Source load power optimization method based on virtual power plant reliability index prediction

Abstract

The invention relates to the field of virtual power plant operation optimization, and provides a virtual power plant reliability index prediction-based source load power optimization method, which comprises the following steps of: measuring parameters required by virtual power plant reliability index prediction, forming a nonlinear time sequence of each parameter, performing normalization processing on the nonlinear time sequence of each parameter, and obtaining a normalized time sequence of each parameter; and according to the nonlinear time sequence of each parameter after normalization processing, calculating a virtual power plant distributed power supply reliability coefficient and a virtual power plant multi-element load reliability coefficient, and predicting a virtual power plant reliability index by using the virtual power plant distributed power supply reliability coefficient and the virtual power plant multi-element load reliability coefficient. According to the method, the reliability index of the virtual power plant is predicted, the source load power of the virtual power plant is optimized according to the prediction result, the income stability of the virtual power plant in the electricity market can be improved, the power grid operation stability is improved, and the new energy utilization rate is improved.

Description

Technical Field

[0001] The present invention relates to the field of virtual power plant operation optimization, and in particular to a source-load power optimization method based on virtual power plant reliability index prediction. Background Art

[0002] Virtual power plant technology can integrate flexible resources such as distributed power sources and multiple controllable loads, participate in the electric energy market and auxiliary service markets such as peak load regulation and frequency regulation, and is an important means to enhance the regulation capacity of the power system and promote energy system reform. Virtual power plants participate in power market transactions as the main body, which further promotes the development of virtual power plant technology. At present, when virtual power plants participate in the power market or power system regulation, the capacity that can be reported or responded is the probabilistic capacity calculated after the aggregation of the regulation capacity of all flexible resources in the virtual power plant. The flexible resources mainly include the grid-connected capacity of photovoltaic and wind power, the total power consumption of temperature control loads, and the total charging power of electric vehicles. The uncertainty of source load power will reduce the reliability of virtual power plants. When the reliability of virtual power plants is poor, the power grid’s trust in the regulation capacity of virtual power plants will decrease. The uncertainty of source load power brings huge risks to the operation of the power grid, resulting in low stability of power grid operation and high operating costs of virtual power plants. The present invention proposes a source-load power optimization method based on the prediction of the virtual power plant reliability index. The virtual power plant reliability index is used to predict the aggregation effect of the flexible resources in the virtual power plant. The source-load power of the virtual power plant is optimized by the root prediction results, thereby solving the problem of low reliability calculation accuracy of the virtual power plant. Summary of the invention

[0003] In order to solve the technical problem that the capacity that can be reported or responded to by virtual power plants in the existing technology during the participation of virtual power plants in the power market or power system regulation is the probabilistic capacity calculated after the aggregation of all flexible resource regulation capabilities in the virtual power plant, and the uncertainty of flexible resources brings huge risks to the operation of the power grid, the embodiment of the present invention provides a source-load power optimization method and device based on the prediction of the reliability index of the virtual power plant. The technical solution is as follows:

[0004] A source-load power optimization method based on virtual power plant reliability index prediction, the method comprising:

[0005] S1. Measure the real-time operation data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant;

[0006] S2. normalizing the measured real-time operation data of the virtual power plant and the environmental parameters affecting the reliability of the virtual power plant;

[0007] S3. Calculate the reliability coefficient of the distributed power source of the virtual power plant at the next moment in the future based on the normalized real-time operation data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant;

[0008] S4. Calculate the multi-load reliability coefficient of the virtual power plant at the next moment in the future based on the normalized real-time operation data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant;

[0009] S5. Calculate the predicted value of the virtual power plant reliability index at the next moment in the future according to the calculated distributed power reliability coefficient of the virtual power plant at the next moment in the future and the calculated multi-load reliability coefficient of the virtual power plant at the next moment in the future;

[0010] S6. Optimize the source-load power of the virtual power plant according to the predicted value of the virtual power plant reliability index at the next moment in the future.

