A multi-objective optimization scheduling method for renewable energy power generation systems considering uncertainty

Abstract

The present invention relates to the field of new energy optimization scheduling, specifically a multi-objective optimization scheduling method for a new energy power generation system taking uncertainty into account. First, the uncertainty in the new energy power generation system is analyzed, and a scenario generation and reduction framework based on an autoregressive moving average model and Kantorovich distance is proposed. Secondly, a multi-objective optimization model is established with the lowest system operating cost and the maximum photovoltaic power generation grid-connected power as the optimization goals. Finally, the effectiveness of the model is verified by comparing the simulation results of multiple scenarios. The simulation results show that the model can effectively reduce the operating cost of the system, improve the absorption power of photovoltaic power generation, and ensure the power supply reliability of the system.

Description

Technical Field

[0001] The present invention relates to the field of new energy optimization scheduling, and in particular to a multi-objective optimization scheduling method for a new energy power generation system taking uncertainty into consideration. Background Art

[0002] Although large-scale renewable energy access to the grid can bring certain economic benefits, its output has characteristics such as randomness, which makes the problem of new energy consumption increasingly prominent. In existing studies, conventional energy such as thermal power and gas turbines are mostly used as basic power sources, and few consider using solar thermal power generation with low environmental pollution and stable output as basic energy to participate in grid dispatch. In the absence of sunlight, the heat storage system of the solar thermal power station can maintain full-load power generation of the power station for about 15 hours, with good dispatch characteristics; and the generator set has a well-controllable heat exchange system, which enables the steam turbine unit to have a fast ramping capability comparable to that of the gas unit, so it has the potential to replace conventional energy as a backup power source. The battery energy storage system can quickly release a large amount of electricity in a short time, and can also discharge continuously at a smaller power for a long time. It can complement the solar thermal power station. It can not only fill the power supply shortage caused by prediction errors in a short time, but also improve the consumption space of new energy through its energy storage characteristics. In order to improve the new energy consumption capacity in Northwest China, a multi-objective optimization dispatching method for new energy power generation system considering uncertainty is proposed. Summary of the invention

[0003] The present invention provides a multi-objective optimization scheduling method for a new energy power generation system considering uncertainty, comprising the following steps:

[0004] S1. Analyze the uncertainty in the renewable energy power generation system;

[0005] S2, describing the uncertainty of renewable energy generation through scenario simulation method;

[0006] S3. Based on the uncertainty of renewable energy power generation, a multi-objective optimization model is established with the lowest system operation cost and the maximum photovoltaic power generation grid-connected power as the optimization goals;

[0007] S4. By comparing the simulation results of multiple scenarios, the effectiveness of the unified multi-objective optimization scheduling is verified.

[0008] In the step S1, the uncertainty in the new energy power generation system is analyzed, specifically including the uncertainty analysis of photovoltaic power generation and the uncertainty analysis of solar thermal power generation.

[0009] The uncertainty analysis of photovoltaic power generation adopts the following method:

[0010] The radiation intensity is described by the Beta distribution function:

[0011]

[0012]

[0013]

[0014] Where: r, r max Respectively represent the radiation intensity and the maximum radiation intensity; α p , β p are the shape distribution parameters of Beta; μ p , σ p are the mean and standard deviation of radiation intensity respectively;

[0015] Photovoltaic power output power P t,PP for:

[0016] P t,PP =S PP η PP r t (4)

[0017] Where: η PP Represents the photoelectric conversion efficiency; S PP is the area of ​​the photovoltaic array; r t is the radiation intensity at any time period t;

[0018] The photovoltaic power output power is described by Beta distribution as:

[0019]

[0020] Where: is the maximum output power of photovoltaic power generation;

[0021] The expected value of photovoltaic power output power E(P t,PP ) and variance D(P t,PP ) is expressed as:

[0022]

[0023]

[0024] The scenario simulation in step S2 includes two parts: scenario generation and scenario reduction. The scenario simulation is generated by an autoregressive sliding average model, and the scenario reduction uses a backward reduction method based on Kantorovich distance to reduce the generated scenario set.

[0025] The method for generating scenario simulation using the autoregressive moving average model is:

[0026] The output scenario of photovoltaic power generation is generated by the autoregressive moving average model:

[0027]

[0028] Where: y t Represents the time series value of a certain period of time t; φ i represents the autoregressive parameter; μ j represents the sliding average parameter; α t Indicates that the mean value is 0 and the variance value is σ 2 A normal white noise process.

