Capacity Planning Method for Energy Storage Power Station

Abstract

The present application discloses a capacity planning method for an energy storage power station, which relates to the field of power technology and is used to reasonably plan the capacity of the energy storage power station. The method includes: obtaining the daily maximum power generation and the daily minimum power generation in a wind-solar-storage system; using the chance-constrained programming method and the multi-objective optimization algorithm, and based on the power and power quantity constraint conditions of the energy storage power station in the wind-solar-storage system, constructing a multi-objective optimal scheduling model; based on the multi-objective optimal scheduling model, using an improved adaptive genetic algorithm based on Monte Carlo simulation to solve and obtain a correction coefficient; the correction coefficient is the theoretical value to ensure that the power generation output of the wind-solar-storage system has the ability to track the change of the electricity load and smooth the peak and valley of wind and solar power generation; determining the planned capacity of the energy storage power station based on the energy storage capacity formula.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of electric power technology, and in particular to a capacity planning method for an energy storage power station. Background Art

[0002] As the scale of wind power generation and photovoltaic power generation continues to increase, the intermittent and volatile nature of wind power generation and photovoltaic power generation has an increasingly greater impact on the stable operation of the power grid.

[0003] In order to solve the peak-shaving pressure on the power grid caused by wind power generation and photovoltaic power generation, it is necessary to configure energy storage power stations with appropriate capacity. However, how to reasonably plan the capacity of energy storage power stations is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present application provides a capacity planning method for an energy storage power station, which is used to reasonably plan the capacity of an energy storage power station.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, a capacity planning method for an energy storage power station is provided, the method comprising: obtaining the maximum daily power generation and the minimum daily power generation in a wind, solar and storage system; using a chance-constrained programming method and a multi-objective optimization algorithm, and based on the power and power constraints of the energy storage power station in the wind, solar and storage system, constructing a multi-objective optimization scheduling model; based on the multi-objective optimization scheduling model, using an improved adaptive genetic algorithm based on Monte Carlo simulation to solve and obtain a correction coefficient; the correction coefficient is a theoretical value that ensures that the power generation output of the wind, solar and storage system has the ability to track changes in power load and smooth out peaks and valleys in wind and solar power generation; determining the planned capacity of the energy storage power station based on an energy storage capacity formula; the energy storage capacity formula satisfies the following relationship: C=K×(N1-N2); wherein C represents the planned capacity of the energy storage power station; K represents the correction coefficient; N1 represents the maximum daily power generation; and N2 represents the minimum daily power generation.

[0007] Optionally, building a multi-objective optimal scheduling model includes: determining a deviation model for minimizing the difference between the actual power and the planned power, minimizing the sum of the investment cost and the operation and maintenance cost, and minimizing the cumulative value of the deviation between the load and the output power of the energy storage power station; wherein, the deviation model for minimizing the difference between the actual power and the planned power is determined by using the least squares method based on the difference between the actual power and the planned power at each time point, and the actual power is composed of the output power of the energy storage power station, the actual output power of wind power generation in the wind-solar-storage system, and the actual output power of photovoltaic power generation in the wind-solar-storage system; the minimization of the sum of the investment cost and the operation and maintenance cost is determined based on the sum of the investment cost and the operation and maintenance cost of the wind-solar-storage system; the minimization of the cumulative value of the deviation between the load and the output power of the energy storage power station is determined based on the cumulative value of the difference between the load and the output power of the energy storage power station within a preset time period; based on the deviation model for minimizing the difference between the actual power and the planned power, the minimization of the sum of the investment cost and the operation and maintenance cost, the minimization of the cumulative value of the deviation between the load and the output power of the energy storage power station, and the power and electricity constraint conditions, a multi-objective optimal scheduling model is built.

[0008] Optionally, the method further includes: inputting the atmospheric data at time point i into the trained neural network model to obtain the photovoltaic power generation output power at time point i and the wind power generation output power at time point i; the atmospheric data includes solar irradiance, temperature, humidity, and wind speed; determining the sum of the photovoltaic power generation output power at time point i and the wind power generation output power at time point i as the planned power at time point i.

[0009] Optionally, the method further includes: obtaining target training sample data; inputting the target training sample data into the neural network model to obtain prediction data; calculating the feature vector similarity between the target training sample data and the prediction data, and optimizing the weight coefficients and thresholds of the neural network model by using the genetic GA optimization algorithm to obtain the trained neural network model.

