Optimization Methods for Wind, Solar and Energy Storage Power Generation Systems under the Influence of Uncertainty in Coal Scenarios

Abstract

This invention discloses a method for optimizing the output of a wind-solar-storage power generation system under the influence of uncertainties in coal-fired power generation scenarios. The method includes: establishing a power clearing model for the power system based on the uncertainties of coal-fired power generation scenarios; inputting the annual and current generation costs of coal-fired power plants into the model and using a solver to obtain cleared power cost parameters under each coal-fired power generation scenario; using a clustering algorithm to obtain the average value and probability distribution of the cleared power cost parameters under each coal-fired power generation scenario; establishing a stochastic optimization model for the wind-solar-storage power generation system; inputting the average value and probability distribution of the cleared power cost parameters into the model; and outputting the demand and supply of the wind-solar-storage power generation system after processing, thereby achieving output optimization. This invention considers the changes in power cleared power cost parameters under the uncertainties of coal-fired power generation scenarios and establishes an output optimization strategy for the wind-solar-storage power generation system through stochastic optimization, enabling the system to achieve optimal output under changing coal-fired power generation scenarios.

Description

Technical Field

[0001] This invention relates to a method for optimizing the output of a wind-solar-storage power generation system, which relates to the field of power optimization, and specifically to a method for optimizing the output of a wind-solar-storage power generation system under the influence of uncertainties in coal-fired power generation scenarios. Background Technology

[0002] In recent years, coal cost parameters have fluctuated dramatically due to various factors. The instability of coal cost parameters, as a primary energy source, directly leads to irregular changes in the power generation cost of coal-fired power units. Coal-fired units constitute a large proportion of the power supply structure. Therefore, changes in coal cost parameters directly affect the final cleared power cost parameters of the power plant. Specifically, an increase in coal cost parameters will lead to an increase in the power generation cost of coal-fired units, thereby increasing the current set cost parameters of coal-fired units and ultimately improving the overall cleared power cost parameters; conversely, a decrease will lead to an overall decrease in the cleared power cost parameters.

[0003] The changing parameters of current cleared electricity costs will have new impacts on the decision-making of other power generation entities. Among them, wind-solar-storage combined generation systems (FSGS), as emerging participants, also face the challenge of decision-making under conditions of fluctuating coal standby parameters and current cost parameters. Although FSGS possesses strong flexibility and can quickly adjust power generation plans and strategies to obtain input costs under different conditions, the current lack of methods for optimizing the output of FSGS under uncertain coal-electricity cost parameters exposes these systems to significant risks and hinders their ability to achieve optimal performance. Summary of the Invention

[0004] To address the problems existing in the background technology, the present invention provides a method for optimizing the output of a wind, solar and energy storage power generation system under the influence of uncertainties in coal-fired power generation scenarios.

[0005] The technical solution adopted in this invention is:

[0006] The present invention provides a method for optimizing the output of a wind, solar, and energy storage power generation system under the influence of uncertainties in coal-fired power generation scenarios, comprising:

[0007] Step 1: Establish a power clearing model for the power system based on uncertainties in coal-fired power scenarios. Input the annual and current generation costs of coal-fired power plants into the power clearing model, and use a solver to process the data to obtain the clearing power cost parameters for the power system under various coal-fired power scenarios. The cost can be specifically measured in terms of electricity volume.

[0008] Step 2: Use clustering algorithms to obtain the average value and probability distribution of the clearing power cost parameters of the power system under various coal scenarios.

[0009] Step 3: Establish a stochastic optimization model for the wind, solar and energy storage power generation system in the power system. Input the average value and probability distribution of the clearing power cost parameters of the power system under various coal scenarios into the stochastic optimization model. After processing, the stochastic optimization model outputs the demand and supply of the wind, solar and energy storage power generation system to achieve power output optimization of the wind, solar and energy storage power generation system.

