundary constraints are met, then the point is determined to be a feasible point, and the dynamic frequency feasible region is composed of all feasible points.

[0060] 4) Establish initial resource allocation weights: For each feasible point, establish initial inertia allocation weights based on node frequency weakness, and establish initial frequency regulation allocation weights based on resource energy margin, energy storage state of charge, and reserve capacity.

[0061] 5) Define two correction coefficients for each type of resource g. , ;

[0062] 6) Construct an objective function that maximizes the feasible region area, use a global heuristic optimization algorithm to solve for the correction coefficients, and calculate the final resource allocation weights based on the optimal correction coefficients. Then, update the correction coefficients and resource allocation weights according to the set rolling update cycle, and distribute the system-level calculation results to the support resources of each frequency.

[0063] In step 3), the dynamic frequency feasible domain Defined as:

[0064]

[0065] In the formula, t is the current running time. The set of correction coefficients; the support strength scanning region D(t) corresponding to t, and the RoCoF constraints of the nodes. Minimum frequency constraint Resource boundary constraints They are defined as follows:

[0066]

[0067]

[0068]

[0069]

[0070] In the formula, This represents the upper limit of the system's virtual inertia support strength scan. This represents the upper limit of the system frequency adjustment intensity scan. For the limit of the rate of change of frequency, For the maximum frequency change rate, The lowest frequency point, The minimum frequency limit. The effective spare capacity of the i-th resource. Let the peak power command of the i-th resource be the power command during the frequency support process. In step 4), the initial inertia allocation weights are constructed based on the maximum frequency change rate obtained in step 3).

[0071]

[0072] In the formula, Let i be the frequency change rate of the node containing the i-th resource. Indicates all Total number of resources.

[0073] The initial frequency regulation allocation weights are constructed based on resource energy margin, energy storage state of charge, and reserve capacity:

[0074]

[0075] In the formula, Let i be the energy margin of the i-th resource. In the state of energy storage charge, For effective reserve capacity.

[0076] Furthermore, in step 5), the present invention defines two correction coefficients for each type of resource g. , The corrected inertia allocation weights and frequency modulation allocation weights are as follows:

[0077]

[0078]

[0079] In the formula, For the category to which the i-th resource belongs, This is the set of correction coefficients.

[0080] The actual virtual inertia and frequency regulation intensity undertaken by each resource after correction satisfy the following:

[0081]

[0082]

[0083] And satisfy resource boundary constraints:

[0084]

[0085]

[0086] In the formula, Let i be the maximum virtual inertia capability of the i-th resource. The maximum frequency regulation strength capability of the i-th resource.

[0087] Furthermore, in step 6), this invention introduces the area of ​​the dynamic frequency feasible region. Under a discrete scanning grid, the area of ​​the feasible region in the period at time t is defined as:

[0088]

[0089] In the formula, , Set the step size for the scan grid. As a feasibility indicator variable, it is defined as:

[0090]

[0091] In the formula, Let t be the dynamic frequency feasible region, and t be the current running time. This is the set of correction coefficients.

[0092] The optimization objective further includes a stability margin term and a correction coefficient offset penalty term, constructing a comprehensive objective function:

[0093]

[0094] In the formula, This is the average stability margin at feasible points, used to select a safer option when the areas are similar. To correct for coefficient offset penalty terms and avoid excessive concentration of results, This is the stability margin weighting coefficient.

[0095] For a single scan point stability margin Defined as the minimum of three constraint margins: frequency change rate margin, frequency minimum point margin, and resource reserve margin.

[0096]

[0097] The three terms in the formula are the frequency change rate margin, the frequency minimum point margin, and the resource reserve margin, respectively. This is the system's rated power.

[0098] average stability margin at feasible points :

[0099]

[0100] Correction coefficient offset penalty term The expression is:

[0101]

[0102] In the formula, , These represent the proportion of virtual inertia support and the proportion of frequency regulation intensity undertaken by the g-th type of resource after optimization, respectively. and These represent the initial inertia load ratio and the initial frequency modulation load ratio of the g-th resource before optimization, respectively. Here, G represents the weighting coefficient for the offset penalty term, and G is the number of resource categories.

[0103] The correction coefficients are optimized using a global heuristic algorithm. and Optimize to obtain the optimal correction coefficient, and send the result to each frequency support resource. Then, update and send the correction coefficient and resource allocation weight according to the set rolling update cycle (e.g., 15 minutes).

[0104] The global heuristic optimization algorithm is one or more of the following: particle swarm optimization, genetic algorithm, Bayesian optimization algorithm, covariance matrix adaptive evolution strategy, or simulated annealing algorithm.

[0105] Figure 2 This is a schematic diagram of an electronic device structure provided in an embodiment of the present invention. The provided electronic device includes: a memory and a processor, wherein the memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions. When the program instructions are loaded and executed by the processor, they implement a resource optimization allocation method for maximizing the dynamic frequency feasible domain area according to the present invention.

[0106] It should be noted that, in addition to Figure 2 In addition to the memory and processor shown, electronic devices may include other hardware depending on their actual functions, which will not be elaborated further.

[0107] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the above-described resource optimization allocation method for maximizing the area of ​​a dynamic frequency feasible domain.