[0010] Optionally, the method further includes: determining the difference between the power generation and supply of the wind-solar-storage system in the current cycle and the difference between the power generation and supply in the previous cycle; the difference between the power generation and supply satisfies the following relationship: W = P2 - P1; where W represents the difference between the power generation and supply; P2 represents the power supply of the power grid; P1 represents the power generation of the wind-solar-storage system; in the case where the difference between the power generation and supply in the current cycle is greater than the difference between the power generation and supply in the previous cycle, controlling the energy storage power station in the wind-solar-storage system to start the power generation mode; in the case where the difference between the power generation and supply in the current cycle is less than the difference between the power generation and supply in the previous cycle, controlling the energy storage power station in the wind-solar-storage system to start the energy storage mode.

[0011] Based on the technical solution provided by this application, by obtaining the daily maximum power generation and the daily minimum power generation in the wind-solar-storage system; using the chance-constrained programming method and the multi-objective optimization algorithm, and based on the power and electricity constraints of the energy storage power station in the wind-solar-storage system, an improved adaptive genetic algorithm based on Monte Carlo simulation is used to solve and obtain the correction coefficient, and the planned capacity of the energy storage power station is determined based on the energy storage capacity formula. In this way, it can maximize the combined output of wind-solar-storage within the scope of the dispatching plan and minimize the energy storage cost. While meeting the energy storage requirements, it also avoids the idle of some energy storage capacity caused by excessive capacity, and improves the rationality of the capacity planning of the energy storage power station.

[0012] In a second aspect, a device for planning the capacity of an energy storage power station is provided. The device includes: an acquisition unit and a processing unit; the acquisition unit is used to obtain the daily maximum power generation and the daily minimum power generation in the wind-solar-storage system; the processing unit is used to use the chance-constrained programming method and the multi-objective optimization algorithm, and based on the power and electricity constraints of the energy storage power station in the wind-solar-storage system, construct a multi-objective optimal dispatching model; the processing unit is also used to, based on the multi-objective optimal dispatching model, use an improved adaptive genetic algorithm based on Monte Carlo simulation to solve and obtain the correction coefficient; the correction coefficient is the theoretical value to ensure that the power generation output of the wind-solar-storage system has the ability to track the change of the electricity load and smooth the peak and valley of wind and solar power generation; the processing unit is also used to determine the planned capacity of the energy storage power station based on the energy storage capacity formula; the energy storage capacity formula satisfies the following relationship: C = K×(N1 - N2); where C represents the planned capacity of the energy storage power station; K represents the correction coefficient; N1 represents the daily maximum power generation; N2 represents the daily minimum power generation.

[0013] Optionally, the processing unit is specifically used for: Optionally, constructing the multi-objective optimal dispatching model includes: determining a deviation model for minimizing the deviation between the actual power and the planned power, minimizing the sum of the investment cost and the operation and maintenance cost, and minimizing the cumulative value of the deviation between the load and the output power of the energy storage power station; where, the deviation model for minimizing the deviation between the actual power and the planned power is determined by using the least squares method based on the difference between the actual power and the planned power at each time point, and the actual power is composed of the output power of the energy storage power station, the actual output power of wind power generation in the wind-solar-storage system, and the actual output power of photovoltaic power generation in the wind-solar-storage system; the minimization of the sum of the investment cost and the operation and maintenance cost is determined based on the sum of the investment cost and the operation and maintenance cost of the wind-solar-storage system; the minimization of the cumulative value of the deviation between the load and the output power of the energy storage power station is determined based on the cumulative value of the difference between the load and the output power of the energy storage power station within a preset time period; based on the deviation model for minimizing the deviation between the actual power and the planned power, minimizing the sum of the investment cost and the operation and maintenance cost, minimizing the cumulative value of the deviation between the load and the output power of the energy storage power station, and the power and electricity constraints, a multi-objective optimal dispatching model is constructed.

[0014] Optionally, the processing unit is further configured to input the atmospheric data at time point i into the trained neural network model to obtain the photovoltaic power generation output power at time point i and the wind power generation output power at time point i; the atmospheric data includes solar irradiance, temperature, humidity, and wind speed; the processing unit is further configured to determine the sum of the photovoltaic power generation output power at time point i and the wind power generation output power at time point i as the planned power at time point i.