[0010] In step 1, the power system includes coal-fired power plants, wind farms, hydropower plants, and wind-solar-storage power generation systems. The power clearing model of the power system includes an objective function and constraints. The objective function is to minimize the power generation cost, which in this case is the total power generation cost of multiple coal-fired power units. A unified clearing method is used for clearing. The specific objective function is as follows:

[0011]

[0012] Where T represents the total power clearing period of the coal-fired power plant; N g N w and N r These represent the sets of coal-fired power plants, wind power plants, and hydropower plants in the power system, respectively. and These represent the preset power generation and preset power cost parameters for the nth coal-fired power plant in the tth time period under the ωth coal scenario; and These represent the preset power generation and preset power generation cost parameters for the w-th large-scale wind farm in the t-th time period, respectively. and These represent the preset electricity generation and preset power generation cost parameters for the r-th hydropower plant during the t-th time period, respectively.

[0013] The constraints of the power system's power clearing model include power system balance and upper and lower limits of coal-fired power unit output constraints, as detailed below:

[0014]

[0015] Among them, D t This represents the load demand of the power system during the t-th time period; and Let represent the maximum and minimum power generation of the coal-fired power plant in the t-th time period, respectively; and Let represent the maximum and minimum power generation of a large wind farm in the t-th time period, respectively. and These represent the maximum and minimum power generation of the hydroelectric power plant during the t-th time period, respectively.

[0016] After solving the power clearing model using the CPLEX solver, the power clearing cost parameters of the power system under various coal scenarios are obtained. The power generation cost of coal-fired power plants is different under each coal scenario.

[0017] In the total power clearing period T of the coal-fired power plant, the preset power cost parameter of the nth coal-fired power plant under the ωth coal scenario. Specifically as follows:

[0018]

[0019] in, and Let represent the annual and current power generation costs of the nth coal-fired power plant under the ωth coal scenario; k1 and k2 represent the first and second cost conversion coefficients of the first stage ladder, respectively; and γ1 and γ2 represent the first and second cost conversion coefficients of the second stage ladder, respectively. denoted as the preset power generation of the nth coal-fired power plant under the ωth coal scenario, h represents the stage-level power generation of the coal-fired power plant, and represents the length of the preset cost parameter interval. Indicates sampling from a normal distribution; and Let represent the average and standard deviation of the annual power generation cost of the nth coal-fired power plant under the ωth coal scenario, respectively; and Let represent the average and standard deviation of the current power generation cost of the nth coal-fired power plant under the ωth coal scenario, respectively.

[0020] In step 2, the K-means clustering algorithm is used to obtain the average value and probability distribution of the clearing power cost parameters of the power system under various coal scenarios, as detailed below:

[0021]

[0022] in, and Let C represent the average value and probability of the k-th type clearing power cost parameter of the power system, respectively. k This represents the power system's clearing electricity cost parameter in the ω-th coal scenario. The set of N k This represents the power system's clearing electricity cost parameter in the ω-th coal scenario. set C k The number of clustered samples, N total This represents the total number of clustered samples in the set of power system clearing electricity cost parameters under various coal scenarios.

[0023] In step 3, the stochastic optimization model for the wind-solar-storage power generation system is as follows:

[0024]

[0025] Among them, F k,profit and Let these represent the average values ​​of the k-th type of cleared electricity cost parameters of the power system in the t-th time period. The supply and demand costs of electricity generated by wind, solar, and energy storage systems; N t This represents the total stochastic optimization time period of the wind-solar-storage power generation system. P represents the probability of the k-th type of clearing energy cost parameter in the power system; t bid and P t ask Let represent the supply and demand of the wind-solar-storage power generation system in the t-th time period, respectively.

[0026] In step 3, the stochastic optimization model of the wind-solar-storage power generation system satisfies the following constraints:

[0027] a) Balance constraints:

[0028]

[0029] in, and P represents the discharge and charge amounts of the energy stored in the wind-solar-storage power generation system during the t-th time period, respectively. t WD and P t PV These represent the power generation of the wind turbine and photovoltaic unit in the wind-solar-storage power generation system during the t-th time period, respectively.

