Power distribution method and device of power grid, storage medium and electronic equipment

Abstract

The invention discloses a power distribution method and device of a power grid, a storage medium and electronic equipment. The method relates to the field of power grid resource allocation, and comprises the steps that a target power value is received, and the target power value comprises a total power value issued by a dispatching center of a power grid for a distributed resource aggregate in a local power grid; a power distribution model of the distributed resource aggregate is obtained, and the power distribution model comprises a target function and a target constraint condition set; based on the power distribution model, a particle swarm optimization algorithm is adopted to decompose the target power value, a power distribution result is obtained, and the power distribution result comprises the power distributed by each distributed resource. According to the method and the device, the technical problem that the adaptation degree of a power distribution result and the distributed resources is low due to the fact that power distribution is directly carried out on each distributed resource in the local power grid by a regulation and control center of the power grid when the local power grid is disconnected from a superior power grid in the related technology is solved.

Description

Technical Field

[0001] The present invention relates to the field of power grid resource allocation, and in particular to a power grid power allocation method, device, storage medium and electronic equipment. Background Art

[0002] Urban local power grids typically feature a high proportion of external power, operate at multiple voltage levels, and coexist with multiple heterogeneous power sources. When extreme events disrupt critical transmission paths, cause voltage loss across substations, or lead to instability in the upstream power grid, the urban local power grid may become disconnected from the upstream grid. The significant active power shortfall at the moment of disconnection can easily lead to significant frequency fluctuations, making off-grid control difficult. Therefore, coordinating the participation of multiple resources within the urban local power grid, including sources, loads, and storage, in emergency frequency control allows the urban local power grid to smoothly transition to islanded operation mode, which is crucial for ensuring uninterrupted power supply to critical loads.

[0003] Urban power grids contain a vast number of small-capacity distributed resources. Related technologies rely on the power grid to directly regulate each distributed resource, which is highly complex. For example, due to the large number of distributed resources and their dispersed access locations, regulation requires real-time communication, state perception, and optimized calculations, which puts the system under pressure from high-dimensional optimization and data exchange. This complexity increases dramatically when rapid response is required after a disaster. Power grid regulation typically relies on centralized control, with a master control center directly acquiring and dispatching state information and power commands for each distributed resource. This is not conducive to efficient post-disaster emergency decision-making.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] An embodiment of the present invention provides a power distribution method, device, storage medium and electronic device for a power grid, so as to at least solve the technical problem in the related art that when a local power grid is disconnected from the upper power grid, the power grid control center directly distributes power to each distributed resource in the local power grid, which is highly complex and leads to low adaptability between the power distribution results and the distributed resources.

[0006] According to one aspect of an embodiment of the present invention, a power distribution method for a power grid is provided, comprising: receiving a target power value, wherein the target power value comprises: a total power value issued by a dispatching center of the power grid to a distributed resource aggregate within a local power grid, wherein the distributed resource aggregate comprises: a plurality of power control resources, wherein the plurality of power control resources comprises at least one of the following: a battery, an energy storage power station, an electric vehicle, and an interruptible load; obtaining a power distribution model for the distributed resource aggregate, wherein the power distribution model comprises: an objective function and a set of objective constraints, wherein the set of objective constraints comprises: a plurality of constraints for constraining the power of the power control resources; based on the power distribution model, decomposing the target power value using a particle swarm optimization algorithm to obtain a power distribution result, wherein the power distribution result comprises: the power allocated to each of the distributed resources.

[0007] Furthermore, the power allocation model is obtained in the following manner, including: collecting device parameters of the power control resources in the distributed resource aggregate to obtain a device parameter set; determining the target constraint condition set based on the device parameter set; and determining the power allocation model based on the target constraint condition set and the device parameter set.

[0008] Furthermore, based on the target constraint set and the device parameter set, the power allocation model is determined, including: classifying the multiple power control resources to obtain classification results; determining the weight of each power control resource based on the classification results; establishing the objective function based on the weight of each power control resource and the device parameter set; and determining the power allocation model based on the objective function and the target constraint set.

[0009] Furthermore, the classification results include: first-class power control resources and second-class power control resources, the number of random factors of the first-class power control resources is lower than the number of random factors of the second-class power control resources, and the random factors include: factors affecting the accuracy of power control. Based on the classification results, the weight of each of the power control resources is determined, including: determining the weight of the first-class power control resources as a first preset weight; determining the weight of the second-class power control resources as a second preset weight, wherein the first preset weight is higher than the second preset weight.

[0010] Furthermore, based on the device parameter set, the target constraint condition set is determined, including: based on the device parameter set, determining the range of the adjustable power of each of the power control resources to obtain an initial constraint condition set, wherein the initial constraint condition set includes: multiple constraints consisting of the range of the adjustable power of each of the power control resources; based on the robust optimization strategy, the constraints in the initial constraint condition set are corrected to obtain a corrected result; based on the corrected result and the initial constraint condition set, the target constraint condition set is determined.

