Virtual power plant resource aggregation scheduling method and device and storage medium

Abstract

The invention provides a virtual power plant resource aggregation scheduling method and device and a storage medium, and belongs to the technical field of power system planning scheduling, and the method comprises the steps: obtaining the day-ahead prediction data of photovoltaic output, wind power output and user load power in a virtual power plant management range, and carrying out the characteristic sampling and per-unit processing, and obtaining per-unit prediction output coefficients of photovoltaic and wind power and a prediction value of a user load. And based on the per-unit prediction output coefficient of the photovoltaic power and the wind power and the prediction value of the user load, establishing a wind-solar capacity ratio model, and solving the model by adopting a particle swarm algorithm to obtain the optimal matching configuration coefficient of the photovoltaic power, the wind power and the user load in the management range of the virtual power plant. And calculating a day-ahead aggregation scheduling allocation set of the internal resources of the virtual power plant according to the optimally matched configuration coefficient. According to the invention, reasonable allocation of independent individual resources in the range of the virtual power plant is realized, and the problem of large difficulty in aggregation allocation and management scheduling of the resources of the virtual power plant is solved.

Description

Technical Field

[0001] The invention relates to a virtual power plant resource aggregation scheduling method, equipment and storage medium, and belongs to the technical field of power system planning and scheduling. Background Art

[0002] In the context of the construction of a new power system, with the gradual expansion of the scale of user-side adjustable resources such as distributed photovoltaics, decentralized wind power, user-side energy storage, and charging piles, the flexible scheduling and priority matching of user-side resources can effectively improve the local consumption capacity of distributed new energy, while alleviating the problem of tight regulation resources in the new power system. As a new model of resource management, virtual power plants are essentially the coordinated management and unified scheduling of user-side resources. The management and scheduling of virtual power plants involves internal resource matching and external resource transactions. There are many management objects and the management is difficult. At present, there is no relatively unified resource aggregation scheduling method. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a virtual power plant resource aggregation scheduling method. According to the virtual power plant's day-ahead forecast data, the optimal matching configuration coefficient of each resource entity is obtained by solving the wind and solar capacity ratio model, and the reasonable allocation of independent individual resources within the virtual power plant is achieved accordingly, solving the problem of the difficulty of virtual power plant resource aggregation allocation and management scheduling.

[0004] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0005] In a first aspect, the present invention provides a virtual power plant resource aggregation scheduling method, comprising:

[0006] Obtain the day-ahead forecast data of distributed photovoltaic output and distributed wind power output within the management scope of the virtual power plant, as well as the day-ahead forecast data of user load power.

[0007] The day-ahead forecast data of the distributed photovoltaic output, the distributed wind power output and the user load power are respectively subjected to characteristic sampling and per-unit processing to obtain the per-unit forecast output coefficients of the distributed photovoltaic and distributed wind power and the forecast value of the user load.

[0008] Based on the normalized predicted output coefficients of the distributed photovoltaic and distributed wind power and the predicted values ​​of user loads, a wind-solar capacity matching model is established.

[0009] The particle swarm algorithm is used to solve the wind-solar capacity ratio model to obtain the optimal matching configuration coefficients of distributed photovoltaic and distributed wind power within the management scope of the virtual power plant.

[0010] The day-ahead aggregated scheduling allocation set of internal resources of the virtual power plant is calculated according to the configuration coefficients of the optimal match of the distributed photovoltaic and distributed wind power.

[0011] Furthermore, the characteristic sampling includes: setting a fixed time interval according to the characteristic distribution of the day-ahead forecast data The day-ahead forecast data is sampled to obtain sampled data representing characteristics of the day-ahead forecast data.

[0012] Furthermore, the fixed time interval 15 minutes, 30 minutes or 60 minutes.