[0029] The method of reducing the scene set generated by the autoregressive moving average model using the back-pushing reduction method based on Kantorovich distance is as follows:

[0030]

[0031]

[0032] Where: t = 1, 2, ..., T is the scheduling period; They represent the photovoltaic power output power of scenes s and s' at any time period t respectively;

[0033] Formula (10) is repeatedly iterated to reduce the number of remaining scenes to meet the requirement.

[0034] The multi-objective optimization model of the new energy power generation system in step S3 includes the following steps:

[0035] S301. Establishing the objective function

[0036] F1=min(C yw +C BS +C cf ) (12)

[0037]

[0038] Where: C yw represents the sum of the operation and maintenance costs of solar thermal and photovoltaic power generation; C BS Indicates the battery operating cost; C cf P represents the penalty cost of photovoltaic power abandonment; t PP represents the amount of photovoltaic power generation connected to the grid at a certain time t; Formula (12) represents the minimization of system operation cost; Formula (13) represents the maximization of the amount of new energy connected to the grid;

[0039] S302, forming constraint conditions for establishing the objective function, which are: power balance constraint, system spinning reserve constraint, photovoltaic operation constraint and battery operation constraint,

[0040] S303, converting the established objective function into a single objective function.

[0041] Another object of the present invention is to provide a device based on a multi-objective optimization scheduling method for a renewable energy power generation system taking uncertainty into consideration, wherein the device includes a multi-objective optimization scheduling method for a renewable energy power generation system taking uncertainty into consideration.

[0042] The beneficial effects of the present invention are as follows: In view of the problem of low utilization rate of new energy in the northwest region, the present invention combines solar thermal power generation, photovoltaic power generation and energy storage system into a new energy power generation system, and proposes a multi-objective optimization scheduling method for a new energy power generation system considering uncertainty. First, the uncertainty in the new energy power generation system is analyzed, and a scenario generation and reduction framework based on the autoregressive moving average model and Kantorovich distance is proposed. Secondly, a multi-objective optimization model is established with the lowest system operating cost and the maximum photovoltaic power generation grid-connected power as the optimization goals. Finally, the effectiveness of the model is verified by comparing the simulation results of multiple scenarios. The simulation results show that the model can effectively reduce the operating cost of the system, improve the absorption power of photovoltaic power generation, and ensure the power supply reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is the load power prediction curve;

[0044] Figure 2 It is the photovoltaic output prediction curve;

[0045] Figure 3 is the light radiation intensity curve;

[0046] Figure 4 This is the scheduling result of scenario 1;

[0047] Figure 5 Scheduling results for scenario 2;

[0048] Figure 6 Thermal storage power curves for different scenarios;

[0049] Figure 7 Provides light and heat output curves for different scenarios;

[0050] Figure 8 Photovoltaic output curves in different scenarios;

[0051] Fig. 9 Comparison of abandoned optical power in different scenarios;

[0052] Fig.10 Comparison of system operating costs in different scenarios; DETAILED DESCRIPTION

[0053] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific embodiments:

[0054] Example 1

[0055] The present invention designs and proposes a multi-objective optimization scheduling method for a new energy power generation system that takes uncertainty into account. First, the uncertainty in the new energy power generation system is analyzed, and a scenario generation and reduction framework based on the autoregressive moving average model and the Kantorovich distance is proposed. Then, a multi-objective optimization model is established with the lowest system operating cost and the maximum photovoltaic power generation grid-connected power as the optimization goals. Finally, the effectiveness of the model is verified by comparing the simulation results of multiple scenarios. The simulation results show that the model can effectively reduce the operating cost of the system, improve the absorption power of photovoltaic power generation, and ensure the power supply reliability of the system. The details are as follows.

[0056] 1. Uncertainty analysis of new energy power generation system

[0057] 1.1 Uncertainty analysis of photovoltaic power generation

[0058] Through the photovoltaic effect, photovoltaic power generation converts solar energy into electrical resources, and the output power is mainly affected by factors such as radiation intensity and photoelectric conversion rate.

[0059] The Beta distribution function is used to describe the radiation intensity:

[0060]

[0061]

[0062]

[0063] Where: r, r max Respectively represent the radiation intensity and the maximum radiation intensity; α p , β p are the shape distribution parameters of Beta; μ p , σ p are the mean and standard deviation of the radiation intensity respectively.