[0011] Furthermore, the correction result includes: a correction result of correcting the constraint conditions of the second type of power control resources, and correcting the constraint conditions in the initial constraint condition set based on the robust optimization strategy to obtain a correction result, including: determining the constraint conditions of the second type of power control resources in the initial constraint condition set to obtain multiple target constraint conditions; obtaining a preset robust coefficient, wherein the preset robust coefficient is used to adjust the performance of the second type of power control resources; based on the preset robust coefficient, using the robust optimization strategy to correct multiple target constraint conditions to obtain the correction result.

[0012] Furthermore, the objective function includes: multiple decision variables, wherein the decision variables are used to determine the power allocated to each power control resource; based on the power allocation model, the target power value is decomposed using a particle swarm optimization algorithm to obtain a power allocation result, including: using the particle swarm optimization algorithm to solve the multiple decision variables in the power allocation model to obtain a solution result, wherein each particle in the particle swarm optimization algorithm represents one of the decision variables, and the position of the particle represents the power allocated to the power control resource corresponding to the decision variable; based on the solution result, the power allocation result is obtained.

[0013] According to another aspect of an embodiment of the present invention, a power distribution device for a power grid is also provided, including: a receiving unit for receiving a target power value, wherein the target power value includes: a total power value issued by a dispatching center of the power grid to a distributed resource aggregate within a local power grid, wherein the distributed resource aggregate includes: a plurality of power control resources, wherein the plurality of power control resources include at least one of the following: batteries, energy storage power stations, electric vehicles, and interruptible loads; an acquisition unit for acquiring a power distribution model of the distributed resource aggregate, wherein the power distribution model includes: an objective function and a set of target constraints, wherein the set of target constraints includes: a plurality of constraints for constraining the power of the power control resources; a decomposition unit for decomposing the target power value based on the power distribution model using a particle swarm optimization algorithm to obtain a power distribution result, wherein the power distribution result includes: the power allocated to each of the distributed resources.

[0014] Furthermore, the power allocation model is obtained through the following units, including: a collection unit, used to collect device parameters of the power control resources in the distributed resource aggregate to obtain a device parameter set; a first determination unit, used to determine the target constraint condition set based on the device parameter set; and a second determination unit, used to determine the power allocation model based on the target constraint condition set and the device parameter set.

[0015] Furthermore, the second determination unit includes: a classification subunit, used to classify multiple power control resources to obtain classification results; a first determination subunit, used to determine the weight of each power control resource based on the classification results; an establishment subunit, used to establish the objective function based on the weight of each power control resource and the device parameter set; and a second determination subunit, used to determine the power allocation model based on the objective function and the target constraint condition set.

[0016] Furthermore, the classification results include: a first type of power control resources and a second type of power control resources, the number of random factors of the first type of power control resources is lower than the number of random factors of the second type of power control resources, and the random factors include: factors affecting the accuracy of power control, and the first determination subunit includes: a first determination module, used to determine that the weight of the first type of power control resources is a first preset weight; a second determination module, used to determine that the weight of the second type of power control resources is a second preset weight, wherein the first preset weight is higher than the second preset weight.

[0017] Furthermore, the first determination unit includes: a first determination subunit, used to determine the range of adjustable power of each of the power control resources based on the device parameter set, and obtain an initial constraint condition set, wherein the initial constraint condition set includes: multiple constraints consisting of the range of adjustable power of each of the power control resources; a correction subunit, used to correct the constraints in the initial constraint condition set based on a robust optimization strategy to obtain a correction result; and a third determination subunit, used to determine the target constraint condition set based on the correction result and the initial constraint condition set.

[0018] Furthermore, the correction result includes: a correction result of correcting the constraints of the second type of power control resources, and the correction subunit includes: a third determination module, used to determine the constraints of the second type of power control resources in the initial constraint condition set, and obtain multiple target constraints; an acquisition module, used to obtain a preset robust coefficient, wherein the preset robust coefficient is used to adjust the performance of the second type of power control resources; a correction module, used to correct the multiple target constraints based on the preset robust coefficient using the robust optimization strategy to obtain the correction result.

[0019] Furthermore, the objective function includes: multiple decision variables, wherein the decision variables are used to determine the power allocated to each power control resource, and the decomposition unit includes: a solving subunit, used to use the particle swarm optimization algorithm to solve the multiple decision variables in the power allocation model to obtain a solution result, wherein each particle in the particle swarm optimization algorithm represents one of the decision variables, and the position of the particle represents the power allocated to the power control resource corresponding to the decision variable; a processing subunit, used to obtain the power allocation result based on the solution result.

[0020] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any one of the above-mentioned power distribution methods for a power grid by executing the executable instructions.

[0021] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, which stores a computer program, wherein when the computer program runs, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned power distribution methods for the power grid.

