System multi-objective optimization strategy considering wind and light uncertainty influence

Abstract

The invention discloses a system multi-objective optimization strategy considering wind and light uncertainty influence, which comprises the following steps of: firstly, acquiring basic parameters and a wiring diagram of a system, constructing a wind and light output, load and energy storage model, determining initial output of each resource of the system, establishing a wind and light uncertainty output interval according to the wind and light output model, and constructing a multi-objective optimization model; the participation of energy storage and load is considered under different output situations of the wind-solar unit, the power of the wind-solar unit is regulated and optimized, the optimal solution of regulation and control between the power of the wind-solar unit and the energy storage and the load under multiple targets is obtained by means of an algorithm, and finally the voltage distribution level of system operation and the system source storage load response situation under the participation of an optimization strategy are compared and analyzed. Through verification, the voltage level of system operation can be effectively improved, the reliability of energy supply is guaranteed, and the influence of wind and light uncertainty on system operation is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of wind and solar energy power supply systems, and specifically to a system multi-objective optimization strategy that takes into account the impact of wind and solar energy uncertainty. Background Art

[0002] At present, the penetration rate of wind and solar resources in the power grid is increasing. However, due to the intermittent and volatile nature of wind and solar resources, there may be excess power in the power grid when wind and solar resources are sufficient, and when wind and solar resources are scarce, the power grid energy demand cannot be met, affecting the stability of the power grid operation. In order to balance the fluctuation of wind and solar resources and ensure the reliability of power grid energy supply, it is necessary to optimize the system affected by wind and solar uncertainty to cope with the impact of wind and solar uncertainty on the system and reduce the impact of wind and solar uncertainty on the system. Summary of the invention

[0003] The purpose of the present invention is to provide a system multi-objective optimization strategy that takes into account the uncertainty of wind and solar power, so as to solve the problem that the existing power grid system proposed in the above background technology is affected by the fluctuation of wind and solar power resources. Due to the intermittent and volatile nature of wind and solar power resources, the power grid may have excess electricity when the wind and solar power resources are sufficient, and cannot meet the energy demand of the power grid when the wind and solar power resources are scarce, thus affecting the stability of the power grid operation.

[0004] To achieve the above object, the present invention provides the following technical solution: a system multi-objective optimization strategy taking into account the influence of wind and solar uncertainty, comprising the following steps:

[0005] Obtain the basic system parameters and wiring diagram, and initialize the energy storage power and load data based on the constructed wind and solar unit output model, load and energy storage model;

[0006] Determine the uncertainty range of wind-solar generator output according to the wind-solar generator output model;

[0007] A multi-objective optimization model including operating cost, voltage stability, and wind and solar power consumption was constructed through a multi-objective particle swarm algorithm;

[0008] Input wind and solar power unit output, energy storage power, load and multiple indicators into the multi-objective optimization model;

[0009] Through operation optimization and algorithm iteration, the solution set of each iteration is obtained;

[0010] The optimal solution is selected with the lowest operating cost, the lowest voltage stability, and the lowest wind speed tolerance, that is, the optimal operation plan of the system is solved.

[0011] As the preferred solution, after selecting the optimal solution to solve the optimal operation plan of the system, the voltage distribution level of the system operation and the source, storage and load response of the system under the participation of the optimization strategy are compared based on the optimal solution to verify the impact of the optimization method on the system operation.

[0012] As a preferred solution, based on the wind and solar power unit power, energy storage power and load value under the optimal solution, a comparative analysis is conducted on the voltage distribution level of the system operation and the source, storage and load response of the system under the participation of the optimization strategy, verifying the effectiveness of the multi-objective optimization model in reducing the uncertainty of wind and solar power in system operation.

[0013] As a preferred solution, the uncertainty output range of the wind-solar generator set is determined according to the wind-solar generator set output model and the output confidence level based on the normal distribution;

[0014] As the preferred solution, the load and energy storage model includes a load model and an energy storage model. In the load model, the load can be divided into three categories: shiftable load, transferable load and reducible load according to the control characteristics of each load. The load control cost is calculated based on the control costs of the above three types of controllable loads. In the energy storage model, the energy storage charging and discharging power is mainly determined by the difference between the output of the wind and solar units and the load power.

[0015] As a preferred solution, the wind-solar unit output model includes a wind power output model and a photovoltaic output model, and the wind-solar unit includes a wind turbine unit and a photovoltaic unit; the wind speed during the period of wind turbine installation is the main factor affecting the output of the wind turbine unit in the photovoltaic output model; temperature and light intensity are the main factors affecting the output of the photovoltaic unit in the photovoltaic output model.