[0108] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described resource optimization allocation method for maximizing the area of ​​the dynamic frequency feasible domain.

[0109] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0111] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0112] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0113] Those skilled in the art to which this invention pertains may make various modifications or additions to the specific embodiments described, or use similar methods to replace them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

[0114] The above embodiments are only used to illustrate the design concept and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes or modifications made based on the principles and design ideas disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A resource optimization allocation method for maximizing the area of ​​the dynamic frequency feasible domain, characterized in that, include: Collect current operating status parameters of the power system, establish initial inertia allocation weights and initial frequency regulation allocation weights for each resource, and define inertia support correction coefficients for each type of resource. and frequency adjustment support correction coefficient ; Strength supported by system virtual inertia and system frequency regulation intensity Using coordinates, establish a dynamic frequency feasible domain scan space; determine the position of each point within the scan space. Whether the preset node RoCoF constraints, minimum frequency point constraints, and resource boundary constraints are met, if they are met, the point is determined to be a feasible point, and the dynamic frequency feasible region is composed of all feasible points. Construct an objective function that maximizes the area of ​​the dynamic frequency feasible domain, and use a global heuristic optimization algorithm to solve for the inertia support correction coefficient and frequency adjustment support correction coefficient of each resource. Based on the obtained inertia support correction coefficients and frequency regulation support correction coefficients for each resource, the inertia allocation weights and frequency regulation allocation weights for each resource are optimized to determine the virtual inertia support strength and frequency regulation strength for each resource.

2. The method according to claim 1, characterized in that, The dynamic frequency feasible region is defined as follows: ； In the formula, t is the current running time. The set of correction coefficients; the support strength scanning region D(t) corresponding to t, and the RoCoF constraint of the nodes. Minimum frequency constraint Resource boundary constraints They are defined as follows: ； ； ； ； In the formula, This represents the upper limit of the system's virtual inertia support strength scan. This represents the upper limit of the system frequency adjustment intensity scan. For the limit of the rate of change of frequency, For the maximum frequency change rate, The lowest frequency point, The minimum frequency limit. The effective spare capacity of the i-th resource. Let be the peak power command of the i-th resource during the frequency support process.

3. The method according to claim 1, characterized in that, The initial inertia allocation weights are constructed based on the node frequency change rate: ； In the formula, For the first The rate of change of the frequency of each resource node Indicates all Total number of resources; The initial frequency regulation allocation weights are constructed based on resource energy margin, energy storage state of charge, and reserve capacity: ； In the formula, Let i be the energy margin of the i-th resource. In the state of energy storage charge, For effective standby capacity.

4. The method according to claim 1, characterized in that, Frequency support resources are categorized into several types based on their resource control patterns and energy attributes, including grid-connected wind power resources, grid-connected energy storage resources, grid-connected flexible DC power resources, grid-built wind power resources, and grid-built energy storage resources; for each type of resource... Define the inertia support correction factor respectively and frequency adjustment support correction coefficient .

5. The method according to claim 4, characterized in that, The corrected inertia allocation weights and frequency modulation allocation weights are as follows: ； ； In the formula, For the category to which the i-th resource belongs, This is the set of correction coefficients.

6. The method according to claim 5, characterized in that, The virtual inertia and frequency modulation intensity of each resource satisfy: ； ； And satisfy resource boundary constraints: ； ； In the formula, Let i be the maximum virtual inertia capability of the i-th resource. The maximum frequency regulation strength capability of the i-th resource.

7. The method according to claim 1, characterized in that, Under a discrete scanning grid, the target area of ​​the dynamic frequency feasible domain in the period at time t is defined as: ； In the formula, , Set the step size for the scan grid. As a feasibility indicator variable, it is defined as: ； In the formula, Let t be the dynamic frequency feasible region, and t be the current running time. This is the set of correction coefficients.

8. The method according to claim 7, characterized in that, Objective function: ； In the formula, This is the average stability margin at feasible points, used to select a safer option when the areas are similar. To correct the coefficients and avoid excessive concentration of results, This is the stability margin weighting coefficient.

9. The method according to claim 8, characterized in that, For a single scan point stability margin Defined as the minimum of three constraint margins: frequency change rate margin, frequency minimum point margin, and resource reserve margin. ； The three terms in the formula are the frequency change rate margin, the frequency minimum point margin, and the resource reserve margin, respectively. This is the system's rated power; average stability margin at feasible points : ； Correction coefficient offset penalty term The expression is: ； In the formula, , These represent the proportion of virtual inertia support and the proportion of frequency regulation intensity borne by the g-th type of resources after optimization, respectively. and These represent the initial inertia load ratio and the initial frequency modulation load ratio of the g-th resource before optimization, respectively. Here, G represents the weighting coefficient for the offset penalty term, and G is the number of resource categories.

10. The method according to claim 1, characterized in that, The method is used for rolling time-domain operation, updating the load level, disturbance intensity, new energy availability, energy storage state of charge, reserve capacity, online ratio of synchronous machines and online status of resources at each rolling moment, and recalculating the dynamic frequency feasible domain, inertia support correction coefficient, frequency regulation support correction coefficient, inertia allocation weight and frequency regulation allocation weight.

Images