Method and device for dispatching power resources and computer readable storage medium

Abstract

The invention discloses a method and device for dispatching power resources and a computer readable storage medium. The method comprises the steps of predicting the power supply power of new energy equipment through a power supply power prediction model at a preset time interval, generating power supply probability distribution corresponding to the probability of each power supply power interval, and predicting the power utilization load through a power utilization power prediction model, generating an electricity utilization probability distribution corresponding to the probability of each electricity utilization power interval; constructing constraint conditions among the new energy equipment, the thermal power generating unit, the energy storage equipment, the national power grid and the electrical load; solving the power supply scheduling model constructed under the constraint condition by using a particle swarm algorithm and taking the minimum power supply cost of the new energy equipment, the thermal power generating unit, the energy storage equipment and the national power grid as a target, and determining the minimum power supply cost; and scheduling power resources of the new energy equipment, the thermal power generating unit, the energy storage equipment and the national power grid according to the minimum power supply cost.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, device and computer-readable storage medium for scheduling electric power resources. Background Art

[0002] As the proportion of renewable energy (such as wind energy and solar energy) in the power system gradually increases, the supply and demand balance and dispatch optimization of the power system face new challenges. As an effective resource aggregation and dispatching means, virtual power plants can achieve supply and demand balance and optimized operation of power dispatching systems by integrating distributed energy resources. At present, the dispatching methods of virtual power plants usually adopt offline optimization algorithms based on historical data, or online optimization algorithms based on real-time data. However, offline optimization algorithms cannot adapt to the real-time changes of power dispatching systems, while online optimization algorithms are difficult to meet the requirements in terms of computational efficiency and real-time performance. Existing virtual power plant dispatching methods have deficiencies in computational efficiency, real-time performance, and optimization accuracy. They are difficult to adapt to the rapidly changing needs of power dispatching systems and cannot achieve the goal of periodic automatic optimization.

[0003] In view of the technical problems that the existing virtual power plant scheduling methods in the above-mentioned prior arts have deficiencies in computing efficiency, real-time performance and optimization accuracy, are difficult to adapt to the rapidly changing needs of the power dispatching system, and cannot achieve the goal of periodic automatic optimization, no effective solution has been proposed so far. Summary of the invention

[0004] The embodiments of the present application provide a method, device and computer-readable storage medium for scheduling power resources, so as to at least solve the technical problems that the existing virtual power plant scheduling methods in the prior art are insufficient in computing efficiency, real-time performance and optimization accuracy, are difficult to adapt to the rapidly changing needs of the power scheduling system, and cannot achieve the goal of periodic automatic optimization.

[0005] According to one aspect of an embodiment of the present application, a method for scheduling electric power resources is provided, comprising: predicting the power supply of new energy equipment through a power supply prediction model at predetermined time intervals, generating a power supply probability distribution corresponding to the probability of each power supply power interval, and predicting the power load through a power consumption prediction model, generating a power consumption probability distribution corresponding to the probability of each power consumption power interval; constructing constraints between new energy equipment, thermal power units, energy storage equipment, national power grid and power load according to a power supply power range pre-set with thermal power units, energy storage equipment and national power grid and according to the power supply probability distribution and the power consumption probability distribution; solving the power supply scheduling model constructed under the constraints with the goal of minimizing the power supply cost of new energy equipment, thermal power units, energy storage equipment and national power grid by using a particle swarm algorithm to determine the minimum power supply cost; and scheduling the electric power resources of new energy equipment, thermal power units, energy storage equipment and national power grid according to the minimum power supply cost at predetermined time intervals.

[0006] According to another aspect of an embodiment of the present application, a device for scheduling electric power resources is also provided, including: a probability generation module, which is used to predict the power supply of new energy equipment through a power supply prediction model at a predetermined time interval, generate a power supply probability distribution corresponding to the probability of each power supply power interval, and predict the power load through a power consumption prediction model to generate a power consumption probability distribution corresponding to the probability of each power consumption power interval; a condition construction module, which is used to construct constraints between new energy equipment, thermal power units, energy storage equipment, national power grids and power loads according to a pre-set power supply power range with thermal power units, energy storage equipment and national power grids and according to the power supply probability distribution and the power consumption probability distribution; a cost determination module, which is used to solve the power supply scheduling model constructed under the constraints with the goal of minimizing the power supply cost of new energy equipment, thermal power units, energy storage equipment and national power grids using a particle swarm algorithm to determine the minimum power supply cost; and a resource scheduling module, which is used to schedule the electric power resources of new energy equipment, thermal power units, energy storage equipment and national power grids according to the minimum power supply cost at a predetermined time interval.

[0007] According to another aspect of an embodiment of the present application, there is also provided an apparatus for scheduling electric power resources, comprising: a processor; and a memory connected to the processor, for providing the processor with instructions for processing the following processing steps: at a predetermined time interval, predicting the power supply of the new energy equipment through a power supply prediction model, generating a power supply probability distribution corresponding to the probability of each power supply power interval, and predicting the power load through a power consumption prediction model, generating a power consumption probability distribution corresponding to the probability of each power consumption power interval; constructing constraints between the new energy equipment, the thermal power units, the energy storage equipment, the national power grid and the power load according to the pre-set power supply power range with the thermal power units, the energy storage equipment and the national power grid and according to the power supply probability distribution and the power consumption probability distribution; solving the power supply scheduling model constructed under the constraints with the particle swarm algorithm as the goal of minimizing the power supply cost of the new energy equipment, the thermal power units, the energy storage equipment and the national power grid, and determining the minimum power supply cost; and at a predetermined time interval, scheduling the electric power resources of the new energy equipment, the thermal power units, the energy storage equipment and the national power grid according to the minimum power supply cost.

[0008] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0009] According to another aspect of the embodiments of the present application, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0010] In the embodiment of the present application, the power dispatching platform predicts the power supply power and power consumption through the power supply power prediction model and power consumption prediction model based on real-time data, thereby enhancing the adaptability and response speed to the real-time changes of the power dispatching system, and solving the problem that the traditional method cannot cope with real-time changes. And the power dispatching platform realizes more refined and efficient power dispatching control by constructing detailed constraints of power supply equipment and power load, maximizing the use of new energy while ensuring the stable operation of the power dispatching system. And this technical solution establishes a power supply dispatching model and solves it using a particle swarm algorithm, so that the particle swarm algorithm can quickly find the optimal solution, quickly find the solution with the minimum power supply cost, and improve the speed and efficiency of determining the minimum power supply cost. Then the goal of periodic automatic optimization at a predetermined time interval is achieved, and the scheduling efficiency and real-time performance of the virtual power plant are improved. Then the existing virtual power plant scheduling method in the prior art is solved. There are deficiencies in calculation efficiency, real-time performance and optimization accuracy, and it is difficult to adapt to the needs of rapid changes in the power dispatching system, and it is impossible to achieve the technical problem of the goal of periodic automatic optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0012] Figure 1 is a hardware structure block diagram of a computing device for implementing the method according to Embodiment 1 of the present application;