[0016] As a preferred solution, by constructing multi-objective indicators including operating cost, voltage stability, and wind and solar power consumption, the effect of the multi-objective optimization model on improving system operation under wind and solar power uncertainty is evaluated.

[0017] Correspondingly, the present invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the multi-objective optimization strategy of the system taking into account the influence of wind and solar uncertainty is implemented.

[0018] Correspondingly, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned system multi-objective optimization strategy taking into account the influence of wind and solar uncertainty.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] The present invention first constructs a wind-solar output, load and energy storage model, determines the initial output of each resource of the system, establishes a wind-solar uncertainty output range based on the wind-solar output model, constructs a multi-objective optimization model, considers the participation of energy storage and load under different wind-solar unit output scenarios, optimizes the power of the wind-solar unit, and uses the algorithm to obtain the optimal solution for the regulation of wind-solar unit power, energy storage and load under multiple objectives. Finally, the voltage distribution level of the system operation and the source-storage-load response of the system under the participation of the optimization strategy are compared and analyzed. It has been verified that the present invention can effectively improve the voltage level of the system operation, reduce the impact of wind-solar uncertainty on the system operation, effectively improve the problem of imbalance between source and load supply and demand of the system, and enhance the system's wind-solar absorption and flexible energy supply capabilities, reduce the impact of wind-solar uncertainty on the system energy supply, and improve the reliability of energy supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of solving a multi-objective optimization strategy for a system taking into account the influence of wind and solar uncertainty provided by the present invention;

[0022] Figure 2 This is a schematic diagram of the wiring diagram structure of a 54-node system used in Example 1;

[0023] Figure 3 It is the uncertainty interval of photovoltaic output in a system multi-objective optimization strategy taking into account the influence of wind and solar uncertainty of the present invention;

[0024] Figure 4 It is the uncertainty interval of photovoltaic output in a system multi-objective optimization strategy taking into account the influence of wind and solar uncertainty of the present invention;

[0025] Figure 5 It is the system voltage distribution level in different scenarios in a system multi-objective optimization strategy taking into account the uncertainty of wind and solar power in the present invention;

[0026] Figure 6 The optimization method provided by the present invention in the standard wind and solar scene responds to the system source storage load in the wind and solar output uncertainty interval;

[0027] Figure 7 The optimization method provided by the present invention in the high-limit scenario of wind and solar power responds to the source-storage-load of the system in the uncertainty interval of wind and solar power output;

[0028] Figure 8 The optimization method provided by the present invention is for the system source-storage-load response in the uncertainty range of wind-solar output in the low-limit scenario of wind-solar. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0030] Example 1

[0031] like Figure 1 As shown, the present invention provides a system multi-objective optimization strategy taking into account the influence of wind and solar uncertainty, including the following steps S101-S106:

[0032] S101 obtains basic system parameters and wiring diagrams, and initializes energy storage power and load data based on the constructed wind and solar generator output model, load and energy storage model;

[0033] In this embodiment, the wiring diagram is as follows Figure 2 As shown, a 54-node system wiring diagram is used, and wind turbines, wind-solar turbines and energy storage devices are used to convert the energy resources of the power grid;

[0034] In this embodiment, the load and energy storage model includes a load model and an energy storage model. In the load model, the load can be divided into three categories: shiftable load, transferable load and reducible load according to the load control characteristics. The load control fee is calculated based on the control costs of the above three types of controllable loads. In the energy storage model, the energy storage charging and discharging power is mainly determined by the difference between the output of the wind and solar units and the load power.

[0035] Specifically, load and energy storage model

[0036] (1) Load model

[0037] According to the control characteristics of each load, it can be divided into three categories: load that can be translated, load that can be transferred, and load that can be reduced. The economic dispatch model of each load is shown in formula (I):

[0038]

[0039] In formula (I), D L is the load regulation fee; D i sh , D i tr and D i re Respectively represent the regulation costs of the three types of controllable loads; δ price,i sh , δ price,i tr , and δ price,i re They represent the cost of adjusting each type of controllable load when adjusting unit power; P i sh and P i,t re The power adjusted for the shiftable and reducible load; P i,t tr , P i,t tr*is the power before and after the transferable load is regulated; n is the number of loads involved in the regulation.

[0040] (2) Energy storage model

[0041] The energy storage charging and discharging power is mainly determined by the difference between the wind and solar power output and the load power ΔP E Determine, when ΔP E When ΔP is greater than 0, the energy storage is in charging state; E When it is less than 0, the energy storage is in the discharge state, and the energy storage charging and discharging power model is shown in formula (II)

[0042]

[0043] In formula (II), P L (t) is the load power at time t, E(t) is the charging and discharging power of the energy storage device at time t; δ is the charging and discharging efficiency of the energy storage device.