[0022] In the present invention, a target power value is received, wherein the target power value includes: a total power value issued by a power grid dispatching center to a distributed resource aggregate within a local power grid, wherein the distributed resource aggregate includes: multiple power control resources, wherein the multiple power control resources include at least one of the following: batteries, energy storage power stations, electric vehicles, and interruptible loads; a power allocation model for the distributed resource aggregate is obtained, wherein the power allocation model includes: an objective function and a set of target constraints, wherein the set of target constraints includes: multiple constraints for constraining the power of the power control resources; based on the power allocation model, a particle swarm optimization algorithm is used to decompose the target power value to obtain a power allocation result, wherein the power allocation result includes: the power allocated to each distributed resource. This solves the technical problem in the related art that when the local power grid is disconnected, the power control center of the power grid directly allocates power to each distributed resource in the local power grid, which is highly complex and leads to low adaptability of the power allocation result to the distributed resources.

[0023] In this application, based on the total power value issued by the dispatching center, power is allocated to the power control resources in the distributed resource aggregate through a power allocation model, avoiding the high complexity of the related technology in which the power grid control center directly allocates power to each distributed resource in the local power grid, thereby achieving the technical effect of reducing the power allocation complexity of the local power grid and improving the accuracy of the power allocation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0025] Figure 1 is a flow chart of an optional power distribution method for a power grid according to an embodiment of the present invention;

[0026] Figure 2 is a flow chart of another optional power distribution method for a power grid according to an embodiment of the present invention;

[0027] Figure 3 is a schematic diagram of an optional power distribution device for a power grid according to an embodiment of the present invention;

[0028] Figure 4 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] It should be noted that the user information (including but not limited to user device information, user personal information, etc.), collected information and data (including but not limited to data used for analysis, stored data, displayed data, electronic medical records, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0032] Example 1

[0033] According to an embodiment of the present invention, a method embodiment of an optional power distribution method for a power grid is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0034] Figure 1 FIG. 1 is a flow chart of an optional power distribution method for a power grid according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0035] Step S101: Receive a target power value, wherein the target power value includes: a total power value issued by a grid dispatching center to a distributed resource aggregate within a local grid, wherein the distributed resource aggregate includes: a plurality of power control resources, wherein the plurality of power control resources includes at least one of the following: a battery, an energy storage power station, an electric vehicle, and an interruptible load, for example,

[0036] The above-mentioned target power value can be the total power value issued by the grid dispatching center to the distributed resource aggregate within the local grid. The above-mentioned distributed resource aggregate can include four types of event-triggered fast power control resources: fuel cells, energy storage power stations, cluster electric vehicles and interruptible loads.

[0037] Step S102 : obtaining a power allocation model of a distributed resource aggregate, wherein the power allocation model includes an objective function and an objective constraint condition set, and the objective constraint condition set includes a plurality of constraint conditions for constraining the power of the power control resource.

[0038] The above-mentioned power allocation model can be used to allocate power command values ​​(corresponding to target power values) among four types of event-triggered fast power control resources: fuel cells, energy storage power stations, cluster electric vehicles, and interruptible loads. The above-mentioned power allocation model can be modified according to whether the power control resources contain uncertain factors.

[0039] It should be noted that for wind power, photovoltaic power, small units and regulating loads, these resources can be driven based on response. During emergency frequency control (i.e., local power grid and upper power grid), the control center does not issue specific power control command values ​​to them.

[0040] Step S103 : Based on the power allocation model, a particle swarm optimization algorithm is used to decompose the target power value to obtain a power allocation result, wherein the power allocation result includes: power allocated to each distributed resource.

[0041] In this embodiment, the objective function of the power allocation model may include multiple decision variables. The particle swarm optimization algorithm may be used to solve the multiple decision variables to decompose the target power value and obtain the power allocation result.

[0042] In this embodiment, through the above steps, based on the total power value issued by the dispatch center, power is allocated to the power control resources in the distributed resource aggregate through the power allocation model, avoiding the high complexity of the power control center directly allocating power to each distributed resource in the local power grid in the related art, thereby achieving the technical effect of reducing the complexity of power allocation in the local power grid and improving the accuracy of the power allocation results. This further solves the technical problem in the related art that when the local power grid level power grid is disconnected, the power control center directly allocates power to each distributed resource in the local power grid, which is high in complexity and leads to low compatibility between the power allocation results and the distributed resources.

[0043] Optionally, the power allocation model is obtained by: collecting device parameters of power control resources in a distributed resource aggregate to obtain a device parameter set; determining a target constraint condition set based on the device parameter set; and determining a power allocation model based on the target constraint condition set and the device parameter set.