[0013] Figure 2 is a schematic diagram of the power dispatching system according to Embodiment 1 of the present application;

[0014] Figure 3 is a flowchart of a method for scheduling power resources according to the first aspect of Embodiment 1 of the present application;

[0015] Figure 4 is a schematic diagram of probability distribution of new energy equipment according to Example 1 of the present application;

[0016] Figure 5 is a schematic diagram of the power supply prediction model according to Example 1 of the present application;

[0017] Figure 6 is a schematic diagram of the electric power prediction model according to Example 1 of the present application;

[0018] Figure 7 is another probability distribution diagram of the new energy device according to Example 1 of the present application;

[0019] Figure 8 is a sequential flow chart of a method for scheduling power resources according to the first aspect of Embodiment 1 of the present application;

[0020] Fig. 9 is a schematic diagram of a device for scheduling power resources according to Embodiment 2 of the present application; and

[0021] Fig.10 It is a schematic diagram of the device for scheduling power resources according to Example 3 of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only embodiments of a part of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application 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 data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising 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.

[0024] Example 1

[0025] According to this embodiment, a method embodiment of a method for scheduling power resources 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.

[0026] The method embodiment provided in this embodiment can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computing device for implementing a method for scheduling power resources. Figure 1 As shown, the computing device may include one or more processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, and a transmission device for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0027] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computing device. As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0028] The memory can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for scheduling power resources in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the method for scheduling power resources of the above-mentioned application program is realized. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0029] The transmission device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computing device. In one example, the transmission device includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet wirelessly.

[0030] The display may be, for example, a touch screen liquid crystal display (LCD) that may enable a user to interact with a user interface of the computing device.

[0031] It should be noted that, in some optional embodiments, the above Figure 1 The computing device shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware elements and software elements. Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computing devices described above.

[0032] Figure 2Schematic diagram of the power dispatching system according to this embodiment. Figure 2 As shown, the system includes: a power dispatching platform 10.

[0033] refer to Figure 2 As shown, the technical solution can be used to implement the dispatching of a regional power grid, specifically, the regional power grid can be, for example, a power grid in a city area. The regional power grid is powered by power generation equipment of different energy types, such as photovoltaic, wind power, energy storage system and thermal power units.

[0034] The power dispatching platform 10 can communicate with photovoltaic equipment, wind power equipment, energy storage system, thermal power unit and power load through the Internet of Things, so as to obtain the power supply power of photovoltaic equipment, wind power equipment, energy storage system and thermal power unit, as well as the power consumption of power load. The power load can include residential power, industrial power, commercial power, agricultural power and transportation power, etc.

[0035] The power dispatching platform 10 can also achieve a balance between the power consumption and power supply of the power load of the regional power grid through power dispatching. Among them, compared with photovoltaic equipment and wind power equipment, thermal power units are relatively stable and controllable. In addition, the energy storage system can be charged during flat and valley periods and discharged during peak and peak periods, thereby realizing power dispatching in a "peak shaving and valley filling" manner. In addition, when the power supply of the power supply equipment of the regional power grid cannot meet the power load, electricity can be purchased from the national power grid. The electricity price of the national power grid also varies according to the peak and valley, with the valley price being lower and the peak price being higher.

[0036] Therefore, the power dispatching platform can balance power supply and power load by dispatching thermal power units and energy storage systems, as well as purchasing electricity from the national power grid.

[0037] It should be noted that the power dispatching platform 10 in the system can be applicable to the hardware structure described above.

[0038] Under the above operating environment, according to a first aspect of this embodiment, a method for scheduling power resources is provided. The method comprises: Figure 2 The power dispatching platform shown in is implemented. Figure 3 A schematic diagram showing the process of the method is shown in FIG. Figure 3 As shown, the method includes:

[0039] S302: at a predetermined time interval, predicting the power supply of the new energy equipment by using the power supply prediction model, generating a power supply probability distribution corresponding to the probability of each power supply power interval, and predicting the power load by using the power consumption prediction model, generating a power consumption probability distribution corresponding to the probability of each power consumption power interval;

[0040] S304: Constructing constraints among new energy equipment, thermal power units, energy storage equipment, the national power grid, and power loads according to the power supply power range pre-set by the thermal power units, energy storage equipment, and the national power grid, and according to the power supply probability distribution and the power consumption probability distribution;

[0041] S306: using a particle swarm algorithm to solve a power supply scheduling model constructed under constraints with the goal of minimizing the power supply cost of new energy equipment, thermal power units, energy storage equipment and national power grids, and determining the minimum power supply cost; and

[0042] S308: At predetermined time intervals, the power resources of new energy equipment, thermal power units, energy storage equipment and the national power grid are dispatched according to the minimum power supply cost.

[0043] Specifically, the power dispatching platform 10 pre-sets a fixed time interval, for example, 15 minutes, so that every 15 minutes, the power dispatching platform 10 dispatches the power supply equipment according to the power supply dispatching scheme corresponding to the minimum power supply cost of the power supply equipment calculated in real time. The power supply equipment includes new energy equipment, thermal power units, energy storage equipment and national power grids.

[0044] First, the power dispatching platform 10 predicts the power supply of the new energy equipment at the next moment through the power supply prediction model at a predetermined time interval according to the weather forecast data at the next moment.

[0045] More specifically, new energy equipment generates electric power resources based on wind power and sunlight, including photovoltaic equipment and wind power equipment. The power dispatching platform 10 collects weather forecast data for the next moment. The weather forecast data includes sunshine intensity data, cloud cover data, temperature data, wind speed, temperature, and air pressure data. Afterwards, the power dispatching platform 10 inputs the weather forecast data for the next moment into a preset power supply prediction model, so that the power supply prediction model outputs the probability pa1~pa9 of the electric power resources generated by the new energy equipment in each power supply power interval at the next moment based on the weather forecast data. And each power supply power interval includes: 1000KWH~900KWH, 900KWH~800KWH,...., 200KWH~100KWH. Afterwards, the power dispatching platform 10 generates the power supply probability distribution of new energy equipment based on the probability of each power supply power interval at the next moment. Figure 4 The power supply probability distribution of new energy devices is shown.

[0046] More specifically, Figure 5 The schematic diagram of the power supply prediction model is shown. The power supply prediction model includes an input layer, a hidden layer and an output layer, wherein the activation function of the output layer is a softmax function. Figure 5As shown, the power dispatching platform 10 generates a corresponding feature vector A[a1, a2, ..., a r ] T Then the power dispatching platform 10 converts the feature vector A[a1,a2,...,a r ] T The input layer is input to the hidden layer. After the hidden layer processes the feature vector, the output layer generates the probabilities pa1~pa9 of each power supply interval through the softmax activation function according to the feature information obtained after the hidden layer processing.