[0013] Furthermore, the standardization processing includes: standardizing the distributed photovoltaic output and the distributed wind power output according to the day-ahead forecast data of the individual output and the individual installed capacity;

[0014] For the ith photovoltaic:

[0015]

[0016] For the jth wind farm:

[0017]

[0018] In the formula, , m is the number of distributed photovoltaics within the management scope of the virtual power plant, for The normalized predicted output coefficient of the ith photovoltaic at the moment, for The sampling data of the ith photovoltaic at the moment, is the installed capacity of the ith photovoltaic power plant; , n is the number of distributed wind power within the management scope of the virtual power plant, for The normalized predicted output coefficient of the jth wind power at time, for The sampling data of the jth wind power at time, is the installed capacity of the jth wind power; , and t is an integer;

[0019] The user load is normalized based on the sum of the sampled data of all user loads within the management scope of the virtual power plant:

[0020] ;

[0021] In the formula, , l is the number of user loads within the management scope of the virtual power plant, for The predicted value of user load at the moment, for The sampling data of the kth user load at the moment, It is the maximum value of the sum sequence of l user load sampling data at each moment within the management scope of the virtual power plant during the predicted day.

[0022] Furthermore, the wind-solar capacity matching model is established, including: based on the principle of optimal matching of power resource allocation with load demand, taking the minimum sum of squares of the difference between the predicted daily new energy output and the user load forecast value as the objective function, the formula is:

[0023] min( ∑ 1 24 ⋅ 60 d [ PG ( t ) − PL ( t )] 2 )

[0024] in, ;

[0025] In the formula, To obtain the minimum value, is the internal configuration ratio of the i-th photovoltaic, is the internal configuration ratio of the jth wind power;

[0026] The constraints for constructing the wind-solar capacity ratio model based on the individual capacity upper limit of independent photovoltaic or wind power resources include:

[0027] .

[0028] Furthermore, the use of a particle swarm algorithm to solve the wind-solar capacity ratio model includes:

[0029] According to the number of distributed photovoltaic and distributed wind power in the virtual power plant, the particle dimension W=m+n is defined;

[0030] Define the fitness function: F = ∑ 1 24 ⋅ 60 d [ PG ( t ) − PL ( t )] 2 ;

[0031] The particle swarm algorithm is adopted to solve the current individual optimal value of the particle and the overall optimal value based on the fitness function: in response to not meeting the preset convergence conditions, the particle speed and position are updated, and the individual optimal value of the particle and the group optimal value are recalculated; in response to meeting the preset convergence conditions, the optimal result is output, and the optimal matching configuration coefficients of each distributed photovoltaic and distributed wind power within the management scope of the virtual power plant are obtained.

[0032] Furthermore, the virtual power plant internal resource day-ahead aggregate scheduling allocation set includes: an internal aggregate scheduling resource set A and an external transaction aggregate scheduling resource set B, and the calculation formula is:

[0033] A= ;

[0034] B= ;

[0035] In the formula, is the configuration coefficient of the mth photovoltaic optimal match, is the optimal matching configuration coefficient of the nth wind power.

[0036] In a second aspect, the present invention provides an electronic device comprising: a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the virtual power plant resource aggregation scheduling method as described in the first aspect is implemented.

[0037] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the virtual power plant resource aggregation scheduling method as described in the first aspect is implemented.

[0038] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the virtual power plant resource aggregation scheduling method provided by the present invention is based on the day-ahead forecast data of the output characteristics of distributed photovoltaic and distributed wind power and the load power consumption characteristics within the management scope of the virtual power plant, and solves the configuration coefficient of the optimal matching of distributed power sources through the wind and solar capacity ratio model, thereby obtaining the aggregation scheduling set of internal self-consumption and external market-based transaction resources of the virtual power plant, which can realize the reasonable allocation of independent individual resources within the scope of the virtual power plant, reduce the support and regulation requirements of the virtual power plant on the power system, improve the internal resource utilization efficiency of the virtual power plant, reduce external transaction costs, and provide a basis for the virtual power plant to conduct resource aggregation allocation, management and scheduling. The corresponding resource allocation ratio can also serve as an important basis for the future distribution of benefits of the virtual power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of the virtual power plant resource aggregation scheduling method provided in Example 1 of the present invention;

[0040] Figure 2 This is the process of solving the wind-solar capacity ratio model using the particle swarm algorithm in Example 1 of the present invention. DETAILED DESCRIPTION

[0041] The terms "first", "second", "third", "fourth", etc. (if any) 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 described herein can be implemented in an order other than that 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 that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. 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 a part of the embodiments of the present application, rather than all of the embodiments.