[0064] From the above, we can know that the photovoltaic power output power P t,PP for:

[0065] P t,PP =S PP η PP r t (4)

[0066] Where: η PP Represents the photoelectric conversion efficiency; S PP is the area of ​​the photovoltaic array; r t is the radiation intensity at any time period t.

[0067] From equations (1) and (4), it can be seen that the photovoltaic power output power can also be described by Beta distribution:

[0068]

[0069] Where: is the maximum output power of photovoltaic power generation.

[0070] It can be seen that the expected value of photovoltaic power output power E(P t,PP ) and variance D(P t,PP ) can be expressed as:

[0071]

[0072]

[0073] From the above, we can see that the output power of photovoltaic power generation is mainly affected by the random changes in light intensity and is uncertain. Therefore, it is necessary to formulate a scheduling plan based on its uncertainty to reduce the operation risk of the power grid.

[0074] 1.2 Uncertainty analysis of CSP

[0075] Since CSP plants are usually equipped with a large-capacity heat storage system, instantaneous changes in light conditions will not directly affect the output of CSP plants. At the same time, the changes in the next day's light radiation can be predicted very accurately, and the uncertainty of light has a very limited impact on the output of CSP, so that the heat storage system will not be disturbed by large weather changes during the heat storage process. Therefore, the impact of light changes on CSP output is ignored here.

[0076] 2. Generation and reduction of uncertainty scenarios

[0077] Since renewable energy generation is uncertain, it is important to formulate dispatch plans in advance through power forecast information to ensure the safe operation of the power grid. The uncertainty of photovoltaic power generation is described by scenario simulation method, which includes scenario generation and scenario reduction.

[0078] The output scenario of photovoltaic power generation is generated through the Auto regressive moving average model (ARMAM):

[0079]

[0080] Where: y t Represents the time series value of a certain period of time t; φ i represents the autoregressive parameter; μ j represents the sliding average parameter; α t Indicates that the mean value is 0 and the variance value is σ 2 A normal white noise process.

[0081] As the number of scenarios continues to increase, the process of solving the multi-objective scheduling model becomes more and more cumbersome. Therefore, considering the impact of solution accuracy and computational complexity, a backward reduction method based on K-distance is used to reduce the scenario set generated by ARMAM.

[0082] Assuming that two large-scale scene sets S and S' are known, the Kantorovich distance L(S, S') can be expressed as:

[0083]

[0084] Where: s, s' represents any scene in the two scene sets S, S'; δ s ,δ s' They represent the occurrence probabilities of any scene s and s' in the scene set respectively; λ(s, s') represents a non-negative distance function; ε(s, s') represents the product of the occurrence probabilities of any scene s and s'.

[0085] Combined with the output power characteristics of photovoltaic power generation, assuming that the original photovoltaic output scene set is S and the target photovoltaic output scene set is S', the above formula can be equivalent to:

[0086]

[0087]

[0088] Where: t = 1, 2, ..., T is the scheduling period; They represent the photovoltaic power output power of scenes s and s' at any time period t.

[0089] Formula (10) is used to iterate repeatedly until the number of remaining scenes meets the requirement.

[0090] 3. Multi-objective optimization model of new energy power generation system

[0091] 3.1 Objective Function

[0092] Under the premise of ensuring the safe and stable operation of the system, it is necessary to ensure the operating efficiency of the system and the maximum utilization of new energy, thus establishing the following objective function:

[0093] F1=min(C yw +C BS +C cf ) (12)

[0094]

[0095] Where: C yw represents the sum of the operation and maintenance costs of solar thermal and photovoltaic power generation; C BSIndicates the battery operating cost; C cf P represents the penalty cost of photovoltaic power abandonment; t PP represents the amount of photovoltaic power generation connected to the grid at a certain time t. Formula (12) represents the minimization of system operation cost; Formula (13) represents the maximization of the amount of new energy connected to the grid. Among them:

[0096]

[0097]

[0098]

[0099]

[0100]

[0101] Where: and Respectively represent the unit maintenance costs of photovoltaic power generation, solar thermal power station collector system power generation and thermal storage system; They represent the power generation power of the solar thermal power station's heat collection system and the heat storage system's heat release power generation power; μ r-d , μ fr They represent the thermoelectric conversion rate of the heat collection system and the heat release loss rate of the heat storage system respectively; They represent the thermal power of thermoelectric conversion in the heat collection system and the heat release power of the heat storage device at a certain time t; c yx 、c zj Respectively represent the unit operation and maintenance cost and depreciation cost of the energy storage system; They represent the charge and discharge of the energy storage system at a certain time t; c cf It represents the cost coefficient of abandoned photovoltaic power in the system.