[0047] Furthermore, the electric power dispatching platform 10 collects the electric power of the electric load at historical moments, and then inputs the electric power into the electric power prediction model. The power supply at historical moments includes: multiple power supplies between the first moment of today and the current moment. Then the electric power dispatching platform 10 predicts the electric power at the next moment through the electric power prediction model, thereby outputting the probability of the electric load at the next moment in each electric power interval. The various electric power intervals include: 1000KWH~900KWH, 900KWH~800KWH, ...., 200KWH~100KWH. Then the electric power dispatching platform 10 generates the electric power probability distribution of the electric load with the probability of each electric power interval corresponding to the next moment (not shown in the figure).

[0048] More specifically, the power consumption prediction model includes an input layer, a hidden layer and an output layer, wherein the activation function of the output layer is a softmax function. e The power consumption of the power load generates the corresponding characteristic vector SY[y1,y2,...,y e ] T Then the power dispatching platform 10 converts the feature vector SY[y1,y2,...,y e ] T The input layer is input to the hidden layer. After the hidden layer processes the feature vector, the output layer generates the probabilities py1~py9 of each power consumption interval through the softmax activation function according to the feature information obtained after the hidden layer processing.

[0049] Furthermore, the power dispatching platform 10 determines the maximum power supply and the minimum power supply of the new energy equipment according to the power supply probability distribution of the new energy equipment, thereby constructing the constraint conditions of the new energy equipment according to the maximum power supply and the minimum power supply. For other power supply equipment such as thermal power units, energy storage equipment and national power grids, the power dispatching platform 10 is pre-set with a power supply range, so that the power dispatching platform uses the pre-set power supply range as the constraint conditions of the thermal power units, energy storage equipment and national power grids. And the power dispatching platform 10 determines the maximum power consumption and the minimum power consumption according to the power consumption probability distribution of the power load, thereby constructing the constraint conditions of the power load according to the maximum power consumption and the minimum power consumption.

[0050] Furthermore, the power dispatching platform 10 calculates the power supply cost at the next moment according to the power supply power of the new energy equipment, thermal power units, energy storage equipment and the national power grid at the next moment under the constraint conditions, and constructs a power supply dispatching model according to the power supply cost. The power dispatching platform 10 then solves the power supply dispatching model through a particle swarm algorithm to determine the minimum power supply cost.

[0051] Furthermore, after obtaining the minimum power supply cost, the power dispatching platform 10 obtains the power supply power of the new energy equipment, thermal power units, energy storage equipment and the national power grid corresponding to the minimum power supply cost, thereby performing corresponding dispatch of the new energy equipment, thermal power units, energy storage equipment and the national power grid.

[0052] As described in the background technology, with the increasing proportion of renewable energy (such as wind energy and solar energy) in the power system, the supply and demand balance and dispatch optimization of the power system face new challenges. As an effective resource aggregation and dispatching means, virtual power plants can achieve supply and demand balance and optimized operation of power dispatching systems by integrating distributed energy resources. At present, the dispatching method of virtual power plants usually adopts an offline optimization algorithm based on historical data, or an online optimization algorithm based on real-time data. However, the offline optimization algorithm cannot adapt to the real-time changes of the power dispatching system, and the online optimization algorithm is difficult to meet the requirements in terms of computational efficiency and real-time performance. The existing virtual power plant dispatching method has deficiencies in computational efficiency, real-time performance and optimization accuracy, and it is difficult to adapt to the rapidly changing needs of the power dispatching system, and it is impossible to achieve the goal of periodic automatic optimization.

[0053] In view of the technical problems described above, through the technical solutions of the embodiments of the present application, the power dispatching platform predicts the power supply power and power consumption through the power supply power prediction model and the power consumption prediction model based on real-time data, thereby enhancing the adaptability and response speed to the real-time changes of the power dispatching system, and solving the problem that the traditional method cannot cope with real-time changes. In addition, the power dispatching platform realizes more refined and efficient power dispatching control by constructing detailed constraints of power supply equipment and power load, and maximizes the use of new energy while ensuring the stable operation of the power dispatching system. In addition, the technical solution establishes a power supply dispatching model and solves it using a particle swarm algorithm, so that the particle swarm algorithm can be used to quickly find the optimal solution, quickly find the solution with the minimum power supply cost, and improve the speed and efficiency of determining the minimum power supply cost. In addition, the goal of periodic automatic optimization at a predetermined time interval is achieved, and the dispatching efficiency and real-time performance of the virtual power plant are improved. In addition, the technical problem that the existing virtual power plant dispatching method in the prior art is insufficient in terms of computational efficiency, real-time performance and optimization accuracy, and it is difficult to adapt to the needs of rapid changes in the power dispatching system, and it is impossible to achieve the goal of periodic automatic optimization is solved.

[0054] Optionally, the operation of constructing constraints between new energy equipment, thermal power units, energy storage equipment, the national power grid and power loads according to a pre-set power supply power range for thermal power units, energy storage equipment and the national power grid and according to a power supply probability distribution and a power consumption probability distribution includes: constructing constraints corresponding to the thermal power units according to a pre-set maximum power supply power and a minimum power supply power of the thermal power units; constructing constraints corresponding to the energy storage equipment according to a pre-set maximum power supply power and a minimum power supply power of the energy storage equipment; constructing constraints corresponding to the national power grid according to a pre-set maximum power supply power and a minimum power supply power of the national power grid; determining the minimum power supply power of the new energy equipment according to the power supply probability distribution of the new energy equipment, and determining the maximum power consumption of the power load according to the power consumption probability distribution of the power load; and constructing constraints between the new energy equipment and the power load according to the minimum power supply power of the new energy equipment and the maximum power consumption of the power load.

[0055] Specifically, the power dispatching platform 10 obtains the power supply probability distribution of the new energy equipment and determines the confidence interval of the power supply probability distribution according to a preset probability threshold. Figure 7 As shown, for example, the probability threshold is 0.2, so the power dispatching platform 10 determines the confidence interval of the power supply probability distribution as UL according to the probability threshold. a ~UR a . After that, the power dispatching platform 10 will UR a As the maximum power supply Xa max , UL aAs the minimum power supply Xa min . Then the power dispatching platform uses the minimum power supply power Xa min Determine the constraints of new energy equipment, that is, Xa = Xa min .

[0056] Furthermore, the power dispatching system is pre-set with m1 thermal power units, namely thermal power units 1 to m1, so that the power supply power of each thermal power unit is Xb u Both correspond to a maximum power supply power Xb u max and minimum power supply Xb u min The power supply range of the composition. u=1~m1. Xb u max represents the maximum power supply of the u-th thermal power unit, Xb u min represents the minimum power supply of the u-th thermal power unit. Then the power dispatching platform uses the maximum power supply Xb u max and minimum power supply Xb u min Determine the constraints of thermal power units 1 to m1, namely Xb u max ≥Xb u ≥Xb u min .