[0042] Example 1

[0043] The present invention provides a virtual power plant resource aggregation scheduling method, such as Figure 1 As shown, the method comprises the following steps:

[0044] Step 1: Obtain the day-ahead forecast data of distributed photovoltaic output and distributed wind power output within the management scope of the virtual power plant, as well as the day-ahead forecast data of user load power.

[0045] Step 2: The day-ahead forecast data of the distributed photovoltaic output, distributed wind power output and user load power are respectively subjected to characteristic sampling and normalization processing to obtain the normalized forecast output coefficients of the distributed photovoltaic and distributed wind power and the forecast value of the user load.

[0046] Specifically, feature sampling includes: setting a fixed time interval according to the feature distribution of the day-ahead forecast data Sampling the day-ahead forecast data to obtain sampled data representing characteristics of the day-ahead forecast data;

[0047] The sampling data of distributed photovoltaic output is: ;

[0048] The sampling data of distributed wind power output is: ;

[0049] The sample data of user load is: ;

[0050] In the formula, m is the number of distributed photovoltaics within the management scope of the virtual power plant, n is the number of distributed wind power within the management scope of the virtual power plant, and l is the number of user loads within the management scope of the virtual power plant; , and t is an integer.

[0051] It should be noted that , and All are sequence data. Contains the sampling data of the output of the mth photovoltaic at all sampling time nodes within the forecast day; similarly, Contains the sampling data of the output of the nth wind power at all sampling time nodes within the forecast day; Contains the sampling data of the lth user load at all sampling time nodes within the forecast day.

[0052] Preferably, a fixed time interval 15 minutes, 30 minutes or 60 minutes.

[0053] Specifically, the normalization process includes:

[0054] For distributed photovoltaic and distributed wind power, standardization is performed based on the sampling data of individual output and individual installed capacity:

[0055] For the ith photovoltaic:

[0056]

[0057] For the jth wind farm:

[0058]

[0059] In the formula, , is the installed capacity of the ith photovoltaic power plant; , is the installed capacity of the jth wind farm.

[0060] For user load, the user load is normalized according to the sum sequence of sampling data of all user loads within the management scope of the virtual power plant:

[0061] ;

[0062] In the formula, It is the maximum value of the sum sequence of l user load sampling data at each time within the management scope of the virtual power plant within the forecast day;

[0063] The obtained per-unit predicted output coefficient of distributed photovoltaic is: , the normalized predicted output coefficient of distributed wind power is: , the predicted value of user load is: .

[0064] Step 3: Based on the normalized predicted output coefficients of the distributed photovoltaic and distributed wind power and the predicted values ​​of user loads, a wind-solar capacity ratio model is established.

[0065] Specifically, a wind-solar capacity ratio model is established, including:

[0066] Based on the principle of optimal matching of power resource allocation with load demand, the objective function is to minimize the sum of squares of the difference between the predicted new energy output and the user load forecast value within the forecast day. The formula is:

[0067] min( ∑ 1 24 ⋅ 60 d [ PG ( t ) − PL ( t )] 2 )

[0068] in, ;

[0069] In the formula, To obtain the minimum value, is the internal configuration ratio of the i-th photovoltaic, is the internal configuration ratio of the jth wind power;

[0070] The constraints for constructing the wind-solar capacity ratio model based on the individual capacity upper limit of independent photovoltaic or wind power resources include:

[0071] .

[0072] Step 4: Use the particle swarm algorithm to solve the wind-solar capacity ratio model to obtain the optimal matching configuration coefficient of distributed photovoltaic and distributed wind power within the management scope of the virtual power plant.

[0073] Specifically, the particle swarm algorithm is used to solve the wind-solar capacity ratio model, such as Figure 2 As shown, including:

[0074] According to the number of distributed photovoltaic and distributed wind power in the virtual power plant, the particle dimension W=m+n is defined;

[0075] Define the fitness function: F = ∑ 1 24 ⋅ 60 d [ PG ( t ) − PL ( t )] 2 ;

[0076] The particle swarm algorithm is adopted to solve the current individual optimal value of the particle and the overall optimal value based on the fitness function: in response to not meeting the preset convergence conditions, the particle speed and position are updated, and the individual optimal value of the particle and the group optimal value are recalculated; in response to meeting the preset convergence conditions, the optimal result is output, and the optimal matching configuration coefficients of each distributed photovoltaic and distributed wind power within the management scope of the virtual power plant are obtained.