[0015] Optionally, the acquisition unit is further configured to acquire target training sample data; the processing unit is further configured to input the target training sample data into the neural network model to obtain prediction data; the processing unit is further configured to calculate the feature vector similarity between the target training sample data and the prediction data, and optimize the weight coefficients and thresholds of the neural network model by using the genetic GA optimization algorithm to obtain the trained neural network model.

[0016] Optionally, the processing unit is further configured to determine the difference between the power generation and supply of the wind-solar-storage system in the current cycle and the difference between the power generation and supply in the previous cycle; the difference between the power generation and supply satisfies the following relationship: W = P2 - P1; where W represents the difference between the power generation and supply; P2 represents the power supply of the power grid; P1 represents the power generation of the wind-solar-storage system; the processing unit is further configured to control the energy storage power station in the wind-solar-storage system to turn on the power generation mode when the difference between the power generation and supply in the current cycle is greater than the difference between the power generation and supply in the previous cycle; the processing unit is further configured to control the energy storage power station in the wind-solar-storage system to turn on the energy storage mode when the difference between the power generation and supply in the current cycle is less than the difference between the power generation and supply in the previous cycle.

[0017] In a third aspect, a capacity planning device for an energy storage power station is provided. The capacity planning device for the energy storage power station can implement the functions performed by the capacity planning device for the energy storage power station in the above aspects or various possible designs. The functions can be implemented by hardware. For example, in a possible design, the capacity planning device for the energy storage power station may include: a processor and a communication interface. The processor can be used to support the capacity planning device for the energy storage power station to implement the functions involved in the above first aspect or any possible design of the first aspect.

[0018] In another possible design, the capacity planning device for the energy storage power station may further include a memory for storing the necessary computer execution instructions and data of the capacity planning device for the energy storage power station. When the capacity planning device for the energy storage power station runs, the processor executes the computer execution instructions stored in the memory to enable the capacity planning device for the energy storage power station to execute the above first aspect or any possible capacity planning method for the energy storage power station of the first aspect.

[0019] Fourthly, a computer-readable storage medium is provided. The computer-readable storage medium can be a readable non-volatile storage medium. The computer-readable storage medium stores computer instructions or programs. When it runs on a computer, it enables the computer to execute the capacity planning method of the energy storage power station in any possible manner of the above first aspect or the above aspects.

[0020] Fifthly, a computer program product containing instructions is provided. When it runs on a computer, it enables the computer to execute the capacity planning method of the energy storage power station in any possible design of the above first aspect or the above aspects.

[0021] Sixthly, an electronic device is provided. The electronic device includes one or more processors and one or more memories. One or more memories are coupled to one or more processors. One or more memories are used to store computer program codes. The computer program codes include computer instructions. When one or more processors execute the computer instructions, it enables the electronic device to execute the capacity planning method of the energy storage power station in any possible design of the above first aspect or the first aspect.

[0022] Seventhly, a chip system is provided. The chip system includes a processor and a communication interface. The chip system can be used to implement the functions executed by the capacity planning device of the energy storage power station in any possible design of the above first aspect or the first aspect. In a possible design, the chip system further includes a memory for storing program instructions and / or data. The chip system can be composed of chips or can include chips and other discrete devices, without limitation. Description of the Drawings

[0023] Figure 1 It is a schematic flowchart of a capacity planning method of an energy storage power station provided by an embodiment of the present application;

[0024] Figure 2 It is a schematic flowchart of another capacity planning method of an energy storage power station provided by an embodiment of the present application;

[0025] Figure 3 It is a schematic flowchart of another capacity planning method of an energy storage power station provided by an embodiment of the present application;

[0026] Figure 4 It is a schematic structural diagram of a capacity planning device of an energy storage power station provided by an embodiment of the present application. Detailed Embodiments

[0027] In order to enable those of ordinary skill in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0028] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of this application are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0029] It should also be understood that the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements and / or components.

[0030] With the continuous increase in the scale of wind power generation and photovoltaic power generation, the impact of the intermittency and volatility of wind power generation and photovoltaic power generation on the stable operation of the power grid is becoming increasingly large.

[0031] To solve the peak shaving pressure caused by wind power generation and photovoltaic power generation on the power grid, it is necessary to configure an energy storage power station with an appropriate capacity. However, how to reasonably plan the capacity of the energy storage power station is a technical problem to be solved urgently.