[0044] In this embodiment, the device capacity information and adjustable power parameters of the fast power control resources (e.g., fuel cells, energy storage power stations, clustered electric vehicles, and interruptible loads) within the distributed resource aggregate can be collected to obtain a device parameter set. Based on the device parameter set, the constraints of each power control resource can be determined, for example, the range of power that each power control resource can allocate. For example, the range of power that each power control resource can allocate can be determined based on the upper and lower limits of the power that each power control resource can allocate. The fast power control resources within the distributed resource aggregate can then be classified according to their reliability. Based on the classification results, the weight of each power control resource can be determined. Then, an objective function can be established based on the weight of each power control resource. The target constraint set and the objective function form the above-mentioned power allocation model, thereby achieving the technical effect of improving the accuracy of power allocation in the power allocation model.

[0045] Optionally, based on a target constraint set and a device parameter set, a power allocation model is determined, including: classifying multiple power control resources to obtain classification results; determining the weight of each power control resource based on the classification results; establishing an objective function based on the weight of each power control resource and the device parameter set; and determining the power allocation model based on the objective function and the target constraint set.

[0046] In this embodiment, multiple power control resources can be classified according to the reliability of each power control resource. Based on the classification results, the weight factor of each power control resource (corresponding to the weight of each power control resource) can be determined. The weight of a power control resource with a high reliability can be higher than that of a power control resource with a low reliability. The reliability can be determined based on the number of random factors. For example, for electric vehicles in a distributed resource aggregate, their charging and discharging behaviors are random. For a disconnecting FL (interruptible load) in a distributed resource aggregate, its operating mode is affected by user comfort and is also random. Therefore, the reliability of electric vehicles and interruptible loads is low.

[0047] After obtaining the weight of each power control resource, a model for the power allocation decision problem of the distributed resource aggregate (i.e., a power allocation model) can be established, specifically including:

[0048] First, we can build a power allocation optimization model to establish an objective function with the goal of minimizing the control cost of distributed resource aggregates, as shown in Formula 1:

[0049]

[0050] After a disturbance occurs (for example, when the local power grid is disconnected from the upper power grid), the frequency response process of the power system can be described by the rotor motion equation, where P FU,l , P DE,i , P FL,j and P EV,k are the decision variables of the lth fuel cell, the ith energy storage power station, the jth interruptible load and the kth cluster electric vehicle, N FU , N DE , N FL and N EV is the number of all fuel cells, energy storage power stations, interruptible loads (FLs) and electric vehicle clusters; a FU , a DE , a FL and a EV The weight factors for fuel cell fast power control resources, energy storage fast power control resources, interruptible load (FL) fast power control resources, and electric vehicle cluster fast power control resources are shown in Figure 2. The power allocation model described above can then be constructed based on the objective function and the set of objective constraints.

[0051] Optionally, the classification results include: first-class power control resources and second-class power control resources, the number of random factors of the first-class power control resources is lower than the number of random factors of the second-class power control resources, the random factors include: factors affecting the accuracy of power control, based on the classification results, the weight of each power control resource is determined, including: determining the weight of the first-class power control resources as a first preset weight; determining the weight of the second-class power control resources as a second preset weight, wherein the first preset weight is higher than the second preset weight.

[0052] In this embodiment, fast power control resources within a distributed resource cluster can be categorized by reliability. For example, fuel cells and energy storage power stations are highly reliable and can be grouped together; electric vehicle clusters and interruptible loads are less reliable than the first two resources and can be grouped together. By assigning different weights to power control resources, resource utilization priorities can be determined, achieving the technical effect of increasing the flexibility of grid resource allocation.

[0053] Optionally, based on the device parameter set, a target constraint condition set is determined, including: based on the device parameter set, determining the range of adjustable power of each power control resource to obtain an initial constraint condition set, wherein the initial constraint condition set includes: multiple constraints consisting of the range of adjustable power of each power control resource; based on a robust optimization strategy, the constraints in the initial constraint condition set are corrected to obtain a corrected result; based on the corrected result and the initial constraint condition set, a target constraint condition set is determined.

[0054] In this embodiment, the adjustable power range of each power control resource can be determined according to the adjustable power parameters in the device parameter set to obtain an initial constraint condition set. For example, the formulas of the multiple constraints in the initial constraint condition set are shown in Formulas (2) to (6):

[0055]

[0056] in, Indicates the lower power limit of the fuel cell power control resource, Indicates the upper power limit of the fuel cell power control resource.

[0057]

[0058] in, Indicates the lower power limit of the power control resource of the energy storage power station. Indicates the power upper limit of the energy storage power station power control resource.

[0059]

[0060] in, Indicates the upper power limit of the interruptible load power control resource.

[0061]

[0062] in, represents the lower power limit of the electric vehicle cluster control resources, Indicates the power upper limit of the electric vehicle cluster power control resources.

[0063]

[0064] Among them, x n is the power control instruction value (i.e., target power value) issued by the control center to the distributed resource aggregate. Formulas (2)-(5) are the control quantity constraints of the fuel cell, energy storage power station, interruptible load, and electric vehicle cluster in the distributed resource aggregate, respectively. Formula (6) is the power control quantity constraint of the distributed resource aggregate as a whole. This constraint indicates that when the distributed resource aggregate distributes power among the internal flexible resources, it should meet the emergency control quantity requirements issued by the control center while ensuring that its own power adjustment range is allowed.