[0077] Step 5: Calculate the day-ahead aggregated scheduling allocation set of internal resources of the virtual power plant according to the configuration coefficients of the optimal match of the distributed photovoltaic and distributed wind power.

[0078] Specifically, the day-ahead aggregate dispatch allocation set of internal resources of the virtual power plant includes: internal aggregate dispatch resource set A and external transaction aggregate dispatch resource set B. The calculation formula is:

[0079] A= ;

[0080] B= .

[0081] Example 2

[0082] Based on Example 1, this example provides resource aggregation scheduling using the method of the present invention in an application scenario where a virtual power plant manages 8 distributed photovoltaic installations, 5 distributed wind power installations, and 2 users.

[0083] A virtual power plant resource aggregation scheduling method comprises the following steps:

[0084] S1: After obtaining the day-ahead forecast data of the virtual power plant, characteristic sampling is performed at a time interval of 60 minutes, so the sampling data of each entity for the whole day is 24, and the output sampling data of each distributed photovoltaic and distributed wind power and the load sampling data of the user are obtained.

[0085] The sampling data of 8 distributed photovoltaic outputs are:

[0086] The sampling data of the five distributed wind power outputs are:

[0087] The sampled data of the 2 user loads are: .

[0088] The normalized data of each resource subject is calculated according to the normalization processing method in Example 1, and is expressed as follows:

[0089] The normalized predicted output coefficients of the eight distributed photovoltaics are:

[0090] The normalized predicted output coefficients of the five distributed wind power plants are:

[0091] The predicted values ​​of the 2 user loads are: .

[0092] S2: Establish a wind-solar capacity ratio model.

[0093] S21: Construct an objective function that reflects the minimum gap between power supply and load within the management scope of the virtual power plant. The specific expression of the objective function is as follows:

[0094] ∑ 1 24 [ PG ( t ) − PL ( t )] 2 ;

[0095] in, ; In the formula, , are the internal configuration ratios of the i-th photovoltaic and j-th wind power, , are the normalized predicted output coefficients of the i-th photovoltaic and j-th wind power at time t respectively.

[0096] S22: Construct model constraints. The configuration scale of photovoltaic and wind power resources cannot exceed the installed capacity. The corresponding model constraints are:

[0097] .

[0098] In this embodiment, the 8 photovoltaic installed capacity scales are: 6 MW, 6 MW, 6 MW, 6 MW, 4 MW, 4 MW, 3 MW, and 3 MW respectively; the 5 wind power installed capacity scales are: 6 MW, 6 MW, 6 MW, 5 MW, and 5 MW respectively.

[0099] S3: The particle swarm optimization algorithm is used to solve the wind-solar capacity ratio model.

[0100] S31: Define particle dimension W=13, define fitness function, and define the maximum number of iterations as 200.

[0101] S32: Through the iterative solution of the particle swarm algorithm, the configuration coefficients of distributed photovoltaic and distributed wind power within the management scope of the virtual power plant are obtained as {0.915, 0.915, 0, 0, 0.122, 0.610, 0.458, 0, 0.485, 0, 0.915, 0, 0}.

[0102] S4: Based on the configuration coefficients of distributed photovoltaic and distributed wind power, the day-ahead aggregate scheduling allocation set of internal resources of the virtual power plant is calculated, including: internal aggregate scheduling resource set A: {6, 6, 0, 0, 0.81, 4, 3, 0, 3.18, 0, 6, 0, 0}, and external market-based transaction aggregate scheduling resource set B: {0, 0, 6, 6, 3.19, 0, 3, 3, 2.82, 6, 0, 5, 5}, in megawatts.

[0103] Example 3

[0104] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the virtual power plant resource aggregation scheduling method as described in Example 1 is implemented.

[0105] Example 4

[0106] This embodiment provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the virtual power plant resource aggregation scheduling method as described in Example 1 is implemented.

[0107] The storage medium may include, for example, a storage component of a tablet computer, a hard disk of a computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer storage medium may be any combination of one or more computer storage media.