[0011] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0012] The embodiment of the present invention measures the parameters required for predicting the reliability index of a virtual power plant to form a nonlinear time series of each parameter, and normalizes the nonlinear time series respectively, and then calculates the reliability coefficient of the distributed power supply of the virtual power plant and the reliability coefficient of the multiple loads of the virtual power plant respectively. Finally, the reliability index of the virtual power plant is predicted by using the distributed power supply reliability coefficient of the virtual power plant and the reliability coefficient of the multiple loads of the virtual power plant, and the source-load power of the virtual power plant is optimized according to the prediction results. This can improve the stability of the income of the virtual power plant in the power market, improve the reliability of flexible resources such as the distributed power supply and multiple controllable loads of the virtual power plant participating in the power market or power system regulation, improve the stability of power grid operation, and improve the utilization rate of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 It is a flow chart of a source-load power optimization method based on virtual power plant reliability index prediction provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0016] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0017] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0018] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0019] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0020] like Figure 1 The flow chart of the source-load power optimization method based on the prediction of the virtual power plant reliability index is shown in FIG. 1 . The processing flow of the method may include the following steps:

[0021] S1: Measure the real-time operating data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant.

[0022] In a feasible implementation, the real-time operation data of the virtual power plant in S1 include: the total photovoltaic output, the total wind power output, the charging status of the storage charge, the total power consumption of the temperature-controlled load and the total charging power of the electric vehicle in the virtual power plant; the environmental parameters affecting the reliability of the virtual power plant in S1 include light intensity, average temperature and average wind speed.

[0023] Optionally, S1 measures the real-time operating data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant, including: measuring the real-time operating data of each virtual power plant including a fixed number according to preset time intervals to form a nonlinear time series and measuring the environmental parameters that affect the reliability of the virtual power plant including a fixed number to form a nonlinear time series.

[0024] According to the preset time intervals, the real-time operation data of each virtual power plant including m are measured and formed into a nonlinear time series, and the environmental parameters affecting the reliability of the virtual power plant including m are measured and formed into a nonlinear time series, that is, at the moments tre1, tre2, …, tre i ,…,tre m , m is a natural number, i is a natural number, i = {1, 2, 3, ..., m}, the number of real-time operation data of virtual power plants included in the nonlinear time series of real-time operation data of each virtual power plant is m, the number of environmental parameters affecting the reliability of virtual power plants in the nonlinear time series of each environmental parameter affecting the reliability of virtual power plants is m, and the nonlinear time series of total photovoltaic output, total wind power output, light intensity, average temperature, average wind speed, storage charge state, total power consumption of temperature-controlled loads, and total charging power of electric vehicles in the virtual power plant are measured as shown in formula (1):

[0025]

[0026] In formula (1), For tre i The total photovoltaic output measured at all times; For tre i Total wind power output measured at all times; For tre i The light intensity measured at all times; For tre i The average temperature measured at all times; For tre i The average wind speed measured at any given moment; For tre i The stored charge state measured at all times; For tre i The total power consumption of the temperature control load measured at all times; For tre i The total charging power of the electric vehicle measured at all times.

[0027] S2. Normalize the measured real-time operation data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant respectively.

[0028] In a feasible implementation manner, the measured real-time operation data of the virtual power plant and the environmental parameters affecting the reliability of the virtual power plant are respectively normalized, including respectively normalizing the nonlinear time series of the measured real-time operation data of each virtual power plant and the nonlinear time series of the environmental parameters affecting the reliability of the virtual power plant;

[0029] Optionally, the measured nonlinear time series of real-time operation data of each virtual power plant and the nonlinear time series of each environmental parameter affecting the reliability of the virtual power plant are normalized as shown in formula (2):

[0030]

[0031] In formula (2), For tre i Normalized value of total photovoltaic output measurement at the moment; For tre i Normalized value of total wind power output measurement at the moment; For tre i Normalized value of the light intensity measurement at each moment; For tre i Normalized value of the average temperature measurement at the moment; For tre i Normalized value of the average wind speed measurement at the moment; For tre i Normalized value of the stored charge state measurement value at each moment; For tre i Normalized value of the total power measurement of the temperature control load at any moment; For tre i The normalized value of the total charging power measurement of the electric vehicle at the moment; PV max is the maximum value of the total photovoltaic output measured during these m fixed time intervals, PV min WP is the minimum value of the total photovoltaic output measured in these m fixed time intervals; max is the maximum value of the total wind power output measured in these m fixed time intervals, WP min is the minimum value of the total wind power output measured in these m fixed time intervals; LM max is the maximum value of the light intensity measurement value in these m fixed time intervals, LM min is the minimum value of the light intensity measurement values ​​in these m fixed time intervals; TP max is the maximum value of the average temperature measurement value in these m fixed time intervals, TP min WS is the minimum value of the average temperature measurement values ​​in these m fixed time intervals; max is the maximum value of the average wind speed measurement values ​​in these m fixed time intervals, WS min The minimum value of the average wind speed measurement value in these m fixed time intervals; SOC max SOC is the maximum value of the state of charge measurement value in these m fixed time intervals. minTCL is the minimum value of the stored charge state measurement value in these m fixed time intervals; max is the maximum value of the total power measurement value of the temperature control load in the m fixed time intervals, TCL min The minimum value of the total power measurement value of the temperature control load in these m fixed time intervals; EV max is the maximum value of the total charging power measurement value of the electric vehicle in these m fixed time intervals, EV min It is the minimum value of the total charging power measurement of the electric vehicle in these m fixed time intervals.

