Virtual power plant aggregation scheduling method and device based on particle swarm optimization

Abstract

The invention provides a virtual power plant aggregation scheduling method and device based on a particle swarm optimization, and the method comprises the steps: obtaining a plurality of to-be-scheduled target resource nodes in a virtual power plant from a distributed resource node list issued by a virtual power plant scheduling platform; based on a particle swarm algorithm, determining a target scheduling strategy of the virtual power plant, the target scheduling strategy being used for indicating a target power value of each target resource node on the corresponding power parameter; and according to the target power value, obtaining an adjustable margin of each target resource node under the target scheduling strategy, and sending the adjustable margin to the virtual power plant scheduling platform. According to the method, the global search capability of the particle swarm algorithm is utilized, the approximate optimal scheduling scheme of the direct control type virtual power plant can be quickly found in a complex solution space, and the scheduling efficiency and accuracy are improved.

Description

Technical Field

[0001] The present application relates to the technical field of virtual power plant optimization scheduling, and in particular to a virtual power plant aggregation scheduling method and device based on particle swarm algorithm. Background Art

[0002] With the continuous adjustment of energy structure, the rapid development of distributed energy resources and the development of smart grid technology, virtual power plants have gradually attracted attention as a new form of energy management. Specifically, direct-controlled virtual power plants achieve flexible adjustment of power systems by optimizing and controlling adjustable energy resources (such as wind power, photovoltaics, energy storage, charging piles, flexible loads, etc.). Among them, direct control commands have certain timeliness requirements, and dynamic aggregation optimization is generally set to be carried out on a rolling basis, automatically sending the aggregatable resources of the nearest time node to the superior dispatching platform. Since it occurs before the direct control command is issued, it can be called a "front-end aggregation optimization architecture."

[0003] However, traditional scheduling methods often fail to achieve good scheduling effects when faced with complex and changeable distributed energy resources and load demands. Therefore, how to aggregate and optimize the scheduling of multiple resources within a direct-controlled virtual power plant to improve energy utilization efficiency, reduce operating costs and ensure the reliability of power supply is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the first objective of this application is to propose a virtual power plant aggregation scheduling method based on particle swarm algorithm.

[0006] The second objective of this application is to propose a virtual power plant aggregation scheduling device based on particle swarm algorithm.

[0007] The third objective of the present application is to provide an electronic device.

[0008] A fourth objective of the present application is to provide a computer-readable storage medium.

[0009] A fifth object of the present application is to provide a computer program product.

[0010] To achieve the above objectives, the first embodiment of the present application proposes a virtual power plant aggregation scheduling method based on a particle swarm algorithm, comprising:

[0011] Obtain multiple target resource nodes to be dispatched in the virtual power plant from the distributed resource node list issued by the virtual power plant dispatching platform;

[0012] Based on the particle swarm optimization algorithm, determine the target scheduling strategy of the virtual power plant, where the target scheduling strategy is used to indicate the target power values of the target resource nodes on the corresponding power parameters;

[0013] According to the target power values, obtain the adjustable margins of the target resource nodes under the target scheduling strategy, and send the adjustable margins to the virtual power plant scheduling platform.

[0014] To achieve the above object, an embodiment of the second aspect of the present application provides a virtual power plant aggregation scheduling device based on the particle swarm optimization algorithm, including:

[0015] An acquisition module, configured to acquire multiple target resource nodes to be scheduled in the virtual power plant from a list of distributed resource nodes sent by the virtual power plant scheduling platform;

[0016] A determination module, configured to determine the target scheduling strategy of the virtual power plant based on the particle swarm optimization algorithm, where the target scheduling strategy is used to indicate the target power values of the target resource nodes on the corresponding power parameters;

[0017] A sending module, configured to obtain the adjustable margins of the target resource nodes under the target scheduling strategy according to the target power values, and send the adjustable margins to the virtual power plant scheduling platform.

[0018] To achieve the above object, an embodiment of the third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement a virtual power plant aggregation scheduling method based on the particle swarm optimization algorithm as described in the first aspect of the embodiments of the present application.

[0019] To achieve the above object, an embodiment of the fourth aspect of the present application provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, they are used to implement a virtual power plant aggregation scheduling method based on the particle swarm optimization algorithm as described in the first aspect of the embodiments of the present application.