[0108] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A virtual power plant resource aggregation scheduling method, characterized in that: include: Obtain the day-ahead forecast data of distributed photovoltaic power output and distributed wind power output within the management scope of the virtual power plant, as well as the day-ahead forecast data of user load power; The day-ahead forecast data of the distributed photovoltaic power output, the distributed wind power output and the user load power are respectively subjected to characteristic sampling and per-unit processing to obtain the per-unit forecast output coefficients of the distributed photovoltaic power output and the distributed wind power output and the forecast value of the user load; Based on the normalized predicted output coefficient of the distributed photovoltaic and distributed wind power and the predicted value of the user load, a wind-solar capacity matching model is established; The particle swarm algorithm is used to solve the wind-solar capacity ratio model to obtain the optimal matching configuration coefficient of distributed photovoltaic and distributed wind power within the management scope of the virtual power plant; The day-ahead aggregated scheduling allocation set of internal resources of the virtual power plant is calculated according to the configuration coefficients of the optimal match of the distributed photovoltaic and distributed wind power.

2. The virtual power plant resource aggregation scheduling method according to claim 1 is characterized in that: The characteristic sampling includes: setting a fixed time interval according to the characteristic distribution of the day-ahead forecast data The day-ahead forecast data is sampled to obtain sampled data representing characteristics of the day-ahead forecast data.

3. The virtual power plant resource aggregation scheduling method according to claim 2 is characterized in that: The fixed time interval 15 minutes, 30 minutes or 60 minutes.

4. The virtual power plant resource aggregation scheduling method according to claim 2 is characterized in that: The standardization processing includes: standardizing the distributed photovoltaic output and the distributed wind power output according to the day-ahead forecast data of the individual output and the individual installed capacity; For the ith photovoltaic: For the jth wind farm: In the formula, , m is the number of distributed photovoltaics within the management scope of the virtual power plant, for The normalized predicted output coefficient of the ith photovoltaic at the moment, for The sampling data of the ith photovoltaic at the moment, is the installed capacity of the ith photovoltaic power plant; , n is the number of distributed wind power within the management scope of the virtual power plant, for The normalized predicted output coefficient of the jth wind power at time, for The sampling data of the jth wind power at time, is the installed capacity of the jth wind power plant; , and t is an integer; The user load is normalized based on the sum of the sampled data of all user loads within the management scope of the virtual power plant: ； In the formula, , l is the number of user loads within the management scope of the virtual power plant, for The predicted value of user load at the moment, for The sampling data of the kth user load at the moment, It is the maximum value of the sum sequence of l user load sampling data at each moment within the management scope of the virtual power plant during the predicted day.

5. The virtual power plant resource aggregation scheduling method according to claim 4 is characterized in that: The wind and solar capacity matching model is established, including: based on the principle of optimal matching of power resource allocation with load demand, taking the minimum sum of squares of the difference between the predicted daily new energy output and the user load forecast value as the objective function, the formula is: in, ; In the formula, To obtain the minimum value, is the internal configuration ratio of the i-th photovoltaic, is the internal configuration ratio of the jth wind power; The constraints for constructing the wind-solar capacity ratio model based on the individual capacity upper limit of independent photovoltaic or wind power resources include: 。 6. The virtual power plant resource aggregation scheduling method according to claim 5 is characterized in that: The particle swarm algorithm is used to solve the wind-solar capacity ratio model, including: According to the number of distributed photovoltaic and distributed wind power in the virtual power plant, the particle dimension W=m+n is defined; Define the fitness function: ; The particle swarm algorithm is adopted to solve the current individual optimal value of the particle and the overall optimal value based on the fitness function: in response to not meeting the preset convergence conditions, the particle speed and position are updated, and the individual optimal value of the particle and the group optimal value are recalculated; in response to meeting the preset convergence conditions, the optimal result is output, and the optimal matching configuration coefficients of each distributed photovoltaic and distributed wind power within the management scope of the virtual power plant are obtained.

7. The virtual power plant resource aggregation scheduling method according to claim 6 is characterized in that: The virtual power plant internal resource day-ahead aggregate scheduling allocation set includes: internal aggregate scheduling resource set A and external transaction aggregate scheduling resource set B, and the calculation formula is: A= ； B= ； In the formula, is the configuration coefficient of the mth photovoltaic optimal match, is the optimal matching configuration coefficient of the nth wind power.

8. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the virtual power plant resource aggregation scheduling method as described in any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the virtual power plant resource aggregation scheduling method according to any one of claims 1 to 7 is implemented.

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