[0044] In this embodiment, the wind-solar unit output model includes a wind power output model and a photovoltaic output model, and the wind-solar unit includes a wind turbine unit and a photovoltaic unit; the wind speed during the period when the wind turbine unit is installed is the main factor affecting the output of the wind turbine unit in the photovoltaic output model; the temperature and light intensity are the main factors affecting the output of the photovoltaic unit in the photovoltaic output model;

[0045] Wind and solar output model

[0046] (1) Wind power output model

[0047] The output of a wind turbine is mainly related to the wind speed during the period of installation. The output power P w (t) is calculated as shown in (III):

[0048]

[0049] In formula (III), P ew is the rated output power of the wind turbine; v(t) is the wind speed at time t; v e 、v q 、v t They are the rated power, start-up and shutdown wind speeds of the wind turbine respectively.

[0050] (2) Photovoltaic output model

[0051] The output of photovoltaic units is mainly affected by factors such as temperature and light. Its output power is greatly affected by light intensity. The output power P of photovoltaic units v (t) The calculation formula is shown in formula (IV):

[0052]

[0053] In formula (IV), Pev is the rated output power of the photovoltaic unit, EV(t) is the light intensity at time t; EV STD is the standard solar radiation (usually 1000W / m 2 ), EV C For a certain radiation point (usually 150W / m 2 ).

[0054] S102 determines the uncertainty range of wind-solar generator output according to the wind-solar generator output model; considering the volatility and intermittency of wind-solar generator output, the output confidence level based on normal distribution is constructed according to the above wind-solar generator output model to determine the uncertainty range of wind-solar generator output as shown in formula (V):

[0055]

[0056] In formula (V), The maximum and minimum wind power output at time t; are the maximum and minimum photovoltaic output at time t.

[0057] Specifically, according to the wind and solar power output model and the output confidence level based on the normal distribution, the confidence level in this embodiment is 0.95, and the uncertainty output range of the wind and solar power unit is determined. The boundary of the output range is used as the extreme scenario for subsequent analysis, where the upper boundary is the high-limit scenario, the lower boundary is the low-limit scenario, and the output mean is used as the standard scenario for subsequent analysis. The uncertainty range of wind power output is as follows: Figure 3 As shown in Figure 2, the uncertainty range of wind and solar power output is as follows: Figure 4 shown.

[0058] S103 uses a multi-objective particle swarm algorithm to build a multi-objective optimization model including operating costs, voltage stability, and wind and solar power consumption;

[0059] In this embodiment, by constructing multi-objective indicators including operating cost, voltage stability, and wind and solar power consumption, the improvement effect of the multi-objective optimization model on system operation under wind and solar power uncertainty is evaluated;

[0060] Multi-objective optimization model

[0061] (1) The system operating cost is shown in formula (VI):

[0062]

[0063] σ1=D L +D wv +D E (VI)

[0064] In formula (VI), S wv Install capacity for wind and solar units; is the unit capacity investment cost and unit capacity maintenance cost of wind and solar power units; wv is the conversion factor; P E 、E ESS are the rated power and rated capacity of the energy storage device respectively; is the unit power investment cost, unit capacity investment cost, and unit capacity maintenance cost of the energy storage device; ESS is the discount rate.

[0065] (2) Voltage stability is shown in formula (VII):

[0066]

[0067] In formula (VII), U f,t+1 , U f,t are the voltage values ​​of node f at time t and t+1 respectively; U f,N is the rated voltage of node f. The smaller σ2 is, the better the system voltage stability is, and vice versa.

[0068] (3) Wind and solar power consumption is shown in formula (VIII):

[0069]

[0070] In formula (VIII), P wv (t), P wv’ (t) are the wind and solar power unit output and the amount of wind and solar power abandoned at time t. The larger σ3 is, the better the wind and solar power absorption level of the system is.

[0071] S104 inputs the operating cost, voltage stability, and wind and solar power consumption parameters into a multi-objective optimization model based on a multi-objective particle swarm algorithm. When the wind and solar power units are at a high-limit scenario, the energy storage participates in the charging and discharging operation, and the excessive wind and solar power in a certain period is transferred to other wind and solar power low-power periods. When the wind and solar power units are at a low-limit scenario, the load can be shifted, transferred, and reduced, and the load with high energy consumption in a certain period is transferred to other periods with low energy demand.