[0020] To achieve the above object, an embodiment of the fifth aspect of the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements a virtual power plant aggregation scheduling method based on the particle swarm optimization algorithm as described in the first aspect of the embodiments of the present application.

[0021] The technical solution provided by the present application at least brings the following beneficial effects:

[0022] From the distributed resource node list sent by the virtual power plant dispatching platform in this application, multiple target resource nodes to be dispatched in the virtual power plant are obtained; based on the particle swarm optimization algorithm, the target dispatching strategy of the virtual power plant is determined, where the target dispatching strategy is used to indicate the target power values of each target resource node on the corresponding power parameters; according to the target power values, the adjustable margins of each target resource node under the target dispatching strategy are obtained, and the adjustable margins are sent to the virtual power plant dispatching platform. This application utilizes the global search ability of the particle swarm optimization algorithm and can quickly find an approximate optimal dispatching scheme for the directly controlled virtual power plant in a complex solution space, improving the efficiency and accuracy of dispatching.

[0023] Additional aspects and advantages of this application will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of this application. Description of the Drawings

[0024] The above and / or additional aspects and advantages of this application will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, where:

[0025] Figure 1 is a schematic flowchart of a virtual power plant aggregation dispatching method based on the particle swarm optimization algorithm provided by an embodiment of this application;

[0026] Figure 2 is an interaction schematic diagram provided by an embodiment of this application;

[0027] Figure 3 is a schematic flowchart of a virtual power plant aggregation dispatching method based on the particle swarm optimization algorithm provided by another embodiment of this application;

[0028] Figure 4 is a schematic structural diagram of a virtual power plant aggregation dispatching device based on the particle swarm optimization algorithm provided by an embodiment of this application;

[0029] Figure 5 is a block diagram of an electronic device provided by an embodiment of this application. Detailed Embodiments

[0030] The embodiments of this application will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are intended to explain this application and should not be construed as limiting this application.

[0031] The virtual power plant aggregation dispatching method and device based on the particle swarm optimization algorithm according to the embodiments of this application will be described below with reference to the drawings.

[0032] Figure 1The figure is a schematic flowchart of a virtual power plant aggregation scheduling method based on the particle swarm optimization algorithm provided by an embodiment of the present application. Among them, the virtual power plant aggregation scheduling method based on the particle swarm optimization algorithm can be applied to a resource aggregator, that is, a virtual power plant distributed resource wide-area aggregation optimization scheduling platform.

[0033] As Figure 1 shown, the virtual power plant aggregation scheduling method based on the particle swarm optimization algorithm includes the following steps:

[0034] Step 101: Obtain multiple target resource nodes to be scheduled in the virtual power plant from the distributed resource node list issued by the virtual power plant scheduling platform.

[0035] In an embodiment of the present application, the virtual power plant scheduling platform may refer to a provincial power grid virtual power plant scheduling platform, a municipal power grid virtual power plant scheduling platform, etc.; the virtual power plant distributed resource wide-area aggregation optimization scheduling platform can obtain the distributed resource node list regularly or irregularly issued by the virtual power plant scheduling platform through Service Interface Class I provided by the power grid; the distributed resource node list may include distributed resource nodes to be scheduled.

[0036] In an embodiment of the present application, the virtual power plant distributed resource wide-area aggregation optimization scheduling platform can perform dynamic aggregation scheduling on the direct control adjustable margin of distributed resource nodes according to the distributed resource node list in accordance with a set scheduling period. Among them, the set scheduling period can be 15 minutes, 30 minutes, 60 minutes, one day, etc.

[0037] In an embodiment of the present application, since the distributed resource nodes in the distributed resource node list may not necessarily have a surplus adjustable margin, the target resource nodes may refer to the distributed resource nodes in the distributed resource node list that can be scheduled and have an adjustable margin; by way of example and not limitation, the target resource nodes may refer to distributed power sources (such as distributed photovoltaics), energy storage devices, controllable loads (adjustable flexible loads), source-network-load-storage / microgrids, and charging and swapping systems.

[0038] Step 102: Determine the target scheduling strategy of the virtual power plant based on the particle swarm optimization algorithm, where the target scheduling strategy is used to indicate the target power values of each target resource node at the corresponding power parameters.