[0057] Furthermore, the power dispatching system is pre-set with m2 energy storage devices, namely energy storage devices 1 to m2, so that the power supply power of each energy storage device is Xc v Both correspond to a maximum power supply power Xc v max and minimum power supply Xc v min The power supply range of the composition. v = 1 ~ m2. Xc v max represents the maximum power supply of the vth energy storage device, Xc v min represents the minimum power supply of the vth energy storage device. Then the power dispatching platform uses the maximum power supply Xc v max and minimum power supply Xc v min Determine the constraints of energy storage devices 1 to m1, namely Xc v max ≥Xc v ≥Xc v min .

[0058] Furthermore, for the national power grid, a range of power supply that can be provided is preset, including a maximum power supply power Xd max and minimum power supply Xd min The minimum power supply power is Xd min is 0. Therefore, the power dispatching platform uses the maximum power supply power Xd max and minimum power supply Xd min Determine the constraints of the national power grid, namely Xd max ≥Xd≥Xd min .

[0059] Furthermore, the power dispatching platform 10 obtains the power consumption probability distribution of the power load, and determines the confidence interval UL of the power consumption probability distribution according to a preset probability threshold. Y ~UR Y . After that, the power dispatching platform 10 will UR Y As the maximum power consumption Y max , UL Y As the minimum power consumption Y min .

[0060] Furthermore, the power dispatching platform 10 calculates the power supply power Xa of the new energy equipment and the power supply power Xb of the thermal power unit. u , power supply power Xc of energy storage equipment v The power supply power Xd of the national power grid and the power consumption power Y of the power load determine the constraints between the power consumption power and the power supply power, that is,

[0061]

[0062] Therefore, when determining the constraints, this technical solution takes the minimum power supply that can be provided by the new energy equipment and the maximum power consumption of the power load, so that no matter which power consumption probability interval the actual power load is in, the power supply provided can meet the requirements of the power load, making the power dispatch model constructed under the constraints more stable.

[0063] Optionally, a particle swarm algorithm is used to solve a power supply scheduling model constructed under constraint conditions with the goal of minimizing the power supply cost of new energy equipment, thermal power units, energy storage equipment and the national power grid, and an operation of determining the minimum power supply cost includes: pre-constructing a power supply scheduling model according to the constraint conditions; and solving the power supply scheduling model with the goal of minimizing the power supply cost of new energy equipment, thermal power units, energy storage equipment and the national power grid with the particle swarm algorithm, and determining the minimum power supply cost, wherein the expression of the particle swarm algorithm is:

[0064] V i k+1 =ω k Vi k +c1r1(P i k -X i k )+c2r2(G k -X i k ) (1)

[0065] X i k+1 =X i k +V i k+1 (2)

[0066] Where k represents the number of iterations; i represents the particle number; V i k , X i k are the velocity and position of particle i before updating; V i k+1 , X i k+1 are the updated speed and position of particle i respectively; P i k , G k are the individual best position of particle i and the global best position of the group respectively; ω k It represents the inertia weight after being updated by the preset PID module; c1 and c2 represent learning factors; r1 and r2 represent random numbers between 0 and 1.

[0067] Specifically, the power dispatching platform 10 pre-builds a power supply dispatching model by using the power supply power of new energy equipment, thermal power units, energy storage equipment and the national power grid under constraint conditions.

[0068] Furthermore, the power dispatching platform 10 solves the power dispatching model through the particle swarm algorithm and determines the minimum power supply cost under the premise of meeting the constraint conditions. The constraint condition is Xa=Xa min , and Xb u max ≥Xb u ≥Xb u min , and Xc v max ≥Xc v ≥Xc v min , and Xd max ≥Xd≥Xd min ,and The particle swarm algorithm is as follows:

[0069] First, the power dispatching platform 10 initializes the initial positions of particles 1 to n generated by the particle swarm algorithm. Initial Speed Initial individual optimal position Initial global best position Initial inertia weight ω0, learning factors c1, c2, random numbers r1, r2. The initial position All meet the constraints.

[0070] And among them

[0071] Afterwards, the power dispatching platform 10 updates the individual optimal position of particle i and the global optimal position of the group according to the iterative formula of the particle swarm algorithm, where the iterative formula of the particle swarm algorithm is:

[0072] V i k+1 =ω k V i k +c1r1(P i k -X i k )+c2r2(G k -X i k ) (1)

[0073] X i k+1 =X i k +V i k+1 (2)

[0074] Where k represents the number of iterations; i represents the particle number, i = 1 to n; V i k , X i k are the velocity and position of particle i before updating; V i k+1 , X i k+1 are the updated speed and position of particle i respectively; P i k , G k are the individual best position of particle i and the global best position of the group respectively; ω k It represents the inertia weight after being updated by the preset PID module; c1 and c2 represent learning factors; r1 and r2 represent random numbers between 0 and 1.

[0075] For example, in the first iteration, the iteration formula of the particle swarm algorithm is:

[0076] V i 1 =ω0V i 0 +c1r1(P i 0 -X i 0 )+c2r2(G0-X i 0 ) (3)

[0077] X i 1 =X i 0 +V i 1 (4)

[0078] Furthermore, the power dispatching platform 10 is pre-set with a PID module, so that the power dispatching platform 10 updates the inertia weight ω0 through the PID module to obtain ω1 for the next iteration, and generates an updated individual optimal position P i 1 and the global optimal position G1.

[0079] More specifically, the power dispatching platform 10 calculates the individual optimal position P i 0 Bring it into the power supply scheduling model to calculate the individual optimal position P i 0 The corresponding power supply cost F(P i 0 ). The power dispatching platform 10 also calculates the position X of each particle. i 0 Bring it into the power supply scheduling model to calculate the position X i 0 The corresponding power supply cost FX(X i 0 ). Afterwards, the power dispatching platform 10 converts the power supply cost F(P i 0 ) and power supply cost F(X i 0 ) is compared, when F(P i 0 )≥F(X0), then the individual best position P for the next iteration i 1 =X i 0 When F(P i 0 )<F(X i0 ), then the individual optimal position P i 1 =P i 0 The power dispatching platform 10 then calculates the individual optimal position P of each particle. i 0 Compare and take the smallest individual best position as the global best position G1. Thus, the power dispatching platform 10 generates an updated individual best position P i 1 and the global optimal position G1.

[0080] Furthermore, the power dispatching platform 10 uses the iterative formula of the particle swarm algorithm to calculate the optimal position P according to the inertia weight ω1 and the individual optimal position P. i 1 The second iteration calculation is continued with the global optimal position G1 until the preset number of iterations is reached.