[0102] 3.2 Constraints

[0103] 1) Power balance constraints

[0104] When grid losses are not considered, the power demand of the electricity load at each moment is equal to the sum of the photovoltaic power generation output, solar thermal power generation output, purchased power, and power shortage, that is,

[0105]

[0106] Where: P t L Indicates the power required by the load at a certain time t;

[0107] 2) System spinning reserve constraints

[0108]

[0109]

[0110] Where: They are the maximum and minimum output of CSP at time t respectively; It represents the maximum power of the energy storage system discharge; n1 and n2 are the upper rotating reserve coefficients of the load and photovoltaic power generation, and n3 is the lower rotating reserve coefficient of photovoltaic power generation.

[0111] 3) PV operation constraints

[0112] 0≤P t PP ≤P t,PP (twenty two)

[0113] 4) Battery operation constraints

[0114]

[0115]

[0116]

[0117] Where: Indicates the initial capacity of the battery; represents the amount of electricity stored in the battery at time t; Indicates the charge and discharge loss rate of the battery; Indicates the maximum charging power of the energy storage system.

[0118] 5) Constraints on CSP

[0119]

[0120]

[0121]

[0122]

[0123] E t0 =E T (30)

[0124] Where: P t CSP represents the power generated by CSP at time t; R D , R U Indicates the maximum up and down climbing limit value of the CSP steam turbine generator; Indicates the minimum heat storage capacity for safe operation of the heat storage system; E t represents the heat storage capacity of the heat storage system at time t; T MFYIndicates the full load operation hours of the heat storage system; η rd It represents the thermoelectric conversion efficiency of the thermal storage system; represents the heat storage and release power of the heat storage system at time t; Indicates the maximum heat storage and heat release power of the heat storage system; E t0 、E T They respectively represent the heat storage amount of the energy storage system at the beginning and end of a scheduling cycle.

[0125] 6) Grid transmission power constraints

[0126] -P fl,max ≤P l,t ≤P zl,max (31)

[0127] Where: P fl,max , P zl,max Respectively represent the reverse and forward transmission power limits of a certain line l; P l,t Represents the transmission power of a certain line l at time t.

[0128] 3.3 Target weight calculation

[0129] The scheduling model has three objective functions, which can be transformed into a single objective function by solving the weight coefficient. The weight coefficient is solved using rough set theory, and the solution process is:

[0130] 1) Constructing a relational data model

[0131] Assume that any objective function f n The target weight coefficient is 1 / n, and the optimal value F suitable for multiple objective functions can be obtained; using this value as a decision attribute, a decision attribute set expressed in the form of D = {F} can be obtained, then there is an information u in the set m is the comprehensive optimal value of multiple objectives, and u m =(f 1m ,f 2m ,···,f im ; F m ), then the sample set can be expressed as U = {u1,u2,···u m}, now you can use f n (u m )=a nm ,F n (u m )=F m To express u m Attributes.

[0132] 2) Solve for R A R D Dependence

[0133]

[0134] In formula (12): R A , R D All are knowledge bases; d() is the cardinality of rough sets; Represents the knowledge base R with F as the decision vector D ; R A () indicates the elimination index a n The previous sample set U.

[0135] 3) Solve for R A right Dependence

[0136]

[0137] 4) Solve the weight coefficient

[0138]

[0139]

[0140] Example 2

[0141] In order to verify the effectiveness of this model, the solar power generation system here consists of a photovoltaic power station, a solar thermal power station and a battery energy storage system. The photovoltaic power generation installed capacity is 100MW. After scene generation and scene reduction, 5 groups of typical photovoltaic output scenes are obtained. The average value of each scene is taken as the photovoltaic predicted output. The load power prediction curve and the photovoltaic output prediction curve are shown in Figure 1 , Figure 2 shown.

[0142] The maximum and minimum output power of the CSP station are 100MW and 10MW respectively, the upper and lower limits of the thermal storage capacity are 1000MW and 100MW respectively, the total capacity of the energy storage system is 50MW, and the maximum charging and discharging power is 8MW. Figure 3 shown.