[0039] In an embodiment of the present application, different target resource nodes may have different corresponding power parameters. For example, the power parameters corresponding to distributed power sources include power generation power, the power parameters corresponding to energy storage devices include charge and discharge power, and the power parameters corresponding to controllable loads include regulation power.

[0040] In the embodiments of the present application, the target scheduling strategy may refer to the optimal scheduling strategy obtained based on the particle swarm optimization algorithm. The target scheduling strategy may include the target power values of each target resource node at the corresponding power parameters. Among them, when searching for the target scheduling strategy based on the particle swarm optimization algorithm, it is necessary to define a fitness function (objective function). As an example but not a limitation, the fitness function may be constructed according to the operating cost of the virtual power plant, and then, with the goal of minimizing the operating cost of the virtual power plant, the target scheduling strategy is solved. Among them, the operating cost of the virtual power plant may include the power generation cost of distributed power sources, the charge and discharge loss cost of energy storage devices, etc.

[0041] Step 103: Obtain the adjustable margin of each target resource node under the target scheduling strategy according to the target power value, and send the adjustable margin to the virtual power plant scheduling platform.

[0042] In the embodiments of the present application, the service interface class III of the power grid may be called according to the target power value corresponding to each target resource node to obtain the adjustable margin of each target resource node under the target scheduling strategy.

[0043] In the embodiments of the present application, the virtual power plant scheduling platform may achieve fast, efficient, and accurate scheduling control through the direct control node according to the adjustable margin sent by the virtual power plant distributed resource wide-area aggregation and optimization scheduling platform.

[0044] As Figure 2 shown, Figure 2 The interaction diagram among the virtual power plant scheduling platform, the virtual power plant distributed resource wide-area aggregation and optimization scheduling platform, and the distributed resource nodes is shown. The aggregation node in the figure is the target resource node in the present application, the dynamic scheduling direct control strategy is the target scheduling strategy in the present application, and the dynamic aggregation optimization algorithm is the particle swarm optimization algorithm in the present application.

[0045] In this embodiment, from the distributed resource node list sent by the virtual power plant scheduling platform, multiple target resource nodes to be scheduled in the virtual power plant are obtained; based on the particle swarm optimization algorithm, the target scheduling strategy of the virtual power plant is determined, where the target scheduling strategy is used to indicate the target power values of each target resource node at the corresponding power parameters; according to the target power value, the adjustable margin of each target resource node under the target scheduling strategy is obtained, and the adjustable margin is sent to the virtual power plant scheduling platform. The present application utilizes the global search ability of the particle swarm optimization algorithm to quickly find an approximate optimal scheduling scheme for the direct control type virtual power plant in a complex solution space, improve the efficiency and accuracy of scheduling, and achieve efficient aggregation and optimization scheduling of distributed energy resources in the virtual power plant, improve energy utilization efficiency, reduce operating costs, and ensure the stability and reliability of power supply.

[0046] This embodiment provides another virtual power plant aggregation scheduling method based on the particle swarm optimization algorithm.Figure 3 Schematic flowchart of a virtual power plant aggregation scheduling method based on particle swarm optimization provided by an embodiment of the present application.

[0047] As Figure 3 shown, the virtual power plant aggregation scheduling method based on particle swarm optimization may include the following steps:

[0048] Step 301: Obtain multiple target resource nodes to be scheduled in the virtual power plant from the distributed resource node list issued by the virtual power plant scheduling platform.

[0049] Step 302: Perform particle coding according to the power parameters corresponding to the multiple target resource nodes, and generate an initial particle swarm in combination with the power parameter ranges corresponding to each target resource node.

[0050] In an embodiment of the present application, the multiple target resource nodes include distributed power sources, energy storage devices, and controllable loads. The power parameters corresponding to the distributed power sources include power generation power, the power parameters corresponding to the energy storage devices include charge and discharge power, and the power parameters corresponding to the controllable loads include regulation power.

[0051] In an embodiment of the present application, particle coding may refer to representing each particle as a multi-dimensional vector, the dimension of which corresponds to the target resource nodes that need to be aggregated and optimized for scheduling in the virtual power plant. For example, the multiple target resource nodes include n distributed power sources, 1 energy storage device, and controllable loads. The particle X can be represented as X = [P g1 , P g2 , …, P gn , P s , P l , where P gj is the power generation power of the jth distributed power source, the value range of j is [1, n], P s is the charge and discharge power of the energy storage device (positive value for charging, negative value for discharging), and P l is the regulation power of the controllable load.