[0065] Due to the different reliability levels of power control resources, in order to improve the accuracy of power allocation performed by the power allocation model, the constraints in the initial constraint set can also be corrected based on the robust optimization strategy to obtain a corrected result; based on the corrected result and the initial constraint set, the target constraint set is determined, thereby achieving the technical effect of improving the accuracy of power allocation of the power allocation model.

[0066] Optionally, the correction result includes: a correction result of correcting the constraints of the second type of power control resources, and correcting the constraints in the initial constraint condition set based on the robust optimization strategy to obtain a correction result, including: determining the constraints of the second type of power control resources in the initial constraint condition set to obtain multiple target constraints; obtaining a preset robust coefficient, wherein the preset robust coefficient is used to adjust the performance of the second type of power control resources; based on the preset robust coefficient, using the robust optimization strategy to correct multiple target constraints to obtain a correction result.

[0067] Uncertain factors in the participation of distributed resource aggregates in emergency frequency control can include PFL and PEV. For electric vehicles in distributed resource aggregates, their charging and discharging behaviors are random. For interruptible flow (FL) in distributed resource aggregates, their operating modes are also random, influenced by user comfort. Considering the uncertainties of electric vehicles and interruptible flow, modifying the constraints can include the following steps:

[0068] Robust optimization can change the robust coefficient and formulate an emergency frequency control scheme to overcome uncertainty. This method is easy to calculate and has strong applicability. Based on the robust stochastic optimization theory (corresponding to the robust optimization strategy), p can be set. F L, p EV To describe the uncertainty parameters of the disconnecting FL and electric vehicle, the expression is as follows:

[0069]

[0070] Among them, p VPP are the uncertainty parameters of the disconnecting FL and electric vehicle cluster in the VPP, and are the upper and lower limits of the power control for these two types of control resources, respectively. Furthermore, considering the differences in power consumption behavior among users within a VPP (distributed resource aggregation) in actual emergency frequency control, the uncertainty parameter often does not reach its maximum. Therefore, in this embodiment, a robust coefficient ΓVPP with free adjustment performance can be introduced, expressed as follows:

[0071]

[0072] Where ΓVPP represents the robust coefficient of the VPP emergency control quantity. By setting the robust coefficient, the distribution interval of the uncertainty factor can be flexibly adjusted, and formulas (4)-(6) are modified to obtain the following formula:

[0073]

[0074]

[0075] When ΓVPP=0, the emergency frequency control scheme does not take into account the uncertainty of VPP, and the robustness of the control scheme is poor; as ΓVPP increases, the robustness of the control scheme is improved, but the control cost also increases.

[0076] After using the robust optimization strategy to modify multiple objective constraints and obtaining the modified results, the power allocation decision model of the VPP can be obtained as shown in Table 1:

[0077] Table 1

[0078]

[0079] Optionally, the objective function includes: multiple decision variables, wherein the decision variables are used to determine the power allocated to each power control resource; based on the power allocation model, a particle swarm optimization algorithm is used to decompose the target power value to obtain a power allocation result, including: using a particle swarm optimization algorithm to solve the multiple decision variables in the power allocation model to obtain a solution result, wherein each particle in the particle swarm optimization algorithm represents one of the decision variables, and the position of the particle represents the power allocated to the power control resource corresponding to the decision variable; based on the solution result, a power allocation result is obtained.

[0080] In this embodiment, to solve the decision variables in the power allocation decision model (corresponding to the power allocation model), the overall power command value (i.e., target power value) of the distributed resource aggregate can be decomposed according to resource reliability priority (i.e., by setting different weights for power control resources), thereby obtaining the command values ​​of the individual components within the aggregate. In this embodiment, a modified PSO algorithm (i.e., particle swarm optimization algorithm) can be used for the solution. Each particle can have two basic attributes: speed and position. The particle's dimension corresponds to the decision variable, and its position represents the current solution.

[0081] In this embodiment, the classic PSO algorithm can be improved by using a dynamically updated inertia weight, with the goal of improving the global search capability and the local search capability in the early and late stages of the algorithm iteration, respectively. In the improved PSO algorithm, the inertia weight is updated in each iteration, and the dynamic update of the inertia weight can be expressed as shown in formulas (12) to (14):

[0082] ω(n)=ω max -n(ω max -ω min ) / n max (12)

[0083] ν i (n+1)=ω(n)ν i (n)+α1β1(x pbest,i -x i (n))+α2β2(x gbest -x i (n)) (13)

[0084] x i (n+1)=x i (n)+η pso ν i (n+1) (14)

[0085] Among them, ω(n) is the dynamic inertia weight calculation formula; n is the current iteration number; n max is the maximum number of iterations; ω max 、ωmin are the maximum and minimum values ​​of the dynamic weight respectively; ν i is the speed of the i-th particle in the population; α1 and α2 are the acceleration coefficients of the particle swarm algorithm that characterize the learning ability of the particle, corresponding to the self-learning ability (i.e., cognitive learning factor) and the social learning ability to learn from the optimal individual in the group (i.e., social learning factor), respectively; β1 and β2 are two random numbers uniformly distributed between (0-1). pbest,i is the historical optimal position of the i-th particle, x gbest is the global optimal position of the particle swarm; η pso is the weight coefficient for speed update.