[0030] b) Energy storage charging and discharging constraints:

[0031]

[0032] in, and These represent the maximum and minimum energy storage discharge power of the wind-solar-storage power generation system, respectively. and This indicates the maximum and minimum charging power of the wind-solar-storage power generation system. and These represent the energy storage discharge and charging amount of the wind-solar-storage power generation system in the t-th time period under the k-th type of cleared energy cost parameter of the power system; and Let represent the energy storage discharge state variable and energy storage charging state variable of the wind-solar-storage power generation system in the t-th time period under the k-th type of cleared electricity cost parameter of the power system, respectively. and These represent the energy storage discharge state variable and the energy storage charging state variable of the wind, solar and energy storage power generation system under the k-th type of cleared electricity cost parameter of the power system, respectively. and Let represent the energy storage capacity of the wind-solar-storage power generation system in the t-th and t-1-th time periods, respectively, under the k-th type of cleared electricity cost parameter of the power system. and η represents the energy storage capacity of the wind-solar-storage power generation system in the initial and final states, respectively, under the k-th type of cleared electricity cost parameter of the power system; η represents the energy storage charging and discharging efficiency of the wind-solar-storage power generation system.

[0033] c) Constraints on wind and solar power output:

[0034] Because the curtailment of wind and solar power generation is taken into account, the wind and solar power output of the wind-solar-storage power generation system is less than the predicted value.

[0035]

[0036] in, and P represents the power generation of wind turbines and photovoltaic units in the wind-solar-storage power generation system during the t-th time period under the k-th type of cleared power cost parameter of the power system; t WD,fore and P t PV,fore Let represent the predicted wind turbine power and the predicted photovoltaic power of the wind-solar-storage power generation system in the t-th time period under the k-th type of cleared power cost parameter of the power system.

[0037] The electronic device of the present invention includes: a memory and a processor coupled to each other, wherein the memory stores program data, and the processor invokes the program data to execute the method described above.

[0038] The present invention provides a computer-readable storage medium having program data stored thereon, which, when executed by a processor, implements the method described above.

[0039] The beneficial effects of this invention are:

[0040] The method of this invention takes into account the changes in the power clearing cost parameter under the uncertainty of the coal scenario. It establishes the power output optimization strategy of the wind, solar and energy storage power generation system through stochastic optimization, which can achieve the optimal power output of the wind, solar and energy storage power generation system under the changing conditions of the coal scenario. Attached Figure Description

[0041] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0042] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] like Figure 1 As shown, the specific method for optimizing the output of a wind-solar-storage power generation system under the influence of uncertainties in coal-fired power generation scenarios according to the present invention is as follows:

[0044] Step 1: The power system includes coal-fired power plants, wind farms, hydropower plants, and wind-solar-storage power generation systems. A power clearing model for the power system based on the uncertainties of the coal scenario is established. The power clearing model includes an objective function and constraints. The objective function minimizes the generation cost, which in this case is the total generation cost of multiple coal-fired power units. A unified clearing method is used for clearing. The specific objective function is as follows:

[0045]

[0046] Where T represents the total power clearing period of the coal-fired power plant; N g N w and N r These represent the sets of coal-fired power plants, wind power plants, and hydropower plants in the power system, respectively. and These represent the preset power generation and preset power cost parameters for the nth coal-fired power plant in the tth time period under the ωth coal scenario; and These represent the preset power generation and preset power generation cost parameters for the w-th large-scale wind farm in the t-th time period, respectively. and These represent the preset electricity generation and preset power generation cost parameters for the r-th hydropower plant during the t-th time period, respectively.

[0047] The constraints of the power system's power clearing model include power system balance and upper and lower limits of coal-fired power unit output constraints, as detailed below:

[0048]

[0049] Among them, D t This represents the load demand of the power system during the t-th time period; and Let represent the maximum and minimum power generation of the coal-fired power plant in the t-th time period, respectively; and Let represent the maximum and minimum power generation of a large wind farm in the t-th time period, respectively. and These represent the maximum and minimum power generation of the hydroelectric power plant during the t-th time period, respectively.

[0050] The annual and current generation costs of coal-fired power plants in the power system are input into the power clearing model, and the clearing power cost parameters of the power system under various coal scenarios are obtained after processing using the CPLEX solver. The generation cost of coal-fired power plants is different under each coal scenario; the cost can be specifically measured in terms of electricity volume.