[0052] In an embodiment of the present application, a set number of particles may be randomly generated to form an initial particle swarm. The position of each particle in the initial particle swarm (i.e., the value of the multi-dimensional vector) is randomly initialized within its corresponding variable value range (power parameter range). It should be noted that the power parameter range may refer to the upper and lower limits of the power generation power of the distributed power source, the upper and lower limits of the charge and discharge power of the energy storage device, and the upper and lower limits of the regulation power of the controllable load. Among them, each particle corresponds to a preset initial velocity vector, and the dimension of the velocity vector is the same as the dimension corresponding to the particle position (multi-dimensional vector).

[0053] It should be noted that all household numbers and adjustable margins (before scheduling) of each target resource node can be obtained through the service interface class I provided by the power grid, and then the power parameter range corresponding to each target resource node can be determined according to the obtained adjustable margin.

[0054] Step 303: Construct a fitness function according to the operating cost of the virtual power plant, the interaction cost with the power grid, and the load satisfaction degree.

[0055] In the embodiment of the present application, the fitness function is used to determine the score of the particle position, and then evaluate the pros and cons of each particle position. By constructing the fitness function according to the operating cost of the virtual power plant, the interaction cost with the power grid, and the load satisfaction degree, the economy (operating cost and interaction cost) of the virtual power plant and the user demand (load satisfaction degree) can be comprehensively considered, which helps to optimize the scheduling strategy of the virtual power plant, improve the operation efficiency, and maximize the overall performance.

[0056] As an example, the operating cost of the virtual power plant, the interaction cost with the power grid, and the load satisfaction degree can be weighted and summed to obtain the fitness function.

[0057] As another example, obtain the interaction power between the virtual power plant and the power grid; obtain the ratio of the actually satisfied load power to the load demand power; construct a fitness function based on the operating cost, interaction power, and ratio.

[0058] For example, the constructed fitness function F can be expressed as:

[0059]

[0060] Among them, C is the operating cost of the virtual power plant, P grid is the interaction power between the virtual power plant and the power grid, P l,ac is the actually satisfied load power, P l,de is the load demand power, λ 1 and λ 2 are weight coefficients used to adjust the relative importance of each factor in the fitness function. For example, λ 1 can be 0.1, and λ 2 can be 0.8.

[0061] Step 304: Update the particle positions of at least one particle in the initial particle swarm, and determine the position scores of each particle position based on the fitness function.

[0062] In the embodiment of the present application, the particles in the initial particle swarm can update their positions according to the corresponding initial velocity vectors, and determine the position scores of the particle positions based on the fitness function after the update. It should be noted that the initial positions of the particles in the initial particle swarm also correspond to position scores.

[0063] In the embodiments of the present application, updating the particle positions of at least one particle in the initial particle swarm includes: for the update of any particle in the initial particle swarm at time t + 1, obtaining the particle position of the particle at time t and the velocity vector of the particle at time t + 1; and performing position update according to the particle position of the particle at time t and the velocity vector at time t + 1 to obtain the particle position of the particle at time t + 1.

[0064] For example, taking particle i in the initial particle swarm as an example, the position update formula of the particle can be: x i (t + 1) = x i (t) + v i (t + 1), where x i (t + 1) is the position of particle i at time t + 1, x i (t) is the position of particle i at time t, and v i (t + 1) is the velocity vector of particle i at time t + 1.

[0065] In the embodiments of the present application, the velocity vector of the particle at each moment may refer to the initial velocity vector or a vector obtained by updating the initial velocity vector.

[0066] As an example, obtaining the velocity vector of the particle at time t + 1 includes: obtaining the first particle position with the highest position score from the particle positions experienced by the particle in the previous t moments; obtaining the second particle position with the highest position score from the particle positions experienced by each particle in the initial particle swarm in the previous t moments; and determining the velocity vector of the particle at time t + 1 according to the velocity vector of the particle at time t, the position difference between the first particle position and the particle position of the particle at time t, and the position difference between the second particle position and the particle position of the particle at time t.