[0086] Particle positions are determined during particle swarm initialization or iteration. Electromechanical transient simulation is then used to obtain trajectory data, and the penalty terms corresponding to each constraint are calculated. During the particle swarm optimization process, particle position updates take very little time, with the majority of the time spent on electromechanical transient simulation to obtain simulation trajectories. Because all particles within the swarm are independent during each iteration, parallel electromechanical transient simulation can be employed to increase solution speed. This can be achieved by using multiple cores on a single machine or by running simulations simultaneously on multiple computers.

[0087] Figure 2 FIG. 1 is a flow chart of another optional power distribution method for a power grid according to an embodiment of the present invention. Figure 2 Shown, including:

[0088] S21: collecting device parameters of the fast power control resources within the distributed resource aggregate and the power command value (corresponding to the target power value) issued by the upper power grid. This step may include the following steps:

[0089] (1) Collecting the power command value issued by the upper power grid received by the distributed resource aggregate;

[0090] (2) Collect the equipment capacity information and adjustable power of the fast power control resources (fuel cells, energy storage power stations, cluster electric vehicles and interruptible loads) within the distributed resource aggregate.

[0091] (3) Rapid power control resources within distributed resource aggregates are classified according to their reliability. Fuel cells and energy storage power stations are highly reliable and are grouped together. Cluster electric vehicles and interruptible loads are less reliable than the first two resources and are grouped together in another category. Resource priority can be determined by assigning different weights to resources.

[0092] S22: Establish a mathematical model for the power allocation decision problem of the distributed resource aggregate (i.e., a power allocation model), as shown in formulas (2) to (6).

[0093] S23: Considering the uncertainties of electric vehicles and disconnecting FLs, the power allocation model of the distributed resource aggregate is modified. The uncertainties of the distributed resource aggregate participating in emergency frequency control include PFL and PEV. For electric vehicles in the distributed resource aggregate, their charging and discharging behaviors are random. For disconnecting FLs in the distributed resource aggregate, their operating modes are also random, influenced by user comfort. Considering the uncertainties of electric vehicles and disconnecting FLs, equations (4) to (6) are modified to obtain equations (7) to (11).

[0094] In this embodiment, based on the total power value issued by the dispatch center, power is allocated to the power control resources in the distributed resource aggregate through a power allocation model. This avoids the high complexity of the power control center directly allocating power to each distributed resource in the local power grid in the related art, thereby achieving the technical effect of reducing the complexity of power allocation in the local power grid and improving the accuracy of the power allocation results. This further solves the technical problem in the related art that when the local power grid level is disconnected, the power control center directly allocates power to each distributed resource in the local power grid, which is high in complexity and leads to low compatibility between the power allocation results and the distributed resources.

[0095] Example 2

[0096] The second embodiment of the present invention provides an optional power distribution device for a power grid, and each implementation unit in the power distribution device corresponds to each implementation step in the first embodiment.

[0097] Figure 3 Schematic diagram of an optional power distribution device for a power grid according to an embodiment of the present invention, Figure 3 The power distribution device includes: a receiving unit 31 , an acquiring unit 32 and a decomposing unit 33 .

[0098] The receiving unit 31 is configured to receive a target power value, wherein the target power value includes: a total power value issued by a grid dispatching center to a distributed resource aggregate within a local grid, wherein the distributed resource aggregate includes: a plurality of power control resources, wherein the plurality of power control resources includes at least one of the following: a battery, an energy storage power station, an electric vehicle, and an interruptible load;

[0099] An acquisition unit 32 is configured to acquire a power allocation model of a distributed resource aggregate, wherein the power allocation model includes an objective function and a set of objective constraints, wherein the set of objective constraints includes a plurality of constraints for constraining the power of the power control resource;

[0100] The decomposition unit 33 is configured to decompose the target power value based on the power allocation model by using a particle swarm optimization algorithm to obtain a power allocation result, wherein the power allocation result includes power allocated to each distributed resource.