[0032] In view of this, the embodiments of this application provide a method for planning the capacity of an energy storage power station, including: obtaining the daily maximum power generation and daily minimum power generation in a wind-solar-storage system; using the chance-constrained programming method and multi-objective optimization algorithm, and based on the power and energy constraints of the energy storage power station in the wind-solar-storage system, constructing a multi-objective optimal scheduling model; based on the multi-objective optimal scheduling model, using an improved adaptive genetic algorithm based on Monte Carlo simulation to solve and obtain a correction coefficient; the correction coefficient is the theoretical value to ensure that the power generation output of the wind-solar-storage system has the ability to track the change of the electricity load and smooth the peak and valley of wind and solar power generation; determining the planned capacity of the energy storage power station based on the energy storage capacity formula; the energy storage capacity formula satisfies the following relationship: C = K×(N1 - N2); where C represents the planned capacity of the energy storage power station; K represents the correction coefficient; N1 represents the daily maximum power generation; N2 represents the daily minimum power generation.

[0033] The method provided by the embodiments of this application will be described in detail below with reference to the drawings of the specification.

[0034] Figure 1 It is a schematic flow chart of a method for planning the capacity of an energy storage power station provided by the embodiments of this application. As Figure 1 shown, the method includes the following S301 - S303:

[0035] S301. Obtain the daily maximum power generation and the daily minimum power generation in the wind-solar-storage system.

[0036] Among them, the daily maximum power generation in the wind-solar-storage system may refer to the daily maximum power generation of the sum of photovoltaic power generation and wind power generation in the wind-solar-storage system. The daily minimum power generation in the wind-solar-storage system may refer to the daily minimum power generation of the sum of photovoltaic power generation and wind power generation in the wind-solar-storage system.

[0037] As a possible implementation, the capacity planning device of the energy storage power station can obtain the daily maximum power generation and the daily minimum power generation in the wind-solar-storage system within a preset time period.

[0038] It should be noted that the preset time period can be set as needed. For example, it can be the past year, etc.

[0039] S302. Use the chance-constrained programming method and the multi-objective optimization algorithm, and based on the power and electricity constraints of the energy storage power station in the wind-solar-storage system, construct a multi-objective optimal scheduling model.

[0040] As a possible implementation, the capacity planning device of the energy storage power station can determine the deviation model of minimizing the difference between the actual power and the planned power, minimize the sum of the investment cost and the operation and maintenance cost, minimize the cumulative value of the deviation between the load and the output power of the energy storage power station, and based on the deviation model of minimizing the difference between the actual power and the planned power, minimizing the sum of the investment cost and the operation and maintenance cost, minimizing the cumulative value of the deviation between the load and the output power of the energy storage power station, and the power and electricity constraints, construct a multi-objective optimal scheduling model.

[0041] Among them, the deviation model of minimizing the difference between the actual power and the planned power is determined by using the least squares method according to the difference between the actual power and the planned power at each time point. The actual power consists of the output power of the energy storage power station, the actual output power of the wind power generation in the wind-solar-storage system, and the actual output power of the photovoltaic power generation in the wind-solar-storage system; the sum of minimizing the investment cost and the operation and maintenance cost is determined according to the sum of the investment cost and the operation and maintenance cost of the wind-solar-storage system; the cumulative value of minimizing the deviation between the load and the output power of the energy storage power station is determined according to the cumulative value of the difference between the load and the output power of the energy storage power station within a preset time period.

[0042] In an example, the multi-objective optimal scheduling model can be:

[0043]

[0044] Among them, represents minimizing the deviation between the actual power and the planned power; represents the output power of the energy storage power station at time point i; Represents the actual output power of wind power generation in the wind-solar-storage system at time point i; Represents the actual output power of photovoltaic power generation in the wind-solar-storage system at time point i; Represents the planned power at time point i;

[0045] Represents minimizing the investment cost And the operation and maintenance cost Sum;

[0046] Represents minimizing the cumulative value of the deviation between the load and the output power of the energy storage power station; Represents a preset coefficient; Represents the load at time point i;

[0047] Represents that the probability that the absolute value of the deviation does not exceed the threshold δ is not less than the preset value α;

[0048] Represents Needs to be between the minimum output power And the maximum output power ;

[0049] Represents the energy storage energy Needs to be between the minimum energy storage energy And the maximum energy storage energy ;

[0050] Represents that the product of the output power of the energy storage power station at time point i and time does not exceed 20% of the maximum energy storage energy.