[0051] In the total power clearing period T of a coal-fired power plant, the preset power cost parameter for the nth coal-fired power plant under the ωth coal scenario is... Specifically as follows:

[0052]

[0053] in, and Let represent the annual and current power generation costs of the nth coal-fired power plant under the ωth coal scenario; k1 and k2 represent the first and second cost conversion coefficients of the first-stage ladder, respectively; and γ1 and γ2 represent the first and second cost conversion coefficients of the second-stage ladder, respectively. denoted as the preset power generation of the nth coal-fired power plant under the ωth coal scenario, h represents the stage-level power generation of the coal-fired power plant, and represents the length of the preset cost parameter interval. Indicates sampling from a normal distribution; and Let represent the average and standard deviation of the annual power generation cost of the nth coal-fired power plant under the ωth coal scenario, respectively; and Let represent the average and standard deviation of the current power generation cost of the nth coal-fired power plant under the ωth coal scenario, respectively.

[0054] Step 2: Use the K-means clustering algorithm to obtain the average value and probability distribution of the clearing power cost parameters of the power system under various coal scenarios, as follows:

[0055]

[0056]

[0057] in, and Let C represent the average value and probability of the k-th type clearing power cost parameter of the power system, respectively. k This represents the power system's clearing electricity cost parameter in the ω-th coal scenario. The set of N k This represents the power system's clearing electricity cost parameter in the ω-th coal scenario. set C k The number of clustered samples, N totalThis represents the total number of clustered samples in the set of power system clearing electricity cost parameters under various coal scenarios.

[0058] After obtaining the uniform clearing power cost parameters for each scenario, cluster analysis is performed on the scenarios: First, the new distance from each sample to the cluster center is calculated and assigned to c. ω Update each cluster center according to the assigned category. Repeat the above steps until... ε represents a preset threshold. Finally, the average clearing power cost parameter and probability for each scenario are calculated, as follows:

[0059]

[0060] Among them, c ω This indicates that the sample is assigned to the category of the nearest cluster center. This represents the mean of the energy cost parameter for all samples in this category. Let represent the set of clearing energy cost parameters for the k-th class in the i-th iteration.

[0061] Step 3: Establish a stochastic optimization model for the wind-solar-storage power generation system within the power system, as detailed below:

[0062]

[0063] Among them, F k,profit and Let these represent the average values ​​of the k-th type of cleared electricity cost parameters of the power system in the t-th time period. The supply and demand costs of electricity generated by wind, solar, and energy storage systems; N t This represents the total stochastic optimization time period of the wind-solar-storage power generation system. P represents the probability of the k-th type of clearing energy cost parameter in the power system; t bid and P t ask Let represent the supply and demand of the wind-solar-storage power generation system in the t-th time period, respectively.

[0064] The stochastic optimization model of the wind-solar-storage power generation system satisfies the following constraints:

[0065] a) Balance constraints:

[0066]

[0067] in, and P represents the discharge and charge amounts of the energy stored in the wind-solar-storage power generation system during the t-th time period, respectively. t WD and P tPV These represent the power generation of the wind turbine and photovoltaic unit in the wind-solar-storage power generation system during the t-th time period, respectively.

[0068] b) Energy storage charging and discharging constraints:

[0069]

[0070] in, and These represent the maximum and minimum energy storage discharge power of the wind-solar-storage power generation system, respectively. and This indicates the maximum and minimum charging power of the wind-solar-storage power generation system. and These represent the energy storage discharge and charging amount of the wind-solar-storage power generation system in the t-th time period under the k-th type of cleared energy cost parameter of the power system; and Let represent the energy storage discharge state variable and energy storage charging state variable of the wind-solar-storage power generation system in the t-th time period under the k-th type of cleared electricity cost parameter of the power system, respectively. and These represent the energy storage discharge state variable and the energy storage charging state variable of the wind, solar and energy storage power generation system under the k-th type of cleared electricity cost parameter of the power system, respectively. and Let represent the energy storage capacity of the wind-solar-storage power generation system in the t-th and t-1-th time periods, respectively, under the k-th type of cleared electricity cost parameter of the power system. and η represents the energy storage capacity of the wind-solar-storage power generation system in the initial and final states, respectively, under the k-th type of cleared electricity cost parameter of the power system; η represents the energy storage charging and discharging efficiency of the wind-solar-storage power generation system.

[0071] c) Constraints on wind and solar power output:

[0072] Because the curtailment of wind and solar power generation is taken into account, the wind and solar power output of the wind-solar-storage power generation system is less than the predicted value.