[0143] During the solution process, the unit operation and maintenance cost of photovoltaic power generation is 30 yuan / MW, and the unit penalty coefficient of abandoned photovoltaic power generation is 100 yuan / MW; the operation and maintenance cost of the solar thermal power generation collection system is 50 yuan / MW, and the operation and maintenance cost of heat storage is 40 yuan / MW; the unit operation and maintenance cost and depreciation cost of the energy storage system are both 25 yuan / MW.

[0144] Since photovoltaic power generation can only be generated during the day, the simulation analysis is only performed from 7:00 to 18:00. The weights of the objective function, that is, the weight coefficients of the objective function of minimizing the system operation cost and the objective function of maximizing the amount of new energy connected to the grid, are 0.7 and 0.3 respectively according to equations (34) and (35).

[0145] Two scenarios are set here for analysis:

[0146] Scenario 1: Basic scenario, which only includes photovoltaic power stations and solar thermal power stations, and does not introduce energy storage systems.

[0147] Scenario 2: Energy storage scenario. In this scenario, an energy storage system is introduced based on the previous scenario.

[0148] Results Analysis

[0149] Figure 4 is the scheduling result of scenario 1. Figure 4 It can be seen that in scenario 1, the photovoltaic power generation output is 343.7MW·h, the solar thermal power generation output is 516.6MW·h, the photovoltaic power generation unabsorbed power is 108.1MW·h, and the photovoltaic power generation absorption rate is only 76.07%, because in this scenario, the solar thermal power station not only needs to operate with load, but also needs to provide backup for photovoltaic power generation to prevent emergencies. Therefore, in order to reduce the risk of power shortage, the system reduces the acceptance ratio of photovoltaic power generation and increases the power generation ratio of solar thermal power generation. Although this can improve the power supply reliability of the system, the acceptance rate of photovoltaic power generation is low, and the benefit potential of photovoltaic power generation has not been fully tapped. Figure 5 This is the scheduling result of scenario 2.

[0150] Depend on Figure 5 It can be seen that in scenario 2, the photovoltaic power generation output is 411.1MW·h, the solar thermal power generation output is 464.9MW·h, the photovoltaic power generation unabsorbed power is only 40.4MW·h, and the photovoltaic power generation grid connection rate reaches 91.06%. This is because the energy storage system has two characteristics: charging energy storage and discharging energy supply. During the low load and photovoltaic power generation peak period, part of the electric energy is stored, and during the peak load and photovoltaic power generation valley period, the discharge relieves the power supply pressure. In addition, the energy storage system can provide backup power for photovoltaic power generation, increase the absorption space of photovoltaic power generation, reduce the power supply pressure of solar thermal power generation, and enable solar thermal power generation to provide more backup power, improve the dispatching flexibility of the dispatching department, and reduce the risk of power shortage in the system. Figure 6 and Figure 7 They are the output curve and heat storage power curve of the CSP station in different scenarios.

[0151] Depend on Figure 6 , Figure 7 It can be seen that the introduction of the energy storage system reduces the power supply pressure of solar thermal power, slightly increases the heat storage capacity of the thermal storage system, and increases the backup capacity of solar thermal power generation. The power generation space created by the reduction in solar thermal power generation can be supplemented by photovoltaic power generation, thereby increasing the grid-connected space for photovoltaic power generation. Figure 8 Photovoltaic output curves for different scenarios.

[0152] Depend on Figure 8It can be seen that the photovoltaic output curve in scenario 2 is higher than the photovoltaic output curve in scenario 1 in each time period, which means that the introduction of the energy storage system has increased the photovoltaic grid-connected power in each time period. At the same time, the proportion of photovoltaic grid-connected power in the system has increased from 39.95% to 46.92%, an increase of 6.97%. Fig. 9 and Fig.10 The comparison of abandoned optical power and system operating cost in different scenarios are shown respectively.

[0153] Depend on Fig. 9 It can be seen that in most scheduling periods, the abandoned optical power in scenario 1 is much greater than that in scenario 2, that is, the renewable energy grid connection rate in scenario 2 is higher and the abandoned optical power is less; Fig.10 It can be seen that the system operation cost of scenario 2 in each dispatch period is slightly lower than that of scenario 1, that is, the economic benefits of the system in scenario 2 are higher than those in scenario 1. Based on the above analysis, it can be seen that the introduction of energy storage system can reduce the system operation cost and improve the system's new energy consumption capacity. Table 1 compares the dispatch results of different scenarios.