[0032] S3. Calculate the reliability coefficient of the distributed power source of the virtual power plant at the next moment in the future based on the normalized real-time operation data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant.

[0033] In a feasible implementation manner, the reliability coefficient of the distributed power supply of the virtual power plant at the next moment in the future is calculated based on the normalized real-time operation data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant, including calculating the reliability coefficient of the distributed power supply of the virtual power plant at the next moment in the future based on the nonlinear time series of the real-time operation data of each virtual power plant after normalization and the nonlinear time series of the environmental parameters that affect the reliability of the virtual power plant. m+1 The reliability coefficient of the virtual power plant distributed power supply is calculated to calculate the reliability coefficient of the next moment in the future m+1 The reliability coefficient of the distributed power generation of the virtual power plant is shown in formula (3):

[0034]

[0035] In formula (3), The next moment in the future m+1 Reliability coefficient of distributed power generation in virtual power plants.

[0036] S4. Calculate the multi-load reliability coefficient of the virtual power plant at the next moment in the future based on the normalized real-time operation data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant.

[0037] In a feasible implementation manner, the reliability coefficient of the virtual power plant multi-load at the next moment in the future is calculated based on the normalized real-time operation data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant, including calculating the reliability coefficient of the virtual power plant multi-load at the next moment in the future based on the nonlinear time series of the normalized real-time operation data of each virtual power plant and the nonlinear time series of the environmental parameters that affect the reliability of the virtual power plant, and calculating the reliability coefficient of the virtual power plant multi-load at the next moment in the future. m+1 The reliability coefficient of the virtual power plant multi-load is shown in formula (4):

[0038]

[0039] In formula (4), For the next moment in the future m+1 Reliability coefficient of multi-load of virtual power plant.

[0040] S5. Calculate the predicted value of the virtual power plant reliability index at the next moment in the future based on the calculated distributed power reliability coefficient of the virtual power plant at the next moment in the future and the calculated multi-load reliability coefficient of the virtual power plant at the next moment in the future.

[0041] In a feasible implementation, the virtual power plant reliability index prediction value at the next moment in the future is calculated based on the virtual power plant distributed power reliability coefficient at the next moment in the future calculated in S3 and the virtual power plant multi-load reliability coefficient at the next moment in the future calculated in S4, including using the virtual power plant reliability index prediction value at the next moment in the future obtained in S3 m+1 The virtual power plant multi-load reliability coefficient and the future next moment tre obtained by S4 m+1 The reliability coefficient of virtual power plant multi-load is calculated at the next moment in the future m+1 The predicted value of the virtual power plant reliability index; calculate the next moment tre m+1 The predicted value of the virtual power plant reliability index is shown in formula (5):

[0042]

[0043] In formula (5), For the next moment in the future m+1 Prediction value of virtual power plant reliability index.

[0044] S6 optimizes the source-load power of the virtual power plant according to the predicted value of the virtual power plant reliability index at the next moment in the future.

[0045] In a feasible implementation, the virtual power plant source-load power is optimized according to the obtained virtual power plant reliability index prediction value at the next moment in the future, including: m+1 Prediction value of virtual power plant reliability index Optimize the source and load power of the virtual power plant according to The size of the grid-connected capacity of photovoltaic and wind power is adjusted, and the total power consumption of temperature-controlled loads and the total charging power of electric vehicles are adjusted.