[0073]

[0074] in, and P represents the power generation of wind turbines and photovoltaic units in the wind-solar-storage power generation system during the t-th time period under the k-th type of cleared power cost parameter of the power system; t WD,fore and P t PV,fore Let represent the predicted wind turbine power and the predicted photovoltaic power of the wind-solar-storage power generation system in the t-th time period under the k-th type of cleared power cost parameter of the power system.

[0075] The average value and probability distribution of the clearing power cost parameters of the power system under various coal scenarios are input into the stochastic optimization model. After processing, the stochastic optimization model outputs the demand and supply of the wind, solar and energy storage power generation system, thereby optimizing the output of the wind, solar and energy storage power generation system.

[0076] The method of this invention first analyzes and models the coal cost under multiple time scales. Based on the annual and current coal-fired power plant, normally distributed coal cost parameters are set, and the cost scenario of the coal required by the current coal-fired power unit is generated by sampling. Then, an electricity clearing model is established. Based on the different cost scenarios generated in the previous step, corresponding preset cost parameters of multiple coal-fired power units are set for unified clearing. Then, a clustering algorithm is used to cluster the clearing cost parameter scenarios to generate typical scenarios of clearing cost parameters. Finally, a stochastic optimization model of the wind-solar-storage power generation system is established. Based on the clearing electricity cost parameters of multiple scenarios and the corresponding scenario probabilities, the model optimizes the decision-making of the corresponding strategy with the goal of maximizing the supply cost of the wind-solar-storage power generation system, thereby maximizing the supply cost of the wind-solar-storage power generation system.

[0077] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented using various computer languages. This application is described with flowcharts of methods, systems, and computer program products according to embodiments of this application.

[0078] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, this invention is intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0079] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if these modifications and variations of this application fall within the scope of the equivalent technology of this invention, this application also intends to include these modifications and variations.

Claims

1. A method for optimizing the output of a wind-solar-storage power generation system under the influence of uncertainties in coal-fired power generation scenarios, characterized in that, include: Step 1: Establish a power clearing model for the power system based on the uncertainty of the coal scenario. Input the annual and current power generation costs of coal-fired power plants in the power system into the power clearing model, and use a solver to process the data to obtain the clearing power cost parameters of the power system under various coal scenarios. Step 2: Use clustering algorithms to obtain the average value and probability distribution of the clearing power cost parameters of the power system under various coal scenarios; Step 3: Establish a stochastic optimization model for the wind, solar and energy storage power generation system in the power system. Input the average value and probability distribution of the clearing power cost parameters of the power system under various coal scenarios into the stochastic optimization model. After processing, the stochastic optimization model outputs the demand and supply of the wind, solar and energy storage power generation system to achieve power output optimization of the wind, solar and energy storage power generation system. In step 1, the power system includes coal-fired power plants, wind farms, hydropower plants, and wind-solar-storage power generation systems. The power clearing model of the power system includes an objective function and constraints. The objective function is as follows: Where T represents the total power clearing period of the coal-fired power plant; N g N w and N r These represent the sets of coal-fired power plants, wind power plants, and hydropower plants in the power system, respectively. and They represent the first time. Preset power generation and preset power cost parameters for the nth coal-fired power plant in the tth time period under a coal scenario; and These represent the preset power generation and preset power generation cost parameters for the w-th wind farm in the t-th time period, respectively. and These represent the preset electricity generation and preset power generation cost parameters for the r-th hydropower plant in the t-th time period, respectively. The specific constraints of the power clearing model for the power system are as follows: in, This represents the load demand of the power system during the t-th time period; and Let represent the maximum and minimum power generation of the coal-fired power plant in the t-th time period, respectively; and Let represent the maximum and minimum power generation of the wind farm in the t-th time period, respectively. and Let represent the maximum and minimum power generation of the hydroelectric power plant in the t-th time period, respectively; After solving the power clearing model using the CPLEX solver, the power clearing cost parameters of the power system under various coal scenarios are obtained. The power generation cost of coal-fired power plants is different under each coal scenario.