[0101] In the power distribution device for a power grid provided in the second embodiment of the present invention, a receiving unit 31 can receive a target power value, wherein the target power value includes: a total power value issued by the power grid dispatching center for a distributed resource aggregate within a local power grid, wherein the distributed resource aggregate includes: multiple power control resources, wherein the multiple power control resources include at least one of the following: a battery, an energy storage power station, an electric vehicle, and an interruptible load. An acquiring unit 32 acquires a power distribution model for the distributed resource aggregate, wherein the power distribution model includes: an objective function and a target constraint set, wherein the target constraint set includes: multiple constraints for constraining the power of the power control resources. A decomposition unit 33 decomposes the target power value using a particle swarm optimization algorithm based on the power distribution model to obtain a power distribution result, wherein the power distribution result includes: the power allocated to each distributed resource. This solves the technical problem in the related art that when a local power grid is disconnected, the power distribution center of the power grid directly distributes power to each distributed resource in the local power grid, which is highly complex and results in poor compatibility between the power distribution result and the distributed resources.

[0102] In this embodiment, based on the total power value issued by the dispatching center, power is allocated to the power control resources in the distributed resource aggregate through the power allocation model, avoiding the high complexity of the related technology in which the power grid control center directly allocates power to each distributed resource in the local power grid, thereby achieving the technical effect of reducing the power allocation complexity of the local power grid and improving the accuracy of the power allocation results.

[0103] Optionally, in the power distribution device of the power grid provided in the second embodiment of the present invention, the power distribution model is obtained through the following units, including: an acquisition unit, used to collect device parameters of power control resources in a distributed resource aggregate to obtain a device parameter set; a first determination unit, used to determine a target constraint condition set based on the device parameter set; and a second determination unit, used to determine the power distribution model based on the target constraint condition set and the device parameter set.

[0104] Optionally, in the power distribution device of the power grid provided in the second embodiment of the present invention, the second determination unit includes: a classification subunit, used to classify multiple power control resources to obtain a classification result; a first determination subunit, used to determine the weight of each power control resource based on the classification result; an establishment subunit, used to establish an objective function based on the weight of each power control resource and a set of equipment parameters; and a second determination subunit, used to determine a power distribution model based on the objective function and a set of objective constraints.

[0105] Optionally, in the power distribution device of the power grid provided in the second embodiment of the present invention, the classification results include: first-class power control resources and second-class power control resources, the number of random factors of the first-class power control resources is lower than the number of random factors of the second-class power control resources, and the random factors include: factors affecting the accuracy of power control, and the first determination subunit includes: a first determination module, used to determine that the weight of the first-class power control resources is a first preset weight; and a second determination module, used to determine that the weight of the second-class power control resources is a second preset weight, wherein the first preset weight is higher than the second preset weight.

[0106] Optionally, in the power distribution device of the power grid provided in Example 2 of the present invention, the first determination unit includes: a first determination sub-unit, used to determine the range of adjustable power of each power control resource based on a set of equipment parameters, and obtain an initial constraint condition set, wherein the initial constraint condition set includes: multiple constraints consisting of the range of adjustable power of each power control resource; a correction sub-unit, used to correct the constraints in the initial constraint condition set based on a robust optimization strategy to obtain a correction result; and a third determination sub-unit, used to determine the target constraint condition set based on the correction result and the initial constraint condition set.

[0107] Optionally, in the power distribution device of the power grid provided in the second embodiment of the present invention, the correction result includes: a correction result of correcting the constraint conditions of the second type of power control resources, and the correction subunit includes: a third determination module, used to determine the constraint conditions of the second type of power control resources in the initial constraint condition set, and obtain multiple target constraint conditions; an acquisition module, used to obtain a preset robust coefficient, wherein the preset robust coefficient is used to adjust the performance of the second type of power control resources; a correction module, used to correct the multiple target constraint conditions based on the preset robust coefficient and adopt a robust optimization strategy to obtain a correction result.

[0108] Optionally, in the power distribution device of the power grid provided in the second embodiment of the present invention, the objective function includes: multiple decision variables, wherein the decision variables are used to determine the power allocated to each power control resource, and the decomposition unit includes: a solving subunit, used to use a particle swarm optimization algorithm to solve multiple decision variables in the power distribution model to obtain a solution result, wherein each particle in the particle swarm optimization algorithm represents one of the decision variables, and the position of the particle represents the power allocated to the power control resource corresponding to the decision variable; a processing subunit, used to obtain a power distribution result based on the solution result.

[0109] The power distribution device of the above-mentioned power grid may also include a processor and a memory. The above-mentioned receiving unit 31, acquisition unit 32 and decomposition unit 33 are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0110] The processor includes a core that retrieves the corresponding program unit from memory. One or more cores can be configured. By adjusting kernel parameters, the system allocates power to power control resources in a distributed resource aggregate based on the total power value issued by the dispatch center using a power allocation model. This avoids the high complexity of direct power allocation by the power control center to each distributed resource in a local power grid, as is commonly done in related technologies. This reduces the complexity of power allocation in the local power grid and improves the accuracy of power allocation results.

[0111] The above-mentioned memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0112] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any one of the above-mentioned power distribution methods for a power grid by executing the executable instructions.

[0113] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, which stores a computer program, wherein when the computer program runs, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned power distribution methods for the power grid.