[0046] Specific optimization process: If the predicted value of the virtual power plant reliability index at the next moment in the future is calculated This means that the virtual power plant is highly reliable in participating in the power market or regulating the power system. At this time, the grid-connected capacity of photovoltaic and wind power should be increased, and the total power consumption of temperature control loads and the total power of electric vehicle charging should be increased. If the predicted value of the virtual power plant reliability index at the next moment in the future is This means that the reliability of the virtual power plant participating in the power market or the power system regulation is moderate. At this time, the grid-connected capacity of photovoltaic and wind power remains unchanged, and the total power consumption of temperature control loads and the total power of electric vehicle charging remain unchanged. If the predicted value of the virtual power plant reliability index at the next moment in the future is This means that the reliability of the virtual power plant in participating in the power market or in regulating the power system is weak. At this time, the grid-connected capacity of photovoltaic and wind power should be reduced, and the total power consumption of temperature control loads and the total power of electric vehicle charging should be reduced. 0.896 and 0.433 are the empirical values ​​of the virtual power plant reliability index.

[0047] In the above embodiment, S1: in the process of measuring the real-time operation data of the virtual power plant and the environmental parameters affecting the reliability of the virtual power plant, the value of m at m fixed time intervals is 6:

[0048] At 6 fixed time intervals tre1, tre2, tre3, tre4, tre5, tre6, the nonlinear time series of total photovoltaic output, total wind power output, light intensity, average temperature, average wind speed, storage charge state, total power consumption of temperature-controlled loads, and total charging power of electric vehicles in the virtual power plant are measured:

[0049]

[0050] S2: Normalize the measured real-time operation data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant

[0051] The calculation formula for normalization is as follows:

[0052]

[0053] Then, the real-time operation data of each virtual power plant and the environmental parameters that affect the reliability of the virtual power plant are given 6 normalized values ​​as follows:

[0054]

[0055] S3: Calculate the reliability coefficient of the distributed power generation of the virtual power plant tre7 at the next moment in the future based on the normalized real-time operation data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant;

[0056]

[0057] S4: Calculate the multi-load reliability coefficient of the virtual power plant at the next moment tre7 in the future according to the normalized real-time operation data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant;

[0058]

[0059] S5: Calculate the predicted value of the virtual power plant reliability index of tre7 at the next moment in the future according to the calculated distributed power reliability coefficient of the virtual power plant at the next moment in the future and the calculated multi-load reliability coefficient of the virtual power plant at the next moment in the future;

[0060]

[0061] S6. Optimize the source-load power of the virtual power plant according to the predicted value of the virtual power plant reliability index at the next future moment tre7.

[0062] The embodiment of the present invention provides a source-load power optimization method based on the prediction of the virtual power plant reliability index. The method first measures the parameters required for the prediction of the virtual power plant reliability index, namely, the real-time operation data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant, to form nonlinear time series of each parameter, and normalizes the nonlinear time series respectively, and then calculates the virtual power plant distributed power reliability coefficient and the virtual power plant multi-load reliability coefficient respectively, and finally predicts the virtual power plant reliability index using the virtual power plant distributed power reliability coefficient and the virtual power plant multi-load reliability coefficient, and optimizes the source-load power of the virtual power plant according to the prediction results. The embodiment of the present invention can improve the stability of the income of the virtual power plant in the power market, improve the reliability of the participation of flexible resources such as the virtual power plant distributed power supply and multi-controllable loads in the power market or the power system regulation, improve the stability of the power grid operation, and improve the utilization rate of new energy.

[0063] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0064] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0065] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0066] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, 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, such as multiple units or components can be combined or integrated into another device, 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 devices or units, which can be electrical, mechanical or other forms.

[0067] 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.

[0068] 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.

[0069] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0070] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A source-load power optimization method based on virtual power plant reliability index prediction, characterized in that: The method comprises: S1. Measure the real-time operation data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant; S2. normalizing the measured real-time operation data of the virtual power plant and the environmental parameters affecting the reliability of the virtual power plant; S3. Calculate the reliability coefficient of the distributed power source of the virtual power plant at the next moment in the future based on the normalized real-time operation data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant; S4. Calculate the multi-load reliability coefficient of the virtual power plant at the next moment in the future based on the normalized real-time operation data of the virtual power plant and the environmental parameters that affect the reliability of the virtual power plant; S5. Calculate the predicted value of the virtual power plant reliability index at the next moment in the future according to the calculated distributed power reliability coefficient of the virtual power plant at the next moment in the future and the calculated multi-load reliability coefficient of the virtual power plant at the next moment in the future; S6. Optimize the source-load power of the virtual power plant according to the predicted value of the virtual power plant reliability index at the next moment in the future.