[0067] For example, the velocity update formula of the particle is:

[0068] v i (t + 1) = w × v i (t) + c 1 × r 1 × (p best,i - x i (t)) + c 2 × r 2 × (g best - x i (t))

[0069] where v i (t + 1) is the velocity vector of particle i at time t + 1, v i(t) is the velocity vector of particle i at time t, w is the inertia weight, c 1 and c 2 are the learning factors, r 1 and r 2 are random numbers in the interval [0, 1], p best,i is the optimal position (first particle position) experienced by particle i itself, and g best is the global optimal position (second particle position) found by the entire particle swarm so far.

[0070] It should be noted that during the iterative update process, the optimal position p best,i of each particle can be updated according to the fitness function value (position score). If the fitness function value of the current particle position is better than the fitness function value of its own optimal position, then p best,i is updated to the current particle position. At the same time, by comparing the fitness function values of all particles, the global optimal position g best is updated.

[0071] Among them, the learning factors c 1 and c 2 can be preset fixed values or can be adaptively adjusted during the update process.

[0072] As an example, the improved learning factor calculation formula in the embodiments of the present application is as follows:

[0073]

[0074] Among them, N_iter is the maximum number of iterations of the particle swarm; n_iter is the current number of iterations; c 1,max 、c 1,min 、c 2,max 、c 2,min are respectively the maximum and minimum values of the preset c 1 and c 2 . It should be noted that n_iter is a variable in this formula.

[0075] The present application improves the particle swarm algorithm and adaptively adjusts the learning factor. The learning factors c 1 and c 2It is no longer a fixed value but is adaptively generated at each iteration, which can balance the global search and local search capabilities, accelerate the convergence speed, improve the algorithm accuracy, and enhance the algorithm robustness. In the initial stage of the particle swarm algorithm, a larger learning factor can make the particles have stronger randomness and be able to explore in a larger search space, which is conducive to finding the general area of the global optimal solution and avoiding the algorithm from converging to the local optimum prematurely. In the later stage of the algorithm operation, a smaller learning factor can enable the particles to conduct fine search near the current optimal solution, improve the local search ability, and further optimize the solution quality.

[0076] It should be noted that during the update process, it is necessary to ensure that the position of the particle (i.e., the value of the multi-dimensional vector) is within the value range of its corresponding variable. If it exceeds the range, it will be adjusted to the boundary value. In addition, when the number of iterations reaches the set maximum number of iterations (such as N_iter = 10000) or the fitness function value converges to the set value (such as the set value Δξ = 10 -4 ), the update process of the particle position can be stopped.

[0077] Step 305: Select the target particle position from each particle position based on the position score.

[0078] In the embodiment of the present application, the target particle position may refer to the global optimal position after the algorithm converges.

[0079] Step 306: Determine the target power value of each target resource node on the corresponding power parameter according to the target particle position.

[0080] In the embodiment of the present application, the target power value of each target resource node on the corresponding power parameter is included in the particle coding vector (the value of the multi-dimensional vector) corresponding to the target particle position.

[0081] Step 307: Obtain the adjustable margin of each target resource node under the target scheduling strategy according to the target power value, and send the adjustable margin to the virtual power plant scheduling platform.

[0082] It should be noted that the relevant content in steps 301 and 307 can refer to the relevant descriptions in other embodiments, and will not be elaborated here.

[0083] For the sake of easy understanding, the technical solution of the present application will be described below with a specific example:

[0084] A directly controlled virtual power plant includes 3 distributed power sources (DG1, DG2, DG3), 1 energy storage device, and a certain number of controllable loads.

[0085] 1) Initial data:

[0086] Power generation limits of distributed power sources: The power range of DG1 is [20, 80] kW, the power range of DG2 is [10, 60] kW, and the power range of DG3 is [30, 100] kW.

[0087] The capacity of the energy storage device is 100 kWh, and the charge and discharge power limit is [-50, 50] kW (negative value for discharge).

[0088] The adjustable range of the controllable load is [-30, 30] kW (negative value for reducing the load).

[0089] The load demand P at the initial moment l,de = 150 kW; the real-time grid electricity price is 0.6 yuan / kWh.

[0090] Power generation cost: The power generation cost of DG1 is 0.3 yuan / kWh, the power generation cost of DG2 is 0.35 yuan / kWh, and the power generation cost of DG3 is 0.4 yuan / kWh. The charge and discharge loss cost of the energy storage device is 0.05 yuan / kWh.