[0072] S105 obtains the solution set for each iteration through operation optimization and algorithm generation. In the algorithm, the power of wind and solar generators is adjusted through energy storage and load operation, and the number of algorithm iterations is set to 80 times.

[0073] S106 selects the optimal solution with the lowest operating cost, the lowest voltage stability, and the lowest wind and solar power absorption, that is, solves the optimal operation plan of the system. Specifically, the operating cost, voltage stability, and wind and solar power absorption indicators in the multi-objective optimization model are used as the algorithm solution targets, and the multi-indicator values ​​and the final wind and solar power unit power, energy storage power and load values ​​under each iteration are solved, and the optimal solution is selected according to the lowest operating cost, the lowest voltage stability, and the lowest wind and solar power absorption.

[0074] In this embodiment, the following steps may also be included:

[0075] After the optimal solution is selected in step 106 to solve the optimal operation plan of the system, the voltage distribution level of the analyzed system operation and the source-storage-load response of the system under the participation of the optimization strategy are compared based on the optimal solution to verify the influence of the optimization method on the system operation.

[0076] As a preferred solution of this embodiment, based on the wind and solar power unit power, energy storage power and load value under the optimal solution, the voltage distribution level of the system operation and the source storage and load response of the system under the participation of the optimization strategy are compared and analyzed to verify the effectiveness of the multi-objective optimization model in reducing the uncertainty of wind and solar power in system operation;

[0077] The present invention is analyzed in depth experimental results such as Figure 5-7 As shown:

[0078] Depend on Figure 5 It can be seen that since the energy storage installation position is consistent with the grid-connected position of the wind and solar units, the network loss of the system during power transmission is minimized, thereby maintaining the voltage drop of the lines in the system. Comparative analysis of the voltage distribution levels of the system in different scenarios shows that the system voltage in the low wind and solar output scenario is 1.70% lower than that in the standard scenario, and the system voltage in the high wind and solar output scenario is 1.84% higher than that in the standard scenario, effectively improving the impact of wind and solar uncertainty on the system voltage stability and maintaining the stable operation of the system.

[0079] Depend on Figure 6 , Figure 7 and Figure 8 It can be seen that in the standard wind and solar output scenario, by adjusting the load and energy storage charging and discharging operations, the load reduction from 9:00 to 20:00 will be partially transferred to 23:00-6:00; in the extreme wind and solar output scenario, due to the uncertainty of wind and solar output, the load and energy storage will participate in the regulation to increase the upward adjustment margin of the system at night and reduce the downward adjustment margin of the system during the day. For example, the power from 12:00 to 14:00 and 18:00 to 21:00 will be transferred to 23:00-1:00 and 6:00-8:00, which will effectively improve the imbalance between the source and load supply and demand of the system, enhance the system's wind and solar absorption and flexible energy supply capabilities, reduce the impact of wind and solar uncertainty on the system's energy supply, and improve the reliability of energy supply.

[0080] Example 2

[0081] It is a system multi-objective optimization device that takes into account the uncertainty of wind and solar power provided by the present invention, including a system initialization module, an optimization module, a solution module and a verification module.

[0082] The system initialization module is used to obtain basic system parameters and wiring information, build a wind and solar generator output model, load and energy storage model, initialize the system's energy storage power and load data, and determine the wind and solar generator output uncertainty interval;

[0083] Optimization module: used to build multi-objective optimization models;

[0084] Solution module: used to input wind and solar power unit output, energy storage power, load and multiple indicators into the multi-objective optimization model to solve the optimal operation plan of the system.

[0085] Verification module: used to compare the voltage distribution level of the analyzed system operation and the source-storage-load response of the system under the participation of the optimization strategy based on the optimal solution, verify the impact of the optimization method on the system operation, and implement the system multi-objective optimization strategy provided in Example 1 through the system multi-objective optimization device.

[0086] Example 3

[0087] It is a terminal device provided by the present invention, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, a system multi-objective optimization strategy taking into account the influence of wind and light uncertainty as described in any one of claims 1 to 7 is implemented.

[0088] The terminal device of this embodiment includes: a processor, a memory, and a computer program and a computer instruction stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above-mentioned embodiment 1 is implemented, for example: Figure 1 Steps S101 to S106 and the final verification step are shown. Alternatively, when the processor executes the computer program, the functions of each module / unit in the above device embodiment are implemented, such as the optimization module.

[0089] Exemplarily, the computer program can be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments that can perform specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device. For example, the optimization module is used to construct a multi-objective optimization model, and by constructing multi-objective indicators including operating cost, voltage stability, and wind and solar power consumption, the improvement effect of the multi-objective optimization model on the operation of the system under wind and solar power uncertainty is evaluated.