[0051] In practical applications, if the power grid has sufficient power peak shaving reserve capacity for the peak shaving of the wind-solar-storage system, differential adjustment can be made according to the peak shaving capacity of the regional power grid.

[0052] S303. Based on the multi-objective optimization scheduling model, an improved adaptive genetic algorithm based on Monte Carlo simulation is used to solve and obtain the correction coefficient.

[0053] Among them, the correction coefficient is the theoretical value to ensure that the power generation output of the wind-solar-storage system has the ability to track the change of the electricity load and smooth the peak and valley of wind and solar power generation.

[0054] S304. Determine the planned capacity of the energy storage power station based on the energy storage capacity formula.

[0055] Among them, the energy storage capacity formula satisfies the following relationship:

[0056] C = K×(N1 - N2);

[0057] Among them, C represents the planned capacity of the energy storage power station; K represents the correction coefficient; N1 represents the maximum daily power generation; N2 represents the minimum daily power generation.

[0058] Based on the technical solution provided in this application, by obtaining the maximum daily power generation and the minimum daily power generation in the wind-solar-storage system; using the chance-constrained programming method and the multi-objective optimization algorithm, and based on the power and electricity constraints of the energy storage power station in the wind-solar-storage system, an improved adaptive genetic algorithm based on Monte Carlo simulation is used to solve for the correction coefficient, and the planned capacity of the energy storage power station is determined based on the energy storage capacity formula. In this way, it is possible to maximize the combined output of wind, solar, and storage within the scope of the scheduling plan and minimize the energy storage cost. While meeting the energy storage requirements, it also avoids the idle of some energy storage capacity caused by excessive capacity, improving the rationality of the capacity planning of the energy storage power station.

[0059] A possible embodiment Figure 2 is a schematic flowchart of another energy storage power station capacity planning method provided by the embodiments of this application. As Figure 2 shown, this application may further include the following S401-S402.

[0060] S401. Input the atmospheric data at time point i into the trained neural network model to obtain the photovoltaic power generation output power at time point i and the wind power generation output power at time point i.

[0061] Among them, the atmospheric data may include solar irradiance, temperature, humidity, and wind speed. The neural network model may be a BP neural network model.

[0062] The capacity planning device of the energy storage power station may input the atmospheric data at time point i into the trained neural network model. The trained neural network model processes the atmospheric data at time point i and outputs the photovoltaic power generation output power at time point i and the wind power generation output power at time point i.

[0063] In practical applications, after inputting the atmospheric data at time point i into the trained neural network model, the output result may further include the load power in the wind-solar-storage system.

[0064] S402. Determine the planned power at time point i as the sum of the photovoltaic power generation output power at time point i and the wind power generation output power at time point i.

[0065] A possible embodiment Figure 3 is a schematic flowchart of another energy storage power station capacity planning method provided by the embodiments of this application. As Figure 3 shown, this application may further include the following S501-S503.

[0066] S501. Obtain the target training sample data;

[0067] Among them, the target training sample data includes sample atmospheric data.

[0068] As a possible implementation manner, the capacity planning device of the energy storage power station can obtain the original training data, screen the original training sample data, and perform data bundling and normalization processing to obtain the target training sample data.

[0069] S502. Input the target training sample data into the neural network model to obtain prediction data.

[0070] Among them, the prediction data may refer to the predicted photovoltaic power generation output power and wind power generation output power in the wind-solar-storage system.

[0071] S503. Calculate the feature vector similarity between the target training sample data and the prediction data, and use the genetic GA optimization algorithm to optimize the weight coefficients and thresholds of the neural network model to obtain the trained neural network model.

[0072] Among them, the calculation method of the feature vector similarity can be set as needed. For example, the feature vector similarity can be determined by cosine similarity. Another example is that the feature vector similarity can be determined by Euclidean distance, etc.

[0073] As a possible implementation manner, the capacity planning device of the energy storage power station can encode the weight coefficients and thresholds of the neural network model into chromosomes in the genetic algorithm, design a fitness function to evaluate the quality of each chromosome, select excellent chromosomes for reproduction according to the fitness function value, perform crossover operations on the selected chromosomes to generate new chromosomes, perform mutation operations on the newly generated chromosomes, and repeat the selection, crossover, and mutation operations until the termination condition is met.