[0114] Figure 4 is a schematic diagram of an electronic device according to an embodiment of the present invention, such as Figure 4As shown, an embodiment of the present invention provides an electronic device 40, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, any one of the above-mentioned power distribution methods for the power grid is implemented.

[0115] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0116] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0117] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0118] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0119] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0120] If the integrated unit 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 invention, or the part that contributes to the prior art, or all or part of the 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 enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0121] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A power distribution method for a power grid, characterized in that: include: Receiving a target power value, wherein the target power value comprises: a total power value issued by a grid dispatching center to a distributed resource aggregate within a local grid, wherein the distributed resource aggregate comprises: a plurality of power control resources, wherein the plurality of power control resources comprises at least one of the following: a battery, an energy storage power station, an electric vehicle, and an interruptible load; Obtaining a power allocation model for the distributed resource aggregate, wherein the power allocation model includes: an objective function and a set of objective constraints, and the set of objective constraints includes: a plurality of constraints for constraining the power of the power control resource; Based on the power allocation model, a particle swarm optimization algorithm is used to decompose the target power value to obtain a power allocation result, wherein the power allocation result includes: power allocated to each of the distributed resources.

2. The power distribution method according to claim 1, wherein: The power distribution model is obtained by: Collecting device parameters of power control resources in the distributed resource aggregate to obtain a device parameter set; Determining the target constraint condition set based on the device parameter set; The power allocation model is determined based on the target constraint set and the device parameter set.

3. The power distribution method according to claim 2, wherein: Determining the power allocation model based on the target constraint set and the device parameter set includes: classifying the plurality of power control resources to obtain a classification result; determining a weight of each of the power control resources based on the classification result; Establishing the objective function based on the weight of each of the power control resources and the set of device parameters; The power allocation model is determined based on the objective function and the set of objective constraints.

4. The power distribution method according to claim 3, wherein: The classification result includes: first-category power control resources and second-category power control resources, the number of random factors of the first-category power control resources is lower than the number of random factors of the second-category power control resources, the random factors include: factors affecting the accuracy of power control, and based on the classification result, determining a weight of each of the power control resources includes: Determining the weight of the first type of power control resources as a first preset weight; The weight of the second-type power control resource is determined to be a second preset weight, wherein the first preset weight is higher than the second preset weight.

5. The power distribution method according to claim 4, characterized in that: Determining the target constraint condition set based on the device parameter set includes: Determining, based on the device parameter set, a range of an adjustable power of each of the power control resources, and obtaining an initial constraint condition set, wherein the initial constraint condition set includes: a plurality of constraints consisting of the range of the adjustable power of each of the power control resources; Modifying the constraints in the initial constraint condition set based on a robust optimization strategy to obtain a modified result; The target constraint condition set is determined based on the correction result and the initial constraint condition set.

6. The power distribution method according to claim 5, characterized in that: The correction result includes: a correction result of correcting the constraint condition of the second type of power control resource, and the correction result is obtained by correcting the constraint condition in the initial constraint condition set based on the robust optimization strategy, including: determining the constraint conditions of the second type of power control resources in the initial constraint condition set to obtain a plurality of target constraint conditions; Obtaining a preset robustness coefficient, wherein the preset robustness coefficient is used to adjust the performance of the second-type power control resource; Based on the preset robust coefficient, the robust optimization strategy is adopted to correct the multiple target constraints to obtain the correction results.

7. The power distribution method according to claim 1, wherein: The objective function includes: a plurality of decision variables, wherein the decision variables are used to determine the power allocated to each power control resource; based on the power allocation model, the target power value is decomposed using a particle swarm optimization algorithm to obtain a power allocation result, including: Solving multiple decision variables in the power allocation model using the particle swarm optimization algorithm to obtain a solution result, wherein each particle in the particle swarm optimization algorithm represents one of the decision variables, and the position of the particle represents the power allocated by the power control resource corresponding to the decision variable; Based on the solution result, the power allocation result is obtained.

8. A power distribution device for a power grid, characterized in that: include: a receiving unit, configured to receive a target power value, wherein the target power value comprises: a total power value issued by a dispatching center of a power grid to a distributed resource aggregate within a local power grid, wherein the distributed resource aggregate comprises: a plurality of power control resources, wherein the plurality of power control resources comprises at least one of the following: a battery, an energy storage power station, an electric vehicle, and an interruptible load; an acquiring unit, configured to acquire a power allocation model of the distributed resource aggregate, wherein the power allocation model includes: an objective function and a set of objective constraints, wherein the set of objective constraints includes: a plurality of constraints for constraining the power of the power control resource; A decomposition unit is used to decompose the target power value based on the power allocation model using a particle swarm optimization algorithm to obtain a power allocation result, wherein the power allocation result includes: the power allocated to each of the distributed resources.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the power distribution method for a power grid according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The method comprises one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the power distribution method of the power grid as described in any one of claims 1 to 7.

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