[0091] 2) Particle swarm initialization:

[0092] Suppose 5 particles are generated, and the initial position x 1 of one particle X 1 (t) is [30, 20, 40, -20, 10], indicating that the power generation of DG1 is 30 kW, the power generation of DG2 is 20 kW, the power generation of DG3 is 40 kW, the discharge power of the energy storage device is 20 kW, and the controllable load is reduced by 10 kW; its initial velocity vector V 1 is supposed to be [5, -3, 4, 3, -2].

[0093] 3) Iterative calculation example:

[0094] Taking the first particle X 1 as an example, suppose the inertia weight w = 0.8, and the learning factors c 1 = c 2 = 1.5, the random numbers r 1 = 0.6, r 2 = 0.8. The current p best,1 of this particle = [35, 22, 45, -15, 8], and the global optimal position g best = [40, 25, 50, -10, 5].

[0095] According to the velocity update formula, the new velocity vector V 1 (t + 1) is calculated, and according to the position update formula, the new position x 1 (t + 1) = [38, 23, 47, -12, 6].

[0096] Calculate the particle position x according to the fitness function 1 The fitness function value (position score) at (t + 1), and update p according to the fitness function value best,i and g best And perform the same operation on other particles, continuously iterate and optimize. After the iteration stops, the globally optimal position obtained is the current optimal aggregation optimization result.

[0097] In this embodiment, from the distributed resource node list issued by the virtual power plant dispatching platform, obtain multiple target resource nodes to be dispatched in the virtual power plant; perform particle coding according to the power parameters corresponding to the multiple target resource nodes, and combine the power parameter ranges corresponding to each target resource node to generate an initial particle swarm; construct a fitness function according to the operating cost of the virtual power plant, the interaction cost with the power grid, and the load satisfaction degree; update the particle positions of at least one particle in the initial particle swarm, and determine the position scores of each particle position based on the fitness function; select the target particle position among each particle position based on the position score; according to the target particle position, determine the target power values of each target resource node on the corresponding power parameters; according to the target power values, obtain the adjustable margins of each target resource node under the target dispatching strategy, and send the adjustable margins to the virtual power plant dispatching platform. By reasonably designing the fitness function, this application comprehensively considers multiple key factors in the operation of the virtual power plant, enabling the dispatching scheme to reduce the operating cost while ensuring the reliability of power supply and the rationality of interaction with the power grid, and having good robustness, capable of adapting to the dispatching requirements of direct-control virtual power plants with different scales and characteristics.

[0098] To implement the above embodiment, an embodiment of this application also proposes a virtual power plant aggregation dispatching device based on the particle swarm algorithm. Figure 4 It is a schematic structural diagram of a virtual power plant aggregation dispatching device based on the particle swarm algorithm provided by an embodiment of this application.

[0099] As Figure 4 shown, the virtual power plant aggregation dispatching device 400 based on the particle swarm algorithm includes:

[0100] An acquisition module 401, configured to obtain multiple target resource nodes to be dispatched in the virtual power plant from the distributed resource node list issued by the virtual power plant dispatching platform;

[0101] A determination module 402, configured to determine the target dispatching strategy of the virtual power plant based on the particle swarm algorithm, where the target dispatching strategy is used to indicate the target power values of each target resource node on the corresponding power parameters;

[0102] A sending module 403, configured to obtain the adjustable margins of each target resource node under a target scheduling policy according to a target power value, and send the adjustable margins to a virtual power plant scheduling platform.

[0103] Optionally, the determining module 402 is specifically configured to: perform particle encoding according to power parameters corresponding to multiple target resource nodes, and generate an initial particle swarm in combination with the power parameter ranges corresponding to each target resource node; construct a fitness function according to the operating cost of the virtual power plant, the interaction cost with the power grid, and the load satisfaction degree; update the particle positions of at least one particle in the initial particle swarm, and determine the position scores of each particle position based on the fitness function; select a target particle position from each particle position based on the position scores; and determine the target power values of each target resource node on the corresponding power parameters according to the target particle position.

[0104] Optionally, the multiple target resource nodes include distributed power sources, energy storage devices, and controllable loads. The power parameters corresponding to the distributed power sources include the power generation, the power parameters corresponding to the energy storage devices include the charge and discharge power, and the power parameters corresponding to the controllable loads include the regulation power.

[0105] Optionally, the determining module 402 is specifically configured to: obtain the interaction power between the virtual power plant and the power grid; obtain the ratio of the actually satisfied load power to the load demand power; and construct a fitness function based on the operating cost, the interaction power, and the ratio.