[0090] The terminal device may be a computing device such as a desktop computer, a notebook, a handheld computer, or a cloud server.

[0091] The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device, and may include more or fewer components than shown in the diagram, or may combine certain components, or different components, for example, the terminal device may also include an input / output device, a network access device, a bus, etc.

[0092] The processor may be a central processing unit (CPU) or any other

[0093] Other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGAs), and other

[0094] Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general processor can be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0095] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile terminal, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0096] Wherein, if the module / unit integrated in the terminal device 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,

[0097] The present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0098] Example 4

[0099] The present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the system multi-objective optimization strategies according to claims 1 to 7 that takes into account the influence of wind and solar uncertainty.

[0100] The calculation conditions, legends, etc. in the embodiments of the present invention are only used to further illustrate the present invention, but are not exhaustive and do not constitute a limitation on the scope of protection of the claims. Those skilled in the art can think of other substantially equivalent alternatives based on the inspiration obtained from the examples of the present invention without creative work, and all of them are within the scope of protection of the present invention.

Claims

1. A multi-objective optimization strategy for a system taking into account the uncertainty of wind and solar power, characterized in that: The following steps are involved: S101 obtains basic system parameters and wiring diagrams, and initializes energy storage power and load data based on the constructed wind and solar generator output model, load and energy storage model; S102 determines the uncertainty interval of wind-solar generator output according to the wind-solar generator output model; S103 uses a multi-objective particle swarm algorithm to build a multi-objective optimization model including operating costs, voltage stability, and wind and solar power consumption; S104 inputs the wind and solar power unit output, energy storage power, load and multiple indicators into the multi-objective optimization model; S105 obtains the solution set in each iteration through operation optimization and algorithm iteration; S106 selects the optimal solution with the lowest operating cost, the lowest voltage stability, and the lowest wind speed tolerance, that is, solves the optimal operation plan of the system.

2. According to claim 1, a system multi-objective optimization strategy taking into account the uncertainty of wind and solar power, characterized in that: After selecting the optimal solution to solve the optimal operation plan of the system, the voltage distribution level of the analyzed system operation and the source, storage and load response of the system under the participation of the optimization strategy are compared based on the optimal solution to verify the impact of the optimization method on the system operation.

3. According to claim 2, a system multi-objective optimization strategy taking into account the uncertainty of wind and solar power, characterized in that: Based on the wind and solar power unit power, energy storage power and load value under the optimal solution, a comparative analysis is conducted on the voltage distribution level of the system operation and the source, storage and load response of the system under the participation of the optimization strategy, verifying the effectiveness of the multi-objective optimization model in reducing the uncertainty of wind and solar power in system operation.

4. According to claim 1, a system multi-objective optimization strategy taking into account the uncertainty of wind and solar power, characterized in that: The uncertainty output range of the wind-solar generator set is determined according to the wind-solar generator set output model and the output confidence level based on normal distribution.

5. According to claim 4, a system multi-objective optimization strategy taking into account the uncertainty of wind and solar power, characterized in that: The load and energy storage model includes the load model and the energy storage model.

6. The multi-objective optimization strategy for a system taking into account the uncertainty of wind and solar power according to claim 5, characterized in that: In the load model, according to the load control characteristics of each load, the load can be divided into three categories: shiftable load, transferable load and curtailable load. The load control cost is calculated based on the control cost of the above three types of controllable loads; in the energy storage model, the energy storage charging and discharging power is mainly determined by the difference between the output of the wind and solar units and the load power.

7. The multi-objective optimization strategy for a system taking into account the uncertainty of wind and solar power according to claim 1, characterized in that: The wind-solar unit output model includes the wind power output model and the photovoltaic output model. The wind-solar unit includes the wind turbine unit and the photovoltaic unit. The wind speed during the installation period of the wind turbine unit is the main factor affecting the output of the wind turbine unit in the photovoltaic output model. The temperature and light intensity are the main factors affecting the output of the photovoltaic unit in the photovoltaic output model.

8. The multi-objective optimization strategy for a system taking into account the uncertainty of wind and solar power according to claim 1, characterized in that: By constructing multi-objective indicators including operating cost, voltage stability, and wind and solar power consumption, the improvement effect of the multi-objective optimization model on system operation under wind and solar power uncertainty is evaluated.

9. A terminal device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, a system multi-objective optimization strategy taking into account the influence of wind and solar uncertainty as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the system multi-objective optimization strategies taking into account the influence of wind and solar uncertainty as claimed in claims 1 to 7.

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