2. The source-load power optimization method based on virtual power plant reliability index prediction according to claim 1 is characterized in that: The real-time operation data of the virtual power plant in S1 include: the total photovoltaic output, the total wind power output, the charging status of the storage charge, the total power consumption of the temperature-controlled load and the total charging power of electric vehicles in the virtual power plant; the environmental parameters that affect the reliability of the virtual power plant in S1 include light intensity, average temperature and average wind speed.

3. The source-load power optimization method based on virtual power plant reliability index prediction according to claim 2 is characterized in that: Measuring the real-time operating data of virtual power plants and environmental parameters that affect the reliability of virtual power plants, including: measuring the real-time operating data of each virtual power plant, including a fixed number, according to preset time intervals to form a nonlinear time series and measuring the environmental parameters that affect the reliability of virtual power plants, including a fixed number, to form a nonlinear time series.

4. The source-load power optimization method based on virtual power plant reliability index prediction according to claim 3 is characterized in that: The measured real-time operation data of the virtual power plant and the environmental parameters affecting the reliability of the virtual power plant are normalized respectively, including the nonlinear time series of the measured real-time operation data of each virtual power plant and the nonlinear time series of the environmental parameters affecting the reliability of the virtual power plant.

5. According to the source-load power optimization method based on the prediction of the virtual power plant reliability index according to claim 4, the reliability coefficient of the distributed power source of the virtual power plant at the next moment in the future is calculated according to the normalized real-time operation data of the virtual power plant and the environmental parameters affecting the reliability of the virtual power plant, including calculating the reliability coefficient of the distributed power source of the virtual power plant at the next moment in the future according to the nonlinear time series of the real-time operation data of each virtual power plant after normalization and the nonlinear time series of the environmental parameters affecting the reliability of the virtual power plant m+1 The reliability coefficient of the virtual power plant distributed power supply is calculated to calculate the reliability coefficient of the next moment in the future m+1 The reliability coefficient of the distributed power generation of the virtual power plant is shown in formula (3): In formula (3), The next moment in the future m+1 Reliability coefficient of distributed power generation of virtual power plant; For tre i Normalized value of total photovoltaic output measurement at the moment; For tre i Normalized value of total wind power output measurement at the moment; For tre i Normalized value of the light intensity measurement at each moment; For tre i Normalized value of the average wind speed measurement at that moment.

6. According to the source-load power optimization method based on the prediction of the virtual power plant reliability index as described in claim 4, the virtual power plant multi-load reliability coefficient at the next moment in the future is calculated according to the normalized real-time operation data of the virtual power plant and the environmental parameters affecting the reliability of the virtual power plant, including calculating the virtual power plant multi-load reliability coefficient at the next moment in the future according to the nonlinear time series of the real-time operation data of each virtual power plant after normalization and the nonlinear time series of the environmental parameters affecting the reliability of the virtual power plant, and calculating the reliability coefficient of the virtual power plant at the next moment in the future. m+1 The reliability coefficient of the virtual power plant multi-load is shown in formula (4): In formula (4), For the next moment in the future m+1 The reliability coefficient of multi-load of virtual power plant; For tre i Normalized value of the average temperature measurement at the moment; For tre i Normalized value of the stored charge state measurement value at each moment; For tre i Normalized value of the total power measurement of the temperature control load at any moment; For tre i Normalized value of the total charging power measurement of the electric vehicle at the moment.

7. A source-load power optimization method based on virtual power plant reliability index prediction according to any one of claims 5-6, characterized in that: S5 calculates the predicted value of the virtual power plant reliability index at the next moment in the future according to the distributed power reliability coefficient of the virtual power plant at the next moment in the future calculated in S3 and the multi-load reliability coefficient of the virtual power plant at the next moment in the future calculated in S4, including the reliability index of the virtual power plant at the next moment in the future obtained by S3. m+1 The virtual power plant multi-load reliability coefficient and the future next moment tre obtained by S4 m+1 The reliability coefficient of virtual power plant multi-load is calculated at the next moment in the future m+1 The predicted value of the virtual power plant reliability index; calculate the next moment tre m+1 The predicted value of the virtual power plant reliability index is shown in formula (5): In formula (5), For the next moment in the future m+1 The predicted value of the virtual power plant reliability index.

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