[0074] Among them, the encoding method and the termination condition can be set as needed. For example, the encoding method can be binary encoding, real number encoding, etc. The termination condition can be reaching the maximum number of iterations, the convergence of the fitness function value, etc.

[0075] It should be noted that in the optimization of the neural network model, the fitness function is usually related to the prediction error of the model, such as mean square error or mean absolute error. The smaller the fitness function value, the higher the fitness of the chromosome.

[0076] In a possible embodiment, in order to enable the wind-solar-storage system to obtain the maximum stable power generation output, this application may further include the following S601-S603.

[0077] S601. Determine the difference between the power generation and supply of the wind-solar-storage system in the current cycle and the difference between the power generation and supply in the previous cycle.

[0078] Among them, the duration of one cycle can be set as needed. For example, it can be 5 minutes, 10 minutes, 30 minutes, etc.

[0079] The difference between the power generation and power supply satisfies the following relationship:

[0080] W = P2 - P1.

[0081] Among them, W represents the difference between the power generation and power supply; P2 represents the power supply of the power grid (which can also be called the load power); P1 represents the power generation of the wind-solar-storage system.

[0082] S602. When the difference between the power generation and power supply in the current cycle is greater than that in the previous cycle, control the energy storage power station in the wind-solar-storage system to turn on the power generation mode.

[0083] S603. When the difference between the power generation and power supply in the current cycle is less than that in the previous cycle, control the energy storage power station in the wind-solar-storage system to turn on the energy storage mode.

[0084] It can be understood that when the power generation output is greater than the power consumption of the electrical load, it will cause the system voltage and frequency to increase, and in severe cases, it will lead to the collapse of the power system; similarly, when the power generation output is less than the power consumption of the electrical load, it will cause the system voltage and frequency to decrease, and in severe cases, it will lead to the collapse of the power system. By adjusting the power generation mode or energy storage mode of the energy storage power station through the difference between the power generation and power supply, a "wind-solar-storage power generation output curve" that is consistent with the waveform of the power grid load curve can be formed to the greatest extent after the balance of the wind-solar-storage, so that the system can obtain the maximum stable power generation output.

[0085] The embodiments of the present application can divide the capacity planning device of the energy storage power station into functional modules or functional units according to the above method examples. For example, each functional module or functional unit can be corresponding to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module or functional unit. Among them, the division of the module or unit in the embodiments of the present application is illustrative, only a logical function division, and there can be other division methods in actual implementation.

[0086] In the case of dividing each functional module corresponding to each function, Figure 4 FIG. shows a schematic structural diagram of another capacity planning device 700 of the energy storage power station. The capacity planning device 700 of the energy storage power station can also be a chip, a processor, etc. applied to the capacity planning device of the energy storage power station. The capacity planning device 700 of the energy storage power station can be used to execute the functions of the capacity planning device of the energy storage power station involved in the above embodiments. Figure 4The capacity planning device 700 of the energy storage power station shown may include: an acquisition unit 701 and a processing unit 702; the acquisition unit 701 is configured to acquire the daily maximum power generation and the daily minimum power generation in the wind-solar-storage system; the processing unit 702 is configured to use the chance-constrained programming method and the multi-objective optimization algorithm, and based on the power and electricity constraint conditions of the energy storage power station in the wind-solar-storage system, use an improved adaptive genetic algorithm based on Monte Carlo simulation to solve and obtain a correction coefficient; the correction coefficient is a theoretical value to ensure that the power generation output of the wind-solar-storage system has the ability to track the change of the electricity load and smooth the peak and valley of wind and solar power generation; the processing unit 702 is further configured to determine the planned capacity of the energy storage power station based on the energy storage capacity formula; the energy storage capacity formula satisfies the following relationship: C = K×(N1 - N2); where C represents the planned capacity of the energy storage power station; K represents the correction coefficient; N1 represents the daily maximum power generation; N2 represents the daily minimum power generation.