[0081] For example, the number of iterations is preset to 3, so the power dispatching platform 10 uses the global optimal position G3 generated by the third iteration as the dispatching scheme with the minimum power supply cost. Thus, the power dispatching platform 10 dispatches the new energy equipment, thermal power units, energy storage equipment and the national power grid accordingly according to the power supply power of the new energy equipment, thermal power units, energy storage equipment and the national power grid corresponding to the global optimal position G3.

[0082] Therefore, the present technical solution determines the minimum power supply cost through the particle swarm algorithm, thereby utilizing the ability of the particle swarm algorithm to quickly find the global optimal solution, thereby improving the speed and efficiency of determining the minimum power supply cost.

[0083] Optionally, according to the constraints, the pre-built power supply scheduling model is:

[0084]

[0085] The power supply scheduling model F represents the total power supply cost, Ca represents the power supply cost of new energy equipment in the power supply scheduling model, and Cb u represents the power supply cost of the uth thermal power unit, m1 represents the number of thermal power units in the power supply scheduling model, u=1~m1, Cc v represents the power supply cost of the vth energy storage device, m2 represents the number of energy storage devices in the power supply scheduling model, v=1~m2, and Cd represents the power supply cost of the national power grid in the power supply scheduling model.

[0086] Specifically, the power dispatching platform 10 calculates the power supply cost Ca of the new energy equipment according to the preset operating cost coefficient θ1 of the new energy equipment and the power supply power Xa of the new energy equipment: Ca = θ1*Xa. Then, the power dispatching platform 10 calculates the power supply cost Ca of the new energy equipment according to the preset operating cost coefficient θ2 of the thermal power unit and the power supply power Xb of the thermal power unit. u , calculate the power supply cost Cb of the thermal power unit u :Cb u =θ2*Xb u Then the power dispatching platform 10 calculates the power supply power Xc based on the preset operation cost coefficient θ3 of the energy storage device and the power supply power Xc of the energy storage device. v , calculate the power supply cost Cc of the energy storage device: Cc v =θ3*Xc v Then, the power dispatching platform 10 calculates the power supply cost Cd of the national power grid according to the power price q of the target period and the power supply power Xd of the national power grid: Cd=q*Xd.

[0087] Furthermore, the power dispatching platform 10 calculates the power supply cost Ca of the new energy equipment and the power supply cost Cb of the thermal power unit. u , the power supply cost of energy storage equipment Cc v The power supply dispatching model is constructed based on the power supply cost Cd of the national power grid, where the power supply dispatching model is:

[0088]

[0089] The power supply scheduling model F represents the total power supply cost, Ca represents the power supply cost of new energy equipment in the power supply scheduling model, and Cb u represents the power supply cost of the uth thermal power unit, m1 represents the number of thermal power units in the power supply scheduling model, u=1~m1, Cc v represents the power supply cost of the vth energy storage device, m2 represents the number of energy storage devices in the power supply scheduling model, v=1~m2, and Cd represents the power supply cost of the national power grid in the power supply scheduling model.

[0090] Therefore, this technical solution constructs a power supply scheduling model according to the power supply cost of the power supply equipment that needs to be scheduled, and then comprehensively judges the power supply power that each power supply equipment needs to provide according to the power supply scheduling model, so that the distribution of power supply of each power supply equipment is more reasonable.

[0091] Optionally, the operation of updating the inertia weight using a preset PID module includes: calculating the distance between the position of each particle and the global optimal position, and calculating the distance average value based on the distance; and updating the inertia weight based on the distance average value using the PID module.

[0092] Specifically, the power dispatching platform 10 needs to update the inertia weight ω kWhen , the position X of each particle i (i = 1 ~ n) in the previous iteration will be obtained i k-1 and the current global optimal position G k-1 , then calculate the position X of each particle i i k-1 With the global optimal position G k-1 The sum of the distances between them is then calculated, and the average of the sum of the distances is generated to generate the average distance L k :

[0093]

[0094] Furthermore, the power dispatching platform 10 uses the PID module to calculate the distance average value L k Calculate and determine the updated inertia weight ω k .

[0095] Therefore, the technical solution accurately determines the inertia weight by calculating the update trend of each particle indicated by the average value of the sum of the distances between the position of each particle and the global optimal position.

[0096] Optionally, the operation of updating the inertia weight according to the distance average value using the PID module includes: determining a proportional value according to the distance average value through the PID module; determining an integral value according to the distance average value through the PID module; determining a differential value according to the distance average value through the PID module; and updating the inertia weight by weighted summing the proportional value, the integral value and the differential value using preset proportional weights, integral weights and differential weights through the PID module.

[0097] Specifically, the PID module includes a proportional unit, an integral unit, and a differential unit. The power dispatching platform 10 uses the proportional unit of the PID module to calculate the proportional value according to the distance average value L k , calculate the ratio value AP k The formula for calculating the ratio is:

[0098] AP k =L k .

[0099] Furthermore, the power dispatching platform 10 uses the integral value calculation formula based on the average value L through the integral unit of the PID module k , calculate the integral value AI k The integral value calculation formula is:

[0100]

[0101] Where L j Represents the average value calculated in the jth iteration.

[0102] Furthermore, the power dispatching platform 10 uses the differential value calculation formula based on the distance average value L through the differential unit of the PID module k , calculate the differential value AD k The differential value calculation formula is:

[0103] AD k =L k -L k-1 .

[0104] Where L k Represents the average value calculated in the k-1th round of iterations.

[0105] Furthermore, the PID module is also preset with a proportional weight WP, an integral weight WI and a differential weight WD, so that the power dispatching platform 10 is based on the proportional weight WP preset by the PID module and the corresponding proportional value AP k , integral weight WI and its corresponding integral value AI k , differential weight WD and its corresponding differential value AD k Perform weighted summation to determine the updated inertia weight ω k :

[0106] ω k =AP k *WP+AI k *WI+AD k *WD.

[0107] Therefore, this technical solution utilizes the PID module to effectively reduce overshoot and ensure the stability of calculation through comprehensive adjustment of the three parts of proportion, integration and differentiation.