[0106] Optionally, for updating any particle in the initial particle swarm at the (t + 1)-th moment, the determining module 402 is specifically configured to: obtain the particle position of the particle at the t-th moment and the velocity vector of the particle at the (t + 1)-th moment; and perform position update according to the particle position of the particle at the t-th moment and the velocity vector at the (t + 1)-th moment to obtain the particle position of the particle at the (t + 1)-th moment.

[0107] Optionally, the determining module 402 is specifically configured to: obtain a first particle position with the highest position score from the particle positions experienced by the particle in the previous t moments; obtain a second particle position with the highest position score from the particle positions experienced by each particle in the initial particle swarm in the previous t moments; and determine the velocity vector of the particle at the (t + 1)-th moment according to the velocity vector of the particle at the t-th moment, the position difference between the first particle position and the particle position of the particle at the t-th moment, and the position difference between the second particle position and the particle position of the particle at the t-th moment.

[0108] It should be noted that the foregoing explanation of the embodiments of the virtual power plant aggregation scheduling method based on the particle swarm algorithm is also applicable to the virtual power plant aggregation scheduling device based on the particle swarm algorithm in this embodiment, and will not be elaborated here.

[0109] Figure 5Schematic diagram of the structure of the electronic device provided by the embodiment of the present application. Among them, the electronic device 500 in this embodiment is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0110] As Figure 5 shown, the above-mentioned electronic device 500 includes:

[0111] A memory 501 and a processor 502, a bus 503 connecting different components (including the memory 501 and the processor 502), and the memory 501 stores a computer program. When the processor 502 executes the program, the virtual power plant aggregation scheduling method based on the particle swarm algorithm of the embodiment of the present application is implemented.

[0112] The bus 503 represents one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any bus structure in a variety of bus structures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0113] The electronic device 500 typically includes a variety of electronic device-readable media. These media can be any available media that can be accessed by the electronic device 500, including volatile and non-volatile media, removable and non-removable media.

[0114] The memory 501 may further include a computer system-readable medium in the form of volatile memory, such as random access memory (RAM) 504 and / or cache memory 505. The electronic device 500 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 506 can be used to read and write non-removable, non-volatile magnetic media ( Figure 5 not shown, commonly referred to as a "hard disk drive"). Although Figure 5Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (such as a "floppy disk") and an optical disk drive for reading and writing a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM or other optical medium) can be provided. In these cases, each drive can be connected to the bus 503 through one or more data medium interfaces. The memory 501 may include at least one program product having a set (such as at least one) of program modules configured to perform the functions of the embodiments of the present application.

[0115] A program / utility 508 having a set (at least one) of program modules 507 can be stored in, for example, the memory 501. Such program modules 507 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. The program modules 507 generally perform the functions and / or methods in the embodiments described in the present application.

[0116] The electronic device 500 can also communicate with one or more external devices 509 (such as a keyboard, a pointing device, a display 511, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 500, and / or communicate with any device that enables the electronic device 500 to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through an input / output (I / O) interface 512. And, the electronic device 500 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and / or a public network, such as the Internet) through a network adapter 513. As Figure 5 shown, the network adapter 513 communicates with other modules of the electronic device 500 through the bus 503. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0117] The processor 502 executes various functional applications and data processing by running the programs stored in the memory 501.

[0118] It should be noted that for the implementation process and technical principle of the electronic device in this embodiment, refer to the foregoing explanation of the virtual power plant aggregation scheduling method based on the particle swarm algorithm in the embodiments of the present application, which will not be elaborated here.

[0119] To implement the above embodiments, the present application also provides a computer-readable storage medium storing computer-executable instructions, which when executed by a processor are used to implement the method provided in the foregoing embodiments.

[0120] To implement the above embodiments, the present application also provides a computer program product including a computer program, which when executed by a processor implements the method provided in the foregoing embodiments.

[0121] The collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information involved in the present application all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0122] It should be noted that personal information from users should be collected for legal and reasonable purposes and not shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after obtaining the informed consent of the user, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization including authorizing relevant user information before the user uses the function. In addition, any necessary steps should be taken to safeguard and protect access to such personal information data and ensure that others with access to the personal information data comply with their privacy policies and procedures.