[0087] Optionally, the processing unit 702 is specifically configured to: determine a deviation model for minimizing the deviation between the actual power and the planned power, minimize the sum of the investment cost and the operation and maintenance cost, and minimize the cumulative value of the deviation between the load and the output power of the energy storage power station; wherein, the deviation model for minimizing the deviation between the actual power and the planned power is determined by using the least squares method based on the difference between the actual power and the planned power at each time point, and the actual power is composed of the output power of the energy storage power station, the actual output power of wind power generation in the wind-solar-storage system, and the actual output power of photovoltaic power generation in the wind-solar-storage system; the sum of the investment cost and the operation and maintenance cost is determined based on the sum of the investment cost and the operation and maintenance cost of the wind-solar-storage system; the cumulative value of the deviation between the load and the output power of the energy storage power station is determined based on the cumulative value of the difference between the load and the output power of the energy storage power station within a preset time period; based on the deviation model for minimizing the deviation between the actual power and the planned power, the sum of the investment cost and the operation and maintenance cost, the cumulative value of the deviation between the load and the output power of the energy storage power station, and the power and electricity constraint conditions, a multi-objective optimal scheduling model is constructed.

[0088] Optionally, the processing unit 702 is further configured to input the atmospheric data at time point i into the trained neural network model to obtain the output power of photovoltaic power generation at time point i and the output power of wind power generation at time point i; the atmospheric data includes solar irradiance, temperature, humidity, and wind speed; the processing unit 702 is further configured to determine the sum of the output power of photovoltaic power generation at time point i and the output power of wind power generation at time point i as the planned power at time point i.

[0089] Optionally, the obtaining unit 701 is further configured to obtain target training sample data; the processing unit 702 is further configured to input the target training sample data into the neural network model to obtain prediction data; the processing unit 702 is further configured to calculate the feature vector similarity between the target training sample data and the prediction data, and optimize the weight coefficients and thresholds of the neural network model by using the genetic algorithm (GA) to obtain the trained neural network model.

[0090] Optionally, the processing unit 702 is further configured to determine the difference between the power generation and supply of the wind-solar-storage system in the current cycle and the difference between the power generation and supply in the previous cycle; the difference between the power generation and supply satisfies the following relationship: W = P2 - P1; where W represents the difference between the power generation and supply; P2 represents the power supply of the power grid; P1 represents the power generation of the wind-solar-storage system; the processing unit 702 is further configured to control the energy storage power station in the wind-solar-storage system to turn on the power generation mode when the difference between the power generation and supply in the current cycle is greater than that in the previous cycle; the processing unit 702 is further configured to control the energy storage power station in the wind-solar-storage system to turn on the energy storage mode when the difference between the power generation and supply in the current cycle is less than that in the previous cycle.

[0091] The embodiment of the present application further provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be completed by a computer program instructing relevant hardware. The program can be stored in the above computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be an internal storage unit of the capacity planning device of the energy storage power station (including the data sending end and / or the data receiving end) in any of the foregoing embodiments, such as the hard disk or memory of the capacity planning device of the energy storage power station. The above computer-readable storage medium can also be an external storage device of the above terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the above terminal device. Further, the above computer-readable storage medium can also include both the internal storage unit of the capacity planning device of the energy storage power station and the external storage device. The above computer-readable storage medium is used to store the above computer program and other programs and data required by the capacity planning device of the energy storage power station. The above computer-readable storage medium can also be used to temporarily store the data that has been output or will be output.

[0092] It should be noted that in the description of the present application, terms such as "first" and "second" in the specification, claims and drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0093] It should be understood that in the present application, "at least one (item)" means one or more, "a plurality" means two or more, "at least two (items)" means two or three or more, and "and / or" is used to describe the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally means that the associated objects before and after are in an "or" relationship. "At least one (item) below" or its similar expression means any combination of these items, including any combination of single item (item) or plural items (items). For example, at least one (item) of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0094] Through the description of the above embodiments, those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above division of each functional module is used as an example. In actual applications, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0095] In several embodiments provided in the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in electrical, mechanical or other forms.

[0096] The unit described as a separating component may or may not be physically separated. The component shown as a unit may be a physical unit or multiple physical units, that is, it may be located in one place or may be distributed to multiple different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0097] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, can also be physically present separately for each unit, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0098] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions to enable a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods in various embodiments of the present application. The aforementioned storage medium includes: USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs and other various media that can store program codes.