2. The method for optimizing the output of a wind-solar-storage power generation system under the influence of uncertainties in coal-fired power generation scenarios as described in claim 1, characterized in that: In the total power clearing period T of the coal-fired power plant, during the first... Preset power cost parameters for the nth coal-fired power plant in a coal-fired scenario Specifically as follows: in, and They represent the first time. The annual and current power generation costs of the nth coal-fired power plant under a coal-fired scenario; and These represent the first and second cost conversion coefficients of the first-stage ladder, respectively. and These represent the first and second cost conversion coefficients of the second-stage ladder, respectively. Indicates the first The preset power generation of the nth coal-fired power plant in a given coal-fired scenario. This indicates the staged, tiered power generation of a coal-fired power plant. Indicates sampling from a normal distribution; and They represent the first time. The average and standard deviation of the annual power generation cost of the nth coal-fired power plant under a coal scenario; and They represent the first time. The average and standard deviation of the current power generation cost of the nth coal-fired power plant under a given coal scenario.

3. The method for optimizing the output of a wind-solar-storage power generation system under the influence of uncertainties in coal-fired power generation scenarios as described in claim 1, characterized in that: In step 2, the K-means clustering algorithm is used to obtain the average value and probability distribution of the clearing power cost parameters of the power system under various coal scenarios, as detailed below: in, and Let represent the average value and probability of the power system's k-th type clearing energy cost parameter, respectively. Indicates the power system in the first Clearing electricity cost parameters in a coal scenario The set, Indicates the power system in the first Clearing electricity cost parameters in a coal scenario set The number of clustered samples in the data. This represents the total number of clustered samples in the set of power system clearing electricity cost parameters under various coal scenarios.

4. The method for optimizing the output of a wind-solar-storage power generation system under the influence of uncertainties in coal-fired power generation scenarios as described in claim 1, characterized in that: In step 3, the stochastic optimization model for the wind-solar-storage power generation system is as follows: in, and Let these represent the average values ​​of the k-th type of cleared electricity cost parameters of the power system in the t-th time period. The supply and demand costs of electricity generated by wind, solar, and energy storage systems; N t This represents the total stochastic optimization time period of the wind-solar-storage power generation system. The probability of the k-th type of clearing power cost parameter in the power system; and Let represent the supply and demand of the wind-solar-storage power generation system in the t-th time period, respectively.

5. The method for optimizing the output of a wind-solar-storage power generation system under the influence of uncertainties in a coal-fired power generation scenario, as described in claim 4, is characterized in that: In step 3, the stochastic optimization model of the wind-solar-storage power generation system satisfies the following constraints: a) Balance constraints: in, and Let represent the discharge and charging amounts of the energy storage system in the t-th time period, respectively. and These represent the power generation of the wind turbine and photovoltaic unit in the wind-solar-storage power generation system during the t-th time period, respectively. b) Energy storage charge and discharge constraints: in, and These represent the maximum and minimum energy storage discharge power of the wind-solar-storage power generation system, respectively. and This indicates the maximum and minimum charging power of the wind-solar-storage power generation system. and These represent the energy storage discharge and charging amount of the wind-solar-storage power generation system in the t-th time period under the k-th type of cleared energy cost parameter of the power system; and Let represent the energy storage discharge state variable and energy storage charging state variable of the wind-solar-storage power generation system in the t-th time period under the k-th type of cleared electricity cost parameter of the power system, respectively. and These represent the energy storage discharge state variable and the energy storage charging state variable of the wind, solar and energy storage power generation system under the k-th type of cleared electricity cost parameter of the power system, respectively. and Let represent the energy storage capacity of the wind-solar-storage power generation system in the t-th and t-1-th time periods, respectively, under the k-th type of cleared electricity cost parameter of the power system. and These represent the energy storage capacity of the wind-solar-storage power generation system in the initial and final states, respectively, under the k-th type of cleared electricity cost parameter of the power system. This indicates the energy storage charging and discharging efficiency of a wind-solar-storage power generation system; c) Wind and solar power output constraints: in, and These represent the power generation of wind turbines and photovoltaic units in the wind-solar-storage power generation system during the t-th time period under the k-th type of clearing power cost parameter of the power system; and Let represent the predicted wind turbine power and the predicted photovoltaic power of the wind-solar-storage power generation system in the t-th time period under the k-th type of cleared power cost parameter of the power system.

6. An electronic device, characterized in that, include: A memory and a processor are coupled to each other, wherein the memory stores program data, and the processor invokes the program data to perform the method as described in any one of claims 1-5.

7. A computer-readable storage medium storing program data thereon, characterized in that, When the program data is executed by the processor, the method as described in any one of claims 1-5 is implemented.

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