[0123] The present application anticipates providing an implementation for users to selectively block the use or access of personal information data. That is, the present disclosure anticipates providing hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting the data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of the user.

[0124] In the description of the foregoing embodiments, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0125] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the technical features indicated. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "a plurality of" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0126] Any process or method description represented in a flowchart or described otherwise herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logical function or process. The scope of the preferred embodiments of the present application includes additional implementations where functions may be executed not in the order shown or discussed, including in a substantially simultaneous manner according to the functions involved or in a reverse order, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0127] The logic and / or steps represented in a flowchart or described otherwise herein, for example, can be considered as an ordered listing of executable instructions for implementing a logical function and can be specifically implemented in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. As used in this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, as the program can be obtained, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or other appropriate processing as necessary to obtain the program in electronic form and then storing it in a computer memory.

[0128] It should be understood that each part of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0129] Those of ordinary skill in the art can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0130] In addition, in each embodiment of the present application, each functional unit can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0131] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A virtual power plant aggregation scheduling method based on particle swarm algorithm, characterized in that: The following steps are involved: Obtain multiple target resource nodes to be dispatched in the virtual power plant from the distributed resource node list issued by the virtual power plant dispatching platform; Based on a particle swarm algorithm, determining a target scheduling strategy for the virtual power plant, wherein the target scheduling strategy is used to indicate a target power value of each target resource node on a corresponding power parameter; According to the target power value, the adjustable margin of each target resource node under the target scheduling strategy is obtained, and the adjustable margin is sent to the virtual power plant scheduling platform.

2. The method according to claim 1, characterized in that The step of determining the target scheduling strategy of the virtual power plant based on a particle swarm algorithm includes: Perform particle encoding according to the power parameters corresponding to the multiple target resource nodes, and generate an initial particle swarm in combination with the power parameter range corresponding to each of the target resource nodes; Constructing a fitness function according to the operation cost of the virtual power plant, the interaction cost with the power grid and the load satisfaction degree; updating a particle position of at least one particle in the initial particle swarm, and determining a position score of each particle position based on the fitness function; selecting a target particle position from among the particle positions based on the position score; According to the target particle position, a target power value of each target resource node on a corresponding power parameter is determined.

3. The method according to claim 2, characterized in that The multiple target resource nodes include distributed power sources, energy storage devices and controllable loads. The power parameters corresponding to the distributed power sources include generated power, the power parameters corresponding to the energy storage devices include charging and discharging power, and the power parameters corresponding to the controllable loads include regulated power.

4. The method according to claim 2, characterized in that: The fitness function is constructed according to the operation cost of the virtual power plant, the interaction cost with the power grid and the load satisfaction degree, including: Obtaining the interactive power between the virtual power plant and the power grid; Obtain the ratio of the load power actually met to the load demand power; The fitness function is constructed based on the operating cost, the interaction power and the ratio.

5. The method according to claim 2, characterized in that: The updating of the particle position of at least one particle in the initial particle group includes: For the update of any particle in the initial particle group at time t+1, the particle position of the particle at time t and the velocity vector of the particle at time t+1 are obtained; According to the particle position of the particle at time t and the velocity vector at time t+1, the position is updated to obtain the particle position of the particle at time t+1.

6. The method according to claim 5, characterized in that Obtaining the velocity vector of the particle at time t+1, including: Obtaining the first particle position with the highest position score from the particle positions that the particle has passed through in the previous t moments; Obtaining the second particle position with the highest position score from the particle positions that each particle in the initial particle group has experienced in the previous t moments; Determine the velocity vector of the particle at time t+1 based on the velocity vector of the particle at time t, the position difference between the first particle position and the particle position of the particle at time t, and the position difference between the second particle position and the particle position of the particle at time t.

7. A virtual power plant aggregation scheduling device based on particle swarm algorithm, characterized in that: include: An acquisition module is used to acquire multiple target resource nodes to be scheduled in the virtual power plant from a distributed resource node list issued by the virtual power plant scheduling platform; A determination module, used to determine a target scheduling strategy of the virtual power plant based on a particle swarm algorithm, wherein the target scheduling strategy is used to indicate a target power value of each target resource node on a corresponding power parameter; A sending module is used to obtain the adjustable margin of each target resource node under the target scheduling strategy according to the target power value, and send the adjustable margin to the virtual power plant scheduling platform.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

Images