[0154] Table 1 Comparison of scheduling results in different scenarios

[0155]

[0156] As shown in Table 1, the operating cost of the system in scenario 2 decreased by 1,728 yuan, or about 2.02%, compared with scenario 1; the abandoned power decreased by 67.7MW, or about 62.63%; and the photovoltaic power consumption increased from 76.07% to 91.06%, or about 15%. From the above data, it can be seen that the model has good economic efficiency and can effectively reduce the abandoned power of the system and improve the photovoltaic power consumption rate.

[0157] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0158] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0159] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0161] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-objective optimization scheduling method for a renewable energy power generation system considering uncertainty, characterized in that: The following steps are involved: S1. Analyze the uncertainty in the renewable energy power generation system; S2, describing the uncertainty of renewable energy generation through scenario simulation method; S3. Based on the uncertainty of renewable energy power generation, a multi-objective optimization model is established with the lowest system operation cost and the maximum photovoltaic power generation grid-connected power as the optimization goals; S4. Verify the multi-objective optimization scheduling of the system by comparing the simulation results of multiple scenarios; The scenario simulation in step S2 includes two parts: scenario generation and scenario reduction. The scenario simulation is generated by an autoregressive sliding average model, and the scenario reduction uses a backward reduction method based on Kantorovich distance to reduce the generated scenario set. The method for generating scenario simulation using the autoregressive moving average model is: The output scenario of photovoltaic power generation is generated by the autoregressive moving average model: Where: y t Represents the time series value of a certain period of time t; φ i represents the autoregressive parameter; μ j represents the sliding average parameter; α t Indicates that the mean value is 0 and the variance value is σ 2 Normal white noise process; The method of reducing the scene set generated by the autoregressive moving average model using the back-pushing reduction method based on Kantorovich distance is as follows: Where: t = 1, 2, ..., T is the scheduling period; They represent the photovoltaic power output power of scenes s and s' at any time period t respectively; Formula (10) is used to iterate repeatedly to reduce the number of remaining scenes to the required number; The multi-objective optimization model of the new energy power generation system in step S3 includes the following steps: S301. Establishing the objective function F1=min(C yw +C BS +C cf ) (12) Where: C yw represents the sum of the operation and maintenance costs of solar thermal and photovoltaic power generation; C BS Indicates the battery operating cost; C cf It represents the penalty cost of abandoned photovoltaic power generation; represents the amount of photovoltaic power generation connected to the grid at a certain time t; Formula (12) represents the minimization of system operation cost; Formula (13) represents the maximization of the amount of new energy connected to the grid; S302, forming constraint conditions for establishing the objective function, which are: power balance constraint, system spinning reserve constraint, photovoltaic operation constraint and battery operation constraint, S303, converting the established objective function into a single objective function.

2. According to claim 1, a multi-objective optimization scheduling method for a renewable energy power generation system taking uncertainty into consideration is characterized in that: In the step S1, the uncertainty in the new energy power generation system is analyzed, specifically including the uncertainty analysis of photovoltaic power generation and the uncertainty analysis of solar thermal power generation.

3. The multi-objective optimization scheduling method for a renewable energy power generation system considering uncertainty according to claim 2 is characterized in that: The uncertainty analysis of photovoltaic power generation adopts the following method: The radiation intensity is described by the Beta distribution function: Where: r, r max Respectively represent the radiation intensity and the maximum radiation intensity; α p , β p are the shape distribution parameters of Beta; μ p , σ p are the mean and standard deviation of radiation intensity respectively; Photovoltaic power output power P t,PP for: P.S t,PP sS PP η PP r t (4) Where: η PP Represents the photoelectric conversion efficiency; S PP is the area of ​​the photovoltaic array; r t is the radiation intensity at any time period t; The photovoltaic power output power is described by Beta distribution as: Where: is the maximum output power of photovoltaic power generation; The expected value of photovoltaic power output power E(P t,PP ) and variance D(P t,PP ) is expressed as:

4. The device of a multi-objective optimization scheduling method for a new energy power generation system considering uncertainty according to claim 1, characterized in that: It includes module A, module B, module C and module D, and module A, module B, module C and module D are connected in sequence; Module A: Analysis of uncertainties in renewable energy power generation systems; Module B: Describing the uncertainty of renewable energy generation through scenario simulation methods; Module C: Based on the uncertainty of renewable energy generation, a multi-objective optimization model is established with the lowest system operating cost and the maximum photovoltaic power generation grid-connected power as the optimization goals; Module D: Verify the system's multi-objective optimization scheduling by comparing the simulation results of multiple scenarios.

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