[0099] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A capacity planning method for an energy storage power station, characterized in that, The method includes: S301. Obtain the daily maximum power generation and the daily minimum power generation in the wind-solar-storage system; S302. Use the chance-constrained programming method and the multi-objective optimization algorithm, and construct a multi-objective optimal scheduling model based on the power and electricity constraints of the energy storage power station in the wind-solar-storage system; The construction of the multi-objective optimal scheduling model includes: Determine the deviation model of minimizing the deviation between the actual power and the planned power, minimize the sum of the investment cost and the operation and maintenance cost, and minimize the cumulative value of the deviation between the load and the output power of the energy storage power station; Among them, the deviation model of minimizing the deviation between the actual power and the planned power is determined by using the least squares method according to the difference between the actual power and the planned power at each time point. The actual power consists of the output power of the energy storage power station, the actual output power of wind power generation in the wind-solar-storage system, and the actual output power of photovoltaic power generation in the wind-solar-storage system; the sum of minimizing the investment cost and the operation and maintenance cost is determined according to the sum of the investment cost and the operation and maintenance cost of the wind-solar-storage system; the cumulative value of minimizing the deviation between the load and the output power of the energy storage power station is determined according to the cumulative value of the difference between the load and the output power of the energy storage power station within a preset time period; Based on the deviation model of minimizing the deviation between the actual power and the planned power, minimizing the sum of the investment cost and the operation and maintenance cost, minimizing the cumulative value of the deviation between the load and the output power of the energy storage power station, and the power and electricity constraints, construct a multi-objective optimal scheduling model; where the multi-objective optimal scheduling model is: Among them, minf1 represents minimizing the deviation between the actual power and the planned power; P bess，i represents the output power of the energy storage power station at time point i; represents the actual output power of wind power generation in the wind-solar-storage system at time point i; represents the actual output power of photovoltaic power generation in the wind-solar-storage system at time point i; P plan_adj，i represents the planned power at time point i; minf2 represents minimizing the sum of the investment cost C IN and the operation and maintenance cost C OM ; minf3 represents the cumulative value of minimizing the deviation between the load and the output power of the energy storage power station; P i represents a preset coefficient; P load，i represents the load at time point i; P r {|Δt|≤δ}≥α means that the probability that the absolute value of the deviation does not exceed the threshold δ is not less than the preset value α; P bess_min ≤P bess，i ≤P bess_max Indicates P bess，i Needs to be between the minimum output power P bess_min and the maximum output power P bess_max ; Indicates the energy storage energy E bess，i Needs to be between the minimum energy storage energy E bess_min and the maximum energy storage energy E bess_max ; P bess，i t ≤ E bess_max *20% means that the product of the output power of the energy storage power station at time point i and time does not exceed 20% of the maximum energy storage capacity; S303. Based on the multi-objective optimal scheduling model, use an improved adaptive genetic algorithm based on Monte Carlo simulation to solve and obtain a correction coefficient; the correction coefficient is a theoretical value that ensures that the power generation output of the wind-solar-storage system has the ability to track the change of the electricity load and smooth the peak and valley of wind and solar power generation; S304. Determine the planned capacity of the energy storage power station based on the energy storage capacity formula; The energy storage capacity formula satisfies the following relationship: C = K×(N1 - N2): Among them, C represents the planned capacity of the energy storage power station; K represents the correction coefficient; N1 represents the daily maximum power generation; N2 represents the daily minimum power generation.

2. The method according to claim 1, characterized in that, The method further includes: Input the atmospheric data at time point i into the trained neural network model to obtain the planned output power of photovoltaic power generation at time point i and the planned output power of wind power generation at time point i; the atmospheric data includes solar irradiance, temperature, humidity, and wind speed; Determine the sum of the planned output power of photovoltaic power generation at time point i and the planned output power of wind power generation at time point i as the planned power at time point i.

3. The method according to claim 2, wherein The method further includes: Obtain target training sample data; Input the target training sample data into the neural network model to obtain prediction data; Calculate the feature vector similarity between the target training sample data and the prediction data, and use the genetic GA optimization algorithm to optimize the weight coefficients and thresholds of the neural network model to obtain the trained neural network model.

4. The method according to claim 1, wherein The method further includes: Determine the difference between the power generation and supply quantity of the wind-solar-storage system in the current cycle and the difference between the power generation and supply quantity in the previous cycle; the difference between the power generation and supply quantity satisfies the following relationship: W = P2 - P1; Wherein, W represents the difference between power generation and power supply; P2 represents the power supply of the power grid; P1 represents the power generation of the wind-solar-storage system; When the difference between power generation and power supply in the current cycle is greater than that in the previous cycle, control the energy storage power station in the wind-solar-storage system to turn on the power generation mode; When the difference between power generation and power supply in the current cycle is less than that in the previous cycle, control the energy storage power station in the wind-solar-storage system to turn on the energy storage mode.

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