[0108] In summary, reference Figure 8 As shown, the power dispatching platform 10 determines the minimum power supply cost and performs the following steps for the corresponding dispatching:

[0109] S801: The power dispatching platform 10 predicts the power supply of the new energy equipment through the power supply prediction model at a predetermined time interval, and generates a power supply probability distribution corresponding to the probability of each power supply power interval;

[0110] S802: The power dispatching platform 10 predicts the power load through the power consumption prediction model, and generates a power consumption probability distribution corresponding to the probability of each power consumption interval;

[0111] S803: The power dispatching platform 10 constructs a constraint condition corresponding to the thermal power unit according to the preset maximum power supply power and minimum power supply power of the thermal power unit;

[0112] S804: The power dispatching platform 10 constructs a constraint condition corresponding to the energy storage device according to the preset maximum power supply power and minimum power supply power of the energy storage device;

[0113] S805: The power dispatching platform 10 constructs constraint conditions corresponding to the national power grid according to the preset maximum power supply power and minimum power supply power of the national power grid;

[0114] S806: The power dispatching platform 10 determines the minimum power supply power of the new energy device according to the power supply probability distribution of the new energy device, determines the maximum power consumption of the power load according to the power consumption probability distribution of the power load, and constructs the constraint conditions between the new energy device and the power load according to the minimum power supply power of the new energy device and the maximum power consumption of the power load;

[0115] S807: The power dispatching platform 10 pre-builds the power supply dispatching model according to the constraint conditions;

[0116] S808: The power dispatching platform 10 calculates the distance between the position of each particle and the global optimal position, and calculates the distance average according to the distance;

[0117] S809: the electric power dispatching platform 10 determines a proportional value according to the average distance value through the PID module, determines an integral value according to the average distance value through the PID module, and determines a differential value according to the average distance value through the PID module;

[0118] S810: The power dispatching platform 10 uses the preset proportional weight, integral weight and differential weight through the PID module to perform weighted summation on the proportional value, the integral value and the differential value to update the inertia weight;

[0119] S811: The power dispatching platform 10 solves the power supply dispatching model by using the particle swarm algorithm that updates the inertia weight to determine the minimum power supply cost;

[0120] S812: The power dispatching platform 10 dispatches the power resources of the new energy equipment, the thermal power generation unit, the energy storage equipment and the national power grid according to the minimum power supply cost.

[0121] In addition, according to a second aspect of this embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0122] In addition, according to a third aspect of this embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0123] Thus, according to this embodiment, the power dispatching platform predicts the power supply power and power consumption through the power supply power prediction model and power consumption prediction model based on real-time data, thereby enhancing the adaptability and response speed to the real-time changes of the power dispatching system, and solving the problem that the traditional method cannot cope with real-time changes. And the power dispatching platform realizes more refined and efficient power dispatching control by constructing detailed constraints of power supply equipment and power load, and maximizes the use of new energy while ensuring the stable operation of the power dispatching system. And this technical solution establishes a power supply dispatching model and solves it by using the particle swarm algorithm, so that the particle swarm algorithm can quickly find the optimal solution, quickly find the solution with the minimum power supply cost, and improve the speed and efficiency of determining the minimum power supply cost. Then the goal of periodic automatic optimization at a predetermined time interval is achieved, and the scheduling efficiency and real-time performance of the virtual power plant are improved. Then the existing virtual power plant scheduling method in the prior art is insufficient in terms of calculation efficiency, real-time performance and optimization accuracy, and it is difficult to adapt to the needs of rapid changes in the power dispatching system, and it is impossible to achieve the technical problem of the goal of periodic automatic optimization.

[0124] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0125] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0126] Example 2

[0127] Fig. 9The apparatus 900 for scheduling power resources according to this embodiment is shown, and the apparatus 900 corresponds to the method according to the first aspect of embodiment 1. Fig. 9 As shown, the device 900 includes: a probability generation module 910, which is used to predict the power supply of the new energy equipment through the power supply prediction model at a predetermined time interval, generate a power supply probability distribution corresponding to the probability of each power supply power interval, and predict the power load through the power consumption prediction model to generate a power consumption probability distribution corresponding to the probability of each power consumption power interval; a condition construction module 920, which is used to construct the constraint conditions between the new energy equipment, the thermal power unit, the energy storage equipment, the national power grid and the power load according to the pre-set power supply power range with the thermal power unit, the energy storage equipment and the national power grid and according to the power supply probability distribution and the power consumption probability distribution; a cost determination module 930, which is used to solve the power supply scheduling model constructed under the constraint conditions with the goal of minimizing the power supply cost of the new energy equipment, the thermal power unit, the energy storage equipment and the national power grid by using a particle swarm algorithm to determine the minimum power supply cost; and a resource scheduling module 940, which is used to schedule the power resources of the new energy equipment, the thermal power unit, the energy storage equipment and the national power grid according to the minimum power supply cost at a predetermined time interval.

[0128] Optionally, the condition construction module 920 includes: a first construction submodule, which is used to construct constraints corresponding to the thermal power units according to a preset maximum power supply power and a minimum power supply power of the thermal power units; a second construction submodule, which is used to construct constraints corresponding to the energy storage devices according to a preset maximum power supply power and a minimum power supply power of the energy storage devices; a third construction submodule, which is used to construct constraints corresponding to the national power grid according to a preset maximum power supply power and a minimum power supply power of the national power grid; a first determination submodule, which is used to determine the minimum power supply power of the new energy equipment according to the power supply probability distribution of the new energy equipment, and determine the maximum power consumption of the power load according to the power consumption probability distribution of the power load; and a fourth construction submodule, which is used to construct constraints between the new energy equipment and the power load according to the minimum power supply power of the new energy equipment and the maximum power consumption of the power load.

[0129] Optionally, the cost determination module 930 includes: a fifth construction submodule, which is used to pre-construct a power supply scheduling model according to the constraint conditions; and a second determination submodule, which is used to solve the power supply scheduling model by using a particle swarm algorithm with the goal of minimizing the power supply cost of new energy equipment, thermal power units, energy storage equipment and national power grids, and determine the minimum power supply cost, wherein the expression of the particle swarm algorithm is:

[0130] V i k+1 =ω k V ik +c1r1(P i k -X i k )+c2r2(G k -X i k ) (1)

[0131] X i k+1 =X i k +V i k+1 (2)

[0132] Where k represents the number of iterations; i represents the particle number; V i k , X i k are the velocity and position of particle i before updating; V i k+1 , X i k+1 are the updated speed and position of particle i respectively; P i k , G k are the individual best position of particle i and the global best position of the group respectively; ω k It represents the inertia weight after being updated by the preset PID module; c1 and c2 represent learning factors; r1 and r2 represent random numbers between 0 and 1.

[0133] Optionally, the fifth building block includes:

[0134]

[0135] Where F represents the total power supply cost, Ca represents the power supply cost of new energy equipment, Cb u represents the power supply cost of the uth thermal power unit, m1 represents the number of thermal power units, u=1~m1, Cc v represents the power supply cost of the vth energy storage device, m2 represents the number of energy storage devices, v=1~m2, and Cd represents the power supply cost of the national power grid.

[0136] Optionally, the updating submodule includes: a first calculation unit, used to calculate the distance between the position of each particle and the global optimal position, and calculate the distance average value based on the distance; and a second calculation unit, used to update the inertia weight according to the distance average value using the PID module.

[0137] Optionally, the second calculation unit includes: determining a proportional value according to a distance average value through a PID module; determining an integral value according to a distance average value through a PID module; determining a differential value according to the distance average value through a PID module; and performing weighted summation of the proportional value, the integral value and the differential value using a preset proportional weight, an integral weight and a differential weight through the PID module to update the inertia weight.

[0138] Thus, according to this embodiment, the power dispatching platform predicts the power supply power and power consumption through the power supply power prediction model and power consumption prediction model based on real-time data, thereby enhancing the adaptability and response speed to the real-time changes of the power dispatching system, and solving the problem that the traditional method cannot cope with real-time changes. And the power dispatching platform realizes more refined and efficient power dispatching control by constructing detailed constraints of power supply equipment and power load, and maximizes the use of new energy while ensuring the stable operation of the power dispatching system. And this technical solution establishes a power supply dispatching model and solves it by using the particle swarm algorithm, so that the particle swarm algorithm can quickly find the optimal solution, quickly find the solution with the minimum power supply cost, and improve the speed and efficiency of determining the minimum power supply cost. Then the goal of periodic automatic optimization at a predetermined time interval is achieved, and the scheduling efficiency and real-time performance of the virtual power plant are improved. Then the existing virtual power plant scheduling method in the prior art is insufficient in terms of calculation efficiency, real-time performance and optimization accuracy, and it is difficult to adapt to the needs of rapid changes in the power dispatching system, and it is impossible to achieve the technical problem of the goal of periodic automatic optimization.

[0139] Example 3

[0140] Fig.10 The apparatus 1000 for scheduling power resources according to this embodiment is shown, and the apparatus 1000 corresponds to the method according to the first aspect of embodiment 1. Fig.10As shown, the device 1000 includes: a processor 1010; and a memory 1020, which is connected to the processor 1010 and is used to provide the processor 1010 with instructions for processing the following processing steps: at a predetermined time interval, the power supply of the new energy equipment is predicted by the power supply prediction model to generate a power supply probability distribution corresponding to the probability of each power supply power interval, and the power load is predicted by the power consumption prediction model to generate a power consumption probability distribution corresponding to the probability of each power consumption power interval; according to the power supply power range pre-set with the thermal power unit, the energy storage device and the national power grid and according to the power supply probability distribution and the power consumption probability distribution, the constraints between the new energy equipment, the thermal power unit, the energy storage device, the national power grid and the power load are constructed; the power supply scheduling model constructed under the constraint conditions is solved by using the particle swarm algorithm with the goal of minimizing the power supply cost of the new energy equipment, the thermal power unit, the energy storage device and the national power grid to determine the minimum power supply cost; and at a predetermined time interval, the power resources of the new energy equipment, the thermal power unit, the energy storage device and the national power grid are dispatched according to the minimum power supply cost.

[0141] Optionally, the operation of constructing constraints between new energy equipment, thermal power units, energy storage equipment, the national power grid and power loads according to a pre-set power supply power range for thermal power units, energy storage equipment and the national power grid and according to a power supply probability distribution and a power consumption probability distribution includes: constructing constraints corresponding to the thermal power units according to a pre-set maximum power supply power and a minimum power supply power of the thermal power units; constructing constraints corresponding to the energy storage equipment according to a pre-set maximum power supply power and a minimum power supply power of the energy storage equipment; constructing constraints corresponding to the national power grid according to a pre-set maximum power supply power and a minimum power supply power of the national power grid; determining the minimum power supply power of the new energy equipment according to the power supply probability distribution of the new energy equipment, and determining the maximum power consumption of the power load according to the power consumption probability distribution of the power load; and constructing constraints between the new energy equipment and the power load according to the minimum power supply power of the new energy equipment and the maximum power consumption of the power load.

[0142] Optionally, a particle swarm algorithm is used to solve a power supply scheduling model constructed under constraint conditions with the goal of minimizing the power supply cost of new energy equipment, thermal power units, energy storage equipment and the national power grid, and an operation of determining the minimum power supply cost includes: pre-constructing a power supply scheduling model according to the constraint conditions; and solving the power supply scheduling model with the goal of minimizing the power supply cost of new energy equipment, thermal power units, energy storage equipment and the national power grid with the particle swarm algorithm, and determining the minimum power supply cost, wherein the expression of the particle swarm algorithm is:

[0143] V i k+1 =ω k Vi k +c1r1(P i k -X i k )+c2r2(G k -X i k ) (1)

[0144] X i k+1 =X i k +V i k+1 (2)

[0145] Where k represents the number of iterations; i represents the particle number; V i k , X i k are the velocity and position of particle i before updating; V i k+1 , X i k+1 are the updated speed and position of particle i respectively; P i k , G k are the individual best position of particle i and the global best position of the group respectively; ω k It represents the inertia weight after being updated by the preset PID module; c1 and c2 represent learning factors; r1 and r2 represent random numbers between 0 and 1.

[0146] Optionally, according to the constraints, the pre-built power supply scheduling model is:

[0147]

[0148] Where F represents the total power supply cost, Ca represents the power supply cost of new energy equipment, Cb u represents the power supply cost of the uth thermal power unit, m1 represents the number of thermal power units, u=1~m1, Cc v represents the power supply cost of the vth energy storage device, m2 represents the number of energy storage devices, v=1~m2, and Cd represents the power supply cost of the national power grid.

[0149] Optionally, the operation of updating the inertia weight using a preset PID module includes: calculating the distance between the position of each particle and the global optimal position, and calculating the distance average value based on the distance; and updating the inertia weight based on the distance average value using the PID module.

[0150] Optionally, the operation of updating the inertia weight according to the distance average value using the PID module includes: determining a proportional value according to the distance average value through the PID module; determining an integral value according to the distance average value through the PID module; determining a differential value according to the distance average value through the PID module; and updating the inertia weight by weighted summing the proportional value, the integral value and the differential value using preset proportional weights, integral weights and differential weights through the PID module.

[0151] Thus, according to this embodiment, the power dispatching platform predicts the power supply power and power consumption through the power supply power prediction model and power consumption prediction model based on real-time data, thereby enhancing the adaptability and response speed to the real-time changes of the power dispatching system, and solving the problem that the traditional method cannot cope with real-time changes. And the power dispatching platform realizes more refined and efficient power dispatching control by constructing detailed constraints of power supply equipment and power load, and maximizes the use of new energy while ensuring the stable operation of the power dispatching system. And this technical solution establishes a power supply dispatching model and solves it by using the particle swarm algorithm, so that the particle swarm algorithm can quickly find the optimal solution, quickly find the solution with the minimum power supply cost, and improve the speed and efficiency of determining the minimum power supply cost. Then the goal of periodic automatic optimization at a predetermined time interval is achieved, and the scheduling efficiency and real-time performance of the virtual power plant are improved. Then the existing virtual power plant scheduling method in the prior art is insufficient in terms of calculation efficiency, real-time performance and optimization accuracy, and it is difficult to adapt to the needs of rapid changes in the power dispatching system, and it is impossible to achieve the technical problem of the goal of periodic automatic optimization.

[0152] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0153] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0154] 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 schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, 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.

[0155] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0156] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0157] 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, in essence, 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, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a 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, RandomAccess Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0158] 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 principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for dispatching electric power resources, It is characterized in that include: At predetermined time intervals, the power supply of the new energy equipment is predicted by the power supply prediction model to generate a power supply probability distribution corresponding to the probability of each power supply power interval, and the power consumption load is predicted by the power consumption prediction model to generate a power consumption probability distribution corresponding to the probability of each power consumption power interval; According to the power supply power range pre-set with the thermal power unit, the energy storage device and the national power grid and according to the power supply probability distribution and the power consumption probability distribution, constraining conditions among the new energy equipment, the thermal power unit, the energy storage device, the national power grid and the power load are constructed; Using a particle swarm algorithm to solve the power supply scheduling model constructed under the constraints with the goal of minimizing the power supply cost of the new energy equipment, the thermal power unit, the energy storage equipment and the national power grid, and determine the minimum power supply cost; as well as At the predetermined time interval, the power resources of the new energy equipment, the thermal power generation unit, the energy storage equipment and the national power grid are dispatched according to the minimum power supply cost.

2. The method according to claim 1, It is characterized in that The operation of constructing constraint conditions among the new energy equipment, the thermal power unit, the energy storage device, the national power grid and the power load according to the power supply power range preset with the thermal power unit, the energy storage device and the national power grid and according to the power supply probability distribution and the power consumption probability distribution includes: Constructing constraint conditions corresponding to the thermal power unit according to the preset maximum power supply power and minimum power supply power of the thermal power unit; Constructing constraint conditions corresponding to the energy storage device according to the preset maximum power supply power and minimum power supply power of the energy storage device; Constructing constraint conditions corresponding to the national power grid according to the preset maximum power supply power and minimum power supply power of the national power grid; Determine the minimum power supply power of the new energy equipment according to the power supply probability distribution of the new energy equipment, and determine the maximum power consumption of the power load according to the power consumption probability distribution of the power load; and According to the minimum power supply power of the new energy equipment and the maximum power consumption of the power load, a constraint condition between the new energy equipment and the power load is constructed.

3. The method according to claim 1, It is characterized in that The particle swarm algorithm is used to solve the power supply scheduling model constructed under the constraints with the goal of minimizing the power supply cost of the new energy equipment, the thermal power unit, the energy storage equipment and the national power grid, and the operation of determining the minimum power supply cost includes: Pre-constructing the power supply scheduling model according to the constraint conditions; and The particle swarm algorithm is used to solve the power supply scheduling model with the goal of minimizing the power supply cost of the new energy equipment, the thermal power unit, the energy storage equipment and the national power grid, and the minimum power supply cost is determined, wherein the expression of the particle swarm algorithm is: V i k+1 =ω k V i k +c 1 r 1 (P i k -X i k )+c 2 r 2 (G k -X i k ) (1) X i k+1 =X i k +V i k+1 (2) Where k represents the number of iterations; i represents the particle number; V i k , X i k are the velocity and position of particle i before updating; V i k+1 , X i k+1 are the updated speed and position of particle i respectively; P i k , G k are the individual best position of particle i and the global best position of the group respectively; ω k Indicates the inertia weight after updating using the preset PID module; c 1 、c 2 represents the learning factor; r 1 、r 2 Represents a random number between 0 and 1.

4. The method according to claim 3, It is characterized in that According to the constraints, the pre-built power supply scheduling model is: Where F represents the total power supply cost, Ca represents the power supply cost of the new energy equipment, Cb u represents the power supply cost of the u-th thermal power unit, m 1 Indicates the number of thermal power units, u = 1 ~ m 1 , Cc v represents the power supply cost of the vth energy storage device, m 2 Indicates the number of energy storage devices, v = 1 ~ m 2 , Cd represents the power supply cost of the national power grid.

5. The method according to claim 3, It is characterized in that The operation of updating the inertia weight using the preset PID module includes: Calculating the distance between the position of each particle and the global optimal position, and calculating the distance average according to the distance; and The inertia weight is updated according to the distance average value by using the PID module.

6. The method according to claim 5, It is characterized in that The operation of updating the inertia weight by using the PID module according to the distance average value includes: Determine a proportional value according to the distance average value by the PID module; Determine an integral value according to the distance average value by the PID module; determining a differential value according to the distance average value by the PID module; and The PID module uses a preset proportional weight, integral weight, and differential weight to perform weighted summation on the proportional value, the integral value, and the differential value to update the inertia weight.

7. A device for dispatching electric power resources, It is characterized in that include: A probability generation module is used to predict the power supply of the new energy equipment at predetermined time intervals through the power supply prediction model, generate a power supply probability distribution corresponding to the probability of each power supply power interval, and predict the power load through the power consumption prediction model, and generate a power consumption probability distribution corresponding to the probability of each power consumption power interval; A condition construction module, for constructing constraint conditions among the new energy equipment, the thermal power unit, the energy storage device, the national power grid and the power load according to a power supply power range preset with the thermal power unit, the energy storage device and the national power grid and according to the power supply probability distribution and the power consumption probability distribution; A cost determination module is used to solve the power supply scheduling model constructed under the constraints by using a particle swarm algorithm with the goal of minimizing the power supply cost of the new energy equipment, the thermal power unit, the energy storage equipment and the national power grid, and determine the minimum power supply cost; as well as The resource scheduling module is used to schedule the power resources of the new energy equipment, the thermal power unit, the energy storage equipment and the national power grid according to the minimum power supply cost at the predetermined time interval.

8. A device for dispatching electric power resources, It is characterized in that include: processor; as well as A memory, connected to the processor, configured to provide the processor with instructions for processing the following processing steps: At predetermined time intervals, the power supply of the new energy equipment is predicted by the power supply prediction model to generate a power supply probability distribution corresponding to the probability of each power supply power interval, and the power consumption load is predicted by the power consumption prediction model to generate a power consumption probability distribution corresponding to the probability of each power consumption power interval; According to the power supply power range pre-set with the thermal power unit, the energy storage device and the national power grid and according to the power supply probability distribution and the power consumption probability distribution, constraining conditions among the new energy equipment, the thermal power unit, the energy storage device, the national power grid and the power load are constructed; Using a particle swarm algorithm to solve the power supply scheduling model constructed under the constraints with the goal of minimizing the power supply cost of the new energy equipment, the thermal power unit, the energy storage equipment and the national power grid, and determine the minimum power supply cost; as well as At the predetermined time interval, the power resources of the new energy equipment, the thermal power generation unit, the energy storage equipment and the national power grid are dispatched according to the minimum power supply cost.

9. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the steps of the method according to claim 1 are implemented.

10. A computer program product comprising a computer program, It is characterized in that When the computer program is executed by a processor, the steps of the method according to claim 1